pwc-archive/papers-with-abstracts huggingface dataset:
SLVideo: A Sign Language Video Moment Retrieval Framework
Authors: Gonçalo Vinagre Martins, João Magalhães, Afonso Quinaz, Carla Viegas, Sofia Cavaco
Date: 2024-07-22 00:00:00
Tasks: Moment Retrieval, Retrieval, Sign Language Recognition

SLVideo is a video moment retrieval system for Sign Language videos that incorporates facial expressions, addressing this gap in existing technology. The system extracts embedding representations for the hand and face signs from video frames to capture the signs in their entirety, enabling users to search for a specific sign language video segment with text queries. A collection of eight hours of annotated Portuguese Sign Language videos is used as the dataset, and a CLIP model is used to generate the embeddings. The initial results are promising in a zero-shot setting. In addition, SLVideo incorporates a thesaurus that enables users to search for similar signs to those retrieved, using the video segment embeddings, and also supports the edition and creation of video sign language annotations. Project web page: https://novasearch.github.io/SLVideo/
Hybrid PHD-PMB Trajectory Smoothing Using Backward Simulation
Authors: Yuxuan Xia, Ángel F. García-Fernández, Lennart Svensson
Date: 2024-07-20 00:00:00
Tasks: Object

The probability hypothesis density (PHD) and Poisson multi-Bernoulli (PMB) filters are two popular set-type multi-object filters. Motivated by the fact that the multi-object filtering density after each update step in the PHD filter is a PMB without approximation, in this paper we present a multi-object smoother involving PHD forward filtering and PMB backward smoothing. This is achieved by first running the PHD filtering recursion in the forward pass and extracting the PMB filtering densities after each update step before the Poisson Point Process approximation, which is inherent in the PHD filter update. Then in the backward pass we apply backward simulation for sets of trajectories to the extracted PMB filtering densities. We call the resulting multi-object smoother hybrid PHD-PMB trajectory smoother. Notably, the hybrid PHD-PMB trajectory smoother can provide smoothed trajectory estimates for the PHD filter without labeling or tagging, which is not possible for existing PHD smoothers. Also, compared to the trajectory PHD filter, which can only estimate alive trajectories, the hybrid PHD-PMB trajectory smoother enables the estimation of the set of all trajectories. Simulation results demonstrate that the hybrid PHD-PMB trajectory smoother outperforms the PHD filter in terms of both state and cardinality estimates, and the trajectory PHD filter in terms of false detections.
Benchmarking deep learning models for bearing fault diagnosis using the CWRU dataset: A multi-label approach
Authors: Rodrigo Kobashikawa Rosa, Danilo Braga, Danilo Silva
Date: 2024-07-19 00:00:00
Tasks: Benchmarking, Binary Classification, Fault Diagnosis, Multi-class Classification

This paper proposes a novel approach for modeling the problem of fault diagnosis using the Case Western Reserve University (CWRU) bearing fault dataset. Although the dataset is considered a standard reference for testing new algorithms, the typical dataset division suffers from data leakage, as shown by Hendriks et al. (2022) and Abburi et al. (2023), leading to papers reporting over-optimistic results. While their proposed division significantly mitigates this issue, it does not eliminate it entirely. Moreover, their proposed multi-class classification task can still lead to an unrealistic scenario by excluding the possibility of more than one fault type occurring at the same or different locations. As advocated in this paper, a multi-label formulation (detecting the presence of each type of fault for each location) can solve both issues, leading to a scenario closer to reality. Additionally, this approach mitigates the heavy class imbalance of the CWRU dataset, where faulty cases appear much more frequently than healthy cases, even though the opposite is more likely to occur in practice. A multi-label formulation also enables a more precise evaluation using prevalence-independent evaluation metrics for binary classification, such as the ROC curve. Finally, this paper proposes a more realistic dataset division that allows for more diversity in the training dataset while keeping the division free from data leakage. The results show that this new division can significantly improve performance while enabling a fine-grained error analysis. As an application of our approach, a comparative benchmark is performed using several state-of-the-art deep learning models applied to 1D and 2D signal representations in time and/or frequency domains.
Psychometric Alignment: Capturing Human Knowledge Distributions via Language Models
Authors: Joy He-Yueya, Wanjing Anya Ma, Kanishk Gandhi, Benjamin W. Domingue, Emma Brunskill, Noah D. Goodman
Date: 2024-07-22 00:00:00

Language models (LMs) are increasingly used to simulate human-like responses in scenarios where accurately mimicking a population's behavior can guide decision-making, such as in developing educational materials and designing public policies. The objective of these simulations is for LMs to capture the variations in human responses, rather than merely providing the expected correct answers. Prior work has shown that LMs often generate unrealistically accurate responses, but there are no established metrics to quantify how closely the knowledge distribution of LMs aligns with that of humans. To address this, we introduce "psychometric alignment," a metric that measures the extent to which LMs reflect human knowledge distribution. Assessing this alignment involves collecting responses from both LMs and humans to the same set of test items and using Item Response Theory to analyze the differences in item functioning between the groups. We demonstrate that our metric can capture important variations in populations that traditional metrics, like differences in accuracy, fail to capture. We apply this metric to assess existing LMs for their alignment with human knowledge distributions across three real-world domains. We find significant misalignment between LMs and human populations, though using persona-based prompts can improve alignment. Interestingly, smaller LMs tend to achieve greater psychometric alignment than larger LMs. Further, training LMs on human response data from the target distribution enhances their psychometric alignment on unseen test items, but the effectiveness of such training varies across domains.
Launch Power Optimization in super-(C+L) Systems
Authors: Yanchao Jiang, Dario Pilori, Antonino Nespola, Alberto Tanzi, Stefano Piciaccia, Mahdi Ranjbar Zefreh, Fabrizio Forghieri, Pierluigi Poggiolini
Date: 2024-07-19 00:00:00

We investigate launch power optimization in 12-THz super-(C+L) systems, using iterative performance evaluation enabled by NLI closed-form models. We find that, despite the strong ISRS, these systems tolerate well easy-to-implement suboptimal launch power profiles, with marginal throughput loss.
RadioRAG: Factual large language models for enhanced diagnostics in radiology using online retrieval augmented generation
Authors: Soroosh Tayebi Arasteh, Mahshad Lotfinia, Keno Bressem, Robert Siepmann, Lisa Adams, Dyke Ferber, Christiane Kuhl, Jakob Nikolas Kather, Sven Nebelung, Daniel Truhn
Date: 2024-07-22 00:00:00
Tasks: Diagnostic, Question Answering, RAG, Retrieval, Retrieval-augmented Generation

Large language models (LLMs) often generate outdated or inaccurate information based on static training datasets. Retrieval augmented generation (RAG) mitigates this by integrating outside data sources. While previous RAG systems used pre-assembled, fixed databases with limited flexibility, we have developed Radiology RAG (RadioRAG), an end-to-end framework that retrieves data from authoritative radiologic online sources in real-time. We evaluate the diagnostic accuracy of various LLMs when answering radiology-specific questions with and without access to additional online information via RAG. Using 80 questions from the RSNA Case Collection across radiologic subspecialties and 24 additional expert-curated questions with reference standard answers, LLMs (GPT-3.5-turbo, GPT-4, Mistral-7B, Mixtral-8x7B, and Llama3 [8B and 70B]) were prompted with and without RadioRAG in a zero-shot inference scenario RadioRAG retrieved context-specific information from www.radiopaedia.org in real-time. Accuracy was investigated. Statistical analyses were performed using bootstrapping. The results were further compared with human performance. RadioRAG improved diagnostic accuracy across most LLMs, with relative accuracy increases ranging up to 54% for different LLMs. It matched or exceeded non-RAG models and the human radiologist in question answering across radiologic subspecialties, particularly in breast imaging and emergency radiology. However, the degree of improvement varied among models; GPT-3.5-turbo and Mixtral-8x7B-instruct-v0.1 saw notable gains, while Mistral-7B-instruct-v0.2 showed no improvement, highlighting variability in RadioRAG's effectiveness. LLMs benefit when provided access to domain-specific data beyond their training data. For radiology, RadioRAG establishes a robust framework that substantially improves diagnostic accuracy and factuality in radiological question answering.
Ultrawideband optical fibre throughput in the presence of total optical power constraints from C to OESCLU spectral bands
Authors: Mindaugas Jarmolovičius, Henrique Buglia, Eric Sillekens, Polina Bayvel, Robert I. Killey
Date: 2024-07-19 00:00:00

Using a recently developed fast integral ultrawideband Gaussian noise model, we quantify the achievable throughput under total optical power constraints for systems ranging from C-band to fully populated OESCLU bands using optimum launch powers, showing conditions when expanding bandwidth provides no additional throughput.
Can GPT-4 learn to analyse moves in research article abstracts?
Authors: Danni Yu, Marina Bondi, Ken Hyland
Date: 2024-07-22 00:00:00
Tasks: Articles, Sentence

One of the most powerful and enduring ideas in written discourse analysis is that genres can be described in terms of the moves which structure a writer's purpose. Considerable research has sought to identify these distinct communicative acts, but analyses have been beset by problems of subjectivity, reliability and the time-consuming need for multiple coders to confirm analyses. In this paper we employ the affordances of GPT-4 to automate the annotation process by using natural language prompts. Focusing on abstracts from articles in four applied linguistics journals, we devise prompts which enable the model to identify moves effectively. The annotated outputs of these prompts were evaluated by two assessors with a third addressing disagreements. The results show that an 8-shot prompt was more effective than one using two, confirming that the inclusion of examples illustrating areas of variability can enhance GPT-4's ability to recognize multiple moves in a single sentence and reduce bias related to textual position. We suggest that GPT-4 offers considerable potential in automating this annotation process, when human actors with domain specific linguistic expertise inform the prompting process.
A Secure and Efficient Distributed Semantic Communication System for Heterogeneous Internet of Things
Authors: Weihao Zeng, Xinyu Xu, Qianyun Zhang, Jiting Shi, Zhenyu Guan, Shufeng Li, Zhijin Qin
Date: 2024-07-19 00:00:00
Tasks: Privacy Preserving, Semantic Communication

Semantic communications are expected to improve the transmission efficiency in Internet of Things (IoT) networks. However, the distributed nature of networks and heterogeneity of devices challenge the secure utilization of semantic communication systems. In this paper, we develop a distributed semantic communication system that achieves the security and efficiency during update and usage phases. A blockchain-based trust scheme for update is designed to continuously train and synchronize the system in dynamic IoT environments. To improve the updating efficiency, we propose a flexible semantic coding method base on compressive semantic knowledge bases. It greatly reduces the amount of data shared among devices for system update, and realizes the flexible adjustment of the size of knowledge bases and the number of transmitted signal symbols in model training and inference stages. In the usage phase, a signature mechanism for lossy semantics is introduced to guarantee the integrity and authenticity of the transmitted semantics in lossy semantic communications. We further design a noise-aware differential privacy mechanism, which introduces optimized noise based on the different channel information available to heterogeneous devices. Experiments on text transmission tasks show that the proposed system achieves the protection of the integrity and privacy for exchanged semantics, and reduces the data to be transmitted in the update phase by about $35\%$ to $88\%$, and in the usage phase by $60\%$ compared with related works.
A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population Mechanism
Authors: Yu Xue, Chenchen Zhu, Mengchu Zhou, Mohamed Wahib, Moncef Gabbouj
Date: 2024-07-22 00:00:00
Tasks: GPU, Neural Architecture Search

Neural architecture search (NAS) enables re-searchers to automatically explore vast search spaces and find efficient neural networks. But NAS suffers from a key bottleneck, i.e., numerous architectures need to be evaluated during the search process, which requires a lot of computing resources and time. In order to improve the efficiency of NAS, a series of methods have been proposed to reduce the evaluation time of neural architectures. However, they are not efficient enough and still only focus on the accuracy of architectures. In addition to the classification accuracy, more efficient and smaller network architectures are required in real-world applications. To address the above problems, we propose the SMEM-NAS, a pairwise com-parison relation-assisted multi-objective evolutionary algorithm based on a multi-population mechanism. In the SMEM-NAS, a surrogate model is constructed based on pairwise compari-son relations to predict the accuracy ranking of architectures, rather than the absolute accuracy. Moreover, two populations cooperate with each other in the search process, i.e., a main population guides the evolution, while a vice population expands the diversity. Our method aims to provide high-performance models that take into account multiple optimization objectives. We conduct a series of experiments on the CIFAR-10, CIFAR-100 and ImageNet datasets to verify its effectiveness. With only a single GPU searching for 0.17 days, competitive architectures can be found by SMEM-NAS which achieves 78.91% accuracy with the MAdds of 570M on the ImageNet. This work makes a significant advance in the important field of NAS.
Large Language Model Enabled Semantic Communication Systems
Authors: Zhenyi Wang, Li Zou, Shengyun Wei, Feifan Liao, Jia Zhuo, Haibo Mi, Rongxuan Lai
Date: 2024-07-19 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model, model, Semantic Communication, Unsupervised Pre-training

Large language models (LLMs) have recently demonstrated state-of-the-art performance across various natural language processing (NLP) tasks, achieving near-human levels in multiple language understanding challenges and aligning closely with the core principles of semantic communication. Inspired by LLMs' advancements in semantic processing, we propose an innovative LLM-enabled semantic communication system framework, named LLM-SC, that applies LLMs directly to the physical layer coding and decoding for the first time. By analyzing the relationship between the training process of LLMs and the optimization objectives of semantic communication, we propose training a semantic encoder through LLMs' tokenizer training and establishing a semantic knowledge base via the LLMs' unsupervised pre-training process. This knowledge base aids in constructing the optimal decoder by providing the prior probability of the transmitted language sequence. Based on this foundation, we derive the optimal decoding criterion for the receiver and introduce the beam search algorithm to further reduce the complexity. Furthermore, we assert that existing LLMs can be employed directly for LLM-SC without additional re-training or fine-tuning. Simulation results demonstrate that LLM-SC outperforms classical DeepSC at signal-to-noise ratios (SNR) exceeding 3 dB, enabling error-free transmission of semantic information under high SNR, which is unattainable by DeepSC. In addition to semantic-level performance, LLM-SC demonstrates compatibility with technical-level performance, achieving approximately 8 dB coding gain for a bit error ratio (BER) of $10^{-3}$ without any channel coding while maintaining the same joint source-channel coding rate as traditional communication systems.
Discrete Flow Matching
Authors: Itai Gat, Tal Remez, Neta Shaul, Felix Kreuk, Ricky T. Q. Chen, Gabriel Synnaeve, Yossi Adi, Yaron Lipman
Date: 2024-07-22 00:00:00
Tasks: HumanEval, mbpp, Prediction

Despite Flow Matching and diffusion models having emerged as powerful generative paradigms for continuous variables such as images and videos, their application to high-dimensional discrete data, such as language, is still limited. In this work, we present Discrete Flow Matching, a novel discrete flow paradigm designed specifically for generating discrete data. Discrete Flow Matching offers several key contributions:(i) it works with a general family of probability paths interpolating between source and target distributions; (ii) it allows for a generic formula for sampling from these probability paths using learned posteriors such as the probability denoiser ($x$-prediction) and noise-prediction ($\epsilon$-prediction); (iii) practically, focusing on specific probability paths defined with different schedulers improves generative perplexity compared to previous discrete diffusion and flow models; and (iv) by scaling Discrete Flow Matching models up to 1.7B parameters, we reach 6.7% Pass@1 and 13.4% Pass@10 on HumanEval and 6.7% Pass@1 and 20.6% Pass@10 on 1-shot MBPP coding benchmarks. Our approach is capable of generating high-quality discrete data in a non-autoregressive fashion, significantly closing the gap between autoregressive models and discrete flow models.
Joint Information and Jamming Beamforming for Securing IoT Networks With Rate-Splitting
Authors: Bin Qiu, Wenchi Cheng, Wei zhang
Date: 2024-07-19 00:00:00

The goal of this paper is to address the physical layer (PHY) security problem for multi-user multi-input single-output (MU-MISO) Internet of Things (IoT) systems in the presence of passive eavesdroppers (Eves). To this end, we propose an artificial noise (AN)-aided rate-splitting (RS)-based secure beamforming scheme. Our design considers the dual use of common messages and places the research emphasis on hiding the private messages for secure communication. In particular, leveraging AN-aided RS-based beamforming, we aim to maximize the focused secrecy sum-rate (F-SSR) by jointly designing transmit information and AN beamforming while satisfying the desired received constraints for the private messages at IoT devices (IoDs), and per-antenna transmit power constraint at base station. Then, we proposed a two-stage algorithm to iteratively find the optimal solution. By transforming non-convex terms into linear terms, we first reformulate the original problem as a convex program. Next, we recast the optimization problem to an unconstrained problem to obtain the global optimal solutions. Utilizing the duality framework, we further develop an efficient algorithm based on a barrier interior point method to solve the reformulated problem. Simulation results validate the superior performance of our proposed schemes.
Unsupervised Robust Cross-Lingual Entity Alignment via Neighbor Triple Matching with Entity and Relation Texts
Authors: Soojin Yoon, Sungho Ko, Tongyoung Kim, SeongKu Kang, Jinyoung Yeo, Dongha Lee
Date: 2024-07-22 00:00:00
Tasks: Entity Alignment, Knowledge Graphs, Relation

Cross-lingual entity alignment (EA) enables the integration of multiple knowledge graphs (KGs) across different languages, providing users with seamless access to diverse and comprehensive knowledge. Existing methods, mostly supervised, face challenges in obtaining labeled entity pairs. To address this, recent studies have shifted towards self-supervised and unsupervised frameworks. Despite their effectiveness, these approaches have limitations: (1) Relation passing: mainly focusing on the entity while neglecting the semantic information of relations, (2) Isomorphic assumption: assuming isomorphism between source and target graphs, which leads to noise and reduced alignment accuracy, and (3) Noise vulnerability: susceptible to noise in the textual features, especially when encountering inconsistent translations or Out-of-Vocabulary (OOV) problems. In this paper, we propose ERAlign, an unsupervised and robust cross-lingual EA pipeline that jointly performs Entity-level and Relation-level Alignment by neighbor triple matching strategy using semantic textual features of relations and entities. Its refinement step iteratively enhances results by fusing entity-level and relation-level alignments based on neighbor triple matching. The additional verification step examines the entities' neighbor triples as the linearized text. This Align-then-Verify pipeline rigorously assesses alignment results, achieving near-perfect alignment even in the presence of noisy textual features of entities. Our extensive experiments demonstrate that the robustness and general applicability of ERAlign improved the accuracy and effectiveness of EA tasks, contributing significantly to knowledge-oriented applications.
Robust Multi-Beam Secure mmWave Wireless Communication for Hybrid Wiretapping Systems
Authors: Bin Qiu, Wenchi Cheng, Wei zhang
Date: 2024-07-19 00:00:00
Tasks: valid

In this paper, we consider the physical layer (PHY) security problem for hybrid wiretapping wireless systems in millimeter wave transmission, where active eavesdroppers (AEs) and passive eavesdroppers (PEs) coexist to intercept the confidential messages and emit jamming signals. To achieve secure and reliable transmission, we propose an artificial noise (AN)-aided robust multi-beam array transceiver scheme. Leveraging beamforming, we aim to minimize transmit power by jointly designing the information and AN beamforming, while satisfying valid reception for legitimate users (LUs), per-antenna power constraints for transmitter, as well as all interception power constraints for eavesdroppers (Eves). In particular, the interception power formulation is taken into account for protecting the information against hybrid Eves with imperfect AE channel state information (CSI) and no PE CSI. In light of the intractability of the problem, we reformulate the considered problem by replacing non-convex constraints with tractable forms. Afterwards, a two-stage algorithm is developed to obtain the optimal solution. Additionally, we design the received beamforming weights by means of minimum variance distortionless response, such that the jamming caused by AEs can be effectively suppressed. Simulation results demonstrate the superiority of our proposed scheme in terms of energy efficiency and security.