April 2023 arXiv papers — page 52

Stéphane Bessy, Johannes Rauch, Dieter Rautenbach, Uéverton S. Souza

We show that every graph $G$ of maximum degree $\Delta$ and sufficiently large order has a vertex cutset $S$ of order at most $\Delta$ that induces a subgraph $G[S]$ of maximum degree at most $\Delta-3$. For $\Delta\in \{ 4,5\}$, we refine this result by considering also the average degree of $G[S]$. If $G$ has no $K_{r,r}$ subgraph, then we show the existen

Kevin Chern, Kelly Boothby, Jack Raymond, Pau Farré

Quantum annealing has emerged as a powerful platform for simulating and optimizing classical and quantum Ising models. Quantum annealers, like other quantum and/or analog computing devices, are susceptible to nonidealities including crosstalk, device variation, and environmental noise. Compensating for these effects through calibration refinement or "shimmin

Bin Zhang, Lijuan Li, Zhiwei Xu, Dapeng Li

In multi-agent reinforcement learning (MARL), self-interested agents attempt to establish equilibrium and achieve coordination depending on game structure. However, existing MARL approaches are mostly bound by the simultaneous actions of all agents in the Markov game (MG) framework, and few works consider the formation of equilibrium strategies via asynchron

Harald Räcke, Stefan Schmid, Ruslan Zabrodin

The performance of many large-scale and data-intensive distributed systems critically depends on the capacity of the interconnecting network. This paper is motivated by the vision of self-adjusting infrastructures whose resources can be adjusted according to the workload they currently serve, in a demand-aware manner. Such dynamic adjustments can be exploite

Yannick Hoga

Systemic risk measures have been shown to be predictive of financial crises and declines in real activity. Thus, forecasting them is of major importance in finance and economics. In this paper, we propose a new forecasting method for systemic risk as measured by the marginal expected shortfall (MES). It is based on first de-volatilizing the observations and,

Clement Sicard, Kajetan Pyszkowski, Victor Gillioz

Recent breakthroughs in NLP largely increased the presence of ASR systems in our daily lives. However, for many low-resource languages, ASR models still need to be improved due in part to the difficulty of acquiring pertinent data. This project aims to help advance research in ASR models for Swiss German dialects, by providing insights about the performance

Bata Vasc, Nithin Raveendran, Bane Vasic

Best and practical watermarking schemes for copyright protection of 3D meshes are required to be blind and robust to attacks and errors. In this paper, we present the latest developments in 3D blind watermarking with a special emphasis on our Ordered Statistics Vertex Extraction and Tracing Algorithm (OSVETA) algorithm and its improvements. OSVETA is based o

Xiaohan Cui, Ruo-Yang Zhang, Xulong Wang, Wei Wang

Lines of exceptional points are robust in the 3-dimensional non-Hermitian parameter space without requiring any symmetry. However, when more elaborate exceptional structures are considered, the role of symmetry becomes critical. One such case is the exceptional chain (EC), which is formed by the intersection or osculation of multiple exceptional lines (ELs).

Julia Rozanova, Marco Valentino, Lucas Cordeiro, Andre Freitas

Probing strategies have been shown to detect the presence of various linguistic features in large language models; in particular, semantic features intermediate to the "natural logic" fragment of the Natural Language Inference task (NLI). In the case of natural logic, the relation between the intermediate features and the entailment label is explicitly known

Matteo Basei, Giorgio Ferrari, Neofytos Rodosthenous

The socioeconomic impact of pollution naturally comes with uncertainty due to, e.g., current new technological developments in emissions' abatement or demographic changes. On top of that, the trend of the future costs of the environmental damage is unknown: Will global warming dominate or technological advancements prevail? The truth is that we do not know w

Jens Hoppe

For the simplest membrane matrix model (corresponding to reduced 3 dimensional SU(2) Yang Mills theory) the form of the ground state wave function is given.

Rafaël Bocquet

We study the conservativity of extensions by additional strict equalities of dependent type theories (and more general second-order generalized algebraic theories). The conservativity of Extensional Type Theory over Intensional Type Theory was proven by Hofmann. Our goal is to generalize such results to type theories without the Uniqueness of Identity Proofs

Marianne Akian, Stéphane Gaubert, Shanqing Liu

We introduce a new numerical method to approximate the solution of a finite horizon deterministic optimal control problem. We exploit two Hamilton-Jacobi-Bellman PDE, arising by considering the dynamics in forward and backward time. This allows us to compute a neighborhood of the set of optimal trajectories, in order to reduce the search space. The solutions

Shaokai Liu, Hao Feng, Wengang Zhou, Houqiang Li

Tremendous efforts have been made on document image rectification, but how to learn effective representation of such distorted images is still under-explored. In this paper, we present DocMAE, a novel self-supervised framework for document image rectification. Our motivation is to encode the structural cues in document images by leveraging masked autoencoder

Han Wang, Ian Harry, Alexander Nitz, Yi-Ming Hu

The measurement of eccentricity would provide strong constraints on the formation channel of stellar-mass binary black holes. However, current ground-based gravitational wave detectors will, in most cases, not be able to measure eccentricity due to orbital circularization. Space-based observatories, in contrast, can determine binary eccentricity at 0.01Hz to

Arturo Amor-Quiroz, William Focillon, Cédric Lorcé, Simone Rodini

We compute all the gravitational form factors in the scalar diquark model at the one-loop level using two different regularization methods. We check explicitly that all the Poincar\'e sum rules are satisfied and we discuss in detail the results for the trace of the energy-momentum tensor. Finally we discuss the spatial distributions of energy and pressure in

Wei Huo, Kam Fai Elvis Tsang, Yamin Yan, Karl Henrik Johansson

In this paper, we study the problem of consensus-based distributed Nash equilibrium (NE) seeking where a network of players, abstracted as a directed graph, aim to minimize their own local cost functions non-cooperatively. Considering the limited energy of players and constrained bandwidths, we propose a stochastic event-triggered algorithm by triggering eac

Krzysztof Palmi, Wojciech Kubinski, Piotr Darnowski

A nuclear reactor based on MIT BEAVRS benchmark was used as a typical power generating Pressurized Water Reactor (PWR). The PARCS v3.2 nodal-diffusion core simulator was used as a full-core reactor physics solver to emulate the operation of a reactor and to generate training, and validation data for the ANN. The ANN was implemented with dedicated Python 3.8

Tommaso Bendinelli, Luca Biggio, Pierre-Alexandre Kamienny

In symbolic regression, the goal is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variables, and constants. However, the combinatorial space of possible expressions can make it challenging for traditional evolutionary algorithms to find the correct expression in a reaso

Giulia Castagnolo, Concetto Spampinato, Francesco Rundo, Daniela Giordano

Continual learning has recently attracted attention from the research community, as it aims to solve long-standing limitations of classic supervisedly-trained models. However, most research on this subject has tackled continual learning in simple image classification scenarios. In this paper, we present a benchmark of state-of-the-art continual learning meth

Antonis Achilleos, Aggeliki Chalki

We present quantitative logics with two-step semantics based on the framework of quantitative logics introduced by Arenas et al. (2020) and the two-step semantics defined in the context of weighted logics by Gastin & Monmege (2018). We show that some of the fragments of our logics augmented with a least fixed point operator capture interesting classes of cou

Qing Yu, Atsushi Hashimoto, Yoshitaka Ushiku

To transfer the knowledge learned from a labeled source domain to an unlabeled target domain, many studies have worked on universal domain adaptation (UniDA), where there is no constraint on the label sets of the source domain and target domain. However, the existing UniDA methods rely on source samples with correct annotations. Due to the limited resources

Miloud Mekkaoui, Ahmed Jellal, Abderrahim El Mouhafid

We investigate the transmissions of fermions through gapped graphene structures by employing a combination of double barrier tilting and a time-oscillating potential. The latter introduces additional sidebands into the transmission probability, which manifest at energy levels determined by the frequency and incident energy. These sidebands arise from the abs

Harish D. Ramachandran, Amar C. Vutha

Precision measurements with atoms and molecules can search for subtle violations of time-reversal symmetry (T) in nuclei, and thereby probe a variety of new physics models. We present a detailed scheme for a nuclear T-violation search experiment using $^{153}$Eu$^{3+}$ ions doped in non-centrosymmetric sites within a Y$_2$SiO$_5$ crystal. The ions in this so

Mukremin Kilic, Alejandro H. Córsico, Adam G. Moss, Gracyn Jewett

We present APO and Gemini time-series photometry of WD J004917.14$-$252556.81, an ultramassive DA white dwarf with $T_{\rm eff} = 13020$ K and $\log{g} = 9.34$. We detect variability at two significant frequencies, making J0049$-$2525 the most massive pulsating white dwarf currently known with $M_\star=1.31~M_{\odot}$ (for a CO core) or $1.26~M_{\odot}$ (for

Jialuo Du, Yidong Ren, Mi Zhang, Yunhao Liu

Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm marked by low-power and long-distance communication. Among them, LoRa is widely deployed for its unique characteristics and open-source technology. By adopting the Chirp Spread Spectrum (CSS) modulation, LoRa enables low signal-to-noise ratio (SNR) communication. The stan

Arielle Marc-Zwecker

In this work, we consider relative character varieties for representations of the 3-punctured sphere group in PU(2,1). We provide necessary and sufficient conditions on the peripheral conjugacy classes, for such a representation to admit a decomposition as products of special elliptic elements. We prove that the representations satisfying these conditions fo

Yifei Jin, Marios Daoutis, Sarunas Girdzijauskas, Aristides Gionis

Cellular coverage quality estimation has been a critical task for self-organized networks. In real-world scenarios, deep-learning-powered coverage quality estimation methods cannot scale up to large areas due to little ground truth can be provided during network design & optimization. In addition they fall short in produce expressive embeddings to adequately

Kristian Gonzalez Barman, Sascha Caron, Tom Claassen, Henk de Regt

Scientific understanding is a fundamental goal of science, allowing us to explain the world. There is currently no good way to measure the scientific understanding of agents, whether these be humans or Artificial Intelligence systems. Without a clear benchmark, it is challenging to evaluate and compare different levels of and approaches to scientific underst

Mingjun Zhao, Mengzhen Wang, Yinglong Ma, Di Niu

Text clustering, as one of the most fundamental challenges in unsupervised learning, aims at grouping semantically similar text segments without relying on human annotations. With the rapid development of deep learning, deep clustering has achieved significant advantages over traditional clustering methods. Despite the effectiveness, most existing deep text

Ali Momeni, Babak Rahmani, Matthieu Mallejac, Philipp Del Hougne

Recent years have witnessed the outstanding success of deep learning in various fields such as vision and natural language processing. This success is largely indebted to the massive size of deep learning models that is expected to increase unceasingly. This growth of the deep learning models is accompanied by issues related to their considerable energy cons

Olga Cherednichenko, Fahad Muhammad, Jérôme Darmont, Cécile Favre

Collaborative Business Analysis (CBA) is a methodology that involves bringing together different stakeholders, including business users, analysts, and technical specialists, to collaboratively analyze data and gain insights into business operations. The primary objective of CBA is to encourage knowledge sharing and collaboration between the different groups

Mehmet Yildirim, Yogesh Langhe

We demonstrate our solution for the 2019 COCO panoptic segmentation task. Our method first performs instance segmentation and semantic segmentation separately, then combines the two to generate panoptic segmentation results. To enhance the performance, we add several expert models of Mask R-CNN in instance segmentation to tackle the data imbalance problem in

B. Chauhan, A. K. Rao, R. P. Malik

We demonstrate the existence of the first-class constraints on the massless Abelian 3-form theory which generate the classical gauge symmetry transformations for this theory in any arbitrary D-dimension of spacetime. We write down the explicit expression for the generator in terms of these first-class constraints. Using the celebrated Noether theorem, corres

Joseph K. L. Lee, Maurice Jamieson, Nick Brown

Leveraging vectorisation, the ability for a CPU to apply operations to multiple elements of data concurrently, is critical for high performance workloads. However, at the time of writing, commercially available physical RISC-V hardware that provides the RISC-V vector extension (RVV) only supports version 0.7.1, which is incompatible with the latest ratified

Guido Mazzuca, Ronan Memin

In this paper, we prove a polynomial Central Limit Theorem for several integrable models, and for the $\beta$-ensembles at high-temperature with polynomial potential. Furthermore, we connect the mean values, the variances and the correlations of the moments of the Lax matrices of these integrable systems with the ones of the $\beta$-ensembles. Moreover, we s

Hao Xie, Dong Li

Reconfigurable intelligent surface (RIS) has been regarded as a promising technique due to its high array gain and low power. However, the traditional passive RIS suffers from the ``double fading'' effect, which has restricted the performance of passive RIS-aided communications. Fortunately, active RIS can alleviate this problem since it can adjust the phase

Hao Zhang, Dan Qu, Keji Shao, Xukui Yang

We introduceDropDim, a structured dropout method designed for regularizing the self-attention mechanism, which is a key component of the transformer. In contrast to the general dropout method, which randomly drops neurons, DropDim drops part of the embedding dimensions. In this way, the semantic information can be completely discarded. Thus, the excessive co

Daniel Ritchie, Paul Guerrero, R. Kenny Jones, Niloy J. Mitra

Procedural models (i.e. symbolic programs that output visual data) are a historically-popular method for representing graphics content: vegetation, buildings, textures, etc. They offer many advantages: interpretable design parameters, stochastic variations, high-quality outputs, compact representation, and more. But they also have some limitations, such as t

Joseph K. L. Lee, Maurice Jamieson, Nick Brown, Ricardo Jesus

Whilst the RISC-V Vector extension (RVV) has been ratified, at the time of writing both hardware implementations and open source software support are still limited for vectorisation on RISC-V. This is important because vectorisation is crucial to obtaining good performance for High Performance Computing (HPC) workloads and, as of April 2023, the Allwinner D1

Zhe Zhao, Leonardo Modesto

In a large class of nonlocal as well as local higher derivative theories minimally coupled to the matter sector, we investigate the exactness of two different classes of homogeneous G\"{o}del-type solutions, which may or may not allow closed time-like curves (CTC). Our analysis is limited to spacetimes solving the Einstein's EoM, thus we can not exclude the

Sachin Kumar Danisetty, Santhosh Reddy Mylaram, Pawan Kumar

We propose a second order gradient based method with ADAM and RMSprop for the training of generative adversarial networks. The proposed method is fastest to obtain similar accuracy when compared to prominent second order methods. Unlike state-of-the-art recent methods, it does not require solving a linear system, or it does not require additional mixed secon

Mingjun Zhao, Yakun Yu, Xiaoli Wang, Lei Yang

Despite the success of deep learning in video understanding tasks, processing every frame in a video is computationally expensive and often unnecessary in real-time applications. Frame selection aims to extract the most informative and representative frames to help a model better understand video content. Existing frame selection methods either individually

Toshiya Namikawa, Blake D. Sherwin

We propose a new probe of inflationary gravitational waves (IGWs): the cross-correlation of the lensing of inflationary $B$-mode polarization with a large-scale structure (LSS) tracer, which can also be a cosmic microwave background (CMB) lensing map. This is equivalent to measuring a three-point function of two CMB $B$-modes and an LSS tracer. We forecast e

Nickos Papadatos

Let $S_n=I_1+\cdots+I_n$ be a sum of independent indicators $I_i$, with $p_i=\Pr(I_i=1)=1-\Pr(I_i=0)$, $i=1,\ldots,n$. It is well-known that the total variation distance between $S_n$ and $Z_\lambda$, where $Z_\lambda$ has a Poisson distribution with mean $\lambda=\sum_{i=1}^n p_i$, is typically of order $\sum_{i=1}^n p_i^2$. In the present work we propose a

Lydia Negka, Angeliki Katsika, Georgios Spathoulas, Vassilis Plagianakos

Public blockchain systems offer security guarantees that cannot be matched by any centralised system. This offering has attracted a lot of interest and has exposed a significant limitation of most blockchain designs with regards to scalability. One of the scaling solutions proposed is state channels which enables serving given applications with minimum numbe

Francesco Ardizzon, Francesco Giurisato, Stefano Tomasin

We propose a novel advantage distillation strategy for physical layer-based secret-key-agreement (SKA). We consider a scenario where Alice and Bob aim at extracting a common bit sequence, which should remain secret to Eve, by quantizing a random number obtained from measurements at their communication channel. We propose an asymmetric advantage distillation

Qin Chao, Eunsoo Kim, Boyang Li

Investments in movie production are associated with a high level of risk as movie revenues have long-tailed and bimodal distributions. Accurate prediction of box-office revenue may mitigate the uncertainty and encourage investment. However, learning effective representations for actors, directors, and user-generated content-related keywords remains a challen

Petra Mengistu, Karen Masters

In the classic Hubble spiral sequence, arm windiness correlates with bulge size; Sa type spiral galaxies with larger bulges also have the most tightly wound spirals. Exceptions to this have long been known, and in recent work using Galaxy Zoo morphologies no strong correlation was seen in a volume limited sample. In this Research Note, we explore the impact

Mingjun Zhao, Shan Lu, Zixuan Wang, Xiaoli Wang

Automated augmentation is an emerging and effective technique to search for data augmentation policies to improve generalizability of deep neural network training. Most existing work focuses on constructing a unified policy applicable to all data samples in a given dataset, without considering sample or class variations. In this paper, we propose a novel two

Hao Zhang, Nianwen Si, Yaqi Chen, Wenlin Zhang

The end-to-end speech translation (E2E-ST) model has gradually become a mainstream paradigm due to its low latency and less error propagation. However, it is non-trivial to train such a model well due to the task complexity and data scarcity. The speech-and-text modality differences result in the E2E-ST model performance usually inferior to the corresponding

Anxo Biasi, Oleg Evnin, Boris A. Malomed

We identify a class of trapping potentials in cubic nonlinear Schr\"{o}dinger equations (NLSEs) that make them non-integrable, but prevent the emergence of power spectra associated with ergodicity. The potentials are characterized by equidistant energy spectra (e.g., the harmonic-oscillator trap), which give rise to a large number of resonances enhancing the

Antonio Bianchi

The increasing interest in environmentally friendly gas mixtures for gaseous particle detectors, especially tetrafluoropropene-based gas mixtures for Resistive Plate Chambers (RPCs), has prompted the need for simulating electron transport coefficients and reaction rates in these mixtures in recent years. MATOQ is a Monte Carlo simulation program that calcula

Polina Karpikova, Radionova Ekaterina, Anastasia Yaschenko, Andrei Spiridonov

Generative DNNs are a powerful tool for image synthesis, but they are limited by their computational load. On the other hand, given a trained model and a task, e.g. faces generation within a range of characteristics, the output image quality will be unevenly distributed among images with different characteristics. It follows, that we might restrain the model

Wenhao Wang, Yifan Sun, Yi Yang

Video Copy Detection (VCD) has been developed to identify instances of unauthorized or duplicated video content. This paper presents our second place solutions to the Meta AI Video Similarity Challenge (VSC22), CVPR 2023. In order to compete in this challenge, we propose Feature-Compatible Progressive Learning (FCPL) for VCD. FCPL trains various models that

Christoph Tremmel, Indu Bodala, M. C. Schraefel

In an effort to make biosignal integration more accessible to explore for more HCI researchers, this paper presents our investigation of how well a standard, near ubiquitous webcam can support remote sensing of heart rate and respiration rate across skin tone ranges. The work contributes: how the webcam can be used for this purpose, its limitations, and how

Alexei Vazquez, Chrysostomos Marasinou, Georgios Kalogridis, Christos Ellinas

Delays in activities completion drive human projects to schedule and cost overruns. It is believed activity delays are the consequence of multiple idiosyncrasies without specific patterns or rules. Here we show that is not the case. Using data for 180 construction project schedules, we demonstrate that activity delays satisfy a universal model that we call t

Clayton Roberts, Rutger IJzermans, David Randell, Matthew Jones

Methane is a powerful greenhouse gas, and a primary target for mitigating climate change in the short-term future due to its relatively short atmospheric lifetime and greater ability to trap heat in Earth's atmosphere compared to carbon dioxide. Top-down observations of atmospheric methane are possible via drone and aircraft surveys as well as satellites suc

Wesley Cowan, Michael N. Katehakis, Sheldon M. Ross

We study new types of dynamic allocation problems the {\sl Halting Bandit} models. As an application, we obtain new proofs for the classic Gittins index decomposition result and recent results of the authors in `Multi-armed bandits under general depreciation and commitment.'

Andrija Kostić, Deaglan J. Bartlett, Harry Desmond

If dark matter annihilates into standard model particles with a cross-section which is velocity dependent, then Local Group dwarf galaxies will not be the best place to search for the resulting gamma ray emission. A greater flux would be produced by more distant and massive halos, with larger velocity dispersions. We construct full-sky predictions for the ga

Nandini Iyer, Ronaldo Menezes, Hugo Barbosa

Cities create potential for individuals from different backgrounds to interact with one another. It is often the case, however, that urban infrastructure obfuscates this potential, creating dense pockets of affluence and poverty throughout a region. The spatial distribution of job opportunities, and how it intersects with the residential landscape, is one of

J. A. Bagger, S. Belomestnykh, P. C. Bhat, J. E. Brau

This statement from the American Linear Collider Committee to the P5 subpanel has three purposes. It presents a brief summary of the case for an $e^+e^-$ Higgs factory that has emerged from Snowmass 2021. It highlights the special virtues of the ILC that are shared with other linear colliders but not with circular colliders. Finally, it calls attention to th

Yingxin Mu, Artem Sapozhnikov

We study visibility inside the vacant set of three models in $\mathbb R^d$ with slow decay of spatial correlations: Brownian interlacements, Poisson cylinders and Boolean model. For each of them, we obtain sharp asymptotic bounds on the probability of visibility to distance $r$ in some direction in terms of the probability of visibility to distance $r$ in a

Lingyuan Meng, Ke Liang, Bin Xiao, Sihang Zhou

Few-shot relation reasoning on knowledge graphs (FS-KGR) aims to infer long-tail data-poor relations, which has drawn increasing attention these years due to its practicalities. The pre-training of previous methods needs to manually construct the meta-relation set, leading to numerous labor costs. Self-supervised learning (SSL) is treated as a solution to ta

Aleksandar Milivojevic

We show that global vanishing of Massey products on a commutative differential graded algebra is not invariant under field extension. Non-vanishing triple Massey products remain non-vanishing upon field extension, while higher Massey products can generally vanish. If the field being extended is algebraically closed, all non-vanishing Massey products remain n

Hao Zhang, Nianwen Si, Yaqi Chen, Wenlin Zhang

Existing techniques often attempt to make knowledge transfer from a powerful machine translation (MT) to speech translation (ST) model with some elaborate techniques, which often requires transcription as extra input during training. However, transcriptions are not always available, and how to improve the ST model performance without transcription, i.e., dat

Taigao Ma, Haozhu Wang, L. Jay Guo

Optical multilayer thin film structures have been widely used in numerous photonic applications. However, existing inverse design methods have many drawbacks because they either fail to quickly adapt to different design targets, or are difficult to suit for different types of structures, e.g., designing for different materials at each layer. These methods al

Nicola Garofalo, Giulio Tralli

In this note we revisit a result in [9], where we established nonlocal isoperimetric inequalities and the related embeddings for Besov spaces adapted to a class of H\"ormander operators of Kolmogorov-type. We provide here a new proof which exploits a weak-type Sobolev embedding established in [11].

Michel Daher Mansour, Jacopo Oswald, Davide Beretta, Michael Stiefe

We report a study on the relationship between structure and electron transport properties of nanoscale graphene/pentacene interfaces. We fabricated graphene/pentacene interfaces from 10-30 nm thick needle-like pentacene nanostructures down to two-three layers (2L-3L) dendritic pentacene islands, and we measured their electron transport properties by conducti

Calliope Ryan-Smith

Stratified formulae were introduced by Quine as an alternative way to attack Russell's Paradox. Instead of limiting comprehension by size (as in $\mathsf{ZF}$ set theory, using its axiom scheme of separation), unlimited comprehension is given to formulae that are in some sense descended from formulae of typed set theory. By keeping variables in a stratified

Yunjiao Gao, Zhenbo Di, Sijie Gao

It is well-known that the mass of a non-asymptotically flat spacetime cannot be uniquely defined. Some mass formulas for the Kerr-AdS black hole have been found and used in studying black hole thermodynamics. However, the derivations usually need a background subtraction to eliminate the divergence at infinity. It is also unknown whether the mass depends on

Ruoqi Zhang, Per Mattsson, Torbjörn Wigren

While reinforcement learning has made great improvements, state-of-the-art algorithms can still struggle with seemingly simple set-point feedback control problems. One reason for this is that the learned controller may not be able to excite the system dynamics well enough initially, and therefore it can take a long time to get data that is informative enough

Florian Le Roux, Andreas Mischok, Francisco Tenopala-Carmona, Malte C. Gather

Non-linearities in organic exciton-polariton microcavities represent an attractive platform for second-generation quantum devices. However, progress in this area hinges on the development of material platforms for high-performance polariton lasing, scalable and sustainable fabrication, and ultimately strategies for electrical pumping. Here, we show how intro

Shili Yan, Haitian Su, Dong Pan, Weijie Li

High-quality free-standing InAs nanosheets are emerging layered semiconductor materials with potentials in designing planar Josephson junction devices for novel physics studies due to their unique properties including strong spin-orbit couplings, large Land\'e g-factors and the two dimensional nature. Here, we report an experimental study of proximity induce

Johannes Pahlke, Ivo F. Sbalzarini

We investigate the computational power of particle methods, a well-established class of algorit hms with applications in scientific computing and computer simulation. The computational power of a compute model determines the class of problems it can solve. Automata theory allows describing the computational power of abstract machines (automata) and the probl

Paul Gorbow

Kaplan and Montague have showed that certain intuitive axioms for a first-order theory of knowledge, formalized as a predicate, are jointly inconsistent. Their arguments rely on self-referential formulas. I offer a consistent first-order theory solving these knower paradoxes, with the following main features: - It solves the knower paradoxes by providing a f

Andrew Houston, Georgina Cosma

Trust is a crucial factor affecting the adoption of machine learning (ML) models. Qualitative studies have revealed that end-users, particularly in the medical domain, need models that can express their uncertainty in decision-making allowing users to know when to ignore the model's recommendations. However, existing approaches for quantifying decision-makin

Gabriel O. Assunção, Rafael Izbicki, Marcos O. Prates

Imbalanced datasets present a significant challenge for machine learning models, often leading to biased predictions. To address this issue, data augmentation techniques are widely used in natural language processing (NLP) to generate new samples for the minority class. However, in this paper, we challenge the common assumption that data augmentation is alwa

Omar Hashash, Christina Chaccour, Walid Saad, Tao Yu

The wireless metaverse will create diverse user experiences at the intersection of the physical, digital, and virtual worlds. These experiences will enable novel interactions between the constituents (e.g., extended reality (XR) users and avatars) of the three worlds. However, remarkably, to date, there is no holistic vision that identifies the full set of m

S. A. Bennett, K. Garett, D. K. Sharp, S. J. Freeman

We demonstrate a new technique for obtaining fission data for nuclei away from $\beta$-stability. These types of data are pertinent to the astrophysical \textit{r-}process, crucial to a complete understanding of the origin of the heavy elements, and for developing a predictive model of fission. These data are also important considerations for terrestrial app

Doru Sticlet, Cătălin Paşcu Moca, Balázs Dóra

We investigate the decay of spatial correlations of $\mathcal{PT}$-symmetric non-Hermitian one-dimensional models that host higher-order exceptional points. Beyond a certain correlation length, they develop anomalous power-law behavior that indicates strong suppression of correlations in the non-Hermitian setups as compared to the Hermitian ones. The correla

S. El Shawish

Intergranular normal stresses (INS) are critical in the initiation and evolution of grain boundary damage in polycrystalline materials. To model the effects of such microstructural damage on a macroscopic scale, knowledge of INS is usually required statistically at each representative volume element subjected to various loading conditions. However, calculati

Yankun Wu, Yuta Nakashima, Noa Garcia

The duality of content and style is inherent to the nature of art. For humans, these two elements are clearly different: content refers to the objects and concepts in the piece of art, and style to the way it is expressed. This duality poses an important challenge for computer vision. The visual appearance of objects and concepts is modulated by the style th

Ruoqi Zhang, Per Mattsson, Torbjörn Wigren

There has recently been an increased interest in reinforcement learning for nonlinear control problems. However standard reinforcement learning algorithms can often struggle even on seemingly simple set-point control problems. This paper argues that three ideas can improve reinforcement learning methods even for highly nonlinear set-point control problems: 1

Ruoqi Zhang, Per Mattson, Torbjörn Wigren

The paper proposes the use of structured neural networks for reinforcement learning based nonlinear adaptive control. The focus is on partially observable systems, with separate neural networks for the state and feedforward observer and the state feedback and feedforward controller. The observer dynamics are modelled by recurrent neural networks while a stan

Marianna Chatzakou, Aparajita Dasgupta, Michael Ruzhansky, Abhilash Tushir

In this paper, we consider a semi-classical version of the nonhomogeneous heat equation with singular time-dependent coefficients on the lattice $\hbar \mathbb{Z}^n$. We establish the well-posedeness of such Cauchy equations in the classical sense when regular coefficients are considered, and analyse how the notion of very weak solution adapts in such equati

Viveka Erlandsson, Juan Souto

Let $\Sigma$ be a closed hyperbolic surface. We study, for fixed $g$, the asymptotics of the number of those periodic geodesics in $\Sigma$ having at most length $L$ and which can be written as the product of $g$ commutators. The basic idea is to reduce these results to being able to count critical realizations of trivalent graphs in $\Sigma$. In the appendi

Hang Yu, Yu Qi, Gang Pan

Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and

IM del Aguila, J del Sagrado

Stakeholders quantification plays a basic role in selecting the appropriate requirements because their judgement is a major criteria since not all of them have the same importance. Original proposals quantified stakeholders assigning them a weight. Nonetheless, actual projects manage a numerous stakeholders community hindering the inclusion of all their weig

M. M. Radhika, Sandip Singh

We determine the existence of cocompact lattices in groups of the form $\V\rtimes\SL_2(\R)$, where $\V$ is a finite dimensional real representation of $\SL_2(\R)$. It turns out that the answer depends on the parity of $\dim(\V)$ when the representation is irreducible.

Quancheng Wang, Xige Zhang, Han Wang, Yuzhe Gu

Caches are used to reduce the speed differential between the CPU and memory to improve the performance of modern processors. However, attackers can use contention-based cache timing attacks to steal sensitive information from victim processes through carefully designed cache eviction sets. And L1 data cache attacks are widely exploited and pose a significant

Ekta Gupta, Varun Gupta, Muskaan Chopra, Prakash Chandra Chhipa

This work presents a novel label-efficient selfsupervised representation learning-based approach for classifying diabetic retinopathy (DR) images in cross-domain settings. Most of the existing DR image classification methods are based on supervised learning which requires a lot of time-consuming and expensive medical domain experts-annotated data for trainin

Sallam Abualhaija, F. BaŞAk Aydemir, Fabiano Dalpiaz, Davide Dell'Anna

[Context]} Natural language processing (NLP) techniques have been widely applied in the requirements engineering (RE) field to support tasks such as classification and ambiguity detection. Despite its empirical vocation, RE research has given limited attention to replication of NLP for RE studies. Replication is hampered by several factors, including the con

Harsh Goel, Yifeng Zhang, Mehul Damani, Guillaume Sartoretti

Many recent works have turned to multi-agent reinforcement learning (MARL) for adaptive traffic signal control to optimize the travel time of vehicles over large urban networks. However, achieving effective and scalable cooperation among junctions (agents) remains an open challenge, as existing methods often rely on extensive, non-generalizable reward shapin

N. Bekheddouma Abdi, R. Bouamrane, K. Khelifa-Kerfa

In this paper, we study the effects of the widths of unstable particles on the one-loop electroweak corrections for the $pp \to WW$ process at the TeV scale within the framework of the complex mass scheme. We also investigate, for this same process, the unitarity of the theory at high energies.

Jianhui Li, Jianmin Li, Haoji Zhang, Shilong Liu

We study the 3D-aware image attribute editing problem in this paper, which has wide applications in practice. Recent methods solved the problem by training a shared encoder to map images into a 3D generator's latent space or by per-image latent code optimization and then edited images in the latent space. Despite their promising results near the input view,

James Mckenna, Vassilis Glenis, Chris Kilsby

The capability to accurately predict flood flows via numerical simulations is a key component of contemporary flood risk management practice. However, modern flood models lack the capacity to accurately model flow interactions with linear features, or hydraulic structures like bridges and gates, which act as partial barriers to flow. Presented within this pa

Sangeetha R. G, Jaya Aravindh V.

An outbreak of the coronavirus disease which occurred three years later and it has hit the world again with many evolutions. The effects on the human race have already been profound. We can only safeguard ourselves against this pandemic by mandating a "Face Mask" also maintaining the "Social Distancing." The necessity of protective face masks in all gatherin

Philipp Strasberg, Teresa E. Reinhard, Joseph Schindler

Within the histories formalism the decoherence functional is a formal tool to investigate the emergence of classicality in isolated quantum systems, yet an explicit evaluation of it from first principles has not been reported. We provide such an evaluation for up to five-time histories based on exact numerical diagonalization of the Schr\"odinger equation. W

Handan Borluk, Gabriele Bruell, Dag Nilsson

Of concern are lump solutions for the fractional Kadomtsev--Petviashvili (fKP) equation. As in the classical Kadomtsev--Petviashvili equation, the fKP equation comes in two versions: fKP-I (strong surface tension case) and fKP-II (weak surface tension case). We prove the existence of nontrivial lump solutions for the fKP-I equation in the energy subcritical