Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning a Latent Simplex in Input-Sparsity Time

We consider the problem of learning a latent $k$-vertex simplex $K\subset\mathbb{R}^d$, given access to $A\in\mathbb{R}^{d\times n}$, which can be viewed as a data matrix with $n$ points that are obtained by randomly perturbing latent points in the simplex $K$ (potentially beyond $K$). A large class of latent variable models, such as adversarial clustering, mixed membership stochastic block models, and topic models can be cast as learning a latent simplex. Bhattacharyya and Kannan (SODA, 2020) give an algorithm for learning such a latent simplex in time roughly $O(k\cdot\textrm{nnz}(A))$, where $\textrm{nnz}(A)$ is the number of non-zeros in $A$. We show that the dependence on $k$ in the running time is unnecessary given a natural assumption about the mass of the top $k$ singular values of $A$, which holds in many of these applications. Further, we show this assumption is necessary, as otherwise an algorithm for learning a latent simplex would imply an algorithmic breakthrough for spectral low rank approximation. At a high level, Bhattacharyya and Kannan provide an adaptive algorithm that makes $k$ matrix-vector product queries to $A$ and each query is a function of all queries preceding it. Since each matrix-vector product requires $\textrm{nnz}(A)$ time, their overall running time appears unavoidable. Instead, we obtain a low-rank approximation to $A$ in input-sparsity time and show that the column space thus obtained has small $\sin\Theta$ (angular) distance to the right top-$k$ singular space of $A$. Our algorithm then selects $k$ points in the low-rank subspace with the largest inner product with $k$ carefully chosen random vectors. By working in the low-rank subspace, we avoid reading the entire matrix in each iteration and thus circumvent the $\Theta(k\cdot\textrm{nnz}(A))$ running time.
X-Vectors with Multi-Scale Aggregation for Speaker Diarization

Speaker diarization is the process of labeling different speakers in a speech signal. Deep speaker embeddings are generally extracted from short speech segments and clustered to determine the segments belong to same speaker identity. The x-vector, which embeds segment-level speaker characteristics by statistically pooling frame-level representations, is one of the most widely used deep speaker embeddings in speaker diarization. Multi-scale aggregation, which employs multi-scale representations from different layers, has recently successfully been used in short duration speaker verification. In this paper, we investigate a multi-scale aggregation approach in an x-vector embedding framework for speaker diarization by exploiting multiple statistics pooling layers from different frame-level layers. Thus, it is expected that x-vectors with multi-scale aggregation have the potential to capture meaningful speaker characteristics from short segments, effectively taking advantage of different information at multiple layers. Experimental evaluation on the CALLHOME dataset showed that our approach provides substantial improvement over the baseline x-vectors.
Optimal Convergence Rates for the Proximal Bundle Method

We study convergence rates of the classic proximal bundle method for a variety of nonsmooth convex optimization problems. We show that, without any modification, this algorithm adapts to converge faster in the presence of smoothness or a H\"older growth condition. Our analysis reveals that with a constant stepsize, the bundle method is adaptive, yet it exhibits suboptimal convergence rates. We overcome this shortcoming by proposing nonconstant stepsize schemes with optimal rates. These schemes use function information such as growth constants, which might be prohibitive in practice. We provide a parallelizable variant of the bundle method that can be applied without prior knowledge of function parameters while maintaining near-optimal rates. The practical impact of this scheme is limited since we incur a (parallelizable) log factor in the complexity. These results improve on the scarce existing convergence rates and provide a unified analysis approach across problem settings and algorithmic details. Numerical experiments support our findings.
A Realizable Filtered Intrusive Polynomial Moment Method

Intrusive uncertainty quantification methods for hyperbolic problems exhibit spurious oscillations at shocks, which leads to a significant reduction of the overall approximation quality. Furthermore, a challenging task is to preserve hyperbolicity of the gPC moment system. An intrusive method which guarantees hyperbolicity is the intrusive polynomial moment (IPM) method, which performs the gPC expansion on the entropy variables. The method, while still being subject to oscillations, requires solving a convex optimization problem in every spatial cell and every time step. The aim of this work is to mitigate oscillations in the IPM solution by applying filters. Filters reduce oscillations by damping high order gPC coefficients. Naive filtering, however, may lead to unrealizable moments, which means that the IPM optimization problem does not have a solution and the method breaks down. In this paper, we propose and analyze two separate strategies to guarantee the existence of a solution to the IPM problem. First, we propose a filter which maintains realizability by being constructed from an underlying Fokker-Planck equation. Second, we regularize the IPM optimization problem to be able to cope with non-realizable gPC coefficients. Consequently, standard filters can be applied to the regularized IPM method. We demonstrate numerical results for the two strategies by investigating the Euler equations with uncertain shock structures in one- and two-dimensional spatial settings. We are able to show a significant reduction of spurious oscillations by the proposed filters.
A Complete Axiomatisation for Divergence Preserving Branching Congruence of Finite-State Behaviours

We present an equational inference system for finite-state expressions, and prove that the system is sound and complete with respect to divergence preserving branching congruence, closing a problem that has been open since 1993. The inference system refines Rob van Glabbeek’s simple and elegant complete axiomatisation for branching bisimulation congruence of finite-state behaviours by joining four simple axioms after dropping one axiom which is unsound under the more refined divergence sensitive semantics.
MultiSports: A Multi-Person Video Dataset of Spatio-Temporally Localized Sports Actions

Spatio-temporal action detection is an important and challenging problem in video understanding. The existing action detection benchmarks are limited in aspects of small numbers of instances in a trimmed video or low-level atomic actions. This paper aims to present a new multi-person dataset of spatio-temporal localized sports actions, coined as MultiSports. We first analyze the important ingredients of constructing a realistic and challenging dataset for spatio-temporal action detection by proposing three criteria: (1) multi-person scenes and motion dependent identification, (2) with well-defined boundaries, (3) relatively fine-grained classes of high complexity. Based on these guide-lines, we build the dataset of MultiSports v1.0 by selecting 4 sports classes, collecting 3200 video clips, and annotating 37701 action instances with 902k bounding boxes. Our datasets are characterized with important properties of high diversity, dense annotation, and high quality. Our Multi-Sports, with its realistic setting and detailed annotations, exposes the intrinsic challenges of spatio-temporal action detection. To benchmark this, we adapt several baseline methods to our dataset and give an indepth analysis on the action detection results in our dataset. We hope our MultiSports can serve as a standard benchmark for spatio-temporal action detection in the future. Our dataset website is at https://deeperaction.github. io/multisports/.
Controlling an Inverted Pendulum with Policy Gradient Methods-A Tutorial

This paper provides the details of implementing two important policy gradient methods to solve the inverted pendulum problem. These are namely the Deep Deterministic Policy Gradient (DDPG) and the Proximal Policy Optimization (PPO) algorithm. The problem is solved by using an actor-critic model where an actor-network is used to learn the policy function and a critic network is to evaluate the actor-network by learning to estimate the Q function. Apart from briefly explaining the mathematics behind these two algorithms, the details of python implementation are provided which helps in demystifying the underlying complexity of the algorithm. In the process, the readers will be introduced to OpenAI/Gym, Tensorflow 2.x and Keras utilities used for implementing the above concepts.
ADMM and spectral proximity operators in hyperspectral broadband phase retrieval for quantitative phase imaging

A novel formulation of the hyperspectral broadband phase retrieval is developed for the scenario where both object and modulation phase masks are spectrally varying. The proposed algorithm is based on a complex domain version of the alternating direction method of multipliers (ADMM) and Spectral Proximity Operators (SPO) derived for Gaussian and Poissonian observations. Computations for these operators are reduced to the solution of sets of cubic (for Gaussian) and quadratic (for Poissonian) algebraic equations. These proximity operators resolve two problems. Firstly, the complex domain spectral components of signals are extracted from the total intensity observations calculated as sums of the signal spectral intensities. In this way, the spectral analysis of the total intensities is achieved. Secondly, the noisy observations are filtered, compromising noisy intensity observations and their predicted counterparts. The ability to resolve the hyperspectral broadband phase retrieval problem and to find the spectrum varying object are essentially defined by the spectral properties of object and image formation operators. The simulation tests demonstrate that the phase retrieval in this formulation can be successfully resolved.
Cross-Cluster Weighted Forests

Adapting machine learning algorithms to better handle clustering or batch effects within training data sets is important across a wide variety of biological applications. This article considers the effect of ensembling Random Forest learners trained on clusters within a single data set with heterogeneity in the distribution of the features. We find that constructing ensembles of forests trained on clusters determined by algorithms such as k-means results in significant improvements in accuracy and generalizability over the traditional Random Forest algorithm. We denote our novel approach as the Cross-Cluster Weighted Forest, and examine its robustness to various data-generating scenarios and outcome models. Furthermore, we explore the influence of the data-partitioning and ensemble weighting strategies the benefits of our method over the existing paradigm. Finally, we apply our approach to cancer molecular profiling and gene expression data sets that are naturally divisible into clusters and illustrate that our approach outperforms classic Random Forest. Code and supplementary material are available at https://github.com/m-ramchandran/cross-cluster.
Confidence Assertions in Cyber-Security for an Information-Sharing Environment

Information sharing is vital in resisting cyberattacks, and the volume and severity of these attacks is increasing very rapidly. Therefore responders must triage incoming warnings in deciding how to act. This study asked a very specific question:"how can the addition of confidence information to alerts and warnings improve overall resistance to cyberattacks. "We sought, in particular, to identify current practices, and if possible, to identify some"best practices. "The research involved literature review and interviews with subject matter experts at every level from system administrators to persons who develop broad principles of policy. An innovative Modified Online Delphi Panel technique was used to elicit judgments and recommendations from experts who were able to speak with each other and vote anonymously to rank proposed practices.
What makes you unique?

This paper proposes a uniqueness Shapley measure to compare the extent to which different variables are able to identify a subject. Revealing the value of a variable on subject $t$ shrinks the set of possible subjects that $t$ could be. The extent of the shrinkage depends on which other variables have also been revealed. We use Shapley value to combine all of the reductions in log cardinality due to revealing a variable after some subset of the other variables has been revealed. This uniqueness Shapley measure can be aggregated over subjects where it becomes a weighted sum of conditional entropies. Aggregation over subsets of subjects can address questions like how identifying is age for people of a given zip code. Such aggregates have a corresponding expression in terms of cross entropies. We use uniqueness Shapley to investigate the differential effects of revealing variables from the North Carolina voter registration rolls and in identifying anomalous solar flares. An enormous speedup (approaching 2000 fold in one example) is obtained by using the all dimension trees of Moore and Lee (1998) to store the cardinalities we need.
1D CNN Architectures for Music Genre Classification

This paper proposes a 1D residual convolutional neural network (CNN) architecture for music genre classification and compares it with other recent 1D CNN architectures. The 1D CNNs learn a representation and a discriminant directly from the raw audio signal. Several convolutional layers capture the time-frequency characteristics of the audio signal and learn various filters relevant to the music genre recognition task. The proposed approach splits the audio signal into overlapped segments using a sliding window to comply with the fixed-length input constraint of the 1D CNNs. As a result, music genre classification can be carried out on a single audio segment or on aggregating the predictions on several audio segments, which improves the final accuracy. The performance of the proposed 1D residual CNN is assessed on a public dataset of 1,000 audio clips. The experimental results have shown that it achieves 80.93% of mean accuracy in classifying music genres and outperforms other 1D CNN architectures.
Neuropharmacokinetics of a new AMPA modulator, S18986, in the rat

The aim of our study was to determine the neuropharmacokinetics of S18986, a positive allosteric modulator of AMPA‐type receptors, in the rat. We focused on its blood‐brain barrier (BBB) uptake and on its brain intra‐ and extra‐cellular fluid (bICF‐bECF) partitioning.
Enhanced Efficacy of Aurora Kinase Inhibitors in G2/M Checkpoint Deficient TP53 Mutant Uterine Carcinomas Is Linked to the Summation of LKB1–AKT–p53 Interactions

Simple Summary Cancers arising from the lining of the uterus, endometrial cancers, are the most common gynecologic malignancy in the United States. Once endometrial cancer escapes the uterus and grows in distant locations, there are limited therapeutic options. The most aggressive and lethal endometrial cancers carry alterations in the protein p53, which is a critical guardian of many cellular functions. The role of these p53 alterations in endometrial cancer is not well understood. The goal of this work was to use p53 altered models of endometrial cancer to understand which, if any, therapeutically targetable vulnerabilities these p53 alterations may confer in endometrial cancer. Here we show that many of these p53 altered cells have problems with cell division which can be targeted with novel single and combination therapies. These discoveries may lead to relevant new therapies for difficult to treat advanced stage endometrial cancers. Abstract Uterine carcinoma (UC) is the most common gynecologic malignancy in the United States. TP53 mutant UCs cause a disproportionate number of deaths due to limited therapies for these tumors and the lack of mechanistic understanding of their fundamental vulnerabilities. Here we sought to understand the functional and therapeutic relevance of TP53 mutations in UC. We functionally profiled targetable TP53 dependent DNA damage repair and cell cycle control pathways in a panel of TP53 mutant UC cell lines and patient-derived organoids. There were no consistent defects in DNA damage repair pathways. Rather, most models demonstrated dependence on defective G2/M cell cycle checkpoints and subsequent upregulation of Aurora kinase-LKB1-p53-AKT signaling in the setting of baseline mitotic defects. This combination makes them sensitive to Aurora kinase inhibition. Resistant lines demonstrated an intact G2/M checkpoint, and combining Aurora kinase and WEE1 inhibitors, which then push these cells through mitosis with Aurora kinase inhibitor-induced spindle defects, led to apoptosis in these cases. Overall, this work presents Aurora kinase inhibitors alone or in combination with WEE1 inhibitors as relevant mechanism driven therapies for TP53 mutant UCs. Context specific functional assessment of the G2/M checkpoint may serve as a biomarker in identifying Aurora kinase inhibitor sensitive tumors.
Structure–Function Relationships of Nanocarbon/Polymer Composites for Chemiresistive Sensing: A Review

Composites of organic compounds and inorganic nanomaterials provide novel sensing platforms for high-performance sensor applications. The combination of the attractive functionalities of nanomaterials with polymers as an organic matrix offers promising materials with tunable electrical, mechanical, and chemisensitive properties. This review mainly focuses on nanocarbon/polymer composites as chemiresistors. We first describe the structure and properties of carbon nanofillers as reinforcement agents used in the manufacture of polymer composites and the sensing mechanism of developed nanocomposites as chemiresistors. Then, the design and synthesizing methods of polymer composites based on carbon nanofillers are discussed. The electrical conductivity, mechanical properties, and the applications of different nanocarbon/polymer composites for the detection of different analytes are reviewed. Lastly, challenges and the future vision for applications of such nanocomposites are described.
Ablation of Bone Tissue by Femtosecond Laser: A Path to High-Resolution Bone Surgery

Femtosecond lasers allow for high-precision, high-quality ablation of biological tissues thanks to their capability of minimizing the thermal loads into the irradiated material. Nevertheless, reported ablation rates remain still too limited to enable their exploitation on a clinical level. This study demonstrates the possibility to upscale the process of fs laser ablation of bone tissue by employing industrially available fs laser sources. A comprehensive parametric study is presented in order to optimize the bone tissue ablation rate while maintaining the tissue health by avoiding excessive thermal loads. Three different absorption regimes are investigated by employing fs laser sources at 1030 nm, 515 nm and 343 nm. The main differences in the three different wavelength regimes are discussed by comparing the evolution of the ablation rate and the calcination degree of the laser ablated tissue. The maximum of the ablation rate is obtained in the visible regime of absorption where a maximum value of 0.66 mm3/s is obtained on a non-calcined tissue for the lowest laser repetition rate and the lowest spatial overlap between successive laser pulses. In this regime, the hemoglobin present in the fresh bone tissue is the main chromophore involved in the absorption process. To the best of our knowledge, this is the highest ablation rate obtained on porcine femur upon fs laser ablation.
Assessing SARS-CoV-2 Infection Rate among Romanian Dental Practitioners

Due to the impact of the Covid-19 pandemic on dental treatments, the present research aimed to assess the infection rate among dental practitioners from Romania and to analyze the economic impact of Covid-19 on dental offices. We designed a web-based survey distributed to dental practitioners from Romania. The survey included questions that assessed demographic data from the dentists who completed the questionnaire, along with economic aspects and epidemiological aspects related to the impact of the COVID-19 pandemic on dental practitioners. Five hundred and seven dentists completed the survey. Three-quarters of the assessed dental offices reported a decrease in the income and patient volume compared to 2019. More than half of the patients visiting the dental office paid more attention to the risk of infection and to prevention methods. Most dental offices implemented specific protective equipment for doctors. Three thousand seven hundred thirty-five dental practitioners were working in the 507 assessed dental offices, and among them, 238 COVID-19 cases of contamination were recorded. High contamination rates were registered in October (48, 20.1%), November (66, 27.7%), and December 2020 (52, 21.85%). Contamination mostly took place at home (114 cases, 47.8%) or resulted from event attendance. This study underlines an overall greater level of safety and an increased patient addressability in dental offices.
Alcohol in Psoriasis—From Bench to Bedside

Alcohol affects the symptoms, compliance and comorbidities as well as the safety and efficacy of treatments in psoriatic patients. In this review, we aim to summarize and link clinical observations with a molecular background, such as signaling pathways at the cellular level and genetic variations, and to provide an overview of how this knowledge could influence our treatment selection and patient management.
Acute Hamstring Injury Prevention Programs in Eleven-a-Side Football Players Based on Physical Exercises: Systematic Review

(1) Objective: To analyze the exercise programs used to prevent of acute hamstring injuries in eleven-a-side football players, and their effectiveness. (2) Methods: A systematic review (PRISMA) was conducted (2008–2020), including RCTs, that exclusively used physical exercises as a prevention method. (3) Results: Ten studies were selected considering 14 interventions, including nine different programs: FIFA11+ (11+), Harmoknee, eccentric Nordic Hamstring Exercise (NHE) exclusively, with eccentric exercises, with stretching or with proprioceptive, New Warm-up Program (NWP), Bounding Exercise Program (BEP), the only one with no positive results, and proprioceptive exercises. Incidence of injuries and strength were the most considered variables, both with favorable evidences. Programs including NHE, which assessed injury incidence, were always effective. The 11+ program was effective in injury incidence and strength; NWP was effective in balance, stability, and strength. (4) Conclusions: The exercise programs discussed were effective to prevent acute hamstring injuries in football players except BEP and partially Harmoknee. Exercises mostly used to reduce the risk of hamstring injuries are those of eccentric force due to its functionality, especially NHE. Only concentric contractions and isometric contractions obtained significant favorable results. The most complete and promising programs were 11+ (in injury incidence and strength) and NWP (strength, balance, and stability). NWP was the best in strength.