Miracle-dz/UniFAR-FTUs huggingface dataset:
Offline coil position denoising enhances detection of TMS effects

OBJECTIVE Transcranial magnetic stimulation (TMS) is extensively used in basic and clinical neuroscience. Previous work has shown substantial residual variability in TMS effects even despite use of on-line visual feedback monitoring of coil position. Here, we aimed to evaluate if off-line denoising of variability induced by neuronavigated coil position and orientation deviations can enhance detection of TMS effects. METHODS Retrospective modeling was used to denoise the impact of common neuronavigated coil position and rotation deviations during TMS experimental sessions on motor evoked potentials (MEP) to single pulse TMS. RESULTS Neuronavigated coil deviations explained approximately 44% of total MEP amplitude variability. Offline denoising led to a 136.71% improvement in the signal to noise ratio (SNR) of corticospinal excitability measurements. CONCLUSIONS: Offline modeling enhanced detection of TMS effects by removing variability introduced by neuronavigated coil deviations. SIGNIFICANCE This approach could allow more accurate determination of TMS effects in cognitive and interventional neuroscience. HIGHLIGHTS Coil deviations impact TMS effects despite use of on-line neuronavigation feedback. Offline denoising of coil deviation impacts on TMS effects significantly reduced variability at trial level. Offline denoising also significantly improved overall SNR of TMS effects.
GSEP: A robust vocal and accompaniment separation system using gated CBHG module and loudness normalization

In the field of audio signal processing research, source separation has been a popular research topic for a long time and the recent adoption of the deep neural networks have shown a significant improvement in performance. The improvement vitalizes the industry to productize audio deep learning based products and services including Karaoke in the music streaming apps and dialogue enhancement in the UHDTV. For these early markets, we defined a set of design principles of the vocal and accompaniment separation model in terms of robustness, quality, and cost. In this paper, we introduce GSEP (Gaudio source SEParation system), a robust vocal and accompaniment separation system using a Gated- CBHG module, mask warping, and loudness normalization and it was verified that the proposed system satisfies all three principles and outperforms the state-of-the-art systems both in objective measure and subjective assessment through experiments.
Is PSR J0855−4644 responsible for the 1.4 TeV electron spectral bump hinted by DAMPE?

DAMPE observation on the cosmic ray electron spectrum hints a narrow excess at $\sim$ 1.4 TeV. Although the excess can be ascribed to dark matter particles, pulsars and pulsar wind nebulae are believed to be a more natural astrophysical origin: electrons injected from nearby pulsars at their early ages can form a bump-like feature in the spectrum due to radiative energy losses. In this paper, with a survey of nearby pulsars, we find 4 pulsars that may have notable contributions to $\sim$ 1.4 TeV cosmic ray electrons. Among them, PSR J0855$-$4644 has a spin down luminosity more than 50 times higher than others and presumably dominates the electron fluxes from them. X-ray observations on the inner compact part (which may represent a tunnel for the transport of electrons from the pulsar) of PWN G267.0$-$01.0 are then used to constrain the spectral index of high energy electrons injected by the pulsar. We show that high-energy electrons released by PSR J0855$-$4644 could indeed reproduce the 1.4 TeV spectral feature hinted by the DAMPE with reasonable parameters.
Runaway stars masquerading as star formation in galactic outskirts

In the outskirts of nearby spiral galaxies, star formation is observed in extremely low gas surface densities. Star formation in these regions, where the interstellar medium is dominated by diffuse atomic hydrogen, is difficult to explain with classic star formation theories. In this work, we introduce runaway stars as an explanation to this observation. Runaway stars, produced by collisional dynamics in young stellar clusters, can travel kilo-parsecs during their main sequence life time. Using galactic-scale hydrodynamic simulations including a treatment of individual stars, we demonstrate that this mechanism enables the ejection of young massive stars into environments where the gas is not dense enough to trigger star formation. This results in the appearance of star formation in regions where it ought to be impossible. We conclude that runaway stars are a contributing, if not dominant, factor to the observations of star formation in the outskirts of spiral galaxies.
Aging-Delaying Plant Extracts Cause Age-Related Changes in Cellular and Organellar Lipidomes of the Chronologically Aging Yeast Saccharomyces cerevisiae

The yeast Saccharomyces cerevisiae has been successfully used to identify genes, signaling pathways and chemical compounds that delay cellular and organismal aging in evolutionarily distant eukaryotes. These findings provided evidence that the mechanisms of biological aging on a cellular level have been conserved in the course of evolution. Recent studies revealed 6 plant extracts that delay chronological aging in S. cerevisiae. All these plant extracts are more efficient aging-delaying interventions than any of the currently known longevity-extending chemicals. As a first step towards uncovering molecular mechanisms through which the 6 plant extracts delay yeast chronological aging, I used quantitative mass spectrometry to compare the concentrations of different classes of lipids in cells and cellular organelles of chronologically aging yeast exposed to each of these extracts or remained untreated. I demonstrate that each of the 6 aging-delaying plant extracts causes age-related changes in cellular and organellar lipidomes of chronologically aging yeast. My findings suggest that each of these extracts differently alters the relative rates of phosphatidic acid flow into the biosynthetic pathways for triacylglycerols in the endoplasmic reticulum, glycerol phospholipids in the endoplasmic reticulum and mitochondria, and cardiolipin in mitochondria. Such re-wiring of phosphatidic acid conversion into triacylglycerols, glycerol phospholipids and cardiolipin delays aging by mechanisms that remain to be established. My study provides important knowledge on which aspects of lipid metabolism are essential for the ability of the 6 plant extracts to delay aging.
No Free Lunch for Approximate MCMC

It is widely known that the performance of Markov chain Monte Carlo (MCMC) can degrade quickly when targeting computationally expensive posterior distributions, such as when the sample size is large. This has motivated the search for MCMC variants that scale well to large datasets. One general approach has been to look at only a subsample of the data at every step. In this note, we point out that well-known MCMC convergence results often imply that these "subsampling" MCMC algorithms cannot greatly improve performance. We apply these generic results to realistic statistical problems and proposed algorithms, and also discuss some design principles suggested by the results.
Online Algorithm for Unsupervised Sequential Selection with Contextual Information

In this paper, we study Contextual Unsupervised Sequential Selection (USS), a new variant of the stochastic contextual bandits problem where the loss of an arm cannot be inferred from the observed feedback. In our setup, arms are associated with fixed costs and are ordered, forming a cascade. In each round, a context is presented, and the learner selects the arms sequentially till some depth. The total cost incurred by stopping at an arm is the sum of fixed costs of arms selected and the stochastic loss associated with the arm. The learner's goal is to learn a decision rule that maps contexts to arms with the goal of minimizing the total expected loss. The problem is challenging as we are faced with an unsupervised setting as the total loss cannot be estimated. Clearly, learning is feasible only if the optimal arm can be inferred (explicitly or implicitly) from the problem structure. We observe that learning is still possible when the problem instance satisfies the so-called 'Contextual Weak Dominance' (CWD) property. Under CWD, we propose an algorithm for the contextual USS problem and demonstrate that it has sub-linear regret. Experiments on synthetic and real datasets validate our algorithm.
Replacing projection on finitely generated convex cones with projection on bounded polytopes.

This paper is devoted to the general problem of projection onto a polyhedral convex cone generated by a finite set of generators.This problem is reformulated into projection onto the polytope obtained by simple truncation of the original cone. Then it can be solved with just two closely related projections onto the same bounded polytope. This approach's computational performance is conditioned by the crucial tool's efficiency for solving the fundamental problem of finding the least norm element in a convex hull of a given finite set of points. In our numerical experiments, we used for this purpose the specialized finite algorithm implemented in the open-source system for matrix-vector calculations octave. This algorithm is practically indifferent to the proportions between the number of generators and their dimensionality and significantly outperformed a general-purpose quadratic programming algorithm of the active-set variety built into octave when the number of points exceeds the dimensionality of the original problem.
Canonization for Bounded and Dihedral Color Classes in Choiceless Polynomial Time

In the quest for a logic capturing PTime the next natural classes of structures to consider are those with bounded color class size. We present a canonization procedure for graphs with dihedral color classes of bounded size in the logic of Choiceless Polynomial Time (CPT), which then captures PTime on this class of structures. This is the first result of this form for non-abelian color classes. The first step proposes a normal form which comprises a "rigid assemblage". This roughly means that the local automorphism groups form 2-injective 3-factor subdirect products. Structures with color classes of bounded size can be reduced canonization preservingly to normal form in CPT. In the second step, we show that for graphs in normal form with dihedral color classes of bounded size, the canonization problem can be solved in CPT. We also show the same statement for general ternary structures in normal form if the dihedral groups are defined over odd domains.
Speech Activity Detection Based on Multilingual Speech Recognition System

To better model the contextual information and increase the generalization ability of a voice detection system, this paper leverages a multi-lingual Automatic Speech Recognition (ASR) system to perform Speech Activity Detection (SAD). Sequence-discriminative training of multi-lingual Acoustic Model (AM) using Lattice-Free Maximum Mutual Information (LF-MMI) loss function, effectively extracts the contextual information of the input acoustic frame. The index of maximum output posterior is considered as a frame-level speech/non-speech decision function. Majority voting and logistic regression are applied to fuse the language-dependent decisions. The leveraged multi-lingual ASR is trained on 18 languages of BABEL datasets and the built SAD is evaluated on 3 different languages. In out-of-domain datasets, the proposed SAD model shows significantly better performance w.r. t. baseline models. In the Ester2 dataset, without using any in-domain data, this model outperforms the WebRTC, phoneme recognizer based VAD (Phn\_Rec), and Pyannote baselines (respectively 7.1, 1.7, and 2.7% absolutely) in Detection Error Rate (DetER) metrics. Similarly, in the LiveATC dataset, this model outperforms the WebRTC, Phn\_Rec, and Pyannote baselines (respectively 6.4, 10.0, and 3.7% absolutely) in DetER metrics.
N A ] 2 2 O ct 2 02 0 AN OVERVIEW OF BLOCK GRAM-SCHMIDT METHODS AND THEIR STABILITY PROPERTIES

Block Gram-Schmidt algorithms comprise essential kernels in many scientiﬁc computing applications, but for many commonly used variants, a rigorous treatment of their stability properties remains open. This survey provides a comprehensive categorization of block Gram-Schmidt algorithms, especially those used in Krylov subspace methods to build orthonormal bases one block vector at a time. All known stability results are assembled, and new results are summarized or conjectured for important communication-reducing variants. A diverse array of numerical illustrations are presented, along with the Matlab code for reproducing the results in a publicly available at repository https://github.com/katlund/BlockStab. A number of open problems are discussed, and an appendix containing all algorithms type-set in a uniform fashion is provided.
Model Interpretability through the Lens of Computational Complexity

In spite of several claims stating that some models are more interpretable than others -- e.g., "linear models are more interpretable than deep neural networks" -- we still lack a principled notion of interpretability to formally compare among different classes of models. We make a step towards such a notion by studying whether folklore interpretability claims have a correlate in terms of computational complexity theory. We focus on local post-hoc explainability queries that, intuitively, attempt to answer why individual inputs are classified in a certain way by a given model. In a nutshell, we say that a class $\mathcal{C}_1$ of models is more interpretable than another class $\mathcal{C}_2$, if the computational complexity of answering post-hoc queries for models in $\mathcal{C}_2$ is higher than for those in $\mathcal{C}_1$. We prove that this notion provides a good theoretical counterpart to current beliefs on the interpretability of models; in particular, we show that under our definition and assuming standard complexity-theoretical assumptions (such as P$\neq$NP), both linear and tree-based models are strictly more interpretable than neural networks. Our complexity analysis, however, does not provide a clear-cut difference between linear and tree-based models, as we obtain different results depending on the particular post-hoc explanations considered. Finally, by applying a finer complexity analysis based on parameterized complexity, we are able to prove a theoretical result suggesting that shallow neural networks are more interpretable than deeper ones.
Listening to Sounds of Silence for Speech Denoising

We introduce a deep learning model for speech denoising, a long-standing challenge in audio analysis arising in numerous applications. Our approach is based on a key observation about human speech: there is often a short pause between each sentence or word. In a recorded speech signal, those pauses introduce a series of time periods during which only noise is present. We leverage these incidental silent intervals to learn a model for automatic speech denoising given only mono-channel audio. Detected silent intervals over time expose not just pure noise but its time-varying features, allowing the model to learn noise dynamics and suppress it from the speech signal. Experiments on multiple datasets confirm the pivotal role of silent interval detection for speech denoising, and our method outperforms several state-of-the-art denoising methods, including those that accept only audio input (like ours) and those that denoise based on audiovisual input (and hence require more information). We also show that our method enjoys excellent generalization properties, such as denoising spoken languages not seen during training.
Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond

Cross-lingual adaptation with multilingual pre-trained language models (mPTLMs) mainly consists of two lines of works: zero-shot approach and translation-based approach, which have been studied extensively on the sequence-level tasks. We further verify the efficacy of these cross-lingual adaptation approaches by evaluating their performances on more fine-grained sequence tagging tasks. After re-examining their strengths and drawbacks, we propose a novel framework to consolidate the zero-shot approach and the translation-based approach for better adaptation performance. Instead of simply augmenting the source data with the machine-translated data, we tailor-make a warm-up mechanism to quickly update the mPTLMs with the gradients estimated on a few translated data. Then, the adaptation approach is applied to the refined parameters and the cross-lingual transfer is performed in a warm-start way. The experimental results on nine target languages demonstrate that our method is beneficial to the cross-lingual adaptation of various sequence tagging tasks.
Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors

False positives (FPs) have been an issue of extreme importance for anti-virus (AV) systems for decades. As more security vendors turn to machine learning, alert deluge has hit critical mass with over 20% of all alerts resulting in FPs and, in some organizations, the number reaches half of all alerts [1]. This increase has resulted in fatigue, frustration, and, worst of all, neglect from security workers on SOC teams. A foundational cause for FPs is that vendors must build one global system to try and satisfy all customers, but have no method to adjust to individual local environments. This leads to outrageous, albeit technically correct, characterization of their platforms being 99.9% effective. Once these systems are deployed the idiosyncrasies of individual, local environments expose blind spots that lead to FPs and uncertainty. We propose a strategy for fixing false positives in production after a model has already been deployed. For too long the industry has tried to combat these problems with inefficient, and at times, dangerous allowlist techniques and excessive model retraining which is no longer enough. We propose using a technique called passive-aggressive learning to alter a malware detection model to an individual's environment, eliminating false positives without sharing any customer sensitive information. We will show how to use passive-aggressive learning to solve a collection of notoriously difficult false positives from a production environment without compromising the malware model's accuracy, reducing the total number of FP alerts by an average of 23x.
Exploring Research Interest in Stack Overflow - A Systematic Mapping Study and Quality Evaluation

Platforms such as Stack Overflow are available for software practitioners to solicit solutions to their challenges and knowledge needs. The practices therein have in recent times however triggered quality related concerns. This is a noteworthy issue when considering that the Stack Overflow platform is used by numerous software developers. Academic research tends to provide validation for the practices and processes employed by Stack Overflow and other such forums. However, previous work did not review the scale of scientific attention that is given to this cause. Continuing from our preliminary work, we conducted a Systematic Mapping study involving 265 papers from six relevant databases to address this gap. In this work, we explored the level of academic interest Stack Overflow has generated, the publication venues that are targeted, the topics that are studied, approaches used, types of contributions and the quality of the publications that are written about Stack Overflow. Outcomes show that Stack Overflow has attracted increasing research interest over the years, with topics relating to both community dynamics and human factors, and technical issues. In addition, research studies have been largely evaluative or proposed solutions; however, the latter approach tends to lack validation. The contributions of these studies are often techniques or answers to a specific problem. Evaluating the quality of all studies that were dedicated to software programming (58 papers), our outcomes show that on average only 58% of the developed quality criteria were met. Notwithstanding that research is continually aiming to understand Stack Overflow and other similar communities, further investigations are required to validate such studies and the solutions they propose.
Enriching Under-Represented Named-Entities To Improve Speech Recognition Performance

Automatic speech recognition (ASR) for under-represented named-entity (UR-NE) is challenging due to such named-entities (NE) have insufficient instances and poor contextual coverage in the training data to learn reliable estimates and representations. In this paper, we propose approaches to enriching UR-NEs to improve speech recognition performance. Specifically, our first priority is to ensure those UR-NEs to appear in the word lattice if there is any. To this end, we make exemplar utterances for those UR-NEs according to their categories (e.g. location, person, organization, etc.), ending up with an improved language model (LM) that boosts the UR-NE occurrence in the word lattice. With more UR-NEs appearing in the lattice, we then boost the recognition performance through lattice rescoring methods. We first enrich the representations of UR-NEs in a pre-trained recurrent neural network LM (RNNLM) by borrowing the embedding representations of the rich-represented NEs (RR-NEs), yielding the lattices that statistically favor the UR-NEs. Finally, we directly boost the likelihood scores of the utterances containing UR-NEs and gain further performance improvement.
Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation

We propose a novel lightweight generative adversarial network for efficient image manipulation using natural language descriptions. To achieve this, a new word-level discriminator is proposed, which provides the generator with fine-grained training feedback at word-level, to facilitate training a lightweight generator that has a small number of parameters, but can still correctly focus on specific visual attributes of an image, and then edit them without affecting other contents that are not described in the text. Furthermore, thanks to the explicit training signal related to each word, the discriminator can also be simplified to have a lightweight structure. Compared with the state of the art, our method has a much smaller number of parameters, but still achieves a competitive manipulation performance. Extensive experimental results demonstrate that our method can better disentangle different visual attributes, then correctly map them to corresponding semantic words, and thus achieve a more accurate image modification using natural language descriptions.
A machine learning based Bayesian optimization solution to non-linear responses in dusty plasmas

Nonlinear frequency response analysis is a widely used method for determining system dynamics in the presence of nonlinearities. In dusty plasmas, the plasma–grain interaction (e.g. grain charging fluctuations) can be characterized by a single-particle non-linear response analysis, while grain–grain non-linear interactions can be determined by a multi-particle non-linear response analysis. Here a machine learning-based method to determine the equation of motion in the non-linear response analysis for dust particles in plasmas is presented. Searching the parameter space in a Bayesian manner allows an efficient optimization of the parameters needed to match simulated non-linear response curves to experimentally measured non-linear response curves.