Experimental Study of Spatial Statistics for Ultra-Reliable Communications

This paper presents an experimental validation for prediction of rare fading events using channel distribution information (CDI) maps that predict channel statistics from measurements acquired at surrounding locations using spatial interpolation. Using experimental channel measurements from 127 locations, we demonstrate the use case of providing statistical guarantees for rate selection in ultra-reliable low-latency communication (URLLC) using CDI maps. By using only the user location and the estimated map, we are able to meet the desired outage probability with a probability between 93.6-95.6% targeting 95%. On the other hand, a model-based baseline scheme that assumes Rayleigh fading meets the target outage requirement with a probability of 77.2%. The results demonstrate the practical relevance of CDI maps for resource allocation in URLLC.

Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing

Common certification methods operate on a flat pre-defined set of fine-grained classes. In this paper, however, we propose a novel, more general, and practical setting, namely adaptive hierarchical certification for image semantic segmentation. In this setting, the certification can be within a multi-level hierarchical label space composed of fine to coarse levels. Unlike classic methods where the certification would abstain for unstable components, our approach adaptively relaxes the certification to a coarser level within the hierarchy. This relaxation lowers the abstain rate whilst providing more certified semantically meaningful information. We mathematically formulate the problem setup and introduce, for the first time, an adaptive hierarchical certification algorithm for image semantic segmentation, that certifies image pixels within a hierarchy and prove the correctness of its guarantees. Since certified accuracy does not take the loss of information into account when traversing into a coarser hierarchy level, we introduce a novel evaluation paradigm for adaptive hierarchical certification, namely the certified information gain metric, which is proportional to the class granularity level. Our evaluation experiments on real-world challenging datasets such as Cityscapes and ACDC demonstrate that our adaptive algorithm achieves a higher certified information gain and a lower abstain rate compared to the current state-of-the-art certification method, as well as other non-adaptive versions of it.

The Effectiveness of Random Forgetting for Robust Generalization

Deep neural networks are susceptible to adversarial attacks, which can compromise their performance and accuracy. Adversarial Training (AT) has emerged as a popular approach for protecting neural networks against such attacks. However, a key challenge of AT is robust overfitting, where the network's robust performance on test data deteriorates with further training, thus hindering generalization. Motivated by the concept of active forgetting in the brain, we introduce a novel learning paradigm called "Forget to Mitigate Overfitting (FOMO)". FOMO alternates between the forgetting phase, which randomly forgets a subset of weights and regulates the model's information through weight reinitialization, and the relearning phase, which emphasizes learning generalizable features. Our experiments on benchmark datasets and adversarial attacks show that FOMO alleviates robust overfitting by significantly reducing the gap between the best and last robust test accuracy while improving the state-of-the-art robustness. Furthermore, FOMO provides a better trade-off between standard and robust accuracy, outperforming baseline adversarial methods. Finally, our framework is robust to AutoAttacks and increases generalization in many real-world scenarios.

FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence

Plain language summarization with LLMs can be useful for improving textual accessibility of technical content. But how factual are these summaries in a high-stakes domain like medicine? This paper presents FactPICO, a factuality benchmark for plain language summarization of medical texts describing randomized controlled trials (RCTs), which are the basis of evidence-based medicine and can directly inform patient treatment. FactPICO consists of 345 plain language summaries of RCT abstracts generated from three LLMs (i.e., GPT-4, Llama-2, and Alpaca), with fine-grained evaluation and natural language rationales from experts. We assess the factuality of critical elements of RCTs in those summaries: Populations, Interventions, Comparators, Outcomes (PICO), as well as the reported findings concerning these. We also evaluate the correctness of the extra information (e.g., explanations) added by LLMs. Using FactPICO, we benchmark a range of existing factuality metrics, including the newly devised ones based on LLMs. We find that plain language summarization of medical evidence is still challenging, especially when balancing between simplicity and factuality, and that existing metrics correlate poorly with expert judgments on the instance level.

RELIANCE: Reliable Ensemble Learning for Information and News Credibility Evaluation

In the era of information proliferation, discerning the credibility of news content poses an ever-growing challenge. This paper introduces RELIANCE, a pioneering ensemble learning system designed for robust information and fake news credibility evaluation. Comprising five diverse base models, including Support Vector Machine (SVM), naive Bayes, logistic regression, random forest, and Bidirectional Long Short Term Memory Networks (BiLSTMs), RELIANCE employs an innovative approach to integrate their strengths, harnessing the collective intelligence of the ensemble for enhanced accuracy. Experiments demonstrate the superiority of RELIANCE over individual models, indicating its efficacy in distinguishing between credible and non-credible information sources. RELIANCE, also surpasses baseline models in information and news credibility assessment, establishing itself as an effective solution for evaluating the reliability of information sources.

A Lightweight Parallel Framework for Blind Image Quality Assessment

Existing blind image quality assessment (BIQA) methods focus on designing complicated networks based on convolutional neural networks (CNNs) or transformer. In addition, some BIQA methods enhance the performance of the model in a two-stage training manner. Despite the significant advancements, these methods remarkably raise the parameter count of the model, thus requiring more training time and computational resources. To tackle the above issues, we propose a lightweight parallel framework (LPF) for BIQA. First, we extract the visual features using a pre-trained feature extraction network. Furthermore, we construct a simple yet effective feature embedding network (FEN) to transform the visual features, aiming to generate the latent representations that contain salient distortion information. To improve the robustness of the latent representations, we present two novel self-supervised subtasks, including a sample-level category prediction task and a batch-level quality comparison task. The sample-level category prediction task is presented to help the model with coarse-grained distortion perception. The batch-level quality comparison task is formulated to enhance the training data and thus improve the robustness of the latent representations. Finally, the latent representations are fed into a distortion-aware quality regression network (DaQRN), which simulates the human vision system (HVS) and thus generates accurate quality scores. Experimental results on multiple benchmark datasets demonstrate that the proposed method achieves superior performance over state-of-the-art approaches. Moreover, extensive analyses prove that the proposed method has lower computational complexity and faster convergence speed.

Large Language Models for Stemming: Promises, Pitfalls and Failures

Text stemming is a natural language processing technique that is used to reduce words to their base form, also known as the root form. The use of stemming in IR has been shown to often improve the effectiveness of keyword-matching models such as BM25. However, traditional stemming methods, focusing solely on individual terms, overlook the richness of contextual information. Recognizing this gap, in this paper, we investigate the promising idea of using large language models (LLMs) to stem words by leveraging its capability of context understanding. With this respect, we identify three avenues, each characterised by different trade-offs in terms of computational cost, effectiveness and robustness : (1) use LLMs to stem the vocabulary for a collection, i.e., the set of unique words that appear in the collection (vocabulary stemming), (2) use LLMs to stem each document separately (contextual stemming), and (3) use LLMs to extract from each document entities that should not be stemmed, then use vocabulary stemming to stem the rest of the terms (entity-based contextual stemming). Through a series of empirical experiments, we compare the use of LLMs for stemming with that of traditional lexical stemmers such as Porter and Krovetz for English text. We find that while vocabulary stemming and contextual stemming fail to achieve higher effectiveness than traditional stemmers, entity-based contextual stemming can achieve a higher effectiveness than using Porter stemmer alone, under specific conditions.

Event-Based Motion Magnification

Detecting and magnifying imperceptible high-frequency motions in real-world scenarios has substantial implications for industrial and medical applications. These motions are characterized by small amplitudes and high frequencies. Traditional motion magnification methods rely on costly high-speed cameras or active light sources, which limit the scope of their applications. In this work, we propose a dual-camera system consisting of an event camera and a conventional RGB camera for video motion magnification, containing temporally-dense information from the event stream and spatially-dense data from the RGB images. This innovative combination enables a broad and cost-effective amplification of high-frequency motions. By revisiting the physical camera model, we observe that estimating motion direction and magnitude necessitates the integration of event streams with additional image features. On this basis, we propose a novel deep network for event-based video motion magnification that addresses two primary challenges: firstly, the high frequency of motion induces a large number of interpolated frames (up to 80), which our network mitigates with a Second-order Recurrent Propagation module for better handling of long-term frame interpolations; and secondly, magnifying subtle motions is sensitive to noise, which we address by utilizing a temporal filter to amplify motion at specific frequencies and reduce noise impact. We demonstrate the effectiveness and accuracy of our dual-camera system and network through extensive experiments in magnifying small-amplitude, high-frequency motions, offering a cost-effective and flexible solution for motion detection and magnification.

ILBiT: Imitation Learning for Robot Using Position and Torque Information based on Bilateral Control with Transformer

Autonomous manipulation in robot arms is a complex and evolving field of study in robotics. This paper introduces an innovative approach to this challenge by focusing on imitation learning (IL). Unlike traditional imitation methods, our approach uses IL based on bilateral control, allowing for more precise and adaptable robot movements. The conventional IL based on bilateral control method have relied on Long Short-Term Memory (LSTM) networks. In this paper, we present the IL for robot using position and torque information based on Bilateral control with Transformer (ILBiT). This proposed method employs the Transformer model, known for its robust performance in handling diverse datasets and its capability to surpass LSTM's limitations, especially in tasks requiring detailed force adjustments. A standout feature of ILBiT is its high-frequency operation at 100 Hz, which significantly improves the system's adaptability and response to varying environments and objects of different hardness levels. The effectiveness of the Transformer-based ILBiT method can be seen through comprehensive real-world experiments.

BitDelta: Your Fine-Tune May Only Be Worth One Bit

Large Language Models (LLMs) are typically trained in two phases: pre-training on large internet-scale datasets, and fine-tuning for downstream tasks. Given the higher computational demand of pre-training, it's intuitive to assume that fine-tuning adds less new information to the model, and is thus more compressible. We explore this assumption by decomposing the weights of fine-tuned models into their pre-trained components and an additional delta. We introduce a simple method, BitDelta, which successfully quantizes this delta down to 1 bit without compromising performance. This interesting finding not only highlights the potential redundancy of information added during fine-tuning, but also has significant implications for the multi-tenant serving and multi-tenant storage of fine-tuned models. By enabling the use of a single high-precision base model accompanied by multiple 1-bit deltas, BitDelta dramatically reduces GPU memory requirements by more than 10x, which can also be translated to enhanced generation latency in multi-tenant settings. We validate BitDelta through experiments across Llama-2 and Mistral model families, and on models up to 70B parameters, showcasing minimal performance degradation over all tested settings.