Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation-invariant synaptic plasticity

Cortical populations of neurons develop sparse representations adapted to the statistics of the environment. While existing synaptic plasticity models reproduce some of the observed receptive-field properties, a major obstacle is the sensitivity of Hebbian learning to omnipresent spurious correlations in cortical networks which can overshadow relevant latent input features. Here we develop a theory for synaptic plasticity that is invariant to second-order correlations in the input. Going beyond classical Hebbian learning, we show how Hebbian long-term depression (LTD) cancels the sensitivity to second-order correlations, so that receptive fields become aligned with features hidden in higher-order statistics. Our simulations demonstrate how correlation-invariance enables biologically realistic models to develop sparse population codes, despite diverse levels of variability and heterogeneity. The theory advances our understanding of local unsupervised learning in cortical circuits and assigns a specific functional role to synaptic LTD mechanisms in pyramidal neurons.
Certification of Iterative Predictions in Bayesian Neural Networks

We consider the problem of computing reach-avoid probabilities for iterative predictions made with Bayesian neural network (BNN) models. Specifically, we leverage bound propagation techniques and backward recursion to compute lower bounds for the probability that trajectories of the BNN model reach a given set of states while avoiding a set of unsafe states. We use the lower bounds in the context of control and reinforcement learning to provide safety certification for given control policies, as well as to synthesize control policies that improve the certification bounds. On a set of benchmarks, we demonstrate that our framework can be employed to certify policies over BNNs predictions for problems of more than $10$ dimensions, and to effectively synthesize policies that significantly increase the lower bound on the satisfaction probability.
Multi-Modal Sarcasm Detection and Humor Classification in Code-Mixed Conversations

Sarcasm detection and humor classification are inherently subtle problems, primarily due to their dependence on the contextual and non-verbal information. Furthermore, existing studies in these two topics are usually constrained in non-English languages such as Hindi, due to the unavailability of qualitative annotated datasets. In this work, we make two major contributions considering the above limitations: (1) we develop a Hindi-English code-mixed dataset, <monospace>MaSaC</monospace>,<sup>1</sup> for the multi-modal sarcasm detection and humor classification in conversational dialog, which to our knowledge is the first dataset of its kind; (2) we propose <monospace>MSH-COMICS</monospace>,<sup>2</sup> a novel attention-rich neural architecture for the utterance classification. We learn efficient utterance representation utilizing a hierarchical attention mechanism that attends to a small portion of the input sentence at a time. Further, we incorporate dialog-level contextual attention mechanism to leverage the dialog history for the multi-modal classification. We perform extensive experiments for both the tasks by varying multi-modal inputs and various submodules of <monospace>MSH-COMICS</monospace>. We also conduct comparative analysis against existing approaches. We observe that <monospace>MSH-COMICS</monospace> attains superior performance over the existing models by <inline-formula><tex-math notation="LaTeX">$>$</tex-math><alternatives><mml:math><mml:mo>></mml:mo></mml:math><inline-graphic xlink:href="akhtar-ieq1-3083522.gif"/></alternatives></inline-formula>1 F1-score point for the sarcasm detection and 10 F1-score points in humor classification. We diagnose our model and perform thorough analysis of the results to understand the superiority and pitfalls.
An Explainable Classification Model for Chronic Kidney Disease Patients

— Currently, Chronic Kidney Disease (CKD) is experiencing a globally increasing incidence and high cost to health systems. A delayed recognition leads to a premature mortality due to progressive loss of kidney function. The employment of data mining to discover subtle patterns in CKD indicators would contribute to an early diagnosis. This work develops a classifier model that would support healthcare professionals in the early diagnosis of CKD patients. Through a data pipeline, an exhaustive search is performed to find the best data mining classifier with different parameters of the data preparation’s sub -stages like data missing or feature selection. Therefore, Extra Trees is selected as the best classifier with a 100% and 99% of accuracy with, respectively, cross-validation technique and with new unseen data. Moreover, the 8 features selected are employed to assess the explainability of the model’s results denoting which features are more relevant in the model’s output.
Sharing Pain: Using Domain Transfer Between Pain Types for Recognition of Sparse Pain Expressions in Horses

Orthopedic disorders are a common cause for euthanasia among horses, which often could have been avoided with earlier detection. These conditions often create varying degrees of subtle but long-term pain. It is challenging to train a visual pain recognition method with video data depicting such pain, since the resulting pain behavior also is subtle, sparsely appearing, and varying, making it challenging for even an expert human labeler to provide accurate ground-truth for the data. We show that transferring features from a dataset of horses with acute nociceptive pain (where labeling is less ambiguous) can aid the learning to recognize more complex orthopedic pain. Moreover, we present a human expert baseline for the problem, as well as an extensive empirical study of various domain transfer methods and of what is detected by the pain recognition method trained on acute pain in the orthopedic dataset. Finally, this is accompanied with a discussion around the challenges posed by real-world animal behavior datasets and how best practices can be established for similar fine-grained action recognition tasks. Our code is available at https://github.com/sofiabroome/ painface-recognition.
Trimming Feature Extraction and Inference for MCU-Based Edge NILM: A Systematic Approach

Nonintrusive load monitoring (NILM) enables the disaggregation of the global power consumption of multiple loads, taken from a single smart electrical meter, into appliance-level details. State-of-the-art approaches are based on machine learning methods and exploit the fusion of time- and frequency-domain features from current and voltage sensors. Unfortunately, these methods are compute-demanding and memory-intensive. Therefore, running low-latency NILM on low-cost resource-constrained microcontroller unit (MCU)-based meters is currently an open challenge. This article addresses the optimization of the feature spaces as well as the computational and storage cost reduction needed for executing state-of-the-art (SoA) NILM algorithms on memory- and compute-limited MCUs. We compare four supervised learning techniques on different classification scenarios and characterize the overall NILM pipeline's implementation on an MCU-based Smart Measurement Node. Experimental results demonstrate that optimizing the feature space enables edge MCU-based NILM with 95.15% accuracy, resulting in a small drop compared to the most accurate feature vector deployment (96.19%) while achieving up to 5.45× speedup and 80.56% storage reduction. Furthermore, we show that low-latency NILM relying only on current measurements reaches almost 80% accuracy, allowing a major cost reduction by removing voltage sensors from the hardware (HW) design.
The effects of binary stars on galaxies and metal-enriched gas during reionization

Binary stars are abundant in nearby galaxies, but are typically unaccounted for in simulations of the high redshift Universe. Stellar population synthesis models that include the effects of binary evolution result in greater relative abundances of ionizing photons that could significantly affect the ambient ionizing background during the epoch of hydrogen reionization, additionally leading to differences in galaxy gas content and star formation. We use hydrodynamic cosmological simulations including in situ multifrequency radiative transfer to evaluate the effects of a high binary fraction in reionization-era galaxies on traits of the early intergalactic medium and the abundance of H I and He II ionizing photons. We further extend this to analyze the traits of enriched gas. In comparing metrics generated using a fiducial simulation assuming single stars with one incorporating a high binary fraction, we find that binary stars cause H I reionization to complete earlier and at an accelerated pace, while also increasing the abundances of high-ionization metals (C IV and Si IV) in simulated absorption spectra while reducing the abundance of low-ionization states (O I, Si II, and C II). However, through increased photoheating of galactic and circumgalactic gas, they simultaneously reduce the rate of star formation in low-mass galaxies, slowing the ongoing process of enrichment and suppressing their own ionizing background. This potentially contributes to a slower He II reionization process at z ≥ 5, and further indicates that self-regulation of galaxies could be underestimated when neglecting binary stellar evolution.
Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates

We examine a pair of graph generative models for the therapeutic design of novel drug candidates targeting SARS-CoV-2 viral proteins. Due to a sense of urgency, we chose well-validated models with unique strengths: an autoencoder that generates molecules with similar structures to a dataset of drugs with anti-SARS activity and a reinforcement learning algorithm that generates highly novel molecules. During generation, we explore optimization toward several design targets to balance druglikeness, synthetic accessability, and anti-SARS activity based on \icfifty. This generative framework\footnote{https://github.com/exalearn/covid-drug-design} will accelerate drug discovery in future pandemics through the high-throughput generation of targeted therapeutic candidates.
Prospects and applications of photonic neural networks

ABSTRACT Neural networks have enabled applications in artificial intelligence through machine learning, and neuromorphic computing. Software implementations of neural networks on conventional computers that have separate memory and processor (and that operate sequentially) are limited in speed and energy efficiency. Neuromorphic engineering aims to build processors in which hardware mimics neurons and synapses in the brain for distributed and parallel processing. Neuromorphic engineering enabled by photonics (optical physics) can offer sub-nanosecond latencies and high bandwidth with low energies to extend the domain of artificial intelligence and neuromorphic computing applications to machine learning acceleration, nonlinear programming, intelligent signal processing, etc. Photonic neural networks have been demonstrated on integrated platforms and free-space optics depending on the class of applications being targeted. Here, we discuss the prospects and demonstrated applications of these photonic neural networks. Graphical Abstract
NVCache: A Plug-and-Play NVMM-based I/O Booster for Legacy Systems

This paper introduces NVCACHE, an approach that uses a non-volatile main memory (NVMM) as a write cache to improve the write performance of legacy applications. We compare NVCACHE against file systems tailored for NVMM (Ext4-DAX and NOVA) and with I/O-heavy applications (SQLite, RocksDB). Our evaluation shows that NVCACHE reaches the performance level of the existing state-of-the-art systems for NVMM, but without their limitations: NVCACHE does not limit the size of the stored data to the size of the NVMM, and works transparently with unmodified legacy applications, providing additional persistence guarantees even when their source code is not available.
Sparse Layered MIMO With Iterative Detection

In this paper, we propose a novel transmission scheme, called <italic>sparse layered MIMO</italic> (SL-MIMO), that combines non-orthogonal transmission and singular value decomposition (SVD) precoding. Non-orthogonality in SL-MIMO allows re-using of the eigen-channels which improves the spectral efficiency and error rate performance of the system through enhancing the coding gain and diversity gain. We also present a low-complexity message-passing (MP) detector for the proposed SL-MIMO system which performs quite close to maximum likelihood (ML). The joint moment generating function (MGF) of the <italic>ordered</italic> eigenvalues is calculated and used to derive a closed-form upper bound on the average word error probability (AWEP) of the SL-MIMO system, and this derived expression is then used to analyze the diversity gain of the system. We use our analytical results to design sub-optimal codebooks to minimize the error rate of the SL-MIMO system. Simulation results in <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$6\times 6$ </tex-math></inline-formula> multiple-input multiple-output (MIMO) systems with 4-ary, 16-ary, and 64-ary constellations show that our proposed SL-MIMO scheme outperforms competing approaches such as X- and Y-codes in terms of system error rate performance. SL-MIMO has 5.6 dB advantage compared to X-codes and 4.7 dB advantage compared to Y-codes in <inline-formula> <tex-math notation="LaTeX">$6\times 6$ </tex-math></inline-formula> MIMO system with a 64-ary constellation.
Balancing the Spread of Two Opinions in Sparse Social Networks

We propose a new discrete model for simultaneously spreading two opinions within a social network inspired by the famous Target Set Selection problem. We are given a social network, a seed-set of agents for each opinion, and two thresholds per agent. The first threshold represents the willingness of an agent to adopt an opinion if she has no opinion at all, while the second threshold states the readiness to acquire a second opinion. The goal is to add as few agents as possible to the initial seed-sets such that, once the process started with these seed-set stabilises, each agent has either both opinions or none. We perform an initial study of its computational complexity. It is not surprising that the problem is NP-hard even in quite restricted settings. Therefore, we investigate the complexity of the problem from the parameterized point-of-view with special focus on sparse networks, which appears often in practice. Among other things, we show that the proposed problem is in the FPT complexity class if we parameterize by the vertex cover number of the underlying graph.
Robust Unsupervised Multi-Object Tracking In Noisy Environments

Physical processes, camera movement, and unpredictable environmental conditions like the presence of dust can induce noise and artifacts in video feeds. We observe that popular unsupervised MOT methods are dependent on noise-free inputs. We show that the addition of a small amount of artificial random noise causes a sharp degradation in model performance on benchmark metrics. We resolve this problem by introducing a robust unsupervised multi-object tracking (MOT) model: AttU-Net. The proposed single-head attention model helps limit the negative impact of noise by learning visual representations at different segment scales. AttU-Net shows better unsupervised MOT tracking performance over variational inference-based state-of-the-art baselines. We evaluate our method in the MNIST-MOT and the Atari game video benchmark. We also provide two extended video datasets: “Kuzushiji-MNIST MOT” which consists of moving Japanese characters and “Fashion-MNIST MOT” to validate the effectiveness of the MOT models.
Loopnet: Musical Loop Synthesis Conditioned on Intuitive Musical Parameters

Loops, seamlessly repeatable musical segments, are a cornerstone of modern music production. Contemporary artists often mix and match various sampled or pre-recorded loops based on musical criteria such as rhythm, harmony and timbral texture to create compositions. Taking such criteria into account, we present LoopNet, a feed-forward generative model for creating loops conditioned on intuitive parameters. We leverage Music Information Retrieval (MIR) models as well as a large collection of public loop samples in our study and use the Wave-U-Net architecture to map control parameters to audio. We also evaluate the quality of the generated audio and propose intuitive controls for composers to map the ideas in their minds to an audio loop.
A Study of the Photometric and Spectroscopic Variations of the Prototypical FU Orionis–type Star V1057 Cyg

Among the low-mass pre-main sequence stars, a small group called FU Orionis–type objects (FUors) are notable for undergoing powerful accretion outbursts. V1057 Cyg, a classical example of an FUor, went into outburst around 1969–1970, after which it faded rapidly, making it the fastest-fading FUor known. Around 1995, a more rapid increase in fading occurred. Since that time, strong photometric modulations have been present. We present nearly 10 yr of source monitoring at Piszkéstető Observatory, complemented with optical/NIR photometry and spectroscopy from the Nordic Optical Telescope, Bohyunsan Optical Astronomy Observatory, Transiting Exoplanet Survey Satellite, and Stratospheric Observatory for Infrared Astronomy. Our light curves show continuation of significant quasi-periodic variability in brightness over the past decade. Our spectroscopic observations show strong wind features, shell features, and forbidden emission lines. All of these spectral lines vary with time. We also report the first detection of [S ii], [N ii], and [O iii] lines in the star.
Repeated pattern of gamma-ray flares in the light curve of the blazar 3C 279

The optical polarization plane of some blazars occasionally exhibits smooth hundred degree long rotations. Multiple theoretical models have been proposed to explain the nature of such events. A deterministic origin of these rotations, however, remains uncertain. We aim to find repeating patterns of flares in gamma-ray light curves of blazars, which accompany optical polarization plane rotations. Such patterns have been predicted to occur by one of the models explaining this phenomenon. For the blazar 3C 279,wheremultiple polarization plane rotations have been reported in the literature, we obtain the Fermi-LAT gamma-ray light curve and analyze its intervals adjacent to polarization plane rotations. We find a complex characteristic pattern of flares in the gamma-ray light curve that is repeated during periods adjacent to three large amplitude EVPA rotation events in 3C 279.We discover a "hidden EVPA rotation", which can only be seen in the relative Stokes parameters plane and that occurred simultaneously with the fourth repetition of the pattern. This finding strongly favors the hypothesis of emission features propagating in the jet as the reason of optical polarization plane rotations. Furthermore, it is compatible with the hypothesis of a sheath in the jet comprised of more slowly propagating emission features.
Evaluating Strategy Exploration in Empirical Game-Theoretic Analysis

In empirical game-theoretic analysis (EGTA), game models are extended iteratively through a process of generating new strategies based on learning from experience with prior strategies. The strategy exploration problem in EGTA is how to direct this process so to construct effective models with minimal iteration. A variety of approaches have been proposed in the literature, including methods based on classic techniques and novel concepts. Comparing the performance of these alternatives can be surprisingly subtle, depending sensitively on criteria adopted and measures employed. We investigate some of the methodological considerations in evaluating strategy exploration, defining key distinctions and identifying a few general principles based on examples and experimental observations. In particular, we emphasize the fact that empirical games create a space of strategies that should be evaluated as a whole. Based on this fact, we suggest that the minimum regret constrained profile (MRCP) provides a particularly robust basis for evaluating a space of strategies, and propose a local search method for MRCP that outperforms previous approaches. However, the computation of MRCP is not always feasible especially in large games. In this scenario, we highlight consistency considerations for comparing across different approaches. Surprisingly, we find that recent works violate these considerations that are necessary for evaluation, which may result in misleading conclusions on the performance of different approaches. For proper evaluation, we propose a new evaluation scheme and demonstrate that our scheme can reveal the true learning performance of different approaches compared to previous evaluation methods.
A Layered Debris Disk around M Star TWA 7 in Scattered Light

We have obtained Hubble Space Telescope (HST) coronagraphic observations of the circumstellar disk around M star TWA 7 using the Space Telescope Imaging Spectrograph (STIS) instrument in visible light. Together with archival observations, including HST/NICMOS using the F160W filter and Very Large Telescope/SPHERE at the H-band in polarized light, we investigate the system in scattered light. By studying this nearly face-on system using geometric disk models and Henyey–Greenstein phase functions, we report a new discovery of a tertiary ring and a clump. We identify a layered architecture: three rings, a spiral, and an ≈150 au2 elliptical clump. The most extended ring peaks at 28 au, and the other components are on its outskirts. Our point-source detection-limit calculations demonstrate the necessity of disk modeling in imaging fainter planets. Morphologically, we witness a clockwise spiral motion, and the motion pattern is consistent with both solid body motion and local Keplerian motion; we also observe underdensity regions for the secondary ring that might result from mean-motion resonance or moving shadows: both call for re-observations to determine their nature. Comparing multi-instrument observations, we obtain blue STIS-NICMOS color, a STIS-SPHERE radial distribution peak difference for the tertiary ring, and a high SPHERE-NICMOS polarization fraction; these aspects indicate that TWA 7 could retain small dust particles. By viewing the debris disk around M star TWA 7 at a nearly face-on vantage point, our study allows for the understanding of such disks in scattered light in both system architecture and dust property.
Restoring Eye Contact to the Virtual Classroom with Machine Learning

Nonverbal communication, in particular eye contact, is a critical element of the music classroom, shown to keep students on task, coordinate musical flow, and communicate improvisational ideas. Unfortunately, this nonverbal aspect to performance and pedagogy is lost in the virtual classroom. In this paper, we propose a machine learning system which uses single instance, single camera image frames as input to estimate the gaze target of a user seated in front of their computer, augmenting the user's video feed with a display of the estimated gaze target and thereby restoring nonverbal communication of directed gaze. The proposed estimation system consists of modular machine learning blocks, leading to a target-oriented (rather than coordinate-oriented) gaze prediction. We instantiate one such example of the complete system to run a pilot study in a virtual music classroom over Zoom software. Inference time and accuracy meet benchmarks for videoconferencing applications, and quantitative and qualitative results of pilot experiments include improved success of cue interpretation and student-reported formation of collaborative, communicative relationships between conductor and musician.