Reviewerly/exHarmony huggingface dataset:
Shadow: A Novel Loss Function for Efficient Training in Siamese Networks

Despite significant recent advances in similarity detection tasks, existing approaches pose substantial challenges under memory constraints. One of the primary reasons for this is the use of computationally expensive metric learning loss functions such as Triplet Loss in Siamese networks. In this paper, we present a novel loss function called Shadow Loss that compresses the dimensions of an embedding space during loss calculation without loss of performance. The distance between the projections of the embeddings is learned from inputs on a compact projection space where distances directly correspond to a measure of class similarity. Projecting on a lower-dimension projection space, our loss function converges faster, and the resulting classified image clusters have higher inter-class and smaller intra-class distances. Shadow Loss not only reduces embedding dimensions favoring memory constraint devices but also consistently performs better than the state-of-the-art Triplet Margin Loss by an accuracy of 5\%-10\% across diverse datasets. The proposed loss function is also model agnostic, upholding its performance across several tested models. Its effectiveness and robustness across balanced, imbalanced, medical, and non-medical image datasets suggests that it is not specific to a particular model or dataset but demonstrates superior performance consistently while using less memory and computation.
Incompleteness of boundedly axiomatizable theories

Our main result (Theorem A) shows the incompleteness of any consistent sequential theory T formulated in a finite language such that T is axiomatized by a collection of sentences of bounded quantifier-alternation-depth. Our proof employs an appropriate reduction mechanism to rule out the possibility of completeness by simply invoking Tarski's Undefinability of Truth theorem. We also use the proof strategy of Theorem A to obtain other incompleteness results (as in Theorems A+; B and B+).
Towards Explainable Strategy Templates using NLP Transformers

This paper bridges the gap between mathematical heuristic strategies learned from Deep Reinforcement Learning (DRL) in automated agent negotiation, and comprehensible, natural language explanations. Our aim is to make these strategies more accessible to non-experts. By leveraging traditional Natural Language Processing (NLP) techniques and Large Language Models (LLMs) equipped with Transformers, we outline how parts of DRL strategies composed of parts within strategy templates can be transformed into user-friendly, human-like English narratives. To achieve this, we present a top-level algorithm that involves parsing mathematical expressions of strategy templates, semantically interpreting variables and structures, generating rule-based primary explanations, and utilizing a Generative Pre-trained Transformer (GPT) model to refine and contextualize these explanations. Subsequent customization for varied audiences and meticulous validation processes in an example illustrate the applicability and potential of this approach.
DPSUR: Accelerating Differentially Private Stochastic Gradient Descent Using Selective Update and Release

Machine learning models are known to memorize private data to reduce their training loss, which can be inadvertently exploited by privacy attacks such as model inversion and membership inference. To protect against these attacks, differential privacy (DP) has become the de facto standard for privacy-preserving machine learning, particularly those popular training algorithms using stochastic gradient descent, such as DPSGD. Nonetheless, DPSGD still suffers from severe utility loss due to its slow convergence. This is partially caused by the random sampling, which brings bias and variance to the gradient, and partially by the Gaussian noise, which leads to fluctuation of gradient updates. Our key idea to address these issues is to apply selective updates to the model training, while discarding those useless or even harmful updates. Motivated by this, this paper proposes DPSUR, a Differentially Private training framework based on Selective Updates and Release, where the gradient from each iteration is evaluated based on a validation test, and only those updates leading to convergence are applied to the model. As such, DPSUR ensures the training in the right direction and thus can achieve faster convergence than DPSGD. The main challenges lie in two aspects -- privacy concerns arising from gradient evaluation, and gradient selection strategy for model update. To address the challenges, DPSUR introduces a clipping strategy for update randomization and a threshold mechanism for gradient selection. Experiments conducted on MNIST, FMNIST, CIFAR-10, and IMDB datasets show that DPSUR significantly outperforms previous works in terms of convergence speed and model utility.
Byzantine Robustness and Partial Participation Can Be Achieved Simultaneously: Just Clip Gradient Differences

Distributed learning has emerged as a leading paradigm for training large machine learning models. However, in real-world scenarios, participants may be unreliable or malicious, posing a significant challenge to the integrity and accuracy of the trained models. Byzantine fault tolerance mechanisms have been proposed to address these issues, but they often assume full participation from all clients, which is not always practical due to the unavailability of some clients or communication constraints. In our work, we propose the first distributed method with client sampling and provable tolerance to Byzantine workers. The key idea behind the developed method is the use of gradient clipping to control stochastic gradient differences in recursive variance reduction. This allows us to bound the potential harm caused by Byzantine workers, even during iterations when all sampled clients are Byzantine. Furthermore, we incorporate communication compression into the method to enhance communication efficiency. Under quite general assumptions, we prove convergence rates for the proposed method that match the existing state-of-the-art (SOTA) theoretical results.
Weight fluctuations in (deep) linear neural networks and a derivation of the inverse-variance flatness relation

We investigate the stationary (late-time) training regime of single- and two-layer linear underparameterized neural networks within the continuum limit of stochastic gradient descent (SGD) for synthetic Gaussian data. In the case of a single-layer network in the weakly underparameterized regime, the spectrum of the noise covariance matrix deviates notably from the Hessian, which can be attributed to the broken detailed balance of SGD dynamics. The weight fluctuations are in this case generally anisotropic, but are subject to an isotropic loss. For a two-layer network, we obtain the stochastic dynamics of the weights in each layer and analyze the associated stationary covariances. We identify the inter-layer coupling as a new source of anisotropy for the weight fluctuations. In contrast to the single-layer case, the weight fluctuations experience an anisotropic loss, the flatness of which is inversely related to the fluctuation variance. We thereby provide an analytical derivation of the recently observed inverse variance-flatness relation in a model of a deep linear neural network.
Finite Volume Features, Global Geometry Representations, and Residual Training for Deep Learning-based CFD Simulation

Computational fluid dynamics (CFD) simulation is an irreplaceable modelling step in many engineering designs, but it is often computationally expensive. Some graph neural network (GNN)-based CFD methods have been proposed. However, the current methods inherit the weakness of traditional numerical simulators, as well as ignore the cell characteristics in the mesh used in the finite volume method, a common method in practical CFD applications. Specifically, the input nodes in these GNN methods have very limited information about any object immersed in the simulation domain and its surrounding environment. Also, the cell characteristics of the mesh such as cell volume, face surface area, and face centroid are not included in the message-passing operations in the GNN methods. To address these weaknesses, this work proposes two novel geometric representations: Shortest Vector (SV) and Directional Integrated Distance (DID). Extracted from the mesh, the SV and DID provide global geometry perspective to each input node, thus removing the need to collect this information through message-passing. This work also introduces the use of Finite Volume Features (FVF) in the graph convolutions as node and edge attributes, enabling its message-passing operations to adjust to different nodes. Finally, this work is the first to demonstrate how residual training, with the availability of low-resolution data, can be adopted to improve the flow field prediction accuracy. Experimental results on two datasets with five different state-of-the-art GNN methods for CFD indicate that SV, DID, FVF and residual training can effectively reduce the predictive error of current GNN-based methods by as much as 41%.
Subnetwork Ensembles

Neural network ensembles have been effectively used to improve generalization by combining the predictions of multiple independently trained models. However, the growing scale and complexity of deep neural networks have led to these methods becoming prohibitively expensive and time consuming to implement. Low-cost ensemble methods have become increasingly important as they can alleviate the need to train multiple models from scratch while retaining the generalization benefits that traditional ensemble learning methods afford. This dissertation introduces and formalizes a low-cost framework for constructing Subnetwork Ensembles, where a collection of child networks are formed by sampling, perturbing, and optimizing subnetworks from a trained parent model. We explore several distinct methodologies for generating child networks and we evaluate their efficacy through a variety of ablation studies and established benchmarks. Our findings reveal that this approach can greatly improve training efficiency, parametric utilization, and generalization performance while minimizing computational cost. Subnetwork Ensembles offer a compelling framework for exploring how we can build better systems by leveraging the unrealized potential of deep neural networks.
Receding Horizon Optimization with PPUM: An Approach for Autonomous Robot Path Planning in Uncertain Environments

The ability to understand spatial-temporal patterns for crowds of people is crucial for achieving long-term autonomy of mobile robots deployed in human environments. However, traditional historical data-driven memory models are inadequate for handling anomalies, resulting in poor reasoning by robot in estimating the crowd spatial distribution. In this article, a Receding Horizon Optimization (RHO) formulation is proposed that incorporates a Probability-related Partially Updated Memory (PPUM) for robot path planning in crowded environments with uncertainties. The PPUM acts as a memory layer that combines real-time sensor observations with historical knowledge using a weighted evidence fusion theory to improve robot's adaptivity to the dynamic environments. RHO then utilizes the PPUM as a informed knowledge to generate a path that minimizes the likelihood of encountering dense crowds while reducing the cost of local motion planning. The proposed approach provides an innovative solution to the problem of robot's long-term safe interaction with human in uncertain crowded environments. In simulation, the results demonstrate the superior performance of our approach compared to benchmark methods in terms of crowd distribution estimation accuracy, adaptability to anomalies and path planning efficiency.
Single-Photon-Based Clock Analysis and Recovery in Quantum Key Distribution

Quantum key distribution is one of the first quantum technologies ready for the market. Current quantum telecommunication systems usually utilize a service channel for synchronizing the transmitter (Alice) and the receiver (Bob). However, the possibility of removing this service channel and exploiting a clock recovery method is intriguing for future implementation, both in fiber and free-space links. In this paper, we investigate criteria to recover the clock in a quantum communication scenario, and experimentally demonstrated the possibility of using a quantum-based clock recovery system in a time-bin quantum key distribution protocol. The performance of the clock recovery technique, in terms of quantum bit error rate and secret key rate, is equivalent to using the service channel for clock sharing.
Experimental anonymous quantum conferencing

Anonymous quantum conference key agreement (AQCKA) allows a group of users within a network to establish a shared cryptographic key without revealing their participation. Although this can be achieved using bi-partite primitives alone, it is costly in the number of network rounds required. By allowing the use of multi-partite entanglement, there is a substantial efficiency improvement. We experimentally implement the AQCKA task in a six-user quantum network using Greenberger-Horne-Zeilinger (GHZ)-state entanglement and obtain a significant resource cost reduction in line with theory when compared to a bi-partite-only approach. We also demonstrate that the protocol retains an advantage in a four-user scenario with finite key effects taken into account.
A New Benchmark and Model for Challenging Image Manipulation Detection

The ability to detect manipulation in multimedia data is vital in digital forensics. Existing Image Manipulation Detection (IMD) methods are mainly based on detecting anomalous features arisen from image editing or double compression artifacts. All existing IMD techniques encounter challenges when it comes to detecting small tampered regions from a large image. Moreover, compression-based IMD approaches face difficulties in cases of double compression of identical quality factors. To investigate the State-of-The-Art (SoTA) IMD methods in those challenging conditions, we introduce a new Challenging Image Manipulation Detection (CIMD) benchmark dataset, which consists of two subsets, for evaluating editing-based and compression-based IMD methods, respectively. The dataset images were manually taken and tampered with high-quality annotations. In addition, we propose a new two-branch network model based on HRNet that can better detect both the image-editing and compression artifacts in those challenging conditions. Extensive experiments on the CIMD benchmark show that our model significantly outperforms SoTA IMD methods on CIMD.
IDD-AW: A Benchmark for Safe and Robust Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather

Large-scale deployment of fully autonomous vehicles requires a very high degree of robustness to unstructured traffic, and weather conditions, and should prevent unsafe mispredictions. While there are several datasets and benchmarks focusing on segmentation for drive scenes, they are not specifically focused on safety and robustness issues. We introduce the IDD-AW dataset, which provides 5000 pairs of high-quality images with pixel-level annotations, captured under rain, fog, low light, and snow in unstructured driving conditions. As compared to other adverse weather datasets, we provide i.) more annotated images, ii.) paired Near-Infrared (NIR) image for each frame, iii.) larger label set with a 4-level label hierarchy to capture unstructured traffic conditions. We benchmark state-of-the-art models for semantic segmentation in IDD-AW. We also propose a new metric called ''Safe mean Intersection over Union (Safe mIoU)'' for hierarchical datasets which penalizes dangerous mispredictions that are not captured in the traditional definition of mean Intersection over Union (mIoU). The results show that IDD-AW is one of the most challenging datasets to date for these tasks. The dataset and code will be available here: http://iddaw.github.io.
An approach to solve the coarse-grained Protein folding problem in a Quantum Computer

Protein folding, which dictates the protein structure from its amino acid sequence, is half a century old problem of biology. The function of the protein correlates with its structure, emphasizing the need of understanding protein folding for studying the cellular and molecular mechanisms that occur within biological systems. Understanding protein structures and enzymes plays a critical role in target based drug designing, elucidating protein-related disease mechanisms, and innovating novel enzymes. While recent advancements in AI based protein structure prediction methods have solved the protein folding problem to an extent, their precision in determining the structure of the protein with low sequence similarity is limited. Classical methods face challenges in generating extensive conformational samplings, making quantum-based approaches advantageous for solving protein folding problems. In this work we developed a novel turn based encoding algorithm that can be run on a gate based quantum computer for predicting the structure of smaller protein sequences using the HP model as an initial framework, which can be extrapolated in its application to larger and more intricate protein systems in future. The HP model best represents a major step in protein folding phenomena - the hydrophobic collapse which brings the hydrophobic amino acid to the interior of a protein. The folding problem is cast in a 3D cubic lattice with degrees of freedom along edges parallel to the orthogonal axes, as well as along diagonals parallel to the axial planes. While, the original formulation with higher order terms can be run on gate based quantum hardwares, the QUBO formulation can give results on both classical softwares employing annealers and IBM CPLEX as well as quantum hardwares.
DEUS: Distributed Electronic Patient File Update System

Inadequate availability of patient information is a major cause for medical errors and affects costs in healthcare. Traditional approaches to information integration in healthcare do not solve the problem. Applying a document-oriented paradigm to systems integration enables inter-institutional information exchange in healthcare. The goal of the proposed architecture is to provide information exchange between strict autonomous healthcare institutions, bridging the gap between primary and secondary care. In a long-term healthcare data distribution scenario, the patient has to maintain sovereignty over any personal health information. Thus, the traditional publish-subscribe architecture is extended by a phase of human mediation within the data flow. DEUS essentially decouples the roles of information author and information publisher into distinct actors, resulting in a triangular data flow. The interaction scenario will be motivated. The significance of human mediation will be discussed. DEUS provides a carefully distinguished actor and role model for mediated pub-sub. The data flow between the participants is factored into distinct phases of information interchange. The artefact model is decomposed into role-dependent constituent parts. Both a domain specific (healthcare) terminology and a generic terminology is provided. From a technical perspective, the system design is presented. The sublayer for network transfer will be highlighted as well as the subsystem for human-machine interaction.
You Only Explain Once

In this paper, we propose a new black-box explainability algorithm and tool, YO-ReX, for efficient explanation of the outputs of object detectors. The new algorithm computes explanations for all objects detected in the image simultaneously. Hence, compared to the baseline, the new algorithm reduces the number of queries by a factor of 10X for the case of ten detected objects. The speedup increases further with with the number of objects. Our experimental results demonstrate that YO-ReX can explain the outputs of YOLO with a negligible overhead over the running time of YOLO. We also demonstrate similar results for explaining SSD and Faster R-CNN. The speedup is achieved by avoiding backtracking by combining aggressive pruning with a causal analysis.
Searching for Snippets of Open-Domain Dialogue in Task-Oriented Dialogue Datasets

Most existing dialogue corpora and models have been designed to fit into 2 predominant categories : task-oriented dialogues portray functional goals, such as making a restaurant reservation or booking a plane ticket, while chit-chat/open-domain dialogues focus on holding a socially engaging talk with a user. However, humans tend to seamlessly switch between modes and even use chitchat to enhance task-oriented conversations. To bridge this gap, new datasets have recently been created, blending both communication modes into conversation examples. The approaches used tend to rely on adding chit-chat snippets to pre-existing, human-generated task-oriented datasets. Given the tendencies observed in humans, we wonder however if the latter do not \textit{already} hold chit-chat sequences. By using topic modeling and searching for topics which are most similar to a set of keywords related to social talk, we explore the training sets of Schema-Guided Dialogues and MultiWOZ. Our study shows that sequences related to social talk are indeed naturally present, motivating further research on ways chitchat is combined into task-oriented dialogues.
Towards Interpretable Classification of Leukocytes based on Deep Learning

Label-free approaches are attractive in cytological imaging due to their flexibility and cost efficiency. They are supported by machine learning methods, which, despite the lack of labeling and the associated lower contrast, can classify cells with high accuracy where the human observer has little chance to discriminate cells. In order to better integrate these workflows into the clinical decision making process, this work investigates the calibration of confidence estimation for the automated classification of leukocytes. In addition, different visual explanation approaches are compared, which should bring machine decision making closer to professional healthcare applications. Furthermore, we were able to identify general detection patterns in neural networks and demonstrate the utility of the presented approaches in different scenarios of blood cell analysis.