Miracle-dz/UniFAR-FTUs huggingface dataset:
Long strings and light scalar fields in the quark-gluon plasma

We review the physics of hot strings close to the Hagedorn temperature and their bearing on the deconfinement phase transition. We stress the importance of light scalar fields describing winding modes and their connection to the infrared degrees of freedom living on the stretched horizon. We comment on their relevance to holographic descriptions of the quark-gluon plasma and their relation with winding modes discovered on the lattice.
Reproducible fabrication and characterization of diamond membranes for photonic crystal cavities

Single crystal diamond has turned out to be a promising material for applications in quantum information processing for its ability to host color centers with optimal properties such as narrow emission lines or long‐lived spin states. However, the mechanical properties of diamond like its hardness and chemical resistance make the fabrication of photonic devices challenging up to date. In this article, a method is presented to produce thin, free‐standing single crystal diamond membranes. These membranes are characterized with four different techniques complementing one another and detecting defects or thickness variations in the diamond films with high accuracy. The precise characterization allows for reproducible fabrication of two‐dimensional photonic crystal cavities by focused ion beam milling in the as‐produced membranes. Furthermore, optimizations in focused ion beam milling are presented, reducing imperfections of the fabricated photonic crystal cavities. Together with post‐processing and implantation of color centers, these diamond nanostructures are a building block for cavity coupling experiments toward reliable spin–photon interfaces or optomechanical devices.
The effectiveness of computerized cognitive behavioural therapy in routine care.

OBJECTIVES AND DESIGN The efficacy of a Computerized Cognitive Behavioural Therapy (CCBT) package, Beating the Blues, has been demonstrated in a large randomized controlled trial. The current study tests the generalizability of this finding in a naturalistic non-randomized trial. METHOD 219 patients with anxiety and/or depression were recruited to receive CCBT in routine care. The Clinical Outcomes in Routine Evaluation-Outcome Measure (CORE-OM) and Work and Social Adjustment scale (WSA) were administered pre-treatment, immediately on completing treatment and at 6 months post-treatment. Single-item self-report measures of anxiety and depression were also collected during each treatment session. RESULTS Completer and intention-to-treat analysis demonstrated statistically and clinically significant improvements on the CORE-OM, WSA and in self-reported anxiety and depression. Intention-to-treat analysis indicated an average 0.29-point drop on the CORE-OM, equating to an uncontrolled pre-post effect size of 0.50. Research completers achieved an average 0.61-point drop equating to an uncontrolled pre-post size of 1.00 on the same measure. Where data was available (18%), these benefits were maintained at week 32 (6 months follow-up). CONCLUSION CCBT can be an effective first line tool within a stepped care framework for the management of common mental health problems.
Degrees of Freedom Region of the (M, N₁, N₂) MIMO Broadcast Channel With Partial CSIT: An Application of Sum-Set Inequalities Based on Aligned Image Sets

The degrees of freedom (DoF) region is characterized for the 2-user multiple input multiple output (MIMO) broadcast channel (BC), where the transmitter is equipped with <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> antennas, the two receivers are equipped with <inline-formula> <tex-math notation="LaTeX">$N_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$N_{2}$ </tex-math></inline-formula> antennas, and the levels of channel state information at the transmitter (CSIT) for the two users are parameterized by <inline-formula> <tex-math notation="LaTeX">$\beta _{1}, \beta _{2}$ </tex-math></inline-formula>, respectively. The achievability of the DoF region was established by Hao, Rassouli and Clerckx, but no proof of optimality was heretofore available. The proof of optimality is provided in this work with the aid of sum-set inequalities based on the aligned image sets (AIS) approach.
A STOPPING CRITERION FOR ITERATIVE SOLUTION OF STOCHASTIC GALERKIN MATRIX EQUATIONS

In this paper we consider generalized polynomial chaos (gPC) based stochastic Galerkin approximations of linear random algebraic equations where the coefficient matrix and the right-hand side are parametrized in terms of a finite number of i.i. d random variables. We show that the standard stopping criterion used in Krylov methods for solving the stochastic Galerkin matrix equations resulting from gPC projection schemes leads to a substantial number of unnecessary and computationally expensive iterations which do not improve the solution accuracy. This trend is demonstrated by means of detailed numerical studies on symmetric and nonsymmetric linear random algebraic equations. We present some theoretical analysis for the special case of linear random algebraic equations with a symmetric positive definite coefficient matrix to gain more detailed insight into this behavior. Finally, we propose a new stopping criterion for iterative Krylov solvers to avoid unnecessary iterations while solving stochastic Galerkin matrix equations. Our numerical studies suggest that the proposed stopping criterion can provide up to a threefold reduction in the computational cost.
Neural, Hormonal and Experiential Control of Sex-Typical Expression of Social Behavior

Expression of social behaviors is regulated by various neuroendocrine and neurochemical factors. Among them, estradiol is known to have a profound influence on female sexual behavior as well as various types of social interactive behaviors, through its binding to two types of estrogen receptors, ER or ER . Since male gonadal hormone, testosterone, is aromatized to estradiol in neuronal cells in the brain, ERs are also essential for the regulation of male-type social behavior and the development of their neural network. In this article, we discuss how each type of ER plays a role in the expression of sex-typical social behavior in males and females by focusing on both organizational and activational action of estradiol. For this purpose we overview behavioral and neuroanatomical studies reported in knockout as well as brain site-specific knockdown models of ER genes. We also discuss how early life experiences may affect subsequent expression of social and socio-emotional behavior.
Identification of potential drug targets for tuberous sclerosis complex by synthetic screens combining CRISPR-based knockouts with RNAi

Combining targeted CRISPR-mediated gene editing with an RNAi-mediated screen identifies candidate drug targets. Double screening for drug targets The tumor suppressors TSC1 and TSC2 form a complex that limits the activity of mTORC1, a multiprotein complex that couples nutrient availability to cell proliferation and growth. Individuals with mutations in either of the TSC-encoding genes develop tumors in various organs. Housden et al. used CRISPR to generate Drosophila cell lines that lacked either TSC1 or TSC2 and performed RNAi screens directed against kinase- and phosphatase-encoding genes in these cell lines to identify those genes that limited the growth of cells lacking either TSC1 or TSC2. Candidate genes that, when knocked down, suppressed the growth of both of the TSC1- and TSC2-deficient Drosophila cell lines (but not that of normal cells) were validated in TSC2-deficient human cells. Because these candidates are evolutionarily conserved, they are more likely to be bona fide drug targets, and this combination of techniques and multispecies screening could be used to identify drug targets for other diseases. The tuberous sclerosis complex (TSC) family of tumor suppressors, TSC1 and TSC2, function together in an evolutionarily conserved protein complex that is a point of convergence for major cell signaling pathways that regulate mTOR complex 1 (mTORC1). Mutation or aberrant inhibition of the TSC complex is common in various human tumor syndromes and cancers. The discovery of novel therapeutic strategies to selectively target cells with functional loss of this complex is therefore of clinical relevance to patients with nonmalignant TSC and those with sporadic cancers. We developed a CRISPR-based method to generate homogeneous mutant Drosophila cell lines. By combining TSC1 or TSC2 mutant cell lines with RNAi screens against all kinases and phosphatases, we identified synthetic interactions with TSC1 and TSC2. Individual knockdown of three candidate genes (mRNA-cap, Pitslre, and CycT; orthologs of RNGTT, CDK11, and CCNT1 in humans) reduced the population growth rate of Drosophila cells lacking either TSC1 or TSC2 but not that of wild-type cells. Moreover, individual knockdown of these three genes had similar growth-inhibiting effects in mammalian TSC2-deficient cell lines, including human tumor-derived cells, illustrating the power of this cross-species screening strategy to identify potential drug targets.
Convolutional Neural Networks on 3D Surfaces Using Parallel Frames

We extend Convolutional Neural Networks (CNNs) on flat and regular domains (e.g. 2D images) to curved surfaces embedded in 3D Euclidean space that are discretized as irregular meshes and widely used to represent geometric data in Computer Vision and Graphics. We define surface convolution on tangent spaces of a surface domain, where the convolution has two desirable properties: 1) the distortion of surface domain signals is locally minimal when being projected to the tangent space, and 2) the translation equi-variance property holds locally, by aligning tangent spaces with the canonical parallel transport that preserves metric. For computation, we rely on a parallel N-direction frame field on the surface that minimizes field variation and therefore is as compatible as possible to and approximates the parallel transport. On the tangent spaces equipped with parallel frames, the computation of surface convolution becomes standard routine. The frames have rotational symmetry which we disambiguate by constructing the covering space of surface induced by the parallel frames and grouping the feature maps into N sets accordingly; convolution is computed on the N branches of the cover space with respective feature maps while the kernel weights are shared. To handle irregular points of a discrete mesh while sharing kernel weights, we make the convolution semi-discrete, i.e. the convolution kernels are polynomial functions, and their convolution with discrete surface points becomes sampling and weighted summation. Pooling and unpooling operations are computed along a mesh hierarchy built through simplification. The presented surface CNNs allow effective deep learning on meshes. We show that for tasks of classification, segmentation and non-rigid registration, surface CNNs using only raw input signals achieve superior performances than previous models using sophisticated input features.
CSI-Net: Unified Human Body Characterization and Action Recognition

We build CSI-Net, a unified Deep Neural Network~(DNN), to learn the representation of WiFi signals. Using CSI-Net, we jointly solved two body characterization problems: biometrics estimation (including body fat, muscle, water, and bone rates) and person recognition. We also demonstrated the application of CSI-Net on two distinctive pose recognition tasks: the hand sign recognition (fine-scaled action of the hand) and falling detection (coarse-scaled motion of the body).
An improved self-calibration algorithm for multilateration coordinates measuring system

It is difficult for the traditional self-calibration algorithm to achieve high precision calibration when measuring over a wide range. To overcome this shortcoming, an improved self-calibration algorithm for multilateration coordinates measuring system is proposed. Different from the traditional self-calibration algorithm, the improved algorithm preselect certain calibration points which is fixedly mounted on stable base, the lengths between specific points are measured precisely by laser interference method. Then the precise lengths are taken as a part of optimization objective function, and then the system parameters are determined by iterative computation. The effectiveness of the proposed algorithm is verified by simulations and experiments. The results show that by using the improved algorithm, the max error is only 8. 9μm, the improved algorithm helps to improve the accuracy of the multilateration system significantly.
Inductive learning of answer set programs

The goal of Inductive Logic Programming (ILP) is to find a hypothesis that explains a set of examples in the context of some pre-existing background knowledge. Until recently, most research on ILP targeted learning definite logic programs. This thesis constitutes the first comprehensive work on learning answer set programs, introducing new learning frameworks, theoretical results on the complexity and generality of these frameworks, algorithms for learning ASP programs, and an extensive evaluation of these algorithms. Although there is previous work on learning ASP programs, existing learning frameworks are either brave – where examples should be explained by at least one answer set – or cautious where examples should be explained by all answer sets. There are cases where brave induction is too weak and cautious induction is too strong. Our proposed frameworks combine brave and cautious learning and can learn ASP programs containing choice rules and constraints. Many applications of ASP use weak constraints to express a preference ordering over the answer sets of a program. Learning weak constraints corresponds to preference learning, which we achieve by introducing ordering examples. We then explore the generality of our frameworks, investigating what it means for a framework to be general enough to distinguish one hypothesis from another. We show that our frameworks are more general than both brave and cautious induction. We also present a new family of algorithms, called ILASP (Inductive Learning of Answer Set Programs), which we prove to be sound and complete. This work concerns learning from both non-noisy and noisy examples. In the latter case, ILASP returns a hypothesis that maximises the coverage of examples while minimising the length of the hypothesis. In our evaluation, we show that ILASP scales to tasks with large numbers of examples finding accurate hypotheses even in the presence of high proportions of noisy examples.
Pressure induced superconductivity in MnSe

The rich phenomena in the FeSe and related compounds have attracted great interests as it provides fertile material to gain further insight into the mechanism of high temperature superconductivity. A natural follow-up work was to look into the possibility of superconductivity in MnSe. It was shown that MnP becomes superconducting with Tc ~ 1 K under pressure. We demonstrated in this work that high pressure can effectively suppress the complex magnetic characters of MnSe crystal when observed at ambient condition. MnSe under pressure is found to undergo several structural transformations: the cubic phase first partially transforms to the hexagonal phase at about 12 GPa, the crystal exhibits the coexistence of cubic, hexagonal and orthorhombic phases from 16 GPa to 30 GPa, and above 30 GPa the crystal shows a single orthorhombic phase. Superconductivity with Tc ~ 5 K was first observed at pressure ~12 GPa by magnetic measurements (~16 GPa by resistive measurements). The highest Tc is ~ 9 K (magnetic result) at ~35 GPa. Our observations suggest the observed superconductivity may closely relate to the pressure-induced structural change. However, the interface between the metallic and insulating boundaries may also play an important role to the pressure induced superconductivity in MnSe.
End-Task Oriented Textual Entailment via Deep Exploring Inter-Sentence Interactions

This work deals with SCITAIL, a natural entailment challenge derived from a multichoice question answering problem. The premises and hypotheses in SCITAIL are generated with no awareness of each other, and not specifically aimed to the entailment task. This makes it more challenging than other entailment tasks and more directly useful to the end-task – question answering. We propose DEISTE (deep exploring inter-sentence interactions for textual entailment) for this entailment task. Given word-to-word interactions between the premise-hypothesis pair (P ,H), DEISTE consists of: (i) A parameterdynamic convolution to make important words in P and H play a dominant role in learnt representations; (ii) A positionaware attentive convolution to encode the representation and position information of the aligned word pairs. Experiments show (i) DEISTE gets ∼ 5% improvement over prior state of the art, (ii) the pre-trained DEISTE on SCITAIL generalizes well on RTE-5.1
Modeling of stable and unstable polarization switching

We examine the stability of polarization switching in polycrystalline ferroelectric/ferroelastic materials subject to continuous electromechanical loading. In case of unstable switching, a finite change of remanent polarization or strain result from an infinitesimally small increment of the applied load. A micromechanical switching model is studied as well as a simple macroscopic boundary value problem in combination with a phenomenological switching law. The micromechanical model of a ferroelastic layered composite is based on an energy switching criterion. Stable response is promoted by (i) homogeneity of mechanical stress, (ii) proximity of local load conditions to mechanical strain control. For the macroscopic boundary value problem of a ferroelectric ring, switching stability depends on the applied load conditions as follows: Charge control result in stable response; voltage control may initiate unstable switching. For voltage control, the mathematical instability disappears if a small amount of ferroelectric 'hardening' is postulated. Nevertheless, an enhanced switching activity is predicted near the former instability.
Taboo Search: An Approach to the Multiple Minima Problem

Described here is a method, based on Glover's taboo search for discrete functions, of solving the multiple minima problem for continuous functions. As demonstrated by model calculations, the algorithm avoids entrapment in local minima and continues the search to give a near-optimal final solution. Unlike other methods of global optimization, this procedure is generally applicable, easy to implement, derivative-free, and conceptually simple.
GNAS : A Greedy Neural Architecture Search Method for Multi-A ribute Learning

A key problem in deep multi-aribute learning is to eectively discover the inter-aribute correlation structures. Typically, the conventional deep multi-aribute learning approaches follow the pipeline of manually designing the network architectures based on task-specic expertise prior knowledge and careful network tunings, leading to the inexibility for various complicated scenarios in practice. Motivated by addressing this problem, we propose an ecient greedy neural architecture search approach (GNAS) to automatically discover the optimal tree-like deep architecture for multi-aribute learning. In a greedy manner, GNAS divides the optimization of global architecture into the optimizations of individual connections step by step. By iteratively updating the local architectures, the global tree-like architecture gets converged where the boom layers are shared across relevant aributes and the branches in top layers more encode aribute-specic features. Experiments on three benchmark multi-aribute datasets show the eectiveness and compactness of neural architectures derived by GNAS, and also demonstrate the eciency of GNAS in searching neural architectures.
Current Applications of Organ-on-a-Chip: A Step Closer to Personalized Medicine

In the pharmaceutical industry, a critical need exists for effective drug development approaches that better account for factors imposed by the physiological microenvironment. Organ-on-a-chip (OOAC)—a revo- lutionary technology that simulates human organs’ physiological milieu and performance on a chip—has applications in curing illnesses and drug screening, and enormous potential to transform the drug discovery workflow. However, the effective integration of this unique engineering system into ordinary pharmacolog-ical and medical contexts remains in development. This Editorial summarizes current research on OOAC systems, and offers insight into future development prospects and the need for a next-generation OOAC framework.
A Tour of Constrained Tensor Canonical Polyadic Decomposition

This paper surveys the use of constraints in tensor decomposition models. Constrained tensor decompositions have been extensively applied to chemometrics and array processing, but there is a growing interest in understanding these methods independently of the application of interest. We suggest a formalism that unifies various instances of constrained tensor decomposition, while shedding light on some possible extensions of existing methods.
Bayesian nonparametrics, convergence and limiting shape of posterior distributions

This manuscript presents a synthesis of my research work over the last few years. It discusses my contributions to the study of the Bayes posterior distribution in statistical models with many or infinitely many parameters, such as nonparametric and semiparametric models. We follow a three-part outline. The first two chapters are a synthesis of the obtained results on convergence rates of posterior distributions. The third chapter considers limiting shape results. The Bernstein-von Mises theorem is established in infinite dimensions, in both semiparametric and nonparametric frameworks.
An Efficient Approximation Algorithm for Minimizing Makespan on Uniformly Related Machines

We give a new and efficient approximation algorithm for scheduling precedence-constrained jobs on machines with different speeds. The problem is as follows. We are given n jobs to be scheduled on a set of m machines. Jobs have processing times and machines have speeds. It takes pj/si units of time for machine i with speed si to process job j with processing requirement pj. Precedence constraints between jobs are given in the form of a partial order. If j? k, processing of job k cannot start until job j's execution is completed. The objective is to find a non-preemptive schedule to minimize the makespan of the schedule. Chudak and Shmoys (1997, “Proceedings of the Eighth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA),” pp. 581? 590) gave an algorithm with an approximation ratio of O(log m), significantly improving the earlier ratio of O(m) due to Jaffe (1980, Theoret. Comput. Sci.26, 1? 17). Their algorithm is based on solving a linear programming relaxation. Building on some of their ideas, we present a combinatorial algorithm that achieves a similar approximation ratio but runs in O(n3) time. Our algorithm is based on a new and simple lower bound which we believe is of independent interest.