Negative / Null Result ReportOpen accessComputer Science
Taeho Kang, Yiyu Chen, Christian Wallraven · 2026 · arXiv
In this paper, we conduct a detailed investigation on the effect of independent component (IC)-based noise rejection methods in neural network classifier-based decoding of electroencephalography (EEG) data in different task datasets. We apply a pipeline matrix of two popular different independent component (IC) decomposition methods (Infomax and Adaptive Mixture Independent Component Analysis (AMICA)) with three different component rejection strategies (none, ICLabel, and multiple artifact rejection algorithm [MARA]) on three different EEG datasets (motor imagery, long-term memory formation, a
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Yu Eric Qian, Wilson S. Geisler, Xue-Xin Wei · 2025 · arXiv
Previous studies have compared neural activities in the visual cortex to representations in deep neural networks trained on image classification. Interestingly, while some suggest that their representations are highly similar, others argued the opposite. Here, we propose a new approach to characterize the similarity of the decision strategies of two observers (models or brains) using decision variable correlation (DVC). DVC quantifies the image-by-image correlation between the decoded decisions based on the internal neural representations in a classification task. Thus, it can capture task-rel
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Dillon Niederhut · 2023 · arXiv
Over the last decade, deep neural networks have achieved state of the art in computer vision tasks. These models, however, are susceptible to unusual inputs, known as adversarial examples, that cause them to misclassify or otherwise fail to detect objects. Here, we provide evidence that the increasing success of adversarial attacks is primarily due to increasing their size. We then demonstrate a method for generating the largest possible adversarial patch by building a adversarial pattern out of repeatable elements. This approach achieves a new state of the art in evading detection by YOLOv2 a
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Courtney Celian, Partha Ryali, Valentino Wilson et al. · 2024 · arXiv
Background: Gravity confounds arm movement ability in post-stroke hemiparesis. Reducing its influence allows effective practice leading to recovery. Yet, there is a scarcity of wearable devices suitable for personalized use across diverse therapeutic activities in the clinic. Objective: In this study, we investigated the safety, feasibility, and efficacy of anti-gravity therapy using the ExoNET device in post-stroke participants. Methods: Twenty chronic stroke survivors underwent six, 45-minute occupational therapy sessions while wearing the ExoNET, randomized into either the treatment (ExoNET
View details →Negative / Null Result ReportOpen accessComputer Science
Adam Brandenburger, Pierfrancesco La Mura · 2025 · arXiv
Any quasi-probability representation of a no-signaling system -- including quantum systems -- can be simulated via a purely classical scheme by allowing signed events and a cancellation procedure. This raises a fundamental question: What properties of the non-classical system does such a classical simulation fail to replicate? We answer by using large deviation theory to show that the probability of a large fluctuation under the classical simulation can be strictly greater than under the actual non-classical system. The key finding driving our result is that negativity in probability relaxes t
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Paolo Bellomo, C. R. Stroud, · 1998 · arXiv
We study the dispersion of the "temporally stable" coherent states for the hydrogen atom introduced by Klauder. These are states which under temporal evolution by the hydrogen atom Hamiltonian retain their coherence properties. We show that in the hydrogen atom such wave packets do not move quasi-classically; i.e., they do not follow with no or little dispersion the Keplerian orbits of the classical electron. The poor quantum-classical correspondence does not improve in the semiclassical limit.
View details →Negative / Null Result ReportOpen accessComputer Science
Cheng Tang, Andrew Arnold · 2020 · arXiv
Recently, Nogueira et al. [2019] proposed a new approach to document expansion based on a neural Seq2Seq model, showing significant improvement on short text retrieval task. However, this approach needs a large amount of in-domain training data. In this paper, we show that this neural document expansion approach can be effectively adapted to standard IR tasks, where labels are scarce and many long documents are present.
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Jose A. R. Cembranos · 2010 · arXiv
There is a non-trivial four-derivative extension of the gravitational spectrum that is free of ghosts and phenomenologically viable. It is the so called $R^2$-gravity since it is defined by the only addition of a term proportional to the square of the scalar curvature. Just the presence of this term does not improve the ultraviolet behaviour of Einstein gravity but introduces one additional scalar degree of freedom that can account for the dark matter of our Universe.
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Anant Sahai · 2007 · arXiv
The traditional information theoretic approach to studying feedback is to consider ideal instantaneous high-rate feedback of the channel outputs to the encoder. This was acceptable in classical work because the results were negative: Shannon pointed out that even perfect feedback often does not improve capacity and in the context of symmetric DMCs, Dobrushin showed that it does not improve the fixed block-coding error exponents in the interesting high rate regime. However, it has recently been shown that perfect feedback does allow great improvements in the asymptotic tradeoff between end-to-e
View details →Failed Experiment ReportOpen accessComputer Science
J. A. Casas, A. Lleyda, C. Muñoz · 1996 · arXiv
The general constraints on the parameter space of soft-breaking terms, in order to avoid dangerous charge and color breaking minima, are applied to the four-dimensional string scenario where the dilaton is the source of supersymmetry breaking (dilaton-dominated limit). The results indicate that the whole parameter space is excluded on these grounds after imposing the present experimental data on the top mass. The inclusion of a non-vanishing cosmological constant does not improve essentially the prospects. Possible way-outs to this situation are briefly discussed.
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Mohan Raj Chanthran, Lay-Ki Soon, Huey Fang Ong et al. · 2024 · arXiv
Malaysian English is a low resource creole language, where it carries the elements of Malay, Chinese, and Tamil languages, in addition to Standard English. Named Entity Recognition (NER) models underperform when capturing entities from Malaysian English text due to its distinctive morphosyntactic adaptations, semantic features and code-switching (mixing English and Malay). Considering these gaps, we introduce MENmBERT and MENBERT, a pre-trained language model with contextual understanding, specifically tailored for Malaysian English. We have fine-tuned MENmBERT and MENBERT using manually annot
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Nima Akhlaghi, Ananya Dhawan, Amir A. Khan et al. · 2018 · arXiv
Objective: The objectives of this paper are to determine the optimal location for ultrasound transducer placement on the anterior forearm for imaging maximum muscle deformations during different hand motions and to investigate the effect of using a sparse set of ultrasound scanlines for motion classification for ultrasound-based muscle computer interfaces (MCIs). Methods: The optimal placement of the ultrasound transducer along the forearm is identified using freehand 3D reconstructions of the muscle thickness during rest and motion completion. From the ultrasound images acquired from the opti
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M. C. Gonzalez-Garcia, S. F. Novaes · 1996 · arXiv
We investigate the effects induced by excited leptons at the one-loop level in the observables measured on the $Z$ peak at LEP. Using a general effective Lagrangian approach to describe the couplings of the excited leptons, we compute their contributions to both oblique parameters and $Z$ partial widths. Our results show that the new effects are comparable to the present experimental sensitivity, but they do not lead to a significant improvement on the available constraints on the couplings and masses of these states.
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Lav R. Varshney, Sanjoy K. Mitter, Vivek K Goyal · 2011 · arXiv
Given the possibility of communication systems failing catastrophically, we investigate limits to communicating over channels that fail at random times. These channels are finite-state semi-Markov channels. We show that communication with arbitrarily small probability of error is not possible. Making use of results in finite blocklength channel coding, we determine sequences of blocklengths that optimize transmission volume communicated at fixed maximum message error probabilities. We provide a partial ordering of communication channels. A dynamic programming formulation is used to show the st
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I. Sahin · 2008 · arXiv
We investigate the potential of $γγ$ collisions to probe scalar unparticle couplings via top-antitop quark pair production. We find 95% confidence level limits on the unparticle couplings with an integrated luminosity of $500 fb^{-1}$ and $\sqrt{s}=1$ TeV energy. We investigate the effect of top quark spin polarization on the unparticle couplings. It is shown that spin polarization of the top quark leads to a significant improvement in the sensitivity limits.
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Mingjian He, Robert Malaney · 2022 · arXiv
Hybrid entanglement between discrete-variable (DV) and continuous-variable (CV) quantum systems is an essential resource for heterogeneous quantum networks. Our previous work showed that in lossy channels the teleportation of DV qubits, via CV-entangled states, can be significantly improved by a new protocol defined by a modified Bell state measurement at the sender. This work explores whether a new, similarly modified, CV-based teleportation protocol can lead to improvement in the transfer of hybrid entangled states. To set the scene, we first determine the performance of such a modified prot
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Joshua Frankie Rayo, Vena Pearl Boñgolan · 2020 · arXiv
The Advanced Circulation (ADCIRC) and Simulating Nearshore Waves (SWAN) coupled model is modified to include a stochastic term in the shallow water equations that represents random external forces from debris carried by surge and short-term local scale atmospheric fluctuations. We added $α$-stable noise, uncorrelated in space and time, in the forcing terms of the coupled model. Inputs to the model are unstructured computational mesh derived from topography and bathymetry, land cover classification, tidal potential constituents and atmospheric forcing. The model simulated surge height of around
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Ian Porada, Alessandro Sordoni, Jackie Chi Kit Cheung · 2021 · arXiv
Transformer models pre-trained with a masked-language-modeling objective (e.g., BERT) encode commonsense knowledge as evidenced by behavioral probes; however, the extent to which this knowledge is acquired by systematic inference over the semantics of the pre-training corpora is an open question. To answer this question, we selectively inject verbalized knowledge into the minibatches of a BERT model during pre-training and evaluate how well the model generalizes to supported inferences. We find generalization does not improve over the course of pre-training, suggesting that commonsense knowled
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Wu Ji · 2026 · arXiv
System prompts for AI coding agents increasingly employ motivational framing -- from neutral task descriptions to fear-driven threats -- yet no controlled study has examined whether such framing affects agent behavior. We present two studies investigating how trust-based versus fear-based motivation framing in system prompts influences AI agent debugging performance. In Study 1, we conducted a controlled manual experiment comparing a trust-framed methodology (NoPUA) against an unframed baseline across 9 debugging scenarios using Claude Sonnet 4. Trust-framed agents found 59% more hidden issues
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Yunquan Dong, Zhengchuan Chen, Shanyun Liu et al. · 2018 · arXiv
We consider an M/M/1 update-and-decide system where Poisson distributed decisions are made based on the received updates. We propose to characterize the freshness of the received updates at decision epochs with Age upon Decisions (AuD). Under the first-come-first-served policy (FCFS), the closed form average AuD is derived. We show that the average AuD of the system is determined by the arrival rate and the service rate, and is independent of the decision rate. Thus, merely increasing the decision rate does not improve the timeliness of decisions. Nevertheless, increasing the arrival rate and
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Aloïs Rautureau, Éric Piette · 2025 · arXiv
While AI systems have equaled or surpassed human performance in a wide variety of games such as Chess, Go, or Dota 2, describing these systems as truly "human-like" remains far-fetched. Despite their success, they fail to replicate the pattern-based, intuitive decision-making processes observed in human cognition. This paper presents an overview of findings from cognitive psychology and previous efforts to model human-like behavior in artificial agents, discusses their applicability to General Game Playing (GGP) and introduces our work-in-progress model based on these observations: CogniPlay.
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G. Chiribella, G. M. D'Ariano, P. Perinotti · 2008 · arXiv
A sequential network of quantum operations is efficiently described by its quantum comb, a non-negative operator with suitable normalization constraints. Here we analyze the case of networks enjoying symmetry with respect to the action of a given group of physical transformations, introducing the notion of covariant combs and testers, and proving the basic structure theorems for these objects. As an application, we discuss the optimal alignment of reference frames (without pre-established common references) with multiple rounds of quantum communication, showing that i) allowing an arbitrary am
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Zhijian Luo, Zihan Huang, Jiahui Tang et al. · 2022 · arXiv
At the age of big data, recommender systems have shown remarkable success as a key means of information filtering in our daily life. Recent years have witnessed the technical development of recommender systems, from perception learning to cognition reasoning which intuitively build the task of recommendation as the procedure of logical reasoning and have achieve significant improvement. However, the logical statement in reasoning implicitly admits irrelevance of ordering, even does not consider time information which plays an important role in many recommendation tasks. Furthermore, recommenda
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Pierre Epron, Adrien Coulet, Mehwish Alam · 2026 · arXiv
Despite their strong linguistic capabilities, Large Language Models (LLMs) are computationally demanding and require substantial resources for fine-tuning, which is unadapted to privacy and budget constraints of many healthcare settings. To address this, we present an experimental analysis focused on Biomedical Named Entity Recognition using lightweight LLMs, we evaluate the impact of different output formats on model performance. The results reveal that lightweight LLMs can achieve competitive performance compared to the larger models, highlighting their potential as lightweight yet effective
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Isabel Segura-Bedmar · 2023 · arXiv
This paper describes our participation in SemEval-2023 Task 10, whose goal is the detection of sexism in social media. We explore some of the most popular transformer models such as BERT, DistilBERT, RoBERTa, and XLNet. We also study different data augmentation techniques to increase the training dataset. During the development phase, our best results were obtained by using RoBERTa and data augmentation for tasks B and C. However, the use of synthetic data does not improve the results for task C. We participated in the three subtasks. Our approach still has much room for improvement, especiall
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Amir Homayounirad, Enrico Liscio, Tong Wang et al. · 2025 · arXiv
Aggregating multiple annotations into a single ground truth label may hide valuable insights into annotator disagreement, particularly in tasks where subjectivity plays a crucial role. In this work, we explore methods for identifying subjectivity in recognizing the human values that motivate arguments. We evaluate two main approaches: inferring subjectivity through value prediction vs. directly identifying subjectivity. Our experiments show that direct subjectivity identification significantly improves the model performance of flagging subjective arguments. Furthermore, combining contrastive l
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Antonious M. Girgis, Deepesh Data, Suhas Diggavi · 2021 · arXiv
We study privacy in a distributed learning framework, where clients collaboratively build a learning model iteratively through interactions with a server from whom we need privacy. Motivated by stochastic optimization and the federated learning (FL) paradigm, we focus on the case where a small fraction of data samples are randomly sub-sampled in each round to participate in the learning process, which also enables privacy amplification. To obtain even stronger local privacy guarantees, we study this in the shuffle privacy model, where each client randomizes its response using a local different
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J. Rehacek, Z. Hradil, M. Dusek et al. · 1999 · arXiv
An experimental comparison of several operational phase concepts is presented. In particular, it is shown that statistically motivated evaluation of experimental data may lead to a significant improvement in phase fitting upon the conventional Noh, Fouge'res and Mandel procedure. The analysis is extended to the asymptotic limit of large intensities, where a strong evidence in favor of multi--dimensional estimation procedures has been found.
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Dou Liu, Ying Long, Sophia Zuoqiu et al. · 2025 · arXiv
Creating high-quality clinical Chains-of-Thought (CoTs) is crucial for explainable medical Artificial Intelligence (AI) while constrained by data scarcity. Although Large Language Models (LLMs) can synthesize medical data, their clinical reliability remains unverified. This study evaluates the reliability of LLM-generated CoTs and investigates prompting strategies to enhance their quality. In a blinded comparative study, senior clinicians in Assisted Reproductive Technology (ART) evaluated CoTs generated via three distinct strategies: Zero-shot, Random Few-shot (using shallow examples), and Se
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Antoni Ramos-Buades, Patricia Schmidt, Geraint Pratten et al. · 2020 · arXiv
The current paradigm for constructing waveforms from precessing compact binaries is to first construct a waveform in a non-inertial, co-precessing binary source frame followed by a time-dependent rotation to map back to the physical, inertial frame. A key insight in the construction of these models is that the co-precessing waveform can be effectively mapped to some equivalent aligned spin waveform. Secondly, the time-dependent rotation implicitly introduces $m$-mode mixing, necessitating an accurate description of higher-order modes in the co-precessing frame. We assess the efficacy of this m
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