Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learning

Federated learning (FL) has been introduced to enable a large number of clients, possibly mobile devices, to collaborate on generating a generalized machine learning model thanks to utilizing a larger number of local samples without sharing to offer certain privacy to collaborating clients. However, due to the participation of a large number of clients, it is often difficult to profile and verify each client, which leads to a security threat that malicious participants may hamper the accuracy of the trained model by conveying poisoned models during the training. Hence, the aggregation framework at the parameter server also needs to minimize the detrimental effects of these malicious clients. A plethora of attack and defence strategies have been analyzed in the literature. However, often the Byzantine problem is analyzed solely from the outlier detection perspective, being oblivious to the topology of neural networks (NNs). In the scope of this work, we argue that by extracting certain side information specific to the NN topology, one can design stronger attacks. Hence, inspired by the sparse neural networks, we introduce a hybrid sparse Byzantine attack that is composed of two parts: one exhibiting a sparse nature and attacking only certain NN locations with higher sensitivity, and the other being more silent but accumulating over time, where each ideally targets a different type of defence mechanism, and together they form a strong but imperceptible attack. Finally, we show through extensive simulations that the proposed hybrid Byzantine attack is effective against 8 different defence methods.

LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation

Early-stage 3D brain tumor segmentation from magnetic resonance imaging (MRI) scans is crucial for prompt and effective treatment. However, this process faces the challenge of precise delineation due to the tumors' complex heterogeneity. Moreover, energy sustainability targets and resource limitations, especially in developing countries, require efficient and accessible medical imaging solutions. The proposed architecture, a Lightweight 3D ATtention U-Net with Parallel convolutions, LATUP-Net, addresses these issues. It is specifically designed to reduce computational requirements significantly while maintaining high segmentation performance. By incorporating parallel convolutions, it enhances feature representation by capturing multi-scale information. It further integrates an attention mechanism to refine segmentation through selective feature recalibration. LATUP-Net achieves promising segmentation performance: the average Dice scores for the whole tumor, tumor core, and enhancing tumor on the BraTS2020 dataset are 88.41%, 83.82%, and 73.67%, and on the BraTS2021 dataset, they are 90.29%, 89.54%, and 83.92%, respectively. Hausdorff distance metrics further indicate its improved ability to delineate tumor boundaries. With its significantly reduced computational demand using only 3.07 M parameters, about 59 times fewer than other state-of-the-art models, and running on a single V100 GPU, LATUP-Net stands out as a promising solution for real-world clinical applications, particularly in settings with limited resources. Investigations into the model's interpretability, utilizing gradient-weighted class activation mapping and confusion matrices, reveal that while attention mechanisms enhance the segmentation of small regions, their impact is nuanced. Achieving the most accurate tumor delineation requires carefully balancing local and global features.

Permissible Knowledge Pooling

Information pooling has been extensively formalised across various logical frameworks in distributed systems, characterized by diverse information-sharing patterns. These approaches generally adopt an intersection perspective, aggregating all possible information, regardless of whether it is known or unknown to the agents. In contrast, this work adopts a unique stance, emphasising that sharing knowledge means distributing what is known, rather than what remains uncertain. This paper introduces a dynamic logic for knowledge pooling or sharing and further discusses a potential framework for permissible knowledge pooling.

Flow Fusion, Exploiting Measurement Redundancy for Smarter Allocation

In petroleum production systems, continuous multiphase flow rates are essential for efficient operation. They provide situational awareness, enable production optimization, improve reservoir management and planning, and form the basis for allocation. Furthermore, they can be crucial to ensure a fair revenue split between stakeholders for complex production systems where operators share the facilities. Yet, due to complex multiphase flow dynamics and uncertain subsurface fluid properties, the flow rates are challenging to obtain with high accuracy. Consequently, flow rate measurement and estimation solutions, such as multiphase flow meters and virtual flow meters, have different degrees of accuracy and suitability, and impact production decisions and production allocation accordingly. We propose a field-proven, data-driven framework for reconciliation and allocation. With data validation and reconciliation as the theoretical backbone, the solution exploits measurement redundancy to fuse together relevant flow rate information to infer the most likely flow rates in the production system based on quantifiable uncertainties. The framework consists of four modules: data-processing, uncertainty estimation, reconciliation, and gross error detection. The latter, being the focus of this paper, is a means to identify and mitigate the effect of measurements subject to systematic error, which can invalidate the reconciliation. In this paper, we highlight that a combination of statistical tests and supporting logic for gross error detection and elimination can be beneficial in obtaining a more justifiable production allocation. Using the maximum power measurement test, the module can be limited in its ability to pinpoint the erroneous measurement. Yet, it is demonstrated that the detections can be convenient indications of gross errors and where these might reside in the production system.

Laue Indexing with Optimal Transport

Laue tomography experiments retrieve the positions and orientations of crystal grains in a polycrystalline samples from diffraction patterns recorded at multiple viewing angles. The use of a broad wavelength spectrum beam can greatly reduce the experimental time, but poses a difficult challenge for the indexing of diffraction peaks in polycrystalline samples; the information about the wavelength of these Bragg peaks is absent and the diffraction patterns from multiple grains are superimposed. To date, no algorithms exist capable of indexing samples with more than about 500 grains efficiently. To address this need we present a novel method: Laue indexing with Optimal Transport (LaueOT). We create a probabilistic description of the multi-grain indexing problem and propose a solution based on Sinkhorn Expectation-Maximization method, which allows to efficiently find the maximum of the likelihood thanks to the assignments being calculated using Optimal Transport. This is a non-convex optimization problem, where the orientations and positions of grains are optimized simultaneously with grain-to-spot assignments, while robustly handling the outliers. The selection of initial prototype grains to consider in the optimization problem are also calculated within the Optimal Transport framework. LaueOT can rapidly and effectively index up to 1000 grains on a single large memory GPU within less than 30 minutes. We demonstrate the performance of LaueOT on simulations with variable numbers of grains, spot position measurement noise levels, and outlier fractions. The algorithm recovers the correct number of grains even for high noise levels and up to 70% outliers in our experiments. We compare the results of indexing with LaueOT to existing algorithms both on synthetic and real neutron diffraction data from well-characterized samples.

End-to-end training of Multimodal Model and ranking Model

Traditional recommender systems heavily rely on ID features, which often encounter challenges related to cold-start and generalization. Modeling pre-extracted content features can mitigate these issues, but is still a suboptimal solution due to the discrepancies between training tasks and model parameters. End-to-end training presents a promising solution for these problems, yet most of the existing works mainly focus on retrieval models, leaving the multimodal techniques under-utilized. In this paper, we propose an industrial multimodal recommendation framework named EM3: End-to-end training of Multimodal Model and ranking Model, which sufficiently utilizes multimodal information and allows personalized ranking tasks to directly train the core modules in the multimodal model to obtain more task-oriented content features, without overburdening resource consumption. First, we propose Fusion-Q-Former, which consists of transformers and a set of trainable queries, to fuse different modalities and generate fixed-length and robust multimodal embeddings. Second, in our sequential modeling for user content interest, we utilize Low-Rank Adaptation technique to alleviate the conflict between huge resource consumption and long sequence length. Third, we propose a novel Content-ID-Contrastive learning task to complement the advantages of content and ID by aligning them with each other, obtaining more task-oriented content embeddings and more generalized ID embeddings. In experiments, we implement EM3 on different ranking models in two scenario, achieving significant improvements in both offline evaluation and online A/B test, verifying the generalizability of our method. Ablation studies and visualization are also performed. Furthermore, we also conduct experiments on two public datasets to show that our proposed method outperforms the state-of-the-art methods.

RAR-b: Reasoning as Retrieval Benchmark

Semantic textual similartiy (STS) and information retrieval tasks (IR) tasks have been the two major avenues to record the progress of embedding models in the past few years. Under the emerging Retrieval-augmented Generation (RAG) paradigm, we envision the need to evaluate next-level language understanding abilities of embedding models, and take a conscious look at the reasoning abilities stored in them. Addressing this, we pose the question: Can retrievers solve reasoning problems? By transforming reasoning tasks into retrieval tasks, we find that without specifically trained for reasoning-level language understanding, current state-of-the-art retriever models may still be far from being competent for playing the role of assisting LLMs, especially in reasoning-intensive tasks. Moreover, albeit trained to be aware of instructions, instruction-aware IR models are often better off without instructions in inference time for reasoning tasks, posing an overlooked retriever-LLM behavioral gap for the research community to align. However, recent decoder-based embedding models show great promise in narrowing the gap, highlighting the pathway for embedding models to achieve reasoning-level language understanding. We also show that, although current off-the-shelf re-ranker models fail on these tasks, injecting reasoning abilities into them through fine-tuning still appears easier than doing so to bi-encoders, and we are able to achieve state-of-the-art performance across all tasks by fine-tuning a reranking model. We release Reasoning as Retrieval Benchmark (RAR-b), a holistic suite of tasks and settings to evaluate the reasoning abilities stored in retriever models. RAR-b is available at https://github.com/gowitheflow-1998/RAR-b.

Identifying Shopping Intent in Product QA for Proactive Recommendations

Voice assistants have become ubiquitous in smart devices allowing users to instantly access information via voice questions. While extensive research has been conducted in question answering for voice search, little attention has been paid on how to enable proactive recommendations from a voice assistant to its users. This is a highly challenging problem that often leads to user friction, mainly due to recommendations provided to the users at the wrong time. We focus on the domain of e-commerce, namely in identifying Shopping Product Questions (SPQs), where the user asking a product-related question may have an underlying shopping need. Identifying a user's shopping need allows voice assistants to enhance shopping experience by determining when to provide recommendations, such as product or deal recommendations, or proactive shopping actions recommendation. Identifying SPQs is a challenging problem and cannot be done from question text alone, and thus requires to infer latent user behavior patterns inferred from user's past shopping history. We propose features that capture the user's latent shopping behavior from their purchase history, and combine them using a novel Mixture-of-Experts (MoE) model. Our evaluation shows that the proposed approach is able to identify SPQs with a high score of F1=0.91. Furthermore, based on an online evaluation with real voice assistant users, we identify SPQs in real-time and recommend shopping actions to users to add the queried product into their shopping list. We demonstrate that we are able to accurately identify SPQs, as indicated by the significantly higher rate of added products to users' shopping lists when being prompted after SPQs vs random PQs.

Use of a Structured Knowledge Base Enhances Metadata Curation by Large Language Models

Metadata play a crucial role in ensuring the findability, accessibility, interoperability, and reusability of datasets. This paper investigates the potential of large language models (LLMs), specifically GPT-4, to improve adherence to metadata standards. We conducted experiments on 200 random data records describing human samples relating to lung cancer from the NCBI BioSample repository, evaluating GPT-4's ability to suggest edits for adherence to metadata standards. We computed the adherence accuracy of field name-field value pairs through a peer review process, and we observed a marginal average improvement in adherence to the standard data dictionary from 79% to 80% (p<0.01). We then prompted GPT-4 with domain information in the form of the textual descriptions of CEDAR templates and recorded a significant improvement to 97% from 79% (p<0.01). These results indicate that, while LLMs may not be able to correct legacy metadata to ensure satisfactory adherence to standards when unaided, they do show promise for use in automated metadata curation when integrated with a structured knowledge base.

MeSA-DRL: Memory-Enhanced Deep Reinforcement Learning for Advanced Socially Aware Robot Navigation in Crowded Environments

Autonomous navigation capabilities play a critical role in service robots operating in environments where human interactions are pivotal, due to the dynamic and unpredictable nature of these environments. However, the variability in human behavior presents a substantial challenge for robots in predicting and anticipating movements, particularly in crowded scenarios. To address this issue, a memory-enabled deep reinforcement learning framework is proposed for autonomous robot navigation in diverse pedestrian scenarios. The proposed framework leverages long-term memory to retain essential information about the surroundings and model sequential dependencies effectively. The importance of human-robot interactions is also encoded to assign higher attention to these interactions. A global planning mechanism is incorporated into the memory-enabled architecture. Additionally, a multi-term reward system is designed to prioritize and encourage long-sighted robot behaviors by incorporating dynamic warning zones. Simultaneously, it promotes smooth trajectories and minimizes the time taken to reach the robot's desired goal. Extensive simulation experiments show that the suggested approach outperforms representative state-of-the-art methods, showcasing its ability to a navigation efficiency and safety in real-world scenarios.