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Efficient Audio Enhancement with a Differentiable Psychoacoustic Loss

https://arxiv.org/html/2608.02918v1
Miranda is also with LCTI, Télécom Paris, IP Paris, France

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https://arxiv.org/pdf/2607.26188v1
Ù¢*(r±¢”ì¢7>^›ò§¯˜Nß$EʯùùÂÜ××DQʯPÑ®îäý®Ó¡5S°g¼Hƒ ¢ Ä/â$)ïÂe§÷vã¼)ï²*`Žíʈêƒzݼ\Â’½Ï¯O>½8ü×ÛãÓå]½àöÙis³™×+¶÷ïÅÕòz¶¸‡HoGáá- J,c°þE³¾ZÍîÛå øóÔv“0¼{g›ËöÇ}8à

Activation-Guided Neuron Intervention to Induce Alzheimer’s-Related

https://arxiv.org/html/2608.03067v1
Beyond their use as predictors, pretrained language models have also become tools for examining how linguistic information is organized in internal representations that can support AD prediction (Balagopalan et al

Report GitHub Issue

https://arxiv.org/html/2608.03154v1
com/COMBINI-Hub/Multi_Agent_Relation_Extraction

Internalizing Academic Writing Workflows for Introduction Generation via

https://arxiv.org/html/2608.03138v1
In academic paper writing, the introduction is the section where a study first presents its motivation, novelty and contribution to readers

Report GitHub Issue

https://arxiv.org/html/2608.03545v1
While effective, the reward signal assigned from majority voting is highly sensitive to consensus strength, defined as the frequency of the most common answer within a rollout group

What Language Does and What the Evidence Supports: A Functional Role

https://arxiv.org/html/2608.03099v1
2 Inspectable Language Is Not Necessarily Correct or Used 5

Guarding a Target Area from a Heterogeneous Group of Cooperative

https://arxiv.org/html/2407.00762v1
The objective of the attacker team is to jointly minimize the weighted sum of their final levels of proximity to the target area, whereas the defender aims to maximize the same value

Testing the effects of an unobservable factor: Do marriage prospects

https://arxiv.org/html/2410.19947v2
In the National Longitudinal Survey of Youth (97) dataset (NLSY97), it is evident that college graduates with different majors have considerable differences in marital status during their 30s

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https://arxiv.org/pdf/2102.13654
üðƒ48#¼vBB¯Òˆ mÅt÷ž={vêÔ Ö³ðĉ{ôèÁWüËk»ví¨À_ªrOR£°²u•>˜ÖüÕüÅÅÅYSÃ\+V,·™ÍßLqO€8µ--”ÛégÏžMq¼––ÛJLLTùwîÜÉÙv²ðÍ›7Ožß·o_pp°8M„ÿíÒ¥ ÷³Ï>ƒ¼°¸uëÖ¼©S§Nƒ úõëÇ+V,]ºôk¯½V¹re ; y‹)‚ñ„¿€põÕWò[Y'0õõõÅA�=zøðáxX,Ÿ={V…àf‡­E‡$0’Q[ºJÍH‚Å*4Z¾|ùìdñ¦oß¾

Optimal Execution with Passive Market Impact

https://arxiv.org/html/2607.28323v1
Our framework is based on two empirical observables: the approximately exponential decay of limit-order fill probabilities with distance from the midprice, and the short-term linear response of price changes to order flow imbalance

Geometric Cross-Modal Token Selection for Latency-Constrained Multimodal

https://arxiv.org/html/2608.03141v1
A representative example is visual question answering (VQA), where an image and a text query are jointly processed to generate an answer

Decoupling Generation and Selection for Budget-Constrained Faithful

https://arxiv.org/html/2608.03655v1
The framework is motivated by the observation that salient and source-supported information often recurs across multiple decoding trajectories, whereas unsupported or weakly grounded content is typically less stable

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https://arxiv.org/pdf/2205.01882v5
žÄ35šÄAâ,:ÿ¾¤-e[ÖdN£�ùÞ#¥h‚ @�ô4(kÁ€±,8§ÀAá-x�² %H-HÒ"}�´É�EA räÔ€V[�°@²[P(¤¥í÷ œ!{Z §Í QÑf ZiJ† µ£$ ´¥z¨�>J@Úò[Ð…¡äŒ0(…‘¦ b`ÐQrFY J‚ц8!Z•0–’*MµúíåK¸úX¯ëvÿÁ³í®Ù6ûº­›ÍÌ'c³#³éÍ»j]î~ÌäsV°7ë‘QI=0R%ŒßàÕ«öc˜}>

You’ve Got to be Efficient: Ambiguity, Misspecification and

https://arxiv.org/html/2604.05327v4
Any researcher or decision-maker who relies on a statistical model to learn about a parameter of interest must therefore contend with the possibility that the likelihood is misspecified

Matching-Driven Deep Reinforcement Learning for Energy-Efficient

https://arxiv.org/html/2407.13076v1
Su is with the School of Cyber Science and Engineering, Xi’an Jiaotong University, Xi’an 710049, China (E-mail: zhousu@ieee

Learning to Control Unknown Strongly Monotone Games

https://arxiv.org/html/2407.00575v3
Ilai Bistritz is with the Department of Industrial Engineering and the School of Electrical Engineering, Tel Aviv Univeristy, Israel

Approximating Choice Data by Discrete Choice Models

https://arxiv.org/html/2205.01882v5
2 Details of the DVD Dataset M Additional Empirical Application: Fishing-site Choice M

When Collaboration Becomes a Trigger:Collective Evidence-Threshold

https://arxiv.org/html/2608.01085v1
Figure 1: A backdoor in one agent is activated by a local input trigger ([TRIGGER] denotes a predefined word or phrase)

Finance-Informed Neural Network: Learning the Geometry of Option Pricing

https://arxiv.org/html/2412.12213v2
Instead of training on observed option prices, FINN is learned through a self-supervised replication objective based on dynamic hedging, ensuring economic consistency by construction

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https://arxiv.org/pdf/2608.02817
�R¼7c…‰å®RpJ –} Ž“�À\Êv>©åp—®fv)»çZ+ÈŠÐPP%:3ÎvÉåÈ÷ⱂT3ÞÙÌ™ŸIêj¸S× œL*û‹Wœ­oÔÌ5#ñF5šÏ½Ö `ظ,àÏ”ß5SY V 3™T4V²q ^6¶dµè�·q^«E�ЭZ6#Ç;mþxå_

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https://arxiv.org/pdf/2005.14165
ØU%S‡SY·o¿1Åäl•& Á_æôãv«)Ð{ã;hL'qê ‹8ÑÎX‰*š ®Ë‘ÓBhMˆÏE�‹Pp³>]¡5¤«[ ÿµÞ¨±çaöyÒ9*löŒ-JˆÕšœ§œ§·¹œ|÷DÇ aèˆOxJ£T‘àëÖæÐ"™¢wö•c…Wó±@L=¥HxÆaªFüH&ò0Ž9k{†Õä2Œ‹bNæ}õâ2ˆ"Êg€µ'å®~

Asking Questions the Right Way: A Multi-Agent Conversational System for

https://arxiv.org/html/2608.01366v2
com Affiliation: Department of Design and Manufacturing, Indian Institute of Science (IISc) , Bangalore , India , Srinidhi Ranjini Girish email: srinidhirgirish@gmail

Producing Policy Recommendations: from Statistical Decision Theory to

https://arxiv.org/html/2607.29281v2
This version is a work in progress; please feel free to contact me if you find a mistake, think I missed an important contribution, or just want to send a comment

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https://arxiv.org/src/2211.10435
’•Ž•ÄCom„²î�Þ4 ߎ÷öÏ(M®W"_xºˆ¥î;;Bu8çñ¶5'UNã�þ°�;hÿ˜ª&‘ú•H—0«Ô�’P/ÙÐ^�/3°ÇKÊO°¿`õ¨GÌC/ +¢Ó„î3j†õ‚�ˆGIt, ;]"}ö3äÜü·«ÑŠ_wË“sÇÝøú&™s]bæ%â¡á#õ-˜¾âIc:‚9Àë€ÿ�¨áPb¸(RGlŸÎÀ•»+Ì�âGÌGØKÆ~XA/(×…šd C‚bB×UCÜGäž8t¬úˆY[”|ôù�A]ƒì['ê�¤Q²[¯S±SôMÓÜöl`–ßÝ/›Jn'퉪ÍÛ|;žx V|ß¶;~0\„ˆÌh„[†ÈªéÎz¼ö˜°)”G°Õ˜º> ~âìû‘þX

Effort-Centric Fairness in Lending Decisions

https://arxiv.org/html/2607.28847v1
A parallel and increasingly intertwined regulatory concern is the explainability of automated credit decisions

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https://arxiv.org/src/2006.03720
ÝePfd,7îò»K¤äd¢¢@ndêóŸþE?MøÙÜ\§ßð³ð{þÛúS«½¹¹¹ÑÞjn@»ÖZ«Ýú“hþ«&äÿäi&!þ”ÄqöP»Çîÿúóä¿õgåÉu/T2™È�Z¹–†÷~½Hân¨ÆâUœŒe¨¿ÈLÇÑoâ:”]Î&|·‘ªÞ|z€ßxVùÔ¼ò©“HdC� óhMÜ)1ITª¢LHѧD ú:Òx_Ä}h¯ÄpÚMt zaœ"í U�‡:Û·ø „ Ó˜zÈC™)zÌÜ:ÊbèþìäôBÜ uo(zq¢«D‡·*

Optimizing Age of Information in Vehicular Edge Computing with Federated

https://arxiv.org/html/2407.02342v1
cn) Pingyi Fan is with the Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology, Tsinghua University, Beijing 100084, China (Email: fpy@tsinghua

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https://arxiv.org/pdf/2608.02758
@Û¬MX–T‰Þ`óëû)iI}¸=Žç g†o)™‘Œp” 9þ‘‚I" ͸"ŠPZ¤ ” AJBE& B¨”ð…Zä@a„eœ‡0–ç„æ„劂0)áSV(ÀáÖ Â)®+ gH €³’0Jx cðYf„qÂ;{ÿžì~¶7ëzòy:ZgÞ�Œi{c®,câ%Л­*Ó�`ÕV÷NW¶wöøL¾�Ý/úf¶W2ø}(^tu0}e¦fY–î]ÝÜÝ3Co¤+´¯iYж³/Ú™g’Ç=nær²ßôÉ\p‹Ö˜EBp

Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially

https://arxiv.org/html/2407.04974v1
The proposed design methodology is supported by an analysis demonstrating that the difference between final outcomes, obtained when the global state is fully observed versus estimated through the social learning method, is ε�\varepsilonitalic_ε-bounded when an appropriate number of iterations of social learning