pwc-archive/papers-with-abstracts huggingface dataset:
Towards Cross-Modal Text-Molecule Retrieval with Better Modality Alignment
Authors: Jia Song, Wanru Zhuang, Yujie Lin, Liang Zhang, Chunyan Li, Jinsong Su, Song He, Xiaochen Bo
Date: 2024-10-31 00:00:00
Tasks: Contrastive Learning, cross-modal alignment, Cross-Modal Retrieval, Drug Design, Retrieval, text similarity

Cross-modal text-molecule retrieval model aims to learn a shared feature space of the text and molecule modalities for accurate similarity calculation, which facilitates the rapid screening of molecules with specific properties and activities in drug design. However, previous works have two main defects. First, they are inadequate in capturing modality-shared features considering the significant gap between text sequences and molecule graphs. Second, they mainly rely on contrastive learning and adversarial training for cross-modality alignment, both of which mainly focus on the first-order similarity, ignoring the second-order similarity that can capture more structural information in the embedding space. To address these issues, we propose a novel cross-modal text-molecule retrieval model with two-fold improvements. Specifically, on the top of two modality-specific encoders, we stack a memory bank based feature projector that contain learnable memory vectors to extract modality-shared features better. More importantly, during the model training, we calculate four kinds of similarity distributions (text-to-text, text-to-molecule, molecule-to-molecule, and molecule-to-text similarity distributions) for each instance, and then minimize the distance between these similarity distributions (namely second-order similarity losses) to enhance cross-modal alignment. Experimental results and analysis strongly demonstrate the effectiveness of our model. Particularly, our model achieves SOTA performance, outperforming the previously-reported best result by 6.4%.
Unveiling User Satisfaction and Creator Productivity Trade-Offs in Recommendation Platforms
Authors: Fan Yao, Yiming Liao, Jingzhou Liu, Shaoliang Nie, Qifan Wang, Haifeng Xu, Hongning Wang
Date: 2024-10-31 00:00:00
Tasks: Diversity

On User-Generated Content (UGC) platforms, recommendation algorithms significantly impact creators' motivation to produce content as they compete for algorithmically allocated user traffic. This phenomenon subtly shapes the volume and diversity of the content pool, which is crucial for the platform's sustainability. In this work, we demonstrate, both theoretically and empirically, that a purely relevance-driven policy with low exploration strength boosts short-term user satisfaction but undermines the long-term richness of the content pool. In contrast, a more aggressive exploration policy may slightly compromise user satisfaction but promote higher content creation volume. Our findings reveal a fundamental trade-off between immediate user satisfaction and overall content production on UGC platforms. Building on this finding, we propose an efficient optimization method to identify the optimal exploration strength, balancing user and creator engagement. Our model can serve as a pre-deployment audit tool for recommendation algorithms on UGC platforms, helping to align their immediate objectives with sustainable, long-term goals.
Learning quantum states prepared by shallow circuits in polynomial time
Authors: Zeph Landau, Yunchao Liu
Date: 2024-10-31 00:00:00

We give a polynomial time algorithm that, given copies of an unknown quantum state $\vert\psi\rangle=U\vert 0^n\rangle$ that is prepared by an unknown constant depth circuit $U$ on a finite-dimensional lattice, learns a constant depth quantum circuit that prepares $\vert\psi\rangle$. The algorithm extends to the case when the depth of $U$ is $\mathrm{polylog}(n)$, with a quasi-polynomial run-time. The key new idea is a simple and general procedure that efficiently reconstructs the global state $\vert\psi\rangle$ from its local reduced density matrices. As an application, we give an efficient algorithm to test whether an unknown quantum state on a lattice has low or high quantum circuit complexity.
Property Estimation in Geotechnical Databases Using Labeled Random Finite Sets
Authors: Changbeom Shim, YoungHo Kim, Craig Butterworth
Date: 2024-10-30 00:00:00

The sufficiency of accurate data is a core element in data-centric geotechnics. However, geotechnical datasets are essentially uncertain, whereupon engineers have difficulty with obtaining precise information for making decisions. This challenge is more apparent when the performance of data-driven technologies solely relies on imperfect databases or even when it is sometimes difficult to investigate sites physically. This paper introduces geotechnical property estimation from noisy and incomplete data within the labeled random finite set (LRFS) framework. We leverage the ability of the generalized labeled multi-Bernoulli (GLMB) filter, a fundamental solution for multi-object estimation, to deal with measurement uncertainties from a Bayesian perspective. In particular, this work focuses on the similarity between LRFSs and big indirect data (BID) in geotechnics as those characteristics resemble each other, which enables GLMB filtering to be exploited potentially for data-centric geotechnical engineering. Experiments for numerical study are conducted to evaluate the proposed method using a public clay database.
Zero-shot Class Unlearning via Layer-wise Relevance Analysis and Neuronal Path Perturbation
Authors: Wenhan Chang, Tianqing Zhu, Ping Xiong, Yufeng Wu, Faqian Guan, Wanlei Zhou
Date: 2024-10-31 00:00:00
Tasks: Machine Unlearning, Privacy Preserving

In the rapid advancement of artificial intelligence, privacy protection has become crucial, giving rise to machine unlearning. Machine unlearning is a technique that removes specific data influences from trained models without the need for extensive retraining. However, it faces several key challenges, including accurately implementing unlearning, ensuring privacy protection during the unlearning process, and achieving effective unlearning without significantly compromising model performance. This paper presents a novel approach to machine unlearning by employing Layer-wise Relevance Analysis and Neuronal Path Perturbation. We address three primary challenges: the lack of detailed unlearning principles, privacy guarantees in zero-shot unlearning scenario, and the balance between unlearning effectiveness and model utility. Our method balances machine unlearning performance and model utility by identifying and perturbing highly relevant neurons, thereby achieving effective unlearning. By using data not present in the original training set during the unlearning process, we satisfy the zero-shot unlearning scenario and ensure robust privacy protection. Experimental results demonstrate that our approach effectively removes targeted data from the target unlearning model while maintaining the model's utility, offering a practical solution for privacy-preserving machine learning.
Systematically Analyzing Prompt Injection Vulnerabilities in Diverse LLM Architectures
Authors: Victoria Benjamin, Emily Braca, Israel Carter, Hafsa Kanchwala, Nava Khojasteh, Charly Landow, Yi Luo, Caroline Ma, Anna Magarelli, Rachel Mirin, Avery Moyer, Kayla Simpson, Amelia Skawinski, Thomas Heverin
Date: 2024-10-28 00:00:00
Tasks: Misinformation

This study systematically analyzes the vulnerability of 36 large language models (LLMs) to various prompt injection attacks, a technique that leverages carefully crafted prompts to elicit malicious LLM behavior. Across 144 prompt injection tests, we observed a strong correlation between model parameters and vulnerability, with statistical analyses, such as logistic regression and random forest feature analysis, indicating that parameter size and architecture significantly influence susceptibility. Results revealed that 56 percent of tests led to successful prompt injections, emphasizing widespread vulnerability across various parameter sizes, with clustering analysis identifying distinct vulnerability profiles associated with specific model configurations. Additionally, our analysis uncovered correlations between certain prompt injection techniques, suggesting potential overlaps in vulnerabilities. These findings underscore the urgent need for robust, multi-layered defenses in LLMs deployed across critical infrastructure and sensitive industries. Successful prompt injection attacks could result in severe consequences, including data breaches, unauthorized access, or misinformation. Future research should explore multilingual and multi-step defenses alongside adaptive mitigation strategies to strengthen LLM security in diverse, real-world environments.
Demonstrating Linked Battery Data To Accelerate Knowledge Flow in Battery Science
Authors: Philipp Dechent, Elias Barbers, Simon Clark, Susanne Lehner, Brady Planden, Masaki Adachi, David A. Howey, Sabine Paarmann
Date: 2024-10-16 00:00:00

Batteries are pivotal for transitioning to a climate-friendly future, leading to a surge in battery research. Scopus (Elsevier) lists 14,388 papers that mention "lithium-ion battery" in 2023 alone, making it infeasible for individuals to keep up. This paper discusses strategies based on structured, semantic, and linked data to manage this information overload. Structured data follows a predefined, machine-readable format; semantic data includes metadata for context; linked data references other semantic data, forming a web of interconnected information. We use a battery-related ontology, BattINFO to standardise terms and enable automated data extraction and analysis. Our methodology integrates full-text search and machine-readable data, enhancing data retrieval and battery testing. We aim to unify commercial cell information and develop tools for the battery community such as manufacturer-independent cycling procedure descriptions and external memory for Large Language Models. Although only a first step, this approach significantly accelerates battery research and digitalizes battery testing, inviting community participation for continuous improvement. We provide the structured data and the tools to access them as open source.
A Method for Constructing Wavelet Functions on the Real Number Field
Authors: Ning li, Lezhi Li
Date: 2024-10-29 00:00:00

A general method to construct wavelet function on real number ffeld is proposed in this article,which is based on finite length sequence.This finite length sequence is called the seed sequence, and the corresponding wavelet function is called the seed sequence wavelet function.The seed sequence wavelet function is continuous and energy concentrated in both time and frequency domains,That is, it has a finite support set in both time and frequency domains. It is proved that if and only if the seed sequence has 0 mean value, the interpolation function satisfy the admissible condition of wavelet function. The conditions corresponding to the higher order vanishing moment of the seed sequence wavelet function are also given in this article. On this basis, the concept of random wavelet function is proposed, and the condition of the regularity of random wavelet is discussed.
Practical Evaluation of Low-Frequency Vibration Energy Harvesting for Creating Green RFID and IoT Devices
Authors: Ivan Froiz-Miguez, Paula Fraga-Lamas, Tiago M. Fernandez-Carames
Date: 2024-10-30 00:00:00

One of the main limitations for the development and deployment of many Green Radio Frequency Identification (RFID) and Internet of Things (IoT) systems is the access to energy sources. In this aspect batteries are the main option to be used in energy constrained scenarios, but their use is limited to certain cases, either because of the constraints imposed by a reduced-form factor, their limited lifespan, or the characteristics of the environment itself (e.g. operating temperature, risk of burning, need for fast response, sudden voltage variations). In this regard, supercapacitors present an interesting alternative for the previously mentioned type of environment, although, due to their short-term capacity, they must be combined with an alternative energy supply mechanism. Energy harvesting mechanisms, in conjunction with ultra-low-power electronics, supercapacitors and various methods to improve the efficiency of communications, have enabled the emergence of battery-less passive electronic devices such as sensors, actuators or transmitters. This paper presents a novel analysis of the performance of an energy harvesting system based on vibrations for Green RFID and IoT applications in the field of maritime transport. The results show that the proposed system allows for charging half of a 1.2 F supercapacitor in about 72 minutes, providing a stable current of around 210 uA and a power output of 0.38 mW.
ArCSEM: Artistic Colorization of SEM Images via Gaussian Splatting
Authors: Takuma Nishimura, Andreea Dogaru, Martin Oeggerli, Bernhard Egger
Date: 2024-10-25 00:00:00
Tasks: Colorization, Novel View Synthesis

Scanning Electron Microscopes (SEMs) are widely renowned for their ability to analyze the surface structures of microscopic objects, offering the capability to capture highly detailed, yet only grayscale, images. To create more expressive and realistic illustrations, these images are typically manually colorized by an artist with the support of image editing software. This task becomes highly laborious when multiple images of a scanned object require colorization. We propose facilitating this process by using the underlying 3D structure of the microscopic scene to propagate the color information to all the captured images, from as little as one colorized view. We explore several scene representation techniques and achieve high-quality colorized novel view synthesis of a SEM scene. In contrast to prior work, there is no manual intervention or labelling involved in obtaining the 3D representation. This enables an artist to color a single or few views of a sequence and automatically retrieve a fully colored scene or video. Project page: https://ronly2460.github.io/ArCSEM
Flexible Demand Manipulation
Authors: Yifan Dai, Andrew Koh
Date: 2024-10-31 00:00:00

We develop a simple framework to analyze how targeted persuasive advertising shapes market power and welfare. A designer flexibly manipulates the demand curve by influencing individual valuations at a cost. A monopolist prices against this manipulated demand curve. We fully characterize the form of optimal advertising plans under ex-ante and ex-post welfare measures. Flexibility per se is powerful, and can substantially harm or benefit consumers vis-a-vis uniform advertising. We discuss implications for regulation, intermediation, and the joint design of manipulation and information.
Network Games Induced Prior for Graph Topology Learning
Authors: Chenyue Zhang, Shangyuan Liu, Hoi-To Wai, Anthony Man-Cho So
Date: 2024-10-31 00:00:00
Tasks: Graph Learning

Learning the graph topology of a complex network is challenging due to limited data availability and imprecise data models. A common remedy in existing works is to incorporate priors such as sparsity or modularity which highlight on the structural property of graph topology. We depart from these approaches to develop priors that are directly inspired by complex network dynamics. Focusing on social networks with actions modeled by equilibriums of linear quadratic games, we postulate that the social network topologies are optimized with respect to a social welfare function. Utilizing this prior knowledge, we propose a network games induced regularizer to assist graph learning. We then formulate the graph topology learning problem as a bilevel program. We develop a two-timescale gradient algorithm to tackle the latter. We draw theoretical insights on the optimal graph structure of the bilevel program and show that they agree with the topology in several man-made networks. Empirically, we demonstrate the proposed formulation gives rise to reliable estimate of graph topology.
The equilibrium properties of obvious strategy profiles in games with many players
Authors: Enxian Chen Bin Wu Hanping Xu
Date: 2024-10-29 00:00:00

This paper studies the equilibrium properties of the ``obvious strategy profile'' in large finite-player games. Each player in such a strategy profile simply adopts a randomized strategy as she would have used in a symmetric equilibrium of an idealized large game. We show that, under a continuity assumption, (i) obvious strategy profiles constitute a convergent sequence of approximate symmetric equilibria as the number of players tends to infinity, and (ii) realizations of such strategy profiles also form a convergent sequence of (pure strategy) approximate equilibria with probability approaching one. Our findings offer a solution that is easily implemented without coordination issues and is asymptotically optimal for players in large finite games. Additionally, we present a convergence result for approximate symmetric equilibria.
Prosody as a Teaching Signal for Agent Learning: Exploratory Studies and Algorithmic Implications
Authors: Matilda Knierim, Sahil Jain, Murat Han Aydoğan, Kenneth Mitra, Kush Desai, Akanksha Saran, Kim Baraka
Date: 2024-10-31 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning

Agent learning from human interaction often relies on explicit signals, but implicit social cues, such as prosody in speech, could provide valuable information for more effective learning. This paper advocates for the integration of prosody as a teaching signal to enhance agent learning from human teachers. Through two exploratory studies--one examining voice feedback in an interactive reinforcement learning setup and the other analyzing restricted audio from human demonstrations in three Atari games--we demonstrate that prosody carries significant information about task dynamics. Our findings suggest that prosodic features, when coupled with explicit feedback, can enhance reinforcement learning outcomes. Moreover, we propose guidelines for prosody-sensitive algorithm design and discuss insights into teaching behavior. Our work underscores the potential of leveraging prosody as an implicit signal for more efficient agent learning, thus advancing human-agent interaction paradigms.
Enhancing EHR Systems with data from wearables: An end-to-end Solution for monitoring post-Surgical Symptoms in older adults
Authors: Heng Sun, Sai Manoj Jalam, Havish Kodali, Subhash Nerella, Ruben D. Zapata, Nicole Gravina, Jessica Ray, Erik C. Schmidt, Todd Matthew Manini, Rashidi Parisa
Date: 2024-10-28 00:00:00

Mobile health (mHealth) apps have gained popularity over the past decade for patient health monitoring, yet their potential for timely intervention is underutilized due to limited integration with electronic health records (EHR) systems. Current EHR systems lack real-time monitoring capabilities for symptoms, medication adherence, physical and social functions, and community integration. Existing systems typically rely on static, in-clinic measures rather than dynamic, real-time patient data. This highlights the need for automated, scalable, and human-centered platforms to integrate patient-generated health data (PGHD) within EHR. Incorporating PGHD in a user-friendly format can enhance patient symptom surveillance, ultimately improving care management and post-surgical outcomes. To address this barrier, we have developed an mHealth platform, ROAMM-EHR, to capture real-time sensor data and Patient Reported Outcomes (PROs) using a smartwatch. The ROAMM-EHR platform can capture data from a consumer smartwatch, send captured data to a secure server, and display information within the Epic EHR system using a user-friendly interface, thus enabling healthcare providers to monitor post-surgical symptoms effectively.
Advancing Free-Space Optical Communication System Architecture: Performance Analysis of Varied Optical Ground Station Network Configurations
Authors: Eugene Rotherham, Connor Casey, Eva Fernandez Rodriguez, Karen Wendy Vidaurre Torrez, Maren Mashor, Isaac Pike
Date: 2024-10-30 00:00:00

This study discusses the current state of FSO technology, as well as global trends and developments in the industrial ecosystem to identify obstacles to the full realization of optical space-to-ground communication networks. Additionally, link performance and network availability trade-off studies are presented, comparing overall system performance between portable and large OGS networks in conjunction with a constellation of small low Earth orbit (LEO) satellites. The paper provides an up-to-date overview and critical analysis of the FSO industry and assesses the feasibility of low-cost portable terminals as an alternative to larger high-capacity OGS systems. This initiative aims to better inform optical communications stakeholders, including governments, academic institutions, satellite operators, manufacturers, and communication service providers
Scaling 6G Subscribers with Fewer BS Antennas using Multi-carrier NOMA in Fixed Wireless Access
Authors: Kamyar Rajabalifardi, Sagnik Bhattacharya, Mehrnaz Afshang, Mohammad Mozaffari, John M. Cioffi
Date: 2024-10-29 00:00:00

This paper introduces a novel power allocation and subcarrier optimization algorithm tailored for fixed wireless access (FWA) networks operating under low-rank channel conditions, where the number of subscriber antennas far exceeds those at the base station (BS). As FWA networks grow to support more users, traditional approaches like orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) struggle to maintain high data rates and energy efficiency due to the limited degrees of freedom in low-rank scenarios. Our proposed solution addresses this by combining optimal power-subcarrier allocation with an adaptive time-sharing algorithm that dynamically adjusts decoding orders to optimize performance across multiple users. The algorithm leverages a generalized decision feedback equalizer (GDFE) approach to effectively manage inter-symbol interference and crosstalk, leading to superior data rates and energy savings. Simulation results demonstrate that our approach significantly outperforms existing OMA and NOMA baselines, particularly in low-rank conditions, with substantial gains in both data rate and energy efficiency. The findings highlight the potential of this method to meet the growing demand for scalable, high-performance FWA networks.
Optimal Power Allocation and Time Sharing in Low Rank Multi-carrier Wi-Fi Channels
Authors: Sagnik Bhattacharya, Kamyar Rajabalifardi, Muhammad Ahmed Mohsin, Rohan Pote, John M. Cioffi
Date: 2024-10-29 00:00:00

The ever-evolving landscape of distributed wireless systems, e.g. multi-user AR/VR systems, demands high data rates (up to 500 Mbps per user) and low power consumption. With increasing number of participating users, uplink data transmission in the situation where the number of transmitter user antennas exceeds the number of access point (AP) antennas presents a low-rank channel problem. Current Wi-Fi standards using orthogonal multiple access (OMA) fail to address these requirements. Non-orthogonal multiple access (NOMA)-based systems, while outperforming the OMA methods, still fall short of the requirement in low-rank channel uplink transmission, because they adhere to a single decoding order for successive interference cancelation (SIC). This paper proposes and develops a novel optimal power-subcarrier allocation algorithm to maximize the achieved data rates for this low rank channel scenario. Additionally, the proposed algorithm implements a novel time-sharing algorithm for simultaneously participating users, which adaptively varies the decoding orders to achieve higher data rates than any single decoding order. Extensive experimental validations demonstrate that the proposed algorithm achieves 39%, 28%, and 16% higher sum data rates than OMA, NOMA, and multi-carrier NOMA baselines respectively, under low-rank channel conditions, under varying SNR values. We further show that the proposed algorithm significantly outperforms the baselines with varying numbers of users or AP antennas, showing the effectiveness of the optimal power allocation and time-sharing.