pwc-archive/papers-with-abstracts huggingface dataset:
Newtonized Orthogonal Matching Pursuit for High-Resolution Target Detection in Sparse OFDM ISAC Systems
Authors: Syed Najaf Haider Shah, Sebastian Semper, Aamir Ullah Khan, Christian Schneider, Joerg Robert
Date: 2024-11-05 00:00:00
Tasks: compressed sensing, Integrated sensing and communication, ISAC, parameter estimation

Integrated Sensing and Communication (ISAC) is a technology paradigm that combines sensing capabilities with communication functionalities in a single device or system. In vehicle-to-everything (V2X) sidelink, ISAC can provide enhanced safety by allowing vehicles to not only communicate with one another but also sense the surrounding environment by using sidelink signals. In ISAC-capable V2X sidelink, the random resource allocation results in an unstructured and sparse distribution of time and frequency resources in the received orthogonal frequency division multiplexing (OFDM) grid, leading to degraded radar detection performance when processed using the conventional 2D-FFT method. To address this challenge, this paper proposes a high-resolution off-grid radar target detection algorithm irrespective of the OFDM grid structure. The proposed method utilizes the Newtonized orthogonal matching pursuit (NOMP) algorithm to effectively detect weak targets masked by the sidelobes of stronger ones and accurately estimates off-grid range and velocity parameters with minimal resources through Newton refinements. Simulation results demonstrate the superior performance of the proposed NOMP-based target detection algorithm compared to existing compressed sensing (CS) methods in terms of detection probability, resolution, and accuracy. Additionally, experimental validation is performed using a bi-static radar setup in a semi-anechoic chamber. The measurement results validate the simulation findings, showing that the proposed algorithm significantly enhances target detection and parameter estimation accuracy in realistic scenarios.
Joint Reconstruction and Spatial Super-Resolution of Hyper-Spectral CTIS Images via Multi-Scale Refinement
Authors: Mazen Mel, Alexander Gatto, Pietro Zanuttigh
Date: 2024-08-20 00:00:00
Proceeding: IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING 2024 8
Tasks: Image Reconstruction, Semantic Segmentation, Super-Resolution

The Computed Tomography Imaging Spectrometer (CTIS) is a snapshot imaging device that captures Hyper-Spectral images as two-dimensional compressed sensor measurements. Computational post-processing algorithms are later needed to recover the latent object cube. However, iterative algorithms typically used to solve this task require large computational resources and, furthermore, these approaches are very sensitive to the presumed system and noise models. In addition, the poor spatial resolution of the 0 th diffraction order image limits the usability of CTIS in favor of other snapshot spectrometers even though it enables higher spectral resolution. In this paper we introduce a learning-based computational model exploiting a reconstruction network with iterative refinement, that is able to recover high quality hyper-spectral images leveraging complementary spatio-spectral information scattered across the CTIS sensor image. We showcase the reconstruction capability of such model beyond the spatial resolution limit of the 0 th diffraction order image. Experimental results are shown both on synthetic data and on real datasets that we acquired using two different CTIS systems coupled with high spatial resolution ground truth hyper-spectral images. Furthermore, we introduce HSIRS, the largest dataset of its kind for joint spectral image reconstruction and semantic segmentation of food items with high quality manually annotated segmentation maps and we showcase how hyper-spectral data allows to efficiently tackle this task.
Multi-frequency antenna for quasi-isotropic radiator and 6G massive IoT
Authors: Bing Xiao, Hang Wong, Kam Man Shum
Date: 2023-12-18 00:00:00

An isotropic antenna radiates and receives electromagnetic wave uniformly in magnitude in 3D space. A multi-frequency quasi-isotropic antenna can serve as a practically feasible solution to emulate an ideal multi-frequency isotropic radiator. It is also an essential technology for mobile smart devices for massive IoT in the upcoming 6G. However, ever since the quasi-isotropic antenna was proposed and achieved more than half a century ago, at most two discrete narrow frequency bands can be achieved, because of the significantly increased structural complexity from multi-frequency isotropic radiation. This limitation impedes numerous related electromagnetic experiments and the advances in wireless communication. Here, for the first time, a design method for multi-band (>2) quasi-isotropic antennas is proposed. An exemplified quasi-isotropic antenna with the desired four frequency bands is also presented for demonstration. The measured results validate excellent performance on both electromagnetics and wireless communications for this antenna.
A Patch-Based Algorithm for Diverse and High Fidelity Single Image Generation
Authors: Nicolas Cherel, Andrés Almansa, Yann Gousseau, Alasdair Newson
Date: 2022-10-16 00:00:00
Proceeding: IEEE International Conference on Image Processing (ICIP) 2022 10
Tasks: Diversity, Image Generation, single-image-generation

Image generation is the task of producing new samples from one or several example images. Until recently, this has been done using large image databases, in particular using Generative Adversarial Networks (GANs). However, Shaham et al. [1] recently proposed the SinGAN method, which achieves this generation using a single image example. At the same time, researchers are realizing that classical patch- based methods can replace certain neural networks, with no costly training. In this paper, we present a purely patch-based method, named Patches for Single image generation (PSin), which requires no training and generates samples in seconds. Our algorithm is based on the minimization of a global, patch- based energy functional, which ensures the visual fidelity of the result to the original image. We also ensure diversity of the results by carefully choosing the initialization of the algorithm. We propose two initialization variants. We compare our results to both the original SinGAN and another recent patch-based image generation approach, both qualitatively and quantitatively using multiple metrics.
Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy
Authors: Alif Bin Abdul Qayyum, Susan D. Mertins, Amanda K. Paulson, Nathan M. Urban, Byung-Jun Yoon
Date: 2024-11-05 00:00:00
Tasks: Drug Design

The data-driven drug design problem can be formulated as an optimization task of a potentially expensive black-box objective function over a huge high-dimensional and structured molecular space. The junction tree variational autoencoder (JTVAE) has been shown to be an efficient generative model that can be used for suggesting legitimate novel drug-like small molecules with improved properties. While the performance of the generative molecular design (GMD) scheme strongly depends on the initial training data, one can improve its sampling efficiency for suggesting better molecules with enhanced properties by optimizing the latent space. In this work, we propose how mechanistic models - such as pathway models described by differential equations - can be used for effective latent space optimization(LSO) of JTVAEs and other similar models for GMD. To demonstrate the potential of our proposed approach, we show how a pharmacodynamic model, assessing the therapeutic efficacy of a drug-like small molecule by predicting how it modulates a cancer pathway, can be incorporated for effective LSO of data-driven models for GMD.
Do transformer models do phonology like a linguist?
Authors: Saliha Muradoglu, Mans Hulden
Date: 2023-07-09 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2023 7

Neural sequence-to-sequence models have been very successful at tasks in phonology and morphology that seemingly require a capacity for intricate linguistic generalisations. In this paper, we perform a detailed breakdown of the power of such models to capture various phonological generalisations and to benefit from exposure to one phonological rule to infer the behaviour of another similar rule. We present two types of experiments, one of which establishes the efficacy of the transformer model on 29 different processes. The second experiment type follows a priming and held-out case split where our model is exposed to two (or more) phenomena; one which is used as a primer to make the model aware of a linguistic category (e.g. voiceless stops) and a second one which contains a rule with a withheld case that the model is expected to infer (e.g. word-final devoicing with a missing training example such as b→p) results show that the transformer model can successfully model all 29 phonological phenomena considered, regardless of perceived process difficulty. We also show that the model can generalise linguistic categories and structures, such as vowels and syllables, through priming processes.
Both Text and Images Leaked! A Systematic Analysis of Multimodal LLM Data Contamination
Authors: Dingjie Song, Sicheng Lai, Shunian Chen, Lichao Sun, Benyou Wang
Date: 2024-11-06 00:00:00

The rapid progression of multimodal large language models (MLLMs) has demonstrated superior performance on various multimodal benchmarks. However, the issue of data contamination during training creates challenges in performance evaluation and comparison. While numerous methods exist for detecting models' contamination in large language models (LLMs), they are less effective for MLLMs due to their various modalities and multiple training phases. In this study, we introduce a multimodal data contamination detection framework, MM-Detect, designed for MLLMs. Our experimental results indicate that MM-Detect is quite effective and sensitive in identifying varying degrees of contamination, and can highlight significant performance improvements due to the leakage of multimodal benchmark training sets. Furthermore, we explore whether the contamination originates from the base LLMs used by MLLMs or the multimodal training phase, providing new insights into the stages at which contamination may be introduced.
Medical Adaptation of Large Language and Vision-Language Models: Are We Making Progress?
Authors: Daniel P. Jeong, Saurabh Garg, Zachary C. Lipton, Michael Oberst
Date: 2024-11-06 00:00:00
Tasks: Medical Question Answering, Question Answering

Several recent works seek to develop foundation models specifically for medical applications, adapting general-purpose large language models (LLMs) and vision-language models (VLMs) via continued pretraining on publicly available biomedical corpora. These works typically claim that such domain-adaptive pretraining (DAPT) improves performance on downstream medical tasks, such as answering medical licensing exam questions. In this paper, we compare seven public "medical" LLMs and two VLMs against their corresponding base models, arriving at a different conclusion: all medical VLMs and nearly all medical LLMs fail to consistently improve over their base models in the zero-/few-shot prompting regime for medical question-answering (QA) tasks. For instance, across the tasks and model pairs we consider in the 3-shot setting, medical LLMs only outperform their base models in 12.1% of cases, reach a (statistical) tie in 49.8% of cases, and are significantly worse than their base models in the remaining 38.2% of cases. Our conclusions are based on (i) comparing each medical model head-to-head, directly against the corresponding base model; (ii) optimizing the prompts for each model separately; and (iii) accounting for statistical uncertainty in comparisons. While these basic practices are not consistently adopted in the literature, our ablations show that they substantially impact conclusions. Our findings suggest that state-of-the-art general-domain models may already exhibit strong medical knowledge and reasoning capabilities, and offer recommendations to strengthen the conclusions of future studies.
Fed-EC: Bandwidth-Efficient Clustering-Based Federated Learning For Autonomous Visual Robot Navigation
Authors: Shreya Gummadi, Mateus V. Gasparino, Deepak Vasisht, Girish Chowdhary
Date: 2024-11-06 00:00:00
Tasks: Federated Learning, Robot Navigation

Centralized learning requires data to be aggregated at a central server, which poses significant challenges in terms of data privacy and bandwidth consumption. Federated learning presents a compelling alternative, however, vanilla federated learning methods deployed in robotics aim to learn a single global model across robots that works ideally for all. But in practice one model may not be well suited for robots deployed in various environments. This paper proposes Federated-EmbedCluster (Fed-EC), a clustering-based federated learning framework that is deployed with vision based autonomous robot navigation in diverse outdoor environments. The framework addresses the key federated learning challenge of deteriorating model performance of a single global model due to the presence of non-IID data across real-world robots. Extensive real-world experiments validate that Fed-EC reduces the communication size by 23x for each robot while matching the performance of centralized learning for goal-oriented navigation and outperforms local learning. Fed-EC can transfer previously learnt models to new robots that join the cluster.
Self-Consistency Preference Optimization
Authors: Archiki Prasad, Weizhe Yuan, Richard Yuanzhe Pang, Jing Xu, Maryam Fazel-Zarandi, Mohit Bansal, Sainbayar Sukhbaatar, Jason Weston, Jane Yu
Date: 2024-11-06 00:00:00
Tasks: GSM8K, Math

Self-alignment, whereby models learn to improve themselves without human annotation, is a rapidly growing research area. However, existing techniques often fail to improve complex reasoning tasks due to the difficulty of assigning correct rewards. An orthogonal approach that is known to improve correctness is self-consistency, a method applied at inference time based on multiple sampling in order to find the most consistent answer. In this work, we extend the self-consistency concept to help train models. We thus introduce self-consistency preference optimization (ScPO), which iteratively trains consistent answers to be preferred over inconsistent ones on unsupervised new problems. We show ScPO leads to large improvements over conventional reward model training on reasoning tasks such as GSM8K and MATH, closing the gap with supervised training with gold answers or preferences, and that combining ScPO with standard supervised learning improves results even further. On ZebraLogic, ScPO finetunes Llama-3 8B to be superior to Llama-3 70B, Gemma-2 27B, and Claude-3 Haiku.
How Transformers Solve Propositional Logic Problems: A Mechanistic Analysis
Authors: Guan Zhe Hong, Nishanth Dikkala, Enming Luo, Cyrus Rashtchian, Xin Wang, Rina Panigrahy
Date: 2024-11-06 00:00:00
Tasks: Logical Reasoning

Large language models (LLMs) have shown amazing performance on tasks that require planning and reasoning. Motivated by this, we investigate the internal mechanisms that underpin a network's ability to perform complex logical reasoning. We first construct a synthetic propositional logic problem that serves as a concrete test-bed for network training and evaluation. Crucially, this problem demands nontrivial planning to solve. We perform our study on two fronts. First, we pursue an understanding of precisely how a three-layer transformer, trained from scratch and attains perfect test accuracy, solves this problem. We are able to identify certain "planning" and "reasoning" mechanisms in the network that necessitate cooperation between the attention blocks to implement the desired logic. Second, we study how pretrained LLMs, namely Mistral-7B and Gemma-2-9B, solve this problem. We characterize their reasoning circuits through causal intervention experiments, providing necessity and sufficiency evidence for the circuits. We find evidence suggesting that the two models' latent reasoning strategies are surprisingly similar, and human-like. Overall, our work systemically uncovers novel aspects of small and large transformers, and continues the study of how they plan and reason.
RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned Vision-Language Models
Authors: Maya Varma, Jean-Benoit Delbrouck, Zhihong Chen, Akshay Chaudhari, Curtis Langlotz
Date: 2024-11-06 00:00:00
Tasks: image-classification, Image Classification, zero-shot-classification, Zero-Shot Learning

Fine-tuned vision-language models (VLMs) often capture spurious correlations between image features and textual attributes, resulting in degraded zero-shot performance at test time. Existing approaches for addressing spurious correlations (i) primarily operate at the global image-level rather than intervening directly on fine-grained image features and (ii) are predominantly designed for unimodal settings. In this work, we present RaVL, which takes a fine-grained perspective on VLM robustness by discovering and mitigating spurious correlations using local image features rather than operating at the global image level. Given a fine-tuned VLM, RaVL first discovers spurious correlations by leveraging a region-level clustering approach to identify precise image features contributing to zero-shot classification errors. Then, RaVL mitigates the identified spurious correlation with a novel region-aware loss function that enables the VLM to focus on relevant regions and ignore spurious relationships during fine-tuning. We evaluate RaVL on 654 VLMs with various model architectures, data domains, and learned spurious correlations. Our results show that RaVL accurately discovers (191% improvement over the closest baseline) and mitigates (8.2% improvement on worst-group image classification accuracy) spurious correlations. Qualitative evaluations on general-domain and medical-domain VLMs confirm our findings.
A Collaborative Content Moderation Framework for Toxicity Detection based on Conformalized Estimates of Annotation Disagreement
Authors: Guillermo Villate-Castillo, Javier Del Ser, Borja Sanz
Date: 2024-11-06 00:00:00
Tasks: Conformal Prediction

Content moderation typically combines the efforts of human moderators and machine learning models. However, these systems often rely on data where significant disagreement occurs during moderation, reflecting the subjective nature of toxicity perception. Rather than dismissing this disagreement as noise, we interpret it as a valuable signal that highlights the inherent ambiguity of the content,an insight missed when only the majority label is considered. In this work, we introduce a novel content moderation framework that emphasizes the importance of capturing annotation disagreement. Our approach uses multitask learning, where toxicity classification serves as the primary task and annotation disagreement is addressed as an auxiliary task. Additionally, we leverage uncertainty estimation techniques, specifically Conformal Prediction, to account for both the ambiguity in comment annotations and the model's inherent uncertainty in predicting toxicity and disagreement.The framework also allows moderators to adjust thresholds for annotation disagreement, offering flexibility in determining when ambiguity should trigger a review. We demonstrate that our joint approach enhances model performance, calibration, and uncertainty estimation, while offering greater parameter efficiency and improving the review process in comparison to single-task methods.
M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models
Authors: Chuhan Li, Ziyao Shangguan, Yilun Zhao, Deyuan Li, Yixin Liu, Arman Cohan
Date: 2024-11-06 00:00:00
Tasks: Information Retrieval, Question Answering

Existing benchmarks for evaluating foundation models mainly focus on single-document, text-only tasks. However, they often fail to fully capture the complexity of research workflows, which typically involve interpreting non-textual data and gathering information across multiple documents. To address this gap, we introduce M3SciQA, a multi-modal, multi-document scientific question answering benchmark designed for a more comprehensive evaluation of foundation models. M3SciQA consists of 1,452 expert-annotated questions spanning 70 natural language processing paper clusters, where each cluster represents a primary paper along with all its cited documents, mirroring the workflow of comprehending a single paper by requiring multi-modal and multi-document data. With M3SciQA, we conduct a comprehensive evaluation of 18 foundation models. Our results indicate that current foundation models still significantly underperform compared to human experts in multi-modal information retrieval and in reasoning across multiple scientific documents. Additionally, we explore the implications of these findings for the future advancement of applying foundation models in multi-modal scientific literature analysis.
Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset
Authors: Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
Date: 2024-11-06 00:00:00
Tasks: Continual Learning, Multi-Armed Bandits, reinforcement-learning, Reinforcement Learning

Neural networks are traditionally trained under the assumption that data come from a stationary distribution. However, settings which violate this assumption are becoming more popular; examples include supervised learning under distributional shifts, reinforcement learning, continual learning and non-stationary contextual bandits. In this work we introduce a novel learning approach that automatically models and adapts to non-stationarity, via an Ornstein-Uhlenbeck process with an adaptive drift parameter. The adaptive drift tends to draw the parameters towards the initialisation distribution, so the approach can be understood as a form of soft parameter reset. We show empirically that our approach performs well in non-stationary supervised and off-policy reinforcement learning settings.
Predicting and Publishing Accurate Imbalance Prices Using Monte Carlo Tree Search
Authors: Fabio Pavirani, Jonas Van Gompel, Seyed Soroush Karimi Madahi, Bert Claessens, Chris Develder
Date: 2024-11-06 00:00:00

The growing reliance on renewable energy sources, particularly solar and wind, has introduced challenges due to their uncontrollable production. This complicates maintaining the electrical grid balance, prompting some transmission system operators in Western Europe to implement imbalance tariffs that penalize unsustainable power deviations. These tariffs create an implicit demand response framework to mitigate grid instability. Yet, several challenges limit active participation. In Belgium, for example, imbalance prices are only calculated at the end of each 15-minute settlement period, creating high risk due to price uncertainty. This risk is further amplified by the inherent volatility of imbalance prices, discouraging participation. Although transmission system operators provide minute-based price predictions, the system imbalance volatility makes accurate price predictions challenging to obtain and requires sophisticated techniques. Moreover, publishing price estimates can prompt participants to adjust their schedules, potentially affecting the system balance and the final price, adding further complexity. To address these challenges, we propose a Monte Carlo Tree Search method that publishes accurate imbalance prices while accounting for potential response actions. Our approach models the system dynamics using a neural network forecaster and a cluster of virtual batteries controlled by reinforcement learning agents. Compared to Belgium's current publication method, our technique improves price accuracy by 20.4% under ideal conditions and by 12.8% in more realistic scenarios. This research addresses an unexplored, yet crucial problem, positioning this paper as a pioneering work in analyzing the potential of more advanced imbalance price publishing techniques.