Reviewerly/exHarmony huggingface dataset:
Contact tracing for COVID-19 in a Swiss canton: analysis of key performance indicators

BACKGROUND: Contact tracing (CT) has played an important role in strategies to control COVID-19. However, there is limited evidence on the performance of digital tools for CT and no consensus on which indicators to use to monitor their performance. We aimed to describe the system and analyse outcomes of CT with a partially automated workflow in the Swiss canton of Solothurn, using key performance indicators (KPIs). METHODS: We describe the process of CT used in the canton of Solothurn between November 2020 and February 2022, including forward and backward CT. We developed 16 KPIs representing CT structure (S1–2), process (P1–11) and outcome (O1–3) based on previous literature to analyse the relative performance of CT. We report the changes in the indicators over waves of SARS-CoV-2 infections caused by several viral variants. RESULTS: The CT team in Solothurn processed 57,363 index cases and 71,809 contacts over a 15-month period. The CT team successfully contacted 99% of positive cases within 24 hours (KPI P7) throughout the pandemic and returned almost all test results on the same or next day (KPI P6), before the delta variant emerged. Three-quarters of contacts were notified within 24 hours of the CT interview with the index (KPI P8) before the emergence of the alpha, delta and omicron variants, when the proportions decreased to 64%, 36% and 54%, respectively. The percentage of new symptomatic cases tested and interviewed within 3 days of symptom onset was high at &gt;70% (KPI P10) and contacts started quarantine within a median of 3 days of index case symptom onset (KPI P3). About a fifth of new index cases had already been in quarantine by the time of their positive test (KPI O1), before the delta variant emerged. The percentage of index cases in isolation by day of testing remained at almost 100% throughout the period of analysis (KPI O2). CONCLUSIONS: The CT in Solothurn used a partially automated workflow and continued to perform well throughout the pandemic, although the relative performance of the CT system declined at higher caseloads. CT remains an important tool for controlling the spread of infectious diseases, but clearer standards should improve the performance, comparability and monitoring of infection in real time as part of pandemic preparedness efforts.
Blockchain Empowered Multi-Agent Systems: Advancing IoT Security and Transaction Efficiency

The rapid growth and escalating complexity of the Internet of Things (IoT) necessitate meticulous attention to ensure efficient and secure transactions among various autonomous components. To address this critical issue, this study proposes the integration of multi-agent systems and blockchain technology within the IoT domain. Uniquely, our approach employs smart contracts to manage exchanges between autonomous entities, thereby offering enhanced security, transparency, and reliability. The study introduces a set of innovative algorithms that regulate agent activities, such as creating blocks, sharing information, and conducting rating processes. Additionally, it provides a detailed analysis of their privacy and security aspects. Compared to traditional multi-agent frameworks, empirical evidence demonstrates significant improvements in efficiency, adaptability, and scalability. This scholarly effort lays a robust foundation for further investigations into applying blockchain to enhance multi-agent systems, potentially paving the way for more sophisticated and context-specific strategies across various IoT fields.
Light-weight Deep Neural Network for Small Vehicle Detection using Model-scale YOLOv4

In this paper, we present a light-weight deep neural network based on an efficiently scaled YOLOv4 model for detecting small objects in drone images. Since drone-captured images mainly contain small objects, we modified the YOLOv4 model by eliminating the head layer responsible for detecting large objects. This modification significantly reduced the model
Fight Fire with Fire: Combating Adversarial Patch Attacks using Pattern-randomized Defensive Patches

Object detection has found extensive applications in various tasks, but it is also susceptible to adversarial patch attacks. Existing defense methods often necessitate modifications to the target model or result in unacceptable time overhead. In this paper, we adopt a counterattack approach, following the principle of "fight fire with fire," and propose a novel and general methodology for defending adversarial attacks. We utilize an active defense strategy by injecting two types of defensive patches, canary and woodpecker, into the input to proactively probe or weaken potential adversarial patches without altering the target model. Moreover, inspired by randomization techniques employed in software security, we employ randomized canary and woodpecker injection patterns to defend against defense-aware attacks. The effectiveness and practicality of the proposed method are demonstrated through comprehensive experiments. The results illustrate that canary and woodpecker achieve high performance, even when confronted with unknown attack methods, while incurring limited time overhead. Furthermore, our method also exhibits sufficient robustness against defense-aware attacks, as evidenced by adaptive attack experiments.
Jump Particle Filtering Framework for Joint Target Tracking and Intent Recognition

This paper presents a Bayesian framework for inferring the posterior of the extended state of a target, incorporating its underlying goal or intent, such as any intermediate waypoints and/or final destination. The methodology is thus for joint tracking and intent recognition. Several novel latent intent models are proposed here within a virtual leader formulation. They capture the influence of the target's hidden goal on its instantaneous behaviour. In this context, various motion models, including for highly maneuvering objects, are also considered. The a priori unknown target intent (e.g. destination) can dynamically change over time and take any value within the state space (e.g. a location or spatial region). A sequential Monte Carlo (particle filtering) approach is introduced for the simultaneous estimation of the target's (kinematic) state and its intent. Rao-Blackwellisation is employed to enhance the statistical performance of the inference routine. Simulated data and real radar measurements are used to demonstrate the efficacy of the proposed techniques.
Dual input stream transformer for eye-tracking line assignment

We introduce a novel Dual Input Stream Transformer (DIST) for the challenging problem of assigning fixation points from eye-tracking data collected during passage reading to the line of text that the reader was actually focused on. This post-processing step is crucial for analysis of the reading data due to the presence of noise in the form of vertical drift. We evaluate DIST against eleven classical approaches on a comprehensive suite of nine diverse datasets. We demonstrate that combining multiple instances of the DIST model in an ensemble achieves high accuracy across all datasets. Further combining the DIST ensemble with the best classical approach yields an average accuracy of 98.17 %. Our approach presents a significant step towards addressing the bottleneck of manual line assignment in reading research. Through extensive analysis and ablation studies, we identify key factors that contribute to DIST's success, including the incorporation of line overlap features and the use of a second input stream. Via rigorous evaluation, we demonstrate that DIST is robust to various experimental setups, making it a safe first choice for practitioners in the field.
Nonparametric consistency for maximum likelihood estimation and clustering based on mixtures of elliptically-symmetric distributions

The consistency of the maximum likelihood estimator for mixtures of elliptically-symmetric distributions for estimating its population version is shown, where the underlying distribution $P$ is nonparametric and does not necessarily belong to the class of mixtures on which the estimator is based. In a situation where $P$ is a mixture of well enough separated but nonparametric distributions it is shown that the components of the population version of the estimator correspond to the well separated components of $P$. This provides some theoretical justification for the use of such estimators for cluster analysis in case that $P$ has well separated subpopulations even if these subpopulations differ from what the mixture model assumes.
Enhancing Instance-Level Image Classification with Set-Level Labels

Instance-level image classification tasks have traditionally relied on single-instance labels to train models, e.g., few-shot learning and transfer learning. However, set-level coarse-grained labels that capture relationships among instances can provide richer information in real-world scenarios. In this paper, we present a novel approach to enhance instance-level image classification by leveraging set-level labels. We provide a theoretical analysis of the proposed method, including recognition conditions for fast excess risk rate, shedding light on the theoretical foundations of our approach. We conducted experiments on two distinct categories of datasets: natural image datasets and histopathology image datasets. Our experimental results demonstrate the effectiveness of our approach, showcasing improved classification performance compared to traditional single-instance label-based methods. Notably, our algorithm achieves 13% improvement in classification accuracy compared to the strongest baseline on the histopathology image classification benchmarks. Importantly, our experimental findings align with the theoretical analysis, reinforcing the robustness and reliability of our proposed method. This work bridges the gap between instance-level and set-level image classification, offering a promising avenue for advancing the capabilities of image classification models with set-level coarse-grained labels.
NP-hard problems are not in BQP

Grover's algorithm can solve NP-complete problems on quantum computers faster than all the known algorithms on classical computers. However, Grover's algorithm still needs exponential time. Due to the BBBV theorem, Grover's algorithm is optimal for searches in the domain of a function, when the function is used as a black box. We analyze the NP-complete set \[\{ (\langle M \rangle, 1^n, 1^t ) \mid \text{ TM }M\text{ accepts an }x\in\{0,1\}^n\text{ within }t\text{ steps}\}.\] If $t$ is large enough, then M accepts each word in $L(M)$ with length $n$ within $t$ steps. So, one can use methods from computability theory to show that black box searching is the fastest way to find a solution. Therefore, Grover's algorithm is optimal for NP-complete problems.
Real-time error mitigation for variational optimization on quantum hardware

In this work we put forward the inclusion of error mitigation routines in the process of training Variational Quantum Circuit (VQC) models. In detail, we define a Real Time Quantum Error Mitigation (RTQEM) algorithm to assist in fitting functions on quantum chips with VQCs. While state-of-the-art QEM methods cannot address the exponential loss concentration induced by noise in current devices, we demonstrate that our RTQEM routine can enhance VQCs' trainability by reducing the corruption of the loss function. We tested the algorithm by simulating and deploying the fit of a monodimensional $\textit{u}$-Quark Parton Distribution Function (PDF) on a superconducting single-qubit device, and we further analyzed the scalability of the proposed technique by simulating a multidimensional fit with up to 8 qubits.
ADaPT: As-Needed Decomposition and Planning with Language Models

Large Language Models (LLMs) are increasingly being used for interactive decision-making tasks requiring planning and adapting to the environment. Recent works employ LLMs-as-agents in broadly two ways: iteratively determining the next action (iterative executors) or generating plans and executing sub-tasks using LLMs (plan-and-execute). However, these methods struggle with task complexity, as the inability to execute any sub-task may lead to task failure. To address these shortcomings, we introduce As-Needed Decomposition and Planning for complex Tasks (ADaPT), an approach that explicitly plans and decomposes complex sub-tasks as-needed, i.e., when the LLM is unable to execute them. ADaPT recursively decomposes sub-tasks to adapt to both task complexity and LLM capability. Our results demonstrate that ADaPT substantially outperforms established strong baselines, achieving success rates up to 28.3% higher in ALFWorld, 27% in WebShop, and 33% in TextCraft -- a novel compositional dataset that we introduce. Through extensive analysis, we illustrate the importance of multilevel decomposition and establish that ADaPT dynamically adjusts to the capabilities of the executor LLM as well as to task complexity.
Generating Pragmatic Examples to Train Neural Program Synthesizers

Programming-by-example is the task of synthesizing a program that is consistent with a set of user-provided input-output examples. As examples are often an under-specification of one's intent, a good synthesizer must choose the intended program from the many that are consistent with the given set of examples. Prior work frames program synthesis as a cooperative game between a listener (that synthesizes programs) and a speaker (a user choosing examples), and shows that models of computational pragmatic inference are effective in choosing the user intended programs. However, these models require counterfactual reasoning over a large set of programs and examples, which is infeasible in realistic program spaces. In this paper, we propose a novel way to amortize this search with neural networks. We sample pairs of programs and examples via self-play between listener and speaker models, and use pragmatic inference to choose informative training examples from this sample.We then use the informative dataset to train models to improve the synthesizer's ability to disambiguate user-provided examples without human supervision. We validate our method on the challenging task of synthesizing regular expressions from example strings, and find that our method (1) outperforms models trained without choosing pragmatic examples by 23% (a 51% relative increase) (2) matches the performance of supervised learning on a dataset of pragmatic examples provided by humans, despite using no human data in training.
The Paradox of Noise: An Empirical Study of Noise-Infusion Mechanisms to Improve Generalization, Stability, and Privacy in Federated Learning

In a data-centric era, concerns regarding privacy and ethical data handling grow as machine learning relies more on personal information. This empirical study investigates the privacy, generalization, and stability of deep learning models in the presence of additive noise in federated learning frameworks. Our main objective is to provide strategies to measure the generalization, stability, and privacy-preserving capabilities of these models and further improve them. To this end, five noise infusion mechanisms at varying noise levels within centralized and federated learning settings are explored. As model complexity is a key component of the generalization and stability of deep learning models during training and evaluation, a comparative analysis of three Convolutional Neural Network (CNN) architectures is provided. The paper introduces Signal-to-Noise Ratio (SNR) as a quantitative measure of the trade-off between privacy and training accuracy of noise-infused models, aiming to find the noise level that yields optimal privacy and accuracy. Moreover, the Price of Stability and Price of Anarchy are defined in the context of privacy-preserving deep learning, contributing to the systematic investigation of the noise infusion strategies to enhance privacy without compromising performance. Our research sheds light on the delicate balance between these critical factors, fostering a deeper understanding of the implications of noise-based regularization in machine learning. By leveraging noise as a tool for regularization and privacy enhancement, we aim to contribute to the development of robust, privacy-aware algorithms, ensuring that AI-driven solutions prioritize both utility and privacy.
Emergence and reconfiguration of modular structure for synaptic neural networks during continual familiarity detection

While advances in artificial intelligence and neuroscience have enabled the emergence of neural networks capable of learning a wide variety of tasks, our understanding of the temporal dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian Feedforward (HebbFF) neural networks in tasks of continual familiarity detection. Drawing inspiration from the field of network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. In particular, we find that the emergence of network modularity is a salient predictor of performance, and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological realms.
Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval

Dense retrieval models have predominantly been studied for English, where models have shown great success, due to the availability of human-labeled training pairs. However, there has been limited success for multilingual retrieval so far, as training data is uneven or scarcely available across multiple languages. Synthetic training data generation is promising (e.g., InPars or Promptagator), but has been investigated only for English. Therefore, to study model capabilities across both cross-lingual and monolingual retrieval tasks, we develop SWIM-IR, a synthetic retrieval training dataset containing 33 (high to very-low resource) languages for training multilingual dense retrieval models without requiring any human supervision. To construct SWIM-IR, we propose SAP (summarize-then-ask prompting), where the large language model (LLM) generates a textual summary prior to the query generation step. SAP assists the LLM in generating informative queries in the target language. Using SWIM-IR, we explore synthetic fine-tuning of multilingual dense retrieval models and evaluate them robustly on three retrieval benchmarks: XOR-Retrieve (cross-lingual), XTREME-UP (cross-lingual) and MIRACL (monolingual). Our models, called SWIM-X, are competitive with human-supervised dense retrieval models, e.g., mContriever, finding that SWIM-IR can cheaply substitute for expensive human-labeled retrieval training data.
Using Azure Quantum Resource Estimator for Assessing Performance of Fault Tolerant Quantum Computation

The resource estimation tools provided by Azure Quantum and Microsoft Quantum Development Kit are described. Using these tools one can automatically evaluate the logical and physical resources required to run algorithms on fault-tolerant quantum computers. An example is given of obtaining resource estimates for quantum fault-tolerant implementations of three different multiplication algorithms.
Tamil-Llama: A New Tamil Language Model Based on Llama 2

Language modeling has witnessed remarkable advancements in recent years, with Large Language Models (LLMs) like ChatGPT setting unparalleled benchmarks in human-like text generation. However, a prevailing limitation is the underrepresentation of languages like Tamil in these cutting-edge models, leading to suboptimal performance in diverse linguistic contexts. This paper addresses this lacuna, enhancing the open-source LLaMA model with an addition of 16,000 Tamil tokens, aiming to achieve superior text generation and comprehension in the Tamil language. We strategically employ the LoRA methodology for efficient model training on a comprehensive Tamil corpus, ensuring computational feasibility and model robustness. Moreover, we introduce a Tamil-translated version of the Alpaca dataset and a subset of the OpenOrca dataset tailored for instruction fine-tuning. Our results showcase significant performance improvements in Tamil text generation, with potential implications for the broader landscape of LLMs in Indian languages. We further underscore our commitment to open research by making our models, datasets, and code publicly accessible, fostering further innovations in language modeling.
Federated Learning with Manifold Regularization and Normalized Update Reaggregation

Federated Learning (FL) is an emerging collaborative machine learning framework where multiple clients train the global model without sharing their own datasets. In FL, the model inconsistency caused by the local data heterogeneity across clients results in the near-orthogonality of client updates, which leads to the global update norm reduction and slows down the convergence. Most previous works focus on eliminating the difference of parameters (or gradients) between the local and global models, which may fail to reflect the model inconsistency due to the complex structure of the machine learning model and the Euclidean space's limitation in meaningful geometric representations. In this paper, we propose FedMRUR by adopting the manifold model fusion scheme and a new global optimizer to alleviate the negative impacts. Concretely, FedMRUR adopts a hyperbolic graph manifold regularizer enforcing the representations of the data in the local and global models are close to each other in a low-dimensional subspace. Because the machine learning model has the graph structure, the distance in hyperbolic space can reflect the model bias better than the Euclidean distance. In this way, FedMRUR exploits the manifold structures of the representations to significantly reduce the model inconsistency. FedMRUR also aggregates the client updates norms as the global update norm, which can appropriately enlarge each client's contribution to the global update, thereby mitigating the norm reduction introduced by the near-orthogonality of client updates. Furthermore, we theoretically prove that our algorithm can achieve a linear speedup property for non-convex setting under partial client participation.Experiments demonstrate that FedMRUR can achieve a new state-of-the-art (SOTA) accuracy with less communication.
Learning Broken Symmetries with Resimulation and Encouraged Invariance

Recognizing symmetries in data allows for significant boosts in neural network training. In many cases, however, the underlying symmetry is present only in an idealized dataset, and is broken in the training data, due to effects such as arbitrary and/or non-uniform detector bin edges. Standard approaches, such as data augmentation or equivariant networks fail to represent the nature of the full, broken symmetry. We introduce a novel data-augmentation scheme that respects the true underlying symmetry and avoids artifacts by augmenting the training set with transformed pre-detector examples whose detector response is then resimulated. In addition, we encourage the network to treat the augmented copies identically, allowing it to learn the broken symmetry. While the technique can be extended to other symmetries, we demonstrate its application on rotational symmetry in particle physics calorimeter images. We find that neural networks trained with pre-detector rotations converge to a solution more quickly than networks trained with standard post-detector augmentation, and that networks modified to encourage similar internal treatment of augmentations of the same input converge even faster.