pwc-archive/papers-with-abstracts huggingface dataset:
An Empirically-parametrized Spatio-Temporal Extended-SIR Model for Combined Dilution and Vaccination Mitigation for Rabies Outbreaks in Wild Jackals
Authors: Teddy Lazebnik, Yehuda Samuel, Jonathan Tichon, Roi Lapid, Roni King, Tomer Nissimian, Orr Spiegel
Date: 2025-01-26 00:00:00

The transmission of zoonotic diseases between animals and humans poses an increasing threat. Rabies is a prominent example with various instances globally, facilitated by a surplus of meso-predators (commonly, facultative synanthropic species e.g., golden jackals [Canis aureus, hereafter jackals]) thanks to the abundance of anthropogenic resources leading to dense populations close to human establishments. To mitigate rabies outbreaks and prevent human infections, authorities target the jackal which is the main rabies vector in many regions, through the dissemination of oral vaccines in known jackals' activity centers, as well as opportunistic culling to reduce population density. Because dilution (i.e., culling) is not selective towards sick or un-vaccinated individuals, these two complementary epizootic intervention policies (EIPs) can interfere with each other. Nonetheless, there is only limited examination of the interactive effectiveness of these EIPs and their potential influence on rabies epizootic spread dynamics, highlighting the need to understand these measures and the spread of rabies in wild jackals. In this study, we introduce a novel spatio-temporal extended-SIR (susceptible-infected-recovered) model with a graph-based spatial framework for evaluating mitigation efficiency. We implement the model in a case study using a jackal population in northern Israel, and using spatial and movement data collected by Advanced Tracking and Localization of Animals in real-life Systems (ATLAS) telemetry. An agent-based simulation approach allows us to explore various biologically-realistic scenarios, and assess the impact of different EIPs configurations. Our model suggests that under biologically-realistic underlying assumptions and scenarios, the effectiveness of both EIPs is not influenced much by the jackal population size but is sensitive to their dispersal between activity centers.
Generating Negative Samples for Multi-Modal Recommendation
Authors: Yanbiao Ji, Yue Ding, Dan Luo, Chang Liu, Jing Tong, Shaokai Wu, Hongtao Lu
Date: 2025-01-25 00:00:00
Tasks: Multi-modal Recommendation, Recommendation Systems

Multi-modal recommender systems (MMRS) have gained significant attention due to their ability to leverage information from various modalities to enhance recommendation quality. However, existing negative sampling techniques often struggle to effectively utilize the multi-modal data, leading to suboptimal performance. In this paper, we identify two key challenges in negative sampling for MMRS: (1) producing cohesive negative samples contrasting with positive samples and (2) maintaining a balanced influence across different modalities. To address these challenges, we propose NegGen, a novel framework that utilizes multi-modal large language models (MLLMs) to generate balanced and contrastive negative samples. We design three different prompt templates to enable NegGen to analyze and manipulate item attributes across multiple modalities, and then generate negative samples that introduce better supervision signals and ensure modality balance. Furthermore, NegGen employs a causal learning module to disentangle the effect of intervened key features and irrelevant item attributes, enabling fine-grained learning of user preferences. Extensive experiments on real-world datasets demonstrate the superior performance of NegGen compared to state-of-the-art methods in both negative sampling and multi-modal recommendation.
ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation
Authors: Qingtian Bian, Marcus Vinícius de Carvalho, Tieying Li, Jiaxing Xu, Hui Fang, Yiping Ke
Date: 2025-01-25 00:00:00
Tasks: Sequential Recommendation

Cross-Domain Sequential Recommendation (CDSR) has recently gained attention for countering data sparsity by transferring knowledge across domains. A common approach merges domain-specific sequences into cross-domain sequences, serving as bridges to connect domains. One key challenge is to correctly extract the shared knowledge among these sequences and appropriately transfer it. Most existing works directly transfer unfiltered cross-domain knowledge rather than extracting domain-invariant components and adaptively integrating them into domain-specific modelings. Another challenge lies in aligning the domain-specific and cross-domain sequences. Existing methods align these sequences based on timestamps, but this approach can cause prediction mismatches when the current tokens and their targets belong to different domains. In such cases, the domain-specific knowledge carried by the current tokens may degrade performance. To address these challenges, we propose the A-B-Cross-to-Invariant Learning Recommender (ABXI). Specifically, leveraging LoRA's effectiveness for efficient adaptation, ABXI incorporates two types of LoRAs to facilitate knowledge adaptation. First, all sequences are processed through a shared encoder that employs a domain LoRA for each sequence, thereby preserving unique domain characteristics. Next, we introduce an invariant projector that extracts domain-invariant interests from cross-domain representations, utilizing an invariant LoRA to adapt these interests into modeling each specific domain. Besides, to avoid prediction mismatches, all domain-specific sequences are aligned to match the domains of the cross-domain ground truths. Experimental results on three datasets demonstrate that our approach outperforms other CDSR counterparts by a large margin. The codes are available in \url{https://github.com/DiMarzioBian/ABXI}.
Multi-Grained Patch Training for Efficient LLM-based Recommendation
Authors: Jiayi Liao, Ruobing Xie, Sihang Li, Xiang Wang, Xingwu Sun, Zhanhui Kang, Xiangnan He
Date: 2025-01-25 00:00:00
Tasks: Language Modeling, Language Modelling, Recommendation Systems, Sequential Recommendation

Large Language Models (LLMs) have emerged as a new paradigm for recommendation by converting interacted item history into language modeling. However, constrained by the limited context length of LLMs, existing approaches have to truncate item history in the prompt, focusing only on recent interactions and sacrificing the ability to model long-term history. To enable LLMs to model long histories, we pursue a concise embedding representation for items and sessions. In the LLM embedding space, we construct an item's embedding by aggregating its textual token embeddings; similarly, we construct a session's embedding by aggregating its item embeddings. While efficient, this way poses two challenges since it ignores the temporal significance of user interactions and LLMs do not natively interpret our custom embeddings. To overcome these, we propose PatchRec, a multi-grained patch training method consisting of two stages: (1) Patch Pre-training, which familiarizes LLMs with aggregated embeddings -- patches, and (2) Patch Fine-tuning, which enables LLMs to capture time-aware significance in interaction history. Extensive experiments show that PatchRec effectively models longer behavior histories with improved efficiency. This work facilitates the practical use of LLMs for modeling long behavior histories. Codes are available at https://github.com/ljy0ustc/PatchRec.
Search results diversification in competitive search
Authors: Tommy Mordo, Itamar Reinman, Moshe Tennenholtz, Oren Kurland
Date: 2025-01-24 00:00:00
Tasks: Diversity, Retrieval

In Web retrieval, there are many cases of competition between authors of Web documents: their incentive is to have their documents highly ranked for queries of interest. As such, the Web is a prominent example of a competitive search setting. Past work on competitive search focused on ranking functions based solely on relevance estimation. We study ranking functions that integrate a results-diversification aspect. We show that the competitive search setting with diversity-based ranking has an equilibrium. Furthermore, we theoretically and empirically show that the phenomenon of authors mimicking content in documents highly ranked in the past, which was demonstrated in previous work, is mitigated when search results diversification is applied.
Multi-Modality Transformer for E-Commerce: Inferring User Purchase Intention to Bridge the Query-Product Gap
Authors: Srivatsa Mallapragada, Ying Xie, Varsha Rani Chawan, Zeyad Hailat, Yuanbo Wang
Date: 2025-01-21 00:00:00
Tasks: Retrieval

E-commerce click-stream data and product catalogs offer critical user behavior insights and product knowledge. This paper propose a multi-modal transformer termed as PINCER, that leverages the above data sources to transform initial user queries into pseudo-product representations. By tapping into these external data sources, our model can infer users' potential purchase intent from their limited queries and capture query relevant product features. We demonstrate our model's superior performance over state-of-the-art alternatives on e-commerce online retrieval in both controlled and real-world experiments. Our ablation studies confirm that the proposed transformer architecture and integrated learning strategies enable the mining of key data sources to infer purchase intent, extract product features, and enhance the transformation pipeline from queries to more accurate pseudo-product representations.
Multi Objective Prioritized Workflow Scheduling Using Deep Reinforcement Based Learning in Cloud Computing
Authors: Sudheer Mangalampalli, SYED SHAKEEL HASHMI, GANESH REDDY KARRI, Tulika Chakrabarti, Martin Margala
Date: 2024-01-08 00:00:00
Proceeding: IEEE Access 2024 1
Tasks: Cloud Computing, Deep Reinforcement Learning, Scheduling

ABSTRACT Workflow Scheduling is a huge challenge in cloud paradigm as many number of workflows
 dynamically generated from various heterogeneous resources and task dependencies in each workflow varies
 from each other. Therefore, if a workflow with more number of dependencies is not scheduled onto an
 appropriate Virtual Machine i.e. with low processing capacity which leads to delay in executing workflows
 and it results in increase of makespan, cost, energy consumption. In order to effectively schedule complex
 workflows i.e. with more task dependencies, we propose a novel multi objective workflow scheduling
 algorithm using Deep reinforcement Learning. Initially, priorities of all workflows calculated based on their
 dependencies and then calculated priorities of VMs based on electricity cost at datacenters to map workflows
 onto precise VMs. These priorities are fed to scheduler which uses Deep Q-Network model to dynamically
 schedule tasks by considering both priorities of tasks and VMs. Extensive simulations carried out on
 workflowsim by considering realtime scientific workflows (Montage, cybershake, Epigenomics, LIGO).
 Our proposed MOPWSDRL compared against existing state of art approaches i.e. Heterogeneous Earliest
 First Deadline, Cat Swarm Optimization, Ant Colony Optimization. Results revealed that our proposed
 MOPDSWRLoutperforms existing state of art algorithms by minimizing makespan, energy consumption.
Investigating Circularity in India's Textile Industry: Overcoming Challenges and Leveraging Digitization for Growth
Authors: Suman Kumar Das
Date: 2025-01-26 00:00:00

India's growing population and economy have significantly increased the demand and consumption of natural resources. As a result, the potential benefits of transitioning to a circular economic model have been extensively discussed and debated among various Indian stakeholders, including policymakers, industry leaders, and environmental advocates. Despite the numerous initiatives, policies, and transnational strategic partnerships of the Indian government, most small and medium enterprises in India face significant challenges in implementing circular economy practices. This is due to the lack of a clear pathway to measure the current state of the circular economy in Indian industries and the absence of a framework to address these challenges. This paper examines the circularity of the 93-textile industry in India using the C-Readiness Tool. The analysis comprehensively identified 9 categories with 34 barriers to adopting circular economy principles in the textile sector through a narrative literature review. The identified barriers were further compared against the findings from a C-readiness tool assessment, which revealed prominent challenges related to supply chain coordination, consumer engagement, and regulatory compliance within the industry's circularity efforts. In response to these challenges, the article proposes a strategic roadmap that leverages digital technologies to drive the textile industry towards a more sustainable and resilient industrial model.
Stabilizability of Markov jump linear systems modeling wireless networked control scenarios (extended version)
Authors: Yuriy Zacchia Lun, Alessandro D'Innocenzo
Date: 2019-07-29 00:00:00

The communication channels used to convey information between the components of wireless networked control systems (WNCSs) are subject to packet losses due to time-varying fading and interference. The WNCSs with missing packets can be modeled as Markov jump linear systems with one time-step delayed mode observations. While the problem of the optimal linear quadratic regulation for such systems has been already solved, we derive the necessary and sufficient conditions for stabilizability. We also show, with an example considering a communication channel model based on WirelessHART (a on-the-market wireless communication standard specifically designed for process automation), that such conditions are essential to the analysis of WNCSs where packet losses are modeled with Bernoulli random variables representing the expected value of the real random process governing the channel.
Enhanced Generalized OFDM with Index Modulation
Authors: A. Atef Ibrahim, Amr A. Nagy, Ashraf Mahran, Amr Abdelaziz
Date: 2025-01-26 00:00:00

In recent years, many attempts have been made to enhance Orthogonal Frequency Multiplexing with Index Modulation (OFDM-IM) in terms of spectral efficiency and error performance. Two challenges typically erupt when using OFDM-IM. First, the degradation in spectral efficiency due to the subcarrier's deactivation, especially when using higher order modulation (M-ary) where every inactive subcarrier will cost $Log_2(M)$ bits loss. Second, using a fixed number of active subcarriers within a sub-block forces the error to be localized within the sub-block. Yet, it loses the advantage of exploiting all possible pattern combinations degrading the overall spectral efficiency. In this paper, we introduce a solution to tackle those problems. The Enhanced Generalized Index Modulation (EGIM) is a simple systematic way to generate and detect the OFDM-IM frame. Unlike the classical OFDM-IM generation by splitting the frame into sub-frames which increases the complexity of the OFDM-IM transmitter and reflects on the receiver Maximum likelihood detector, EGIM Makes full use of all possible combinations of active subcarriers within the frame by using variable active subcarriers (k) depending on the incoming data. The EGIM is still susceptible to error propagation if the OFF symbol is wrongly mapped to one of the ON symbols or vice versa. For that reason, we offer an OFDM-IM autoencoder to overcome this problem. The encoder generates the (ON/OFF) symbols systematically to achieve the advantage of sending all possible frame indices patterns depending on the input bit stream offering an average of 3dB gain in terms of power efficiency. The proposed encoder performance was compared to the standard encoder with the same effective coding rate using soft and hard decision Viterbi decoding utilizing the power gain achieved.
Central and Periodic Multi-Scale Discrete Radon Transforms
Authors: Óscar Gómez-Cárdenes, José G. Marichal-Hernández, Jonas Phillip Lüke, José M. Rodríguez-Ramos
Date: 2021-11-11 00:00:00
Proceeding: Applied Sciences 2021 11

The multi-scale discrete Radon transform (DRT) calculates, with linearithmic complexity, the summation of pixels, through a set of discrete lines, covering all possible slopes and intercepts in an image, exclusively with integer arithmetic operations. An inversion algorithm exists and is exact and fast, in spite of being iterative. In this work, the DRT forward and backward pair is evolved to propose two faster algorithms: central DRT, which computes only the central portion of intercepts; and periodic DRT, which computes the line integrals on the periodic extension of the input. Both have an output of size 𝑁×4𝑁, instead of 3𝑁×4𝑁, as in the original algorithm. Periodic DRT is proven to have a fast inversion, whereas central DRT does not. An interesting application of periodic DRT is its use as building a block of discrete curvelet transform. Central DRT can provide almost a 2× speedup over conventional DRT, probably becoming the faster Radon transform algorithm available, at the cost of ignoring 15% of the summations in the corners.
Artificial Intelligence Framework for Simulating Clinical Decision-Making: A Markov Decision Process Approach
Authors: Casey C. Bennett, Kris Hauser
Date: 2013-01-10 00:00:00
Tasks: Decision Making

In the modern healthcare system, rapidly expanding costs/complexity, the growing myriad of treatment options, and exploding information streams that often do not effectively reach the front lines hinder the ability to choose optimal treatment decisions over time. The goal in this paper is to develop a general purpose (non-disease-specific) computational/artificial intelligence (AI) framework to address these challenges. This serves two potential functions: 1) a simulation environment for exploring various healthcare policies, payment methodologies, etc., and 2) the basis for clinical artificial intelligence - an AI that can think like a doctor. This approach combines Markov decision processes and dynamic decision networks to learn from clinical data and develop complex plans via simulation of alternative sequential decision paths while capturing the sometimes conflicting, sometimes synergistic interactions of various components in the healthcare system. It can operate in partially observable environments (in the case of missing observations or data) by maintaining belief states about patient health status and functions as an online agent that plans and re-plans. This framework was evaluated using real patient data from an electronic health record. Such an AI framework easily outperforms the current treatment-as-usual (TAU) case-rate/fee-for-service models of healthcare (Cost per Unit Change: $189 vs. $497) while obtaining a 30-35% increase in patient outcomes. Tweaking certain model parameters further enhances this advantage, obtaining roughly 50% more improvement for roughly half the costs. Given careful design and problem formulation, an AI simulation framework can approximate optimal decisions even in complex and uncertain environments. Future work is described that outlines potential lines of research and integration of machine learning algorithms for personalized medicine.
Punch Out Model Synthesis: A Stochastic Algorithm for Constraint Based Tiling Generation
Authors: Zzyv Zzyzek
Date: 2025-01-05 00:00:00

As an artistic aid in tiled level design, Constraint Based Tiling Generation (CBTG) algorithms can help to automatically create level realizations from a set of tiles and placement constraints. Merrell's Modify in Blocks Model Synthesis (MMS) and Gumin's Wave Function Collapse (WFC) have been proposed as Constraint Based Tiling Generation (CBTG) algorithms that work well for many scenarios but have limitations in problem size, problem setup and solution biasing. We present Punch Out Model Synthesis (POMS), a Constraint Based Tiling Generation algorithm, that can handle large problem sizes, requires minimal assumptions for setup and can help mitigate solution biasing. POMS attempts to resolve indeterminate grid regions by trying to progressively realize sub-blocks, performing a stochastic boundary erosion on previously resolved regions should sub-block resolution fail. We highlight the results of running a reference implementation on different tile sets and discuss a tile correlation length, implied by the tile constraints, and its role in choosing an appropriate block size to aid POMS in successfully finding grid realizations.
A VM-HDL Co-Simulation Framework for Systems with PCIe-Connected FPGAs
Authors: Shenghsun Cho, Mrunal Patel, Basavaraj Kaladagi, Han Chen, Tapti Palit, Michael Ferdman, Peter Milder
Date: 2025-01-19 00:00:00

PCIe-connected FPGAs are gaining popularity as an accelerator technology in data centers. However, it is challenging to jointly develop and debug host software and FPGA hardware. Changes to the hardware design require a time-consuming FPGA synthesis process, and modification to the software, especially the operating system and device drivers, can frequently cause the system to hang, without providing enough information for debugging. The combination of these problems results in long debug iterations and a slow development process. To overcome these problems, we designed a VM-HDL co-simulation framework, which is capable of running the same software, operating system, and hardware designs as the target physical system, while providing full visibility and significantly shorter debug iterations.
ZKLoRA: Efficient Zero-Knowledge Proofs for LoRA Verification
Authors: Bidhan Roy, Peter Potash, Marcos Villagra
Date: 2025-01-21 00:00:00

Low-Rank Adaptation (LoRA) is a widely adopted method for customizing large-scale language models. In distributed, untrusted training environments, an open source base model user may want to use LoRA weights created by an external contributor, leading to two requirements: (1) the base model user must confirm that the LoRA weights are effective when paired with the intended base model, and (2) the LoRA contributor must keep their proprietary weights private until compensation is assured. We present ZKLoRA, a zero-knowledge verification protocol that relies on succinct proofs and our novel Multi-Party Inference procedure to verify LoRA-base model compatibility without exposing LoRA weights. ZKLoRA produces deterministic correctness guarantees and validates each LoRA module in only 1-2 seconds on state-of-the-art large language models. This low-latency approach enables nearly real-time verification and promotes secure collaboration among geographically decentralized teams and contract-based training pipelines. The protocol ensures that the delivered LoRA module works as claimed, safeguarding the contributor's intellectual property while providing the base model user with verification of compatibility and lineage.