Miracle-dz/UniFAR-FTUs huggingface dataset:
Freeway to Memory Level Parallelism in Slice-Out-of-Order Cores

Exploiting memory level parallelism (MLP) is crucial to hide long memory and last level cache access latencies. While out-of-order (OoO) cores, and techniques building on them, are effective at exploiting MLP, they deliver poor energy efficiency due to their complex and energy-hungry hardware. This work revisits slice-out-of-order (sOoO) cores as an energy efficient alternative for MLP exploitation. sOoO cores achieve energy efficiency by constructing and executing \textit{slices} of MLP generating instructions out-of-order only with respect to the rest of instructions; the slices and the remaining instructions, by themselves, execute in-order. However, we observe that existing sOoO cores miss significant MLP opportunities due to their dependence-oblivious in-order slice execution, which causes dependent slices to frequently block MLP generation. To boost MLP generation, we introduce Freeway, a sOoO core based on a new dependence-aware slice execution policy that tracks dependent slices and keeps them from blocking subsequent independent slices and MLP extraction. The proposed core incurs minimal area and power overheads, yet approaches the MLP benefits of fully OoO cores. Our evaluation shows that Freeway delivers 12% better performance than the state-of-the-art sOoO core and is within 7% of the MLP limits of full OoO execution.
Drug repurposing for SARS-COV-2: A molecular docking, molecular dynamics, machine learning, and ab initio study (preprint)/ en

A molecule of size 125nm has caused around 272 million infections and 5.3 million deaths worldwide. Those numbers are 50.4 million and 802,951 in the USA. It is called SARS-CoV-2, which causes a disease known as COVID-19. The high damaging cost of the pandemic had incentivized various private, government, and academic entities to work towards finding a cure for this disease. As an outcome, multiple vaccine candidates are discovered to avoid the infection in the first place. But so far, there has been no success in finding therapeutics candidates. In this paper, we attempted to provide multiple therapy candidates based upon a sophisticated multi-scale in-silico framework, which increases the probability of the candidates surviving an in-vivo trial. We have selected a group of ligands from the ZINC database based upon previously partially successful candidates, i.e., Hydroxychloroquine, Lopinavir, Remdesivir, Ritonavir. We have used the following robust framework to screen the ligands;Step-I: high throughput molecular docking, Step-II: molecular dynamics analysis, Step-III: density functional theory analysis. In total, we have analyzed 242,000(ligands)*9(proteins)= 2.178 million unique protein binding site/ligand combinations. The proteins were selected based on recent studies evaluating potential inhibitor binding sites. Step-I had filtered that number down to 10 ligands/protein-based on molecular docking binding energy, which is further filtered down to 2 ligands/protein based on drug-likeness analysis. Additionally, these two ligands per protein were analyzed in Step-II with a molecular dynamics modeling-based RMSD filter of less than 1Å. It finally suggested three ligands (ZINC001176619532, ZINC000517580540, ZINC000952855827) attacking different binding sites of the same protein(7BV2), which were further analyzed in Step III to find the rationale behind higher ligand stability.
On the exact linearisation and control of flat discrete-time systems

The paper addresses the exact linearization of flat nonlinear discrete-time systems by generalized static or dynamic feedbacks which may also depend on forward-shifts of the new input. We first investigate the question which forward-shifts of a given flat output can be chosen in principle as a new input, and subsequently how to actually introduce the new input by a suitable feedback. With respect to the choice of a feasible input, easily verifiable conditions are derived. Introducing such a new input requires a feedback which may in general depend not only on this new input itself but also on its forward-shifts. This is similar to the continuous-time case, where feedbacks which depend on time derivatives of the closed-loop input - and in particular quasi-static ones - have already been used successfully for the exact linearization of flat systems since the nineties of the last century. For systems with a flat output that does not depend on forward-shifts of the input, it is shown how to systematically construct a new input such that the total number of the corresponding forward-shifts of the flat output is minimal. Furthermore, it is shown that in this case the calculation of a linearizing feedback is particularly simple, and the subsequent design of a discrete-time flatness-based tracking control is discussed. The presented theory is illustrated by the discretized models of a wheeled mobile robot and a 3DOF helicopter.
A Mixed Integer Programming Approach to Training Dense Neural Networks

Artiﬁcial Neural Networks (ANNs) are prevalent machine learning models that are applied across various real world classiﬁcation tasks. However, training ANNs is time-consuming and the resulting models take a lot of memory to deploy. In order to train more parsimonious ANNs, we propose a novel mixed-integer programming (MIP) formulation for training fully-connected ANNs. Our formulations can account for both binary and rectiﬁed linear unit (ReLU) activations, and for the use of a log-likelihood loss. We present numerical experiments comparing our MIP-based methods against existing approaches and show that we are able to achieve competitive out-of-sample performance with more parsimonious models.
Deep Nonparametric Estimation of Operators between Infinite Dimensional Spaces

Learning operators between infinitely dimensional spaces is an important learning task arising in wide applications in machine learning, imaging science, mathematical modeling and simulations, etc. This paper studies the nonparametric estimation of Lipschitz operators using deep neural networks. Non-asymptotic upper bounds are derived for the generalization error of the empirical risk minimizer over a properly chosen network class. Under the assumption that the target operator exhibits a low dimensional structure, our error bounds decay as the training sample size increases, with an attractive fast rate depending on the intrinsic dimension in our estimation. Our assumptions cover most scenarios in real applications and our results give rise to fast rates by exploiting low dimensional structures of data in operator estimation. We also investigate the influence of network structures (e.g., network width, depth, and sparsity) on the generalization error of the neural network estimator and propose a general suggestion on the choice of network structures to maximize the learning efficiency quantitatively.
Convergence of a class of nonmonotone descent methods for KL optimization problems

This paper is concerned with a class of nonmonotone descent methods for minimizing a proper lower semicontinuous KL function $\Phi$, which generates a sequence satisfying a nonmonotone decrease condition and a relative error tolerance. Under suitable assumptions, we prove that the whole sequence converges to a limiting critical point of $\Phi$ and, when $\Phi$ is a KL function of exponent $\theta\in[0,1)$, the convergence admits a linear rate if $\theta\in[0,1/2]$ and a sublinear rate associated to $\theta$ if $\theta\in(1/2,1)$. The required assumptions are shown to be sufficient and necessary if $\Phi$ is also weakly convex on a neighborhood of stationary point set. Our results resolve the convergence problem on the iterate sequence generated by a class of nonmonotone line search algorithms for nonconvex and nonsmooth problems, and also extend the convergence results of monotone descent methods for KL optimization problems. As the applications, we achieve the convergence of the iterate sequence for the nonmonotone line search proximal gradient method with extrapolation and the nonmonotone line search proximal alternating minimization method with extrapolation. Numerical experiments are conducted for zero-norm and column $\ell_{2,0}$-norm regularized problems to validate their efficiency.
Formal Verification of Unknown Dynamical Systems via Gaussian Process Regression

Leveraging autonomous systems in safety-critical scenarios requires verifying their behaviors in the presence of uncertainties and black-box components that influence the system dynamics. In this article, we develop a framework for verifying partially-observable, discrete-time dynamical systems with unmodelled dynamics against temporal logic specifications from a given input-output dataset. The verification framework employs Gaussian process (GP) regression to learn the unknown dynamics from the dataset and abstract the continuous-space system as a finite-state, uncertain Markov decision process (MDP). This abstraction relies on space discretization and transition probability intervals that capture the uncertainty due to the error in GP regression by using reproducible kernel Hilbert space analysis as well as the uncertainty induced by discretization. The framework utilizes existing model checking tools for verification of the uncertain MDP abstraction against a given temporal logic specification. We establish the correctness of extending the verification results on the abstraction to the underlying partially-observable system. We show that the computational complexity of the framework is polynomial in the size of the dataset and discrete abstraction. The complexity analysis illustrates a trade-off between the quality of the verification results and the computational burden to handle larger datasets and finer abstractions. Finally, we demonstrate the efficacy of our learning and verification framework on several case studies with linear, nonlinear, and switched dynamical systems.
Learning Free Gait Transition for Quadruped Robots Via Phase-Guided Controller

Gaits and transitions are key components in legged locomotion. For legged robots, describing and reproducing gaits as well as transitions remain longstanding challenges. Reinforcement learning has become a powerful tool to formulate controllers for legged robots. Learning multiple gaits and transitions, nevertheless, is related to the multi-task learning problems. In this work, we present a novel framework for training a simple control policy for a quadruped robot to locomote in various gaits. Four independent phases are used as the interface between the gait generator and the control policy, which characterizes the movement of four feet. Guided by the phases, the quadruped robot is able to locomote according to the generated gaits, such as walk, trot, pacing and bounding, and to make transitions among those gaits. More general phases can be used to generate complex gaits, such as mixed rhythmic dancing. With the control policy, the Black Panther robot, a medium-dog-sized quadruped robot, can perform all learned motor skills while following the velocity commands smoothly and robustly in natural environment.
Transposed BiHom-Poisson algebras

Abstract In this paper, we introduce the concept of transposed BiHom-Poisson (abbr. TBP) algebras which can be constructed by BiHom-Novikov-Poisson algebras. Several useful identities for TBP algebras are provided. We also prove that the tensor products of two (T)BP algebras are closed. The notions of BP 3-Lie algebras and TBP 3-Lie algebras are presented and TBP algebras can induce TBP 3-Lie algebras by two approaches. Finally, we give some examples for the TBP algebras of dimension 2.
Echo chambers in the Ising model and implications on the mean magnetization

The echo-chamber effect is a common term in opinion dynamic modeling to describe how a person’s opinion might be artificially enhanced as it is reflected back at her through social interactions. Here, we study the existence of this effect in statistical mechanics models, which are commonly used to study opinion dynamics. We show that the Ising model does not exhibit echo-chambers, but this result is a consequence of a special symmetry. We then distinguish between three types of models: (i) those with a strong echo-chamber symmetry, that have no echo-chambers at all; (ii) those with a weak echo-chamber symmetry that can exhibit echo-chambers but only if there are external fields in the system, and (iii) models without echo-chamber symmetry that generically have echo-chambers. We use these results to construct an efficient algorithm to efficiently and precisely calculate magnetization in arbitrary tree networks. Finally, we apply this algorithm to study two systems: phase transitions in the random field Ising model on a Bethe lattice and the influence optimization problem in social networks.
Online Regularization towards Always-Valid High-Dimensional Dynamic Pricing

Devising dynamic pricing policy with always valid online statistical learning procedure is an important and as yet unresolved problem. Most existing dynamic pricing policy, which focus on the faithfulness of adopted customer choice models, exhibit a limited capability for adapting the online uncertainty of learned statistical model during pricing process. In this paper, we propose a novel approach for designing dynamic pricing policy based regularized online statistical learning with theoretical guarantees. The new approach overcomes the challenge of continuous monitoring of online Lasso procedure and possesses several appealing properties. In particular, we make the decisive observation that the always-validity of pricing decisions builds and thrives on the online regularization scheme. Our proposed online regularization scheme equips the proposed optimistic online regularized maximum likelihood pricing (OORMLP) pricing policy with three major advantages: encode market noise knowledge into pricing process optimism; empower online statistical learning with always-validity over all decision points; envelop prediction error process with time-uniform non-asymptotic oracle inequalities. This type of non-asymptotic inference results allows us to design more sample-efficient and robust dynamic pricing algorithms in practice. In theory, the proposed OORMLP algorithm exploits the sparsity structure of high-dimensional models and secures a logarithmic regret in a decision horizon. These theoretical advances are made possible by proposing an optimistic online Lasso procedure that resolves dynamic pricing problems at the process level, based on a novel use of non-asymptotic martingale concentration. In experiments, we evaluate OORMLP in different synthetic and real pricing problem settings, and demonstrate that OORMLP advances the state-of-the-art methods.
Long non-coding RNA HOXA11 antisense RNA upregulates spermatogenesis-associated serine-rich 2-like to enhance cisplatin resistance in laryngeal squamous cell carcinoma by suppressing microRNA-518a

ABSTRACT Long noncoding RNAs (LncRNAs) are closely associated with the chemoresistance of laryngeal squamous cell carcinoma (LSCC). Previous studies indicated that HOXA11-AS could function as a vital regulator in human cancers. However, the regulatory mechanisms of HOXA11-AS in the chemoresistance of LSCC remain unclear. In this study, it was found that HOXA11-AS expression was upregulated in cisplatin (CDDP)-resistant LSCC tissues and cells. Loss-of-function assays revealed that HOXA11-AS knockdown inhibited the viability, migration, and invasion, but promoted the apoptosis of CDDP-resistant LSCC cells. Meanwhile, we identified miR-518a as a downstream gene of HOXA11-AS in LSCC, and miR-518a silencing reversed the promotive effect of HOXA11-AS knockdown on CDDP sensitivity of LSCC cells. In addition, miR-518a could inhibit spermatogenesis-associated serine-rich 2-like (SPATS2L) expression by direct interaction, and upregulation of SPATS2L abrogated the inhibitory effect of HOXA11-AS silencing or miR-518a overexpression on CDDP resistance of CDDP-resistant LSCC cells. In sum, our results demonstrated that HOXA11-AS enhanced CDDP resistance of LSCC via miR-518a/SPATS2L axis, which might offer novel therapeutic strategies for CDDP-resistant LSCC.
An Evaluation of the Factors Affecting Failure of the Procedure in Reoperated Thyroidectomies: A Retrospective Analysis

Background The aim of this study was to determine the factors affecting procedure failure in revision thyroidectomy surgery. Methods A total of 148 patients applied with revision surgery were separated into 2 groups according to the surgical success status. Comparisons were made of the 2 groups of patients where residual tissue was totally excised (Group 1, n:132) and patients where residual tissue could not be completely excised (Group 2, n:16). The patients were examined in respect of factors affecting the success of the procedure. Results The patients comprised 133 (89.9%) females and 15 (10.1%) males with a mean age of 49.68±12.02 years. Surgical failure was observed in 7 patients as the lesion could not be determined despite the use of intraoperative USG, and in 9 patients because of weak signal or signal loss. The determination of residual tissue ≤25mm on preoperative USG examination was seen to have a significant negative effect on surgical success (r=-0.329, p0.001). The patient having undergone ≥3 previous operations was determined to have a negative effect on surgical success (r=-0.229, p=0.005), and nerve damage on the opposite side to the lesion in a previous surgical procedure was determined to be the most important factor with a negative effect on surgical success (r=-0.571, p<0.001). In multinomial logistic regression analysis of the factors affecting success, the preoperative presence of nerve damage in the contralateral lobe to the lesion (OR: 33.11, 95% CI: 4.22-192.28, p<0.001) and lesion size ≤25 mm (OR: 10.10, 95% CI: 3.54-75.01, p=0.001) were determined to contribute significantly to surgical failure. Conclusion The results of this study clearly showed that as residual tissue size ≤25mm and contralateral nerve damage in the preoperative ultrasonographic evaluation are associated with surgical failure, alternative treatment methods such as radioactive iodine ablation may be preferred in these patients.
A robust, accurate, sensitive LC–MS/MS method to measure indoxyl sulfate, validated for plasma and kidney cells

Abstract Proximal tubular damage is an important prognostic determinant in various chronic kidney diseases (CKDs). Currently available diagnostic methods do not allow for early disease detection and are neither efficient. Indoxyl sulfate (IS) is an endogenous metabolite and protein‐bound uremic toxin that is eliminated via renal secretion, but accumulates in plasma during tubular dysfunction. Therefore, it may be suitable as a tubular function marker. To evaluate this, a fast bioanalytical method was developed and validated for IS in various species and a kidney cell line using LC–MS/MS. An isotope‐labeled IS potassium salt as an internal standard and acetonitrile (ACN) as a protein precipitant were used for sample pretreatment. The analyte was separated on a Polaris 3 C18‐A column by gradient elution using 0.1% formic acid in water and ACN, and detected by negative electrospray ionization in selected reaction monitoring mode. The within‐day (≤ 4.0%) and between‐day (≤ 4.3%) precisions and accuracies (97.7 to 107.3%) were within the acceptable range. The analyte showed sufficient stability at all conditions investigated. Finally, applying this assay, significantly higher plasma and lower urine concentrations of IS were observed in mice with diabetic nephropathy with tubular damage, which encourages validation toward its use as a biomarker.
Astrocyte‐targeting RNA interference against mutated superoxide dismutase 1 induces motoneuron plasticity and protects fast‐fatigable motor units in a mouse model of amyotrophic lateral sclerosis

In amyotrophic lateral sclerosis (ALS) caused by SOD1 gene mutations, both cell‐autonomous and noncell‐autonomous mechanisms lead to the selective degeneration of motoneurons (MN). Here, we evaluate the therapeutic potential of gene therapy targeting mutated SOD1 in mature astrocytes using mice expressing the mutated SOD1G93A protein. An AAV‐gfaABC1D vector encoding an artificial microRNA is used to deliver RNA interference against mutated SOD1 selectively in astrocytes. The treatment leads to the progressive rescue of neuromuscular junction occupancy, to the recovery of the compound muscle action potential in the gastrocnemius muscle, and significantly improves neuromuscular function. In the spinal cord, gene therapy targeting astrocytes protects a small pool of the most vulnerable fast‐fatigable MN until disease end stage. In the gastrocnemius muscle of the treated SOD1G93A mice, the fast‐twitch type IIB muscle fibers are preserved from atrophy. Axon collateral sprouting is observed together with muscle fiber type grouping indicative of denervation/reinnervation events. The transcriptome profiling of spinal cord MN shows changes in the expression levels of factors regulating the dynamics of microtubules. Gene therapy delivering RNA interference against mutated SOD1 in astrocytes protects fast‐fatigable motor units and thereby improves neuromuscular function in ALS mice.
Influence of maternal attitudes and parenting style on children`s dental caries experience.

OBJECTIVE To evaluate the effect parental attitudes have on the dental caries experience of children. METHODS The cross-sectional observational study was conducted at Yeditepe University, Istanbul, Turkey, from March to September 2019, and comprised mothers of children who came to the Department of Paediatric Dentistry for treatment of children. Multi-variable poisson regression model was run to predict prevalence of dental caries. Mothers' attitudes in family were measured using the parental attitude research instrument scores. Data was analysed using Stata 15. RESULTS There were 258 mothers of as many children aged 6-15 years. Among the children, 150(58%) were boys and 108(42%) were girls. 'Rejection of traditional housewife role' and 'level of discipline' had a statistically negative correlation (p<0.05), while 'level of marital conflict' had a statistically positive correlation with the prevalence of caries. CONCLUSIONS Healthy family environment and empowering parental style had a positive effect on the oral health of children.