May 2023 arXiv papers — page 55

Christopher Johann Clarke

Music Representing Corpus Virtual (MRCV) is an open source software suite designed to explore the capabilities of Artificial Intelligence (AI) and Machine Learning (ML) in Music Generation, Sound Design, and Virtual Instrument Creation (MGSDIC). The software is accessible to users of varying levels of experience, with an emphasis on providing an explorative

Qinyuan Ye, Harvey Yiyun Fu, Xiang Ren, Robin Jia

We investigate the predictability of large language model (LLM) capabilities: given records of past experiments using different model families, numbers of parameters, tasks, and numbers of in-context examples, can we accurately predict LLM performance on new experiment configurations? Answering this question has practical implications for LLM users (e.g., de

Seyed Mohammadhossein Tabatabaee, Anne Bouillard, Jean-Yves Le Boudec

Time-sensitive networks require timely and accurate monitoring of the status of the network. To achieve this, many devices send packets periodically, which are then aggregated and forwarded to the controller. Bounding the aggregate burstiness of the traffic is then crucial for effective resource management. In this paper, we are interested in bounding this a

S. -A. Biehs, G. S. Agarwal

The nonreciprocal propagation of light typically requires use of materials like ferrites or magneto-optical media with a strong magnetic bias or methods based on material nonlinearities which require use of strong electromagnetic fields. A simpler possibility to produce nonreciprocity is to use spatio-temporal modulations to produce magnetic fields in synthe

Sander Gribling, Sven Polak, Lucas Slot

The moment-sum-of-squares (moment-SOS) hierarchy is one of the most celebrated and widely applied methods for approximating the minimum of an n-variate polynomial over a feasible region defined by polynomial (in)equalities. A key feature of the hierarchy is that, at a fixed level, it can be formulated as a semidefinite program of size polynomial in the numbe

Louis Sharrock, Lester Mackey, Christopher Nemeth

We introduce a suite of new particle-based algorithms for sampling in constrained domains which are entirely learning rate free. Our approach leverages coin betting ideas from convex optimisation, and the viewpoint of constrained sampling as a mirrored optimisation problem on the space of probability measures. Based on this viewpoint, we also introduce a uni

Tomi K. Baikie, Philip Calado, Krzysztof Galkowski, Zahra Andaji-Garmaroudi

Metal halide perovskite solar cells have gained widespread attention due to their high efficiency and high defect tolerance. The absorbing perovskite layer is as a mixed electron-ion conductor that supports high rates of ion and charge transport at room temperature, but the migration of mobile defects can lead to degradation pathways. We combine experimental

Michael Seifert, Tomáš Rauch, Miguel A. L. Marques, Silvana Botti

Zincblende copper iodide has attracted significant interest as a potential material for transparent electronics, thanks to its exceptional light transmission capabilities in the visible range and remarkable hole conductivity. However, remaining challenges hinder the utilization of copper iodide's unique properties in real-world applications. To address this,

Siddhartha Ganguly, Souvik Das, Debasish Chatterjee, Ravi Banavar

Limited bandwidth and limited saturation in actuators are practical concerns in control systems. Mathematically, these limitations manifest as constraints being imposed on the control actions, their rates of change, and more generally, the global behavior of their paths. While the problem of actuator saturation has been studied extensively, little attention

Jianzhou Luo, Dingchuan Yang, Ke Wei

The Sinkhorn algorithm is a widely used method for solving the optimal transport problem, and the Greenkhorn algorithm is one of its variants. While there are modified versions of these two algorithms whose computational complexities are $O({n^2\|C\|_\infty^2\log n}/{\varepsilon^2})$ to achieve an $\varepsilon$-accuracy, to the best of our knowledge, the exi

Minje Choi, Jiaxin Pei, Sagar Kumar, Chang Shu

Large language models (LLMs) have been shown to perform well at a variety of syntactic, discourse, and reasoning tasks. While LLMs are increasingly deployed in many forms including conversational agents that interact with humans, we lack a grounded benchmark to measure how well LLMs understand \textit{social} language. Here, we introduce a new theory-driven

Hannah Bast, Matthias Hertel, Natalie Prange

Existing evaluations of entity linking systems often say little about how the system is going to perform for a particular application. There are two fundamental reasons for this. One is that many evaluations only use aggregate measures (like precision, recall, and F1 score), without a detailed error analysis or a closer look at the results. The other is that

Cleo Matzken, Steffen Eger, Ivan Habernal

Protecting privacy in contemporary NLP models is gaining in importance. So does the need to mitigate social biases of such models. But can we have both at the same time? Existing research suggests that privacy preservation comes at the price of worsening biases in classification tasks. In this paper, we explore the extent to which this tradeoff really holds

Timon Ziegenbein, Shahbaz Syed, Felix Lange, Martin Potthast

Online discussion moderators must make ad-hoc decisions about whether the contributions of discussion participants are appropriate or should be removed to maintain civility. Existing research on offensive language and the resulting tools cover only one aspect among many involved in such decisions. The question of what is considered appropriate in a controver

Muhammad Khalifa, Lajanugen Logeswaran, Moontae Lee, Honglak Lee

In the context of multi-step reasoning, e.g., with chain-of-thought, language models (LMs) can easily assign a high likelihood to incorrect steps. As a result, decoding strategies that optimize for solution likelihood often yield incorrect solutions. To address this issue, we propose Guiding chain-of-thought ReAsoning with a CorrectnEss Discriminator (GRACE)

Rui-Chen Zheng, Yang Ai, Zhen-Hua Ling

Audio-visual speech enhancement (AV-SE) aims to enhance degraded speech along with extra visual information such as lip videos, and has been shown to be more effective than audio-only speech enhancement. This paper proposes further incorporating ultrasound tongue images to improve lip-based AV-SE systems' performance. Knowledge distillation is employed at th

Fani Derveni, Arefeh Abbasi, Pedro M. Reis

We perform finite element simulations to study the impact of defect-defect interactions on the pressure-induced buckling of thin, elastic, spherical shells containing two dimpled imperfections. Throughout, we quantify the critical buckling pressure of these shells using their knockdown factor. We examine cases featuring either identical or different geometri

ATLAS Collaboration

A search for Majorana neutrinos in same-sign $WW$ scattering events is presented. The analysis uses $\sqrt{s}= 13$ TeV proton-proton collision data with an integrated luminosity of 140 fb$^{-1}$ recorded during 2015-2018 by the ATLAS detector at the Large Hadron Collider. The analysis targets final states including exactly two same-sign muons and at least tw

Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto, Eric Schulz

In everyday conversations, humans can take on different roles and adapt their vocabulary to their chosen roles. We explore whether LLMs can take on, that is impersonate, different roles when they generate text in-context. We ask LLMs to assume different personas before solving vision and language tasks. We do this by prefixing the prompt with a persona that

EunJeong Hwang, Bodhisattwa Prasad Majumder, Niket Tandon

An important aspect of developing LLMs that interact with humans is to align models' behavior to their users. It is possible to prompt an LLM into behaving as a certain persona, especially a user group or ideological persona the model captured during its pertaining stage. But, how to best align an LLM with a specific user and not a demographic or ideological

Kellin Pelrine, Anne Imouza, Camille Thibault, Meilina Reksoprodjo

Misinformation poses a critical societal challenge, and current approaches have yet to produce an effective solution. We propose focusing on generalization, uncertainty, and how to leverage recent large language models, in order to create more practical tools to evaluate information veracity in contexts where perfect classification is impossible. We first de

Rui-Qian Li, Yi-Wei Shen, Bao-De Lin, Jingyi Yu

Photonic reservoir computing (PRC) is a special hardware recurrent neural network, which is featured with fast training speed and low training cost. This work shows a wavelength-multiplexing PRC architecture, taking advantage of the numerous longitudinal modes in a Fabry-Perot semiconductor laser. These modes construct connected physical neurons in parallel,

Xingchen Wan, Ruoxi Sun, Hootan Nakhost, Hanjun Dai

A hallmark of modern large language models (LLMs) is their impressive general zero-shot and few-shot abilities, often elicited through in-context learning (ICL) via prompting. However, while highly coveted and being the most general, zero-shot performances in LLMs are still typically weaker due to the lack of guidance and the difficulty of applying existing

S. D. Katore, S. P. Hatkar, D. P. Tadas

The purpose of this paper is to study the Kaluza-Klein universe in the context of the $f(R,T)$ gravity theory using magnetized strange quark matter (MSQM). To obtain exact solutions of field equations, we assume two types of volumetric expansion: power law and exponential law volumetric expansions. The violation of energy conditions has been studied. The phy

Zhenyu Zhang, Yehui Hou, Minyong Guo

In a recent work by Fernandes [arXiv:2305.10382], an exact stationary and axisymmetric solution was discovered in semiclassical gravity with type-A trace anomaly, identified as a quantum-corrected version of the Kerr black hole. In this study, we explore the observational signatures of this black hole solution. Our investigation reveals that there exist prog

M. Calixto, A. Mayorgas, N. A. Cordero, E. Romera

We analyze the magneto-optical conductivity (and related magnitudes like transmittance and Faraday rotation of the irradiated polarized light) of some elemental two-dimensional Dirac materials of group IV (graphene analogues, buckled honeycomb lattices, like silicene, germanene, stannane, etc.), group V (phosphorene), and zincblende heterostructures (like Hg

Matteo Colangeli, Antonio Di Francesco, Lamberto Rondoni

We consider several Hamiltonian systems perturbed by external agents, that preserve their Hamiltonian structure. We investigate the corrections to the canonical statistics resulting from coupling such systems with possibly large but finite reservoirs, and from the onset of processes breaking the time reversal symmetry. We analyze exactly solvable oscillators

Juanjuan Xu, Huanshui Zhang

In this paper, we study the linear quadratic (LQ) optimal control problem of linear systems with private input and measurement information. The main challenging lies in the unavailability of other regulators' historical input information. To overcome this difficulty, we introduce a kind of novel observers by using the private input and measurement informatio

Andreas Döpp, Igor Andriyash, Kim Ta Phuoc

All-optical Compton sources combine laser wakefield accelerators and intense scattering pulses to generate ultrashort bursts of backscattered radiation. The scattering pulse plays the role of a short-period undulator in which relativistic electrons oscillate and emit x-ray radiation. To date, most of the working laser-plasma accelerators operate preferably a

Bishal Santra, Sakya Basak, Abhinandan De, Manish Gupta

The use of large language models (LLMs) in natural language processing (NLP) tasks is rapidly increasing, leading to changes in how researchers approach problems in the field. To fully utilize these models' abilities, a better understanding of their behavior for different input protocols is required. With LLMs, users can directly interact with the models thr

Xingxing Zuo, Nan Yang, Nathaniel Merrill, Binbin Xu

Incrementally recovering 3D dense structures from monocular videos is of paramount importance since it enables various robotics and AR applications. Feature volumes have recently been shown to enable efficient and accurate incremental dense reconstruction without the need to first estimate depth, but they are not able to achieve as high of a resolution as de

Gijs Wijnholds, Michael Moortgat

This paper addresses structural ambiguity in Dutch relative clauses. By investigating the task of disambiguation by grounding, we study how the presence of a prior sentence can resolve relative clause ambiguities. We apply this method to two parsing architectures in an attempt to demystify the parsing and language model components of two present-day neural p

Louis Sharrock, Daniel Dodd, Christopher Nemeth

We introduce two new particle-based algorithms for learning latent variable models via marginal maximum likelihood estimation, including one which is entirely tuning-free. Our methods are based on the perspective of marginal maximum likelihood estimation as an optimization problem: namely, as the minimization of a free energy functional. One way to solve thi

Harald Garcke, Dennis Trautwein

In this work, we present a phase-field model for tumour growth, where a diffuse interface separates a tumour from the surrounding host tissue. In our model, we consider transport processes by an internal, non-solenoidal velocity field. We include viscoelastic effects with the help of a general Oldroyd-B type description with relaxation and possible stress ge

Zhitong Xiong, Sining Chen, Yi Wang, Lichao Mou

Geometric information in the normalized digital surface models (nDSM) is highly correlated with the semantic class of the land cover. Exploiting two modalities (RGB and nDSM (height)) jointly has great potential to improve the segmentation performance. However, it is still an under-explored field in remote sensing due to the following challenges. First, the

Tingting Ma, Qianhui Wu, Huiqiang Jiang, Börje F. Karlsson

Cross-lingual named entity recognition (NER) aims to train an NER system that generalizes well to a target language by leveraging labeled data in a given source language. Previous work alleviates the data scarcity problem by translating source-language labeled data or performing knowledge distillation on target-language unlabeled data. However, these methods

Yu-Bang Zheng, Xi-Le Zhao, Junhua Zeng, Chao Li

Tensor network (TN) representation is a powerful technique for computer vision and machine learning. TN structure search (TN-SS) aims to search for a customized structure to achieve a compact representation, which is a challenging NP-hard problem. Recent "sampling-evaluation"-based methods require sampling an extensive collection of structures and evaluating

Jianqing Chen, Qian Zhang

This paper is concerned with a quasilinear Schr\"{o}dinger system in $\mathbb R^{N}$ $$\left\{\aligned &-\Delta u+A(x)u-\frac{1}{2}\triangle(u^{2})u=\frac{2\alpha}{\alpha+\beta}|u|^{\alpha-2}u|v|^{\beta},\\ &-\Delta v+B(x)v-\frac{1}{2}\triangle(v^{2})v=\frac{2\beta}{\alpha+\beta}|u|^{\alpha}|v|^{\beta-2}v,\\ & u(x)\to 0\ \hbox{and}\quad v(x)\to 0\ \hbox{as}\

Qin Liu, Fei Wang, Chaowei Xiao, Muhao Chen

Language models are often at risk of diverse backdoor attacks, especially data poisoning. Thus, it is important to investigate defense solutions for addressing them. Existing backdoor defense methods mainly focus on backdoor attacks with explicit triggers, leaving a universal defense against various backdoor attacks with diverse triggers largely unexplored.

Lin Guan, Karthik Valmeekam, Sarath Sreedharan, Subbarao Kambhampati

There is a growing interest in applying pre-trained large language models (LLMs) to planning problems. However, methods that use LLMs directly as planners are currently impractical due to several factors, including limited correctness of plans, strong reliance on feedback from interactions with simulators or even the actual environment, and the inefficiency

Anthony Chen, Panupong Pasupat, Sameer Singh, Hongrae Lee

The remarkable capabilities of large language models have been accompanied by a persistent drawback: the generation of false and unsubstantiated claims commonly known as "hallucinations". To combat this issue, recent research has introduced approaches that involve editing and attributing the outputs of language models, particularly through prompt-based editi

Shivanshu Gupta, Matt Gardner, Sameer Singh

In-context learning (ICL), the ability of large language models to perform novel tasks by conditioning on a prompt with a few task examples, requires these examples to be informative about the test instance. The standard approach of independently ranking and selecting the most similar examples selects redundant examples while omitting important information.

Alba García-Ruiz

The Random Wave Conjecture of M. V. Berry is the heuristic that eigenfunctions of a classically chaotic system should behave like Gaussian random fields, in the large eigenvalue limit. In this work we collect some definitions and properties of Gaussian random fields, and show that the formulation of the Berry's conjecture proposed using local weak limits is

Tinatin Baratashvili, Christine Verbeke, Rony Keppens, Stefaan Poedts

Coronal mass ejections (CMEs) are large eruptions close to the solar surface, where plasma is ejected outwards into space at large speeds. When directed towards Earth, they interfere with Earth's magnetic fields and cause strong geo-effective storms. In order to mitigate the potential damage, forecasting tools are implemented. Recently, a novel heliospheric

Alexander Spangher, Nanyun Peng, Jonathan May, Emilio Ferrara

News articles are driven by the informational sources journalists use in reporting. Modeling when, how and why sources get used together in stories can help us better understand the information we consume and even help journalists with the task of producing it. In this work, we take steps toward this goal by constructing the largest and widest-ranging annota

P. Vezio, M. Bonaldi, A. Borrielli, F. Marino

Thermal noise is a major obstacle to observing quantum behavior in macroscopic systems. To mitigate its effect, quantum optomechanical experiments are typically performed in a cryogenic environment. However, this condition represents a considerable complication in the transition from fundamental research to quantum technology applications. It is therefore in

Yuxia Wang, Jonibek Mansurov, Petar Ivanov, Jinyan Su

Large language models (LLMs) have demonstrated remarkable capability to generate fluent responses to a wide variety of user queries. However, this has also raised concerns about the potential misuse of such texts in journalism, education, and academia. In this study, we strive to create automated systems that can detect machine-generated texts and pinpoint p

Wang Zhu, Jesse Thomason, Robin Jia

We train a language model (LM) to robustly answer multistep questions by generating and answering sub-questions. We propose Chain-of-Questions, a framework that trains a model to generate sub-questions and sub-answers one at a time by leveraging human annotated question decomposition meaning representation (QDMR). The key technical challenge is that QDMR onl

Jasper Ischebeck

We give theorems about asymptotic normality of general additive functionals on patricia tries in an i.i.d. setting, derived from results on tries by Janson (2022). These theorems are applied to show asymptotic normality of the distribution of random fringe trees in patricia tries. Formulas for asymptotic mean and variance are given. The proportion of fringe

Luis Guerrero-Mendez, Anxo Lema-Saavedra, Elena Jimenez, Antonio Fernandez-Ramos

Our automated reaction discovery program, AutoMeKin, has been utilized to investigate the formation of glycolonitrile (HOCH$_{2}$CN) in the gas phase under the low temperatures of the interstellar medium (ISM). The feasibility of a proposed pathway depends on the absence of barriers above the energy of reactants and the availability of the suggested precurso

Keming Lu, Xiaoman Pan, Kaiqiang Song, Hongming Zhang

We consider the problem of Open-world Information Extraction (Open-world IE), which extracts comprehensive entity profiles from unstructured texts. Different from the conventional closed-world setting of Information Extraction (IE), Open-world IE considers a more general situation where entities and relations could be beyond a predefined ontology. More impor

Amita Kamath, Jack Hessel, Kai-Wei Chang

Performant vision-language (VL) models like CLIP represent captions using a single vector. How much information about language is lost in this bottleneck? We first curate CompPrompts, a set of increasingly compositional image captions that VL models should be able to capture (e.g., single object, to object+property, to multiple interacting objects). Then, we

Krzysztof Oleszkiewicz

Motivated by a recent paper of Kevin Tanguy, in which the concept of second order influences on the discrete cube and Gauss space has been investigated in detail, the present note studies it in a more specific context of Boolean functions on the discrete cube. Some bounds which Tanguy obtained as applications of his more general approach are extended and com

Z. X. Ling, X. J. Sun, C. Zhang, S. L. Sun

The Lobster Eye Imager for Astronomy (LEIA), a pathfinder of the Wide-field X-ray Telescope of the Einstein Probe (EP) mission, was successfully launched onboard the SATech-01 satellite of the Chinese Academy of Sciences on 27 July 2022. In this paper, we introduce the design and on-ground test results of the LEIA instrument. Using state-of-the-art Micro-Por

Andreas Bott, Tim Janke, Florian Steinke

Flexible district heating grids form an important part of future, low-carbon energy systems. We examine probabilistic state estimation in such grids, i.e., we aim to estimate the posterior probability distribution over all grid state variables such as pressures, temperatures, and mass flows conditional on measurements of a subset of these states. Since the p

Samir Sahoo, Bosiljka Tadic, Malayaja Chutani, Neelima Gupte

The hidden geometry of simplicial complexes can influence the collective dynamics of nodes in different ways depending on the simplex-based interactions of various orders and competition between local and global structural features. We study a system of phase oscillators attached to nodes of 4-dimensional simplicial complexes and interacting via positive/neg

Najmeh Sadat Mirian, Elham Salehi, Frank Zimmermann

The Large Hadron electron Collider (LHeC) is a proposed future particle physics project colliding 60 GeV electrons from a six-pass recirculating energy-recovery linac (ERL) with 7 TeV protons stored in the LHC. The ERL technology allows for much higher beam current and, therefore, higher luminosity than a traditional linac. The high-current, high-energy elec

Zhi-Hao Lai, Tian-Hao Zhang, Qi Liu, Xinyuan Qian

The local and global features are both essential for automatic speech recognition (ASR). Many recent methods have verified that simply combining local and global features can further promote ASR performance. However, these methods pay less attention to the interaction of local and global features, and their series architectures are rigid to reflect local and

Mohammad Rowshan, Jinhong Yuan

The ordered-reliability bits (ORB) variant of guessing random additive noise decoding (GRAND), known as ORBGRAND, achieves remarkably low time complexity at high code rates compared to other GRAND variants. However, its computational complexity remains higher than other near-ML universal decoders like ordered-statistics decoding (OSD). To address this, we pr

Luka Pavlović

In this work, we propose a method for extracting text spans that may indicate one of the BIG5 psychological traits using a question-answering task with examples that have no answer for the asked question. We utilized the RoBERTa model fine-tuned on SQuAD 2.0 dataset. The model was further fine-tuned utilizing comments from Reddit. We examined the effect of t

Arne F. Nix, Max F. Burg, Fabian H. Sinz

Knowledge distillation (KD) is a simple and successful method to transfer knowledge from a teacher to a student model solely based on functional activity. However, current KD has a few shortcomings: it has recently been shown that this method is unsuitable to transfer simple inductive biases like shift equivariance, struggles to transfer out of domain genera

Ziang Xiao, Susu Zhang, Vivian Lai, Q. Vera Liao

We address a fundamental challenge in Natural Language Generation (NLG) model evaluation -- the design and evaluation of evaluation metrics. Recognizing the limitations of existing automatic metrics and noises from how current human evaluation was conducted, we propose MetricEval, a framework informed by measurement theory, the foundation of educational test

Yangsibo Huang, Samyak Gupta, Zexuan Zhong, Kai Li

Retrieval-based language models (LMs) have demonstrated improved interpretability, factuality, and adaptability compared to their parametric counterparts, by incorporating retrieved text from external datastores. While it is well known that parametric models are prone to leaking private data, it remains unclear how the addition of a retrieval datastore impac

Khalil Zakeri, Christophe Berthod

The scattering theory of low-energy (slow) electrons has been developed by Evans and Mills [Phys. Rev. B 5, 4126 (1972)]. The formalism is merely based on the electrostatic Coulomb interaction of the scattering electrons with the charge-density fluctuations above the surface and can describe most of the interesting features observed in the high-resolution el

Jiajia Chen, Jiancan Wu, Jiawei Chen, Xin Xin

Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns. Although improving the overall accuracy, GCNs unfortunately amplify popularity bias -- tail items are less likely to be recommended. This effect prevents the GCN-based RS from making precise and fair recommendation

Chuhao Liu, Shaojie Shen

Loop detection plays a key role in visual Simultaneous Localization and Mapping (SLAM) by correcting the accumulated pose drift. In indoor scenarios, the richly distributed semantic landmarks are view-point invariant and hold strong descriptive power in loop detection. The current semantic-aided loop detection embeds the topology between semantic instances t

Stavros Garoufalidis, Matthias Storzer, Campbell Wheeler

We prove that a formal power series associated to an ideally triangulated cusped hyperbolic 3-manifold (together with some further choices) is a topological invariant. This formal power series is conjectured to agree to all orders in perturbation theory with two important topological invariants of hyperbolic knots, namely the Kashaev invariant and the Anders

Simeon Ball, Ricard Vilar

We prove that certain classical cyclic redundancy check codes can be used for classical error correction and not just classical error detection. We extend the idea of classical cyclic redundancy check codes to quantum cyclic redundancy check codes. This allows us to construct quantum stabiliser codes which can correct burst errors where the burst length atta

Xingyu Fu, Ben Zhou, Sihao Chen, Mark Yatskar

Recent advances in multimodal large language models (LLMs) have shown extreme effectiveness in visual question answering (VQA). However, the design nature of these end-to-end models prevents them from being interpretable to humans, undermining trust and applicability in critical domains. While post-hoc rationales offer certain insight into understanding mode

Nicolas Staudenmaier, Anjusha Vijayakumar-Sreeja, Genko Genov, Daniel Cohen

Diffusion noise represents a major constraint to successful liquid state nano-NMR spectroscopy. Using the Fisher information as a faithful measure, we theoretically calculate and experimentally show that phase sensitive protocols are superior in most experimental scenarios, as they maximize information extraction from correlations in the sample. We derive th

Shuting Yan, Pingping Chen, Honghui Chen, Huan Mao

Anomaly detection is represented as an unsupervised learning to identify deviated images from normal images. In general, there are two main challenges of anomaly detection tasks, i.e., the class imbalance and the unexpectedness of anomalies. In this paper, we propose a multiresolution feature guidance method based on Transformer named GTrans for unsupervised

Ruoyao Wang, Graham Todd, Eric Yuan, Ziang Xiao

In this work, we investigate the capacity of language models to generate explicit, interpretable, and interactive world models of scientific and common-sense reasoning tasks. We operationalize this as a task of generating text games, expressed as hundreds of lines of Python code. To facilitate this task, we introduce ByteSized32 (Code: github.com/cognitiveai

Vikas Raunak, Amr Sharaf, Yiren Wang, Hany Hassan Awadallah

While Neural Machine Translation (NMT) represents the leading approach to Machine Translation (MT), the outputs of NMT models still require translation post-editing to rectify errors and enhance quality under critical settings. In this work, we formalize the task of direct translation post-editing with Large Language Models (LLMs) and explore the use of GPT-

Sohee Yang, Jonghyeon Kim, Joel Jang, Seonghyeon Ye

Previous works in prompt engineering for large language models have introduced different gradient-free probability-based prompt selection methods that aim to choose the optimal prompt among the candidates for a given task but have failed to provide a comprehensive and fair comparison between each other. In this paper, we propose a unified framework to interp

Yige Li, Xixiang Lyu, Xingjun Ma, Nodens Koren

Deep neural networks (DNNs) have been found to be vulnerable to backdoor attacks, raising security concerns about their deployment in mission-critical applications. While existing defense methods have demonstrated promising results, it is still not clear how to effectively remove backdoor-associated neurons in backdoored DNNs. In this paper, we propose a nov

Andreas Förster, Alarith Uhde, Mathias Komesker, Christina Komesker

LoopBoxes is an accessible digital musical instrument designed to create an intuitive access to loop based music making for children with special educational needs (SEN). This paper describes the evaluation of the instrument in the form of a pilot study during a music festival in Berlin, Germany, as well as a case study with children and music teachers in a

Peter Jansen

In this work, we show that contemporary language models have a previously unknown skill -- the capacity for electronic circuit design from high-level textual descriptions, akin to code generation. We introduce two benchmarks: Pins100, assessing model knowledge of electrical components, and Micro25, evaluating a model's capability to design common microcontro

Ming Ji, Holger F. Hofmann

In quantum theory, a measurement context is defined by an orthogonal basis in a Hilbert space, where each basis vector represents a specific measurement outcome. The precise quantitative relation between two different measurement contexts can thus be characterized by the inner products of nonorthogonal states in that Hilbert space. Here, we use measurement o

Janek Groß, Michael Kläs, Lisa Jöckel, Pascal Gerber

As the use of Artificial Intelligence (AI) components in cyber-physical systems is becoming more common, the need for reliable system architectures arises. While data-driven models excel at perception tasks, model outcomes are usually not dependable enough for safety-critical applications. In this work,we present a timeseries-aware uncertainty wrapper for de

Yuwei Zhang, Zihan Wang, Jingbo Shang

We introduce ClusterLLM, a novel text clustering framework that leverages feedback from an instruction-tuned large language model, such as ChatGPT. Compared with traditional unsupervised methods that builds upon "small" embedders, ClusterLLM exhibits two intriguing advantages: (1) it enjoys the emergent capability of LLM even if its embeddings are inaccessib

Siddharth Kumar Sahoo, Neeraj Yadav, Indrani Banerjee

Einstein-Maxwell dilaton-axion (EMDA) gravity provides a simple framework to investigate the signatures of string theory. The axion and the dilaton fields arising in EMDA gravity have important implications in inflationary cosmology and in addressing the late time acceleration of the universe. It is therefore instructive to explore the implications of such a

Weiqi Wang, Tianqing Fang, Wenxuan Ding, Baixuan Xu

The task of zero-shot commonsense question answering evaluates models on their capacity to reason about general scenarios beyond those presented in specific datasets. Existing approaches for tackling this task leverage external knowledge from CommonSense Knowledge Bases (CSKBs) by pretraining the model on synthetic QA pairs constructed from CSKBs. In these a

Ganghwi Kim, Dae-Han Jung, Hee-Sung Han, Ki-Suk Lee

The dynamic behaviors of magnetic domain walls have significant implications for developing advanced spintronic devices. In this study, we investigate the intriguing resonance phenomenon within the magnetic domain wall structure and its profound influence on dynamic motion, focusing on the dissipation mechanism. By applying a static external magnetic field,

Rodrigo Diaz, Charalampos Saitis, Mark Sandler

In this work, we propose a method for the controllable synthesis of real-time contact sounds using neural resonators. Previous works have used physically inspired statistical methods and physical modelling for object materials and excitation signals. Our method incorporates differentiable second-order resonators and estimates their coefficients using a neura

Douadi Drihem

The aim of this paper is to present some necessary conditions for the boundedness of the mapping $f\mapsto |f|^{\mu },\mu >0$ on Besov spaces equipped with power weights.

Na Zhang, Kun Yue, Chao Fang

As a transformative general-purpose technology, AI has empowered various industries and will continue to shape our lives through ubiquitous applications. Despite the enormous benefits from wide-spread AI deployment, it is crucial to address associated downside risks and therefore ensure AI advances are safe, fair, responsible, and aligned with human values.

Ananya Harsh Jha, Tom Sherborne, Evan Pete Walsh, Dirk Groeneveld

Large language models (LLMs) enable unparalleled few- and zero-shot reasoning capabilities but at a high computational footprint. A growing assortment of methods for compression promises to reduce the computational burden of LLMs in deployment, but so far, only quantization approaches have been demonstrated to be effective for LLM compression while maintaini

M. Glowacki, K. Lee-Waddell, A. T. Deller, N. Deg

We report on the commensal ASKAP detection of a fast radio burst (FRB), FRB20211127I, and the detection of neutral hydrogen (HI) emission in the FRB host galaxy, WALLABYJ131913-185018 (hereafter W13-18). This collaboration between the CRAFT and WALLABY survey teams marks the fifth, and most distant, FRB host galaxy detected in HI, not including the Milky Way

M. Muñoz-Echeverría, J. F. Macías-Pérez, E. Artis, W. Cui

The determination of the mass of galaxy clusters from observations is subject to systematic uncertainties. Beyond the errors due to instrumental and observational systematic effects, in this work we investigate the bias introduced by modelling assumptions. In particular, we consider the reconstruction of the mass of galaxy clusters from convergence maps empl

Fengjun Zhuo, Jian Kang, Aurélien Manchon, Zhenxiang Cheng

Magnonics or magnon spintronics is an emerging field focusing on generating, detecting, and manipulating magnons. As charge-neutral quasi-particles, magnons are promising information carriers because of their low energy dissipation and long coherence length. In the past decade, topological phases in magnonics have attracted intensive attention due to their f

J. R. Alvarado García, D. Rosales Herrera, A. Fernández Téllez, Bogar Díaz

We investigate the structure of the medium formed in heavy ion collisions using three different models: the Color String Percolation Model (CSPM), the Core-Shell-Color String Percolation Model (CSCSPM), and the Color Glass Condensate (CGC) framework. We analyze the radial distribution function of the transverse representation of color flux tubes in each mode

Huang Bojun, Fei Yuan

It is typically understood that the training of modern neural networks is a process of fitting the probability distribution of desired output. However, recent paradoxical observations in a number of language generation tasks let one wonder if this canonical probability-based explanation can really account for the empirical success of deep learning. To resolv

Mohsen Pourvali, Yao Meng, Chen Sheng, Yangzhou Du

In this paper, we investigate the effectiveness of integrating a hierarchical taxonomy of labels as prior knowledge into the learning algorithm of a flat classifier. We introduce two methods to integrate the hierarchical taxonomy as an explicit regularizer into the loss function of learning algorithms. By reasoning on a hierarchical taxonomy, a neural networ

Alexander Droob, Daniel Morratz, Frederik Langkilde Jakobsen, Jacob Carstensen

The broad development and usage of edge devices has highlighted the importance of creating resilient and computationally advanced environments. When working with edge devices these desiderata are usually achieved through replication and offloading. This paper reports on the design and implementation of Workrs, a fault tolerant service that enables the offloa

Zixuan Jiang, Jiaqi Gu, Hanqing Zhu, David Z. Pan

Transformers have achieved great success in machine learning applications. Normalization techniques, such as Layer Normalization (LayerNorm, LN) and Root Mean Square Normalization (RMSNorm), play a critical role in accelerating and stabilizing the training of Transformers. While LayerNorm recenters and rescales input vectors, RMSNorm only rescales the vector

Akari Asai, Sneha Kudugunta, Xinyan Velocity Yu, Terra Blevins

Despite remarkable advancements in few-shot generalization in natural language processing, most models are developed and evaluated primarily in English. To facilitate research on few-shot cross-lingual transfer, we introduce a new benchmark, called BUFFET, which unifies 15 diverse tasks across 54 languages in a sequence-to-sequence format and provides a fixe

Žiga Babnik, Naser Damer, Vitomir Štruc

Contemporary face recognition (FR) models achieve near-ideal recognition performance in constrained settings, yet do not fully translate the performance to unconstrained (realworld) scenarios. To help improve the performance and stability of FR systems in such unconstrained settings, face image quality assessment (FIQA) techniques try to infer sample-quality

Tasneem Saleem, Salleh Ahmad, Jean-Baptiste Cizel, Christophe De La Taille

Medical applications, such as Positron Emission Tomography (PET), and space applications, such as Light Detection and Ranging (LIDAR), are in need of highly specialized ASICs. Weeroc, in collaboration with different partners, is highly involved in developing a new generation of front-end ASICs. In the context of a joined LIDAR project among Weeroc, CNES, and

Zhi-Peng Xing, Xiao-Gang He, Fei Huang, Chang Yang

A large amount of data on hadronic two body weak decays of anti-triplet charmed baryons $T_{c\bar 3}$ to an octet baryon $T_8$ and an octet or singlet pseudoscalar meson $P$, $T_{c \bar 3} \to T_8 P$, have been measured. The SU(3) flavor symmetry has been applied to study these decays to obtain insights about weak interactions for charm physics. However not

Kaijian Sha, Yun Wang, Chunjing Xie

In this paper, we prove the uniform nonlinear structural stability of Poiseuille flows with suitably large flux for the steady Navier-Stokes system in a two-dimensional strip with arbitrary period. Furthermore, the well-posedness theory for the Navier-Stokes system is also proved even when the $L^2$-norm of the external force is large. In particular, if the