Reviewerly/exHarmony huggingface dataset:
CRAFT: Concept Recursive Activation FacTorization for Explainability

Attribution methods, which employ heatmaps to identify the most influential regions of an image that impact model decisions, have gained widespread popularity as a type of explainability method. However, recent research has exposed the limited practical value of these methods, attributed in part to their narrow focus on the most prominent regions of an image - revealing “where” the model looks, but failing to elucidate “what” the model sees in those areas. In this work, we try to fill in this gap with CRAFT - a novel approach to identify both “what” and “where” by generating concept-based explanations. We introduce 3 new ingredients to the automatic concept extraction literature: (i) a recursive strategy to detect and decompose concepts across layers, (ii) a novel method for a more faithful estimation of concept importance using Sobol indices, and (iii) the use of implicit differentiation to unlock Concept Attribution Maps. We conduct both human and computer vision experiments to demonstrate the benefits of the proposed approach. We show that the proposed concept importance estimation technique is more faithful to the model than previous methods. When evaluating the usefulness of the method for human experimenters on a human-centered utility benchmark, we find that our approach significantly improves on two of the three test scenarios.
A spinwave Ising machine

Abstract Time-multiplexed Coherent Ising Machines (CIMs) have demonstrated promising results in rapidly solving large-scale combinatorial problems. However, CIMs remain relatively large and power-demanding. Here, we demonstrate a spinwave-based Ising machine (SWIM) that due to the low spinwave group velocity allows for sufficient miniaturization and reduced power consumption. The SWIM is implemented using a 10-mm-long 5- μ m-thick Yttrium Iron Garnet film with off-the-shelf microwave components and can support an 8-spin MAX-CUT problem and solve it in less than 4 μ s consuming only 7 μ J. As the SWIM minimizes its energy, we observe that the spin states can demonstrate both uniform and domain-propagation-like switching. The developed SWIM has the potential for substantial further miniaturization with reduction of power consumption, scalability in the number of supported spins, increase of operational speed, and may become a versatile platform for commercially feasible high-performance solvers of combinatorial optimization problems.
Federated Learning under Heterogeneous and Correlated Client Availability

The enormous amount of data produced by mobile and IoT devices has motivated the development of federated learning (FL), a framework allowing such devices (or clients) to collaboratively train machine learning models without sharing their local data. FL algorithms (like FedAvg) iteratively aggregate model updates computed by clients on their own datasets. Clients may exhibit different levels of participation, often correlated over time and with other clients. This paper presents the first convergence analysis for a FedAvg-like FL algorithm under heterogeneous and correlated client availability. Our analysis highlights how correlation adversely affects the algorithm’s convergence rate and how the aggregation strategy can alleviate this effect at the cost of steering training toward a biased model. Guided by the theoretical analysis, we propose CA-Fed, a new FL algorithm that tries to balance the conflicting goals of maximizing convergence speed and minimizing model bias. To this purpose, CA-Fed dynamically adapts the weight given to each client and may ignore clients with low availability and large correlation. Our experimental results show that CA-Fed achieves higher time-average accuracy and a lower standard deviation than state-of-the-art AdaFed and F3AST, both on synthetic and real datasets.
Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages

Back-translation is widely known for its effectiveness in neural machine translation when there is little to no parallel data. In this approach, a source-to-target model is coupled with a target-to-source model trained in parallel. The target-to-source model generates noisy sources, while the source-to-target model is trained to reconstruct the targets and vice versa. Recent developments of multilingual pre-trained sequence-to-sequence models for programming languages have been very effective for a broad spectrum of downstream software engineering tasks. Hence, training them to build programming language translation systems via back-translation is compelling. However, these models cannot be further trained via back-translation since they learn to output sequences in the same language as the inputs during pre-training. As an alternative, we propose performing back-translation via code summarization and generation. In code summarization, a model learns to generate natural language (NL) summaries given code snippets. In code generation, the model learns to do the opposite. Therefore, target-to-source generation in back-translation can be viewed as a target-to-NL-to-source generation. We show that our proposed approach performs competitively with state-of-the-art methods. We have made the code publicly available.
FedEHR: A Federated Learning Approach towards the Prediction of Heart Diseases in IoT-Based Electronic Health Records

In contemporary healthcare, the prediction and identification of cardiac diseases is crucial. By leveraging the capabilities of Internet of Things (IoT)-enabled devices and Electronic Health Records (EHRs), the healthcare sector can largely benefit to improve patient outcomes by increasing the accuracy of disease prediction. However, protecting data privacy is essential to promote participation and adhere to rules. The suggested methodology combines EHRs with IoT-generated health data to predict heart disease. For its capacity to manage high-dimensional data and choose pertinent features, a soft-margin L1-regularised Support Vector Machine (sSVM) classifier is used. The large-scale sSVM problem is successfully solved using the cluster primal–dual splitting algorithm, which improves computational complexity and scalability. The integration of federated learning provides a cooperative predictive analytics methodology that upholds data privacy. The use of a federated learning framework in this study, with a focus on peer-to-peer applications, is crucial for enabling collaborative predictive modeling while protecting the confidentiality of each participant’s private medical information.
A scoping review of artificial intelligence-based methods for diabetes risk prediction

The increasing prevalence of type 2 diabetes mellitus (T2DM) and its associated health complications highlight the need to develop predictive models for early diagnosis and intervention. While many artificial intelligence (AI) models for T2DM risk prediction have emerged, a comprehensive review of their advancements and challenges is currently lacking. This scoping review maps out the existing literature on AI-based models for T2DM prediction, adhering to the PRISMA extension for Scoping Reviews guidelines. A systematic search of longitudinal studies was conducted across four databases, including PubMed, Scopus, IEEE-Xplore, and Google Scholar. Forty studies that met our inclusion criteria were reviewed. Classical machine learning (ML) models dominated these studies, with electronic health records (EHR) being the predominant data modality, followed by multi-omics, while medical imaging was the least utilized. Most studies employed unimodal AI models, with only ten adopting multimodal approaches. Both unimodal and multimodal models showed promising results, with the latter being superior. Almost all studies performed internal validation, but only five conducted external validation. Most studies utilized the area under the curve (AUC) for discrimination measures. Notably, only five studies provided insights into the calibration of their models. Half of the studies used interpretability methods to identify key risk predictors revealed by their models. Although a minority highlighted novel risk predictors, the majority reported commonly known ones. Our review provides valuable insights into the current state and limitations of AI-based models for T2DM prediction and highlights the challenges associated with their development and clinical integration.
AI’s challenge of understanding the world

In thinking about the challenge of getting artificial intelligence (AI) to understand our complex world, I recalled a Twitter post from a user of Tesla's self-driving system. The user tweeted that his car kept stopping abruptly at a particular location for no apparent reason. Then he noticed a billboard advertisement on the side of the road, featuring a sheriff holding up a stop sign. The car's vision system had interpreted this as an actual stop sign, and slammed on the brakes.
Evaluation of Oropharyngeal Cancer Information from Revolutionary Artificial Intelligence Chatbot

Objective With burgeoning popularity of artificial intelligence‐based chatbots, oropharyngeal cancer patients now have access to a novel source of medical information. Because chatbot information is not reviewed by experts, we sought to evaluate an artificial intelligence‐based chatbot's oropharyngeal cancer‐related information for accuracy. Methods Fifteen oropharyngeal cancer‐related questions were developed and input into ChatGPT version 3.5. Four physician‐graders independently assessed accuracy, comprehensiveness, and similarity to a physician response using 5‐point Likert scales. Responses graded lower than three were then critiqued by physician‐graders. Critiques were analyzed using inductive thematic analysis. Readability of responses was assessed using Flesch Reading Ease (FRE) and Flesch–Kincaid Reading Grade Level (FKRGL) scales. Results Average accuracy, comprehensiveness, and similarity to a physician response scores were 3.88 (SD = 0.99), 3.80 (SD = 1.14), and 3.67 (SD = 1.08), respectively. Posttreatment‐related questions were most accurate, comprehensive, and similar to a physician response, followed by treatment‐related, then diagnosis‐related questions. Posttreatment‐related questions scored significantly higher than diagnosis‐related questions in all three domains ( p &lt; 0.01). Two themes of the physician critiques were identified: suboptimal education value and potential to misinform patients. The mean FRE and FKRGL scores both indicated greater than an 11th grade readability level—higher than the 6th grade level recommended for patients. Conclusion ChatGPT responses may not educate patients to an appropriate degree, could outright misinform them, and read at a more difficult grade level than is recommended for patient material. As oropharyngeal cancer patients represent a vulnerable population facing complex, life‐altering diagnoses, and treatments, they should be cautious when consuming chatbot‐generated medical information. Level of Evidence N/A Laryngoscope , 2023
Cluster Analysis on Longitudinal Data of Patients with Kidney Dialysis using a Smoothing Cubic B-Spline Model

Longitudinal data analysis is gaining prominence, particularly in medicine and economics. This research centers on collecting and analyzing longitudinal data, specifically cluster analysis. The thesis emphasizes the nonparametric cubic B-spline model, known for its smoothness, flexibility, and ability to capture intricate patterns and data fluctuations due to the continuity of its derivatives. The penalization method was employed to accomplish clustering. It categorizes longitudinally balanced data by penalizing the pairwise distances between cubic B-spline model coefficients using a penalization function, such as the recently devised concave penalization function. The cubic spline penalty CSP, part of the pair distance penalty, employs the nonparametric pairwise grouping (NPG) method. Model selection criteria, like Bayesian Information Criteria (BIC), help determine the number of clusters. Optimization methods, including the alternative direction method of the multiplier ADMM algorithm, are applied to approximate solutions within the R statistical program. A simulation study generated balanced longitudinal data for 60 and 100 subjects with ten replicates each. The experiments demonstrated the effectiveness of the CSP penalty function in the clustering process. For practical application, the study involved the analysis of data from kidney failure patients collected from Ibn Sina Teaching Hospital for Dialysis in Mosul over seven consecutive months in 2023. The NPG aggregation method and CSP penalty functions were used, resulting in two groups based on the glomerular filtration rate of the kidneys. According to medical criteria, this rate determines the required dialysis frequency twice a week or thrice.
Evaluation of features for the automatic recognition of OFDM signals in monitoring or cognitive receivers

The automatic recognition of signal types is an important task of monitoring receivers and also cognitive receivers. Several modulation recognition or classification procedures exist for single channel signal types while a simple robust procedure for automatic recognition of OFDM signals is lacking because of its numerous frequency channelslying close together. The task considered in this paper is the discrimination between OFDM (or multi-channel) signals and other signal types. The number of frequency channels of the OFDM signals is assumed to be unknown a priori. So, together with the automatic OFDM detection the estimation of the number of frequency channels is treated. Several discrimination features have been examined and the most promising ones are described: measures of the variation, of the skewness, of the kurtosis, and of the specific picket-fence shape of the spectrum which is typical for many OFDM signals. For a number of real-world OFDM samples, recorded from the high frequency range, results are presented. An automatic discrimination from single channel or noise like signals is achieved and the number of system channels can be estimated.
QoS Conditions for VoIP and VoD

This paper concerns quality evaluation of the telecommunication services: VoIP (representing the RT interactive class) and VoD (representing the MM streaming class). Subjective and objective methods and tools for perceived quality measurement are analyzed and compared. Subjective tests are performed for selected video sequences using the Double-Stimulus Impairment Scale (DSIS) method. Thus the objective algorithms (VQM and VQmon) are calibrated. Speech quality is measured using the objective methods: PESQ and POLQA. Threshold values for network parameters (packet loss rate, delay jitter) are set, that guarantee the acceptable service quality.
Lexical Disambiguation Based on Distributed Representations of Context Frequency

A model for lexical disambiguation is presented that is based on combining the frequencies of past contexts of ambiguous words. The frequences are encoded in the word representations and define the words'' semantics. A simple Recurrent Network (SRN) parser combines the context frequences output. This disambiguation process is most striking when the interpretation involves semantic flipping, that is, an alternation between two opposing meanings as more words are read in. The sense of throwing a ball alternates between dance and baseball as indicators such as the agent, location, and recipient are input. The SRN parser demonstrates how the context frequencies are dynamically combined to determine the interpretation of such sentences. We hypothesize that other aspects of ambiguity resolution are based on similar mechanisms are well, and can be naturally approached from the distributed connectionist viewpoint.
A Context-Based Framework for Mental Representation

In this paper we present a context-based family of formal systems (called MultiContext Systems) which we propose to use as a formal framework for a theory of mental representation. We start with an intuitive notion of context as a subset of the complete (cognitive) state of an individual. Then we introduce two general principles which we believe are at the core of any logic of context, namely the principles of locality and compatibility. We show how these principles can be formalized in the framework of MultiContext Systems, and argue that this conceptesl/logical framework can be used to account for a variety of phenomena in a theory of mental representation. Finally, we compare our framework with previous work.
A rounding theorem for unique binary tomographic reconstruction

Discrete tomography deals with the reconstruction of images from projections collected along a few given directions. Different approaches can be considered, according to different models. In this paper we adopt the grid model, where pixels are lattice points with integer coordinates, X-rays are discrete lattice lines, and projections are obtained by counting the number of lattice points intercepted by X-rays taken in the assigned directions. We move from a theoretical result that allows uniqueness of reconstruction in the grid with just four suitably selected X-ray directions. In this framework, the structure of the allowed ghosts is studied and described. This leads to a new result, stating that the unique binary solution can be explicitly and exactly retrieved from the minimum Euclidean norm solution by means of a rounding method based on some special entries, which are precisely determined. A corresponding iterative algorithm has been implemented, and tested on a few phantoms having different characteristics and structure.
Recent results of quantum ergodicity on graphs and further investigation

We outline some recent proofs of quantum ergodicity on large graphs and give new applications in the context of irregular graphs. We also discuss some remaining questions.
On marginal and conditional parameters in logistic regression models

Summary We derive the exact formula linking the parameters of marginal and conditional logistic regression models with binary mediators when no conditional independence assumptions can be made. The formula has the appealing property of being the sum of terms that vanish whenever parameters of the conditional models vanish, thereby recovering well-known results as particular cases. It also permits the disentangling of direct and indirect effects as well as quantifying the distortion induced by the omission of relevant covariates, opening the way to sensitivity analysis. As the parameters of the conditional models are multiplied by terms that are always bounded, the derivations may also be used to construct reasonable bounds on the parameters of interest when relevant intermediate variables are unobserved. We assume that, conditionally on a set of covariates, the data-generating process can be represented by a directed acyclic graph. We also show how the results presented here lead to the extension of path analysis to a system of binary random variables.
Automated Region of Interest Detection Method in Scintigraphic Glomerular Filtration Rate Estimation

The glomerular filtration rate (GFR) is a crucial index to measure renal function. In daily clinical practice, the GFR can be estimated using the Gates method, which requires the clinicians to define the region of interest (ROI) for the kidney and the corresponding background in dynamic renal scintigraphy. The manual placement of ROIs to estimate the GFR is subjective and labor-intensive, however, making it an undesirable and unreliable process. This work presents a fully automated ROI detection method to achieve accurate and robust GFR estimations. After image preprocessing, the ROI for each kidney was delineated using a shape prior constrained level set (spLS) algorithm and then the corresponding background ROIs were obtained according to the defined kidney ROIs. In computer simulations, the spLS method had the best performance in kidney ROI detection compared with the previous threshold method (threshold) and the Chan-Vese level set (cvLS) method. In further clinical applications, 223 sets of 99mTc-diethylenetriaminepentaacetic acid renal scintigraphic images from patients with abnormal renal function were reviewed. Compared with the former ROI detection methods (threshold and cvLS), the GFR estimations based on the ROIs derived by the spLS method had the highest consistency and correlations (r = 0.98, p < 0.001) with the reference estimated by experienced physicians. The results indicate that the proposed automated ROI detection method has great potential in automated ROI detection for accurate and robust GFR estimation in dynamic renal scintigraphy.
Fast amplification and rephasing of entangled cat states in a qubit-oscillator system

We study a qubit-oscillator system, with a time-dependent coupling coefficient, and present a fast scheme for generating entangled Schr\"odinger-cat states with large mean photon numbers and also a scheme that protects the cat states against dephasing caused by the nonlinearity in the system. We focus on the case where the qubit frequency is small compared to the oscillator frequency. We first present the exact quantum state evolution in the limit of infinitesimal qubit frequency. We then analyze the first-order effect of the nonzero qubit frequency. Our scheme works for a wide range of coupling strength values, including the recently achieved deep-strong-coupling regime.
A Declarative Grammar of Flexible Volume Visualization Pipelines

This paper presents a declarative grammar for conveniently and effectively specifying advanced volume visualizations. Existing methods for creating volume visualizations either lack the flexibility to specify sophisticated visualizations or are difficult to use for those unfamiliar with volume rendering implementation and parameterization. Our design provides the ability to quickly create expressive visualizations without knowledge of the volume rendering implementation. It attempts to capture aspects of those difficult but powerful methods while remaining flexible and easy to use. As a proof of concept, our current implementation of the grammar allows users to combine multiple data variables in various ways and define transfer functions for diverse input data. The grammar also has the ability to describe advanced shading effects and create animations. We demonstrate the power and flexibility of our approach using multiple practical volume visualizations.
Nonlinear Asymmetric Multi-Valued Hashing

Most existing hashing methods resort to binary codes for large scale similarity search, owing to the high efficiency of computation and storage. However, binary codes lack enough capability in similarity preservation, resulting in less desirable performance. To address this issue, we propose Nonlinear Asymmetric Multi-Valued Hashing (NAMVH) supported by two distinct non-binary embeddings. Specifically, a real-valued embedding is used for representing the newly-coming query by an ideally nonlinear transformation. Besides, a multi-integer-embedding is employed for compressing the whole database, which is modeled by Binary Sparse Representation (BSR) with fixed sparsity. With these two non-binary embeddings, NAMVH preserves more precise similarities between data points and enables access to the incremental extension with database samples evolving dynamically. To perform meaningful asymmetric similarity computation for efficient semantic search, these embeddings are jointly learnt by preserving the pairwise label-based similarity. Technically, this results in a mixed integer programming problem, which is efficiently solved by a well-designed alternative optimization method. Extensive experiments on seven large scale datasets demonstrate that our approach not only outperforms the existing binary hashing methods in search accuracy, but also retains their query and storage efficiency.