Miracle-dz/UniFAR-FTUs huggingface dataset:
TILING DIRECTED GRAPHS WITH TOURNAMENTS

The Hajnal–Szemerédi theorem states that for any positive integer $r$ and any multiple $n$ of $r$ , if $G$ is a graph on $n$ vertices and $\unicode[STIX]{x1D6FF}(G)\geqslant (1-1/r)n$ , then $G$ can be partitioned into $n/r$ vertex-disjoint copies of the complete graph on $r$ vertices. We prove a very general analogue of this result for directed graphs: for any positive integer $r$ with $r\neq 3$ and any sufficiently large multiple $n$ of $r$ , if $G$ is a directed graph on $n$ vertices and every vertex is incident to at least $2(1-1/r)n-1$ directed edges, then $G$ can be partitioned into $n/r$ vertex-disjoint subgraphs of size $r$ each of which contain every tournament on $r$ vertices (the case $r=3$ is different and was handled previously). In fact, this result is a consequence of a tiling result for standard multigraphs (that is multigraphs where there are at most two edges between any pair of vertices). A related Turán-type result is also proven.
A hybrid method for solving systems of operator inclusion problems

In this paper, we propose an algorithm combining the forward-backward splitting method and the alternative projection method for solving the system of splitting inclusion problem. We want to find a point in the interception of a finite number of sets that we don't know, the solution of each component of the system. The algorithm consists of approximate the sets involved in the problem by separates halfspaces which are a known strategy. By finding these halfspaces in each iteration we use only one inclusion problem of the system. The iterations consist of two parts, the first contains an explicit Armijo-type search in the spirit of the extragradient-like methods for variational inequalities. In the iterative process, the operator forward-backward is computed only one time for each inclusion problem, this represents a great computational saving because the computational cost of this operator is nothing cheap. The second part consists of special projection step, projecting in the separating halfspace. The convergence analysis of the proposed scheme is given assuming monotonicity all operators, without any Lipschitz continuity assumption.
A Class of Solvable Stationary Singular Stochastic Control Problems

We consider the determination of the optimal stationary singular stochastic control of a linear diffusion for a class of average cumulative cost minimization problems arising in various financial and economic applications of stochastic control theory. We present a set of conditions under which the optimal policy is of the standard local time reflection type and state the first order conditions from which the boundaries can be determined. Since the conditions do not require symmetry or convexity of the costs, our results cover also the cases where costs are asymmetric and non-convex. We also investigate the comparative static properties of the optimal policy and delineate circumstances under which higher volatility expands the continuation region where utilizing the control is suboptimal.
Pumping a photoionization atomic inner-shell x-ray laser by x-ray free-electron laser radiation

Focusing an x-ray free electron laser (XFEL) pulse into a gas target, a plasma of transiently core excited ions can be created within a few fs, building a pathway to an inner-shell keV x-ray laser. Varying the XFEL parameters, a wide variety of pulse structures can be created with comparable peak-intensities to XFELs: isolated pulses of subfs duration, trains of pulses with increased temporal coherence, and trains of fs pulses of different wavelengths. We present self-consistent gain and amplification calculations, tailored to predict first experiments on lasing in neon pumped by the Linac Coherent Light Source at Stanford.
Random effects and variances as a synthesis of nonlinear regression analyses of mitochondrial electron transport

SUMMARY This cross-disciplinary paper develops random effects and variances methods, especially for small samples within effects, and applies them to mitochondrial enzyme kinetics. A new force flux kinetic model is introduced for the alternative oxidase of plant mitochondria, the results of 42 experiments are published and the force flux function is fitted by nonlinear regression for each experiment. The results of the regression analyses are synthesized by a random effects model specifying random nonlinear regression coefficients and random variances within experiments distributed in an inverse gamma distribution. The paper shows how random nonlinear regression coefficients analysis can summarize the results of a long series of similar biological experiments and sharpen the analysis of the results of each. A large data set often consists of a family of components each of which can be fitted with the same model. There is a within-component variation for each compo- nent and random effects variation between components. The interpretation of the data requires not only regression analysis of each component but also a subsequent synthesis of the results of such analysis into an overall summary, achieved by specifying random regression coefficients and random error variances. In the example which follows, the individual samples are so small that random variances are specified and contracted predictors of them are computed which are more accurate than the individual estimates. They are required for weights in the analysis. The residual maximum likelihood estimates of the random effects variance matrices are sometimes singular. A Lagrange multiplier method is introduced to compute them. The strength of the integrated analysis comes, however, at the price of a complex set of decisions as to how the different components of the computation are to be fitted together. The paper shows how this is done by a flow diagram for the computations, as well as describing each individual component.
Lorentz microscopy of micron‐sized laser‐written magnetic domains in TbFe

High resolution images of laser‐written magnetic domains in thin sputtered films of terbium iron have been obtained using Lorentz microscopy. The domains, written with a GaAs diode laser, are generally circular in shape with diameters in the range 0.9–2.2 μm. Conditions necessary for the observation of highly detailed images are described. It was found that with certain values of laser power and applied magnetic field, smaller irregular domains appear within the principal domains.
The effect of temporary workers and works councils on process innovation

PurposeThis study aims to investigate the effects of temporary workers and works councils on process innovations at manufacturing sites. The impact of temporary workers, commonly viewed as a means of operational flexibility and cost savings, on firms’ ability to innovate is underexplored. Works councils represent and help integrate temporary workers, but are often equated with unions, which have been criticized as barriers to innovation, especially in the US.Design/methodology/approachThe authors use secondary data collected by the Institute for Employment Research (IAB) of the German Federal Employment Agency. Specifically, the authors conduct a series of regression analyses using 11-year panel data covering the period 2009–2019 with 11,641 manufacturing site-year observations.FindingsThe results suggest that the use of temporary workers initially promotes process innovation, but at too high a level, it impairs firms’ ability to innovate. Furthermore, the results suggest that works councils have a positive impact on innovation and dampen the curvilinear effect found with respect to temporary workers.Originality/valueResearch has largely focused on the cost and flexibility benefits of temporary workers. The authors analyze the effectiveness of temporary workers in terms of innovativeness. By including works councils, the study also consider the contextual environment in which temporary workers are employed. Finally, the results reject the assumption that works councils have a similar negative impact as unions on innovation; in fact, the authors find the opposite.
Low-complexity colour-space for capsule endoscopy image compression

A novel and low-complexity colour-space for the compression of wireless capsule endoscopy (WCE) images is presented. The colour-space is developed by analysing the unique features of endoscopic images. Compared with existing standard colour-spaces, the proposed colour-space shows better performance for WCE image compression.
Endothermic and exothermic chemically reacting plumes

We develop a model for a turbulent plume in an unbounded ambient that takes into account a general exothermic or endothermic chemical reaction. These reactions can have an important effect on the plume dynamics since the entrainment rate, which scales with the vertical velocity, will be a function of the heat release or absorption. Specifically, we examine a second-order non-reversible reaction, where one species is present in the plume from a pure source and the other is in the environment. For uniform ambient density and species fields the reaction has an important effect on the deviation from pure plume behaviour as defined by the source parameter Γ. In the case of an exothermic reaction the density difference between the plume and the reference density increases and the plume is ‘lazy’, whereas for an endothermic reaction this difference decreases and the plume is more jet-like. Furthermore, for chemical and density-stratified environments, the reaction will have an important effect on the buoyancy flux because the entrainment rate will not necessarily decrease with distance from the source, as in traditional models. As a result, the maximum rise height of the plume for exothermic reactions may actually decrease with reaction rate if this occurs in a region of high ambient density. In addition, we investigate non-Boussinesq effects, which are important when the heat of reaction is large enough.
Some Consequences of Detailed Junction Modeling in Road Traffic Assignment

Until recently, analyses of the assignment problem have considered only restricted forms of the functions which relate the costs of travel to volumes of traffic. These analyses are not applicable to networks which include detailed models of either uncontrolled junctions or signal-control policies which react to changes in traffic volumes. Recent assignment procedures are capable of modeling both these kinds of junction interaction, and it is therefore of interest to determine under what conditions the behavior of such procedures is assured. In doing so, use is made of a recently established sufficient condition for good behavior in any network. In broad terms, this condition is that the cost of using a link depends mainly on the flow on that link. The condition is discussed, and a related but weaker condition is shown to be necessary to guarantee good behavior. Analysis of detailed models of priority junctions and roundabouts shows that they give rise to cost functions which just satisfy the necessary condition but fail to satisfy the sufficient one. At signal-controlled junctions, the policy which determines how the signal-settings are calculated influences whether or not these conditions are satisfied: various policies are analyzed, and all but one are shown to lead to a violation of the condition which is necessary for good behavior in any network.
Modern Speech Enhancement Techniques in Text -Independent Speaker Verification

— The noise robustness of speaker veriﬁcation systems is crucial for real applications although only few articles have tackled this problem. In this paper, we study the performance of several modern speech enhancement solutions including wavelet– based speech denoising. We use these algorithms as a preprocessing stage in a text–independent speaker veriﬁcation system. The results are presented after exhaustive simulations.
Stochastic Primal-Dual Q-Learning

In this work, we present a new model-free and off-policy reinforcement learning (RL) algorithm, that is capable of finding a near-optimal policy with state-action observations from arbitrary behavior policies. Our algorithm, called the stochastic primal-dual Q-learning (SPD Q-learning), hinges upon a new linear programming formulation and a dual perspective of the standard Q-learning. In contrast to previous primal-dual RL algorithms, the SPD Q-learning includes a Q-function estimation step, thus allowing to recover an approximate policy from the primal solution as well as the dual solution. We prove a first-of-its-kind result that the SPD Q-learning guarantees a certain convergence rate, even when the state-action distribution is time-varying but sub-linearly converges to a stationary distribution. Numerical experiments are provided to demonstrate the off-policy learning abilities of the proposed algorithm in comparison to the standard Q-learning.
Metaplectic covers of Kac–Moody groups and Whittaker functions

Starting from some linear algebraic data (a Weyl-group invariant bilinear form) and some arithmetic data (a bilinear Steinberg symbol), we construct a cover of a Kac-Moody group generalizing the work of Matsumoto. Specializing our construction over non-archimedean local fields, for each positive integer n we obtain the notion of $n$-fold metaplectic covers of Kac-Moody groups. In this setting, we prove a Casselman-Shalika type formula for Whittaker functions.
Near infrared spectroscopy to study the brain: an overview

This paper gives an overview of principles, technologies, and applications using near infrared spectrometry and imaging (NIRS and NIRI) to study brain function. The physical background is reviewed and technologies and their properties are discussed. Advantages and limitations of NIRI are described. The basic functional signals obtained by NIRI, the neuronal and the hemodynamic signal are described and in particular publications about the former are reviewed. Applications in adults and neonates are reviewed, too.
A deep learning approach for construction vehicles fill factor estimation and bucket detection in extreme environments

The development of autonomous detection technology is imperative in the field of construction. The bucket fill factor is one of the main indicators for evaluating the productivity of construction vehicles. Bucket detection is a prerequisite for bucket trajectory planning. However, previous studies have been conducted under ideal environments, a specific single environment, and several normal environments without considering the actual harsh environments at construction sites. Therefore, seven extreme environments are set in this paper to fill this gap, and an effective method is proposed. First, a novel framework for image restoration under extreme environments is proposed. It applies to all tasks conducted by vision on construction sites. Second, a combination of segmentation and classification networks is used for the first time in this area. Multitask learning is used to discover a positive correlation between fill factor estimation and bucket detection. Furthermore, probabilistic methods and transfer learning were introduced, and excellent results were achieved (97.40% accuracy in fill factor estimation and 99.76% accuracy in bucket detection for seven extreme environments).
Magnetic Energies and Feynman–Kac–Itô Formulas for Symmetric Markov Processes

Given a (conservative) symmetric Markov process on a metric space we consider related bilinear forms that generalize the energy form for a particle in an electromagnetic field. We obtain one bilinear form by semigroup approximation and another, closed one, by using a Feynman–Kac–Itô formula. If the given process is Feller, its energy measures have densities and its jump measure has a kernel, then the two forms agree on a core and the second is a closed extension of the first. In this case we provide the explicit form of the associated Hamiltonian.
Gradient-free two-points optimal method for non smooth stochastic convex optimization problem with additional small noise

Using double-smoothing technique and stochastic mirror descent with inexact oracle we built an optimal algorithm (up to a multiplicative factor) for two-points gradient-free non-smooth stochastic convex programming. We investigate how much can be the level of noise (the nature of this noise isn't necessary stochastic) for the rate of convergence to be maintained (up to a multiplicative factor).
Dickman approximation in simulation, summations and perpetuities

The generalized Dickman distribution ${\cal D}_\theta$ with parameter $\theta>0$ is the unique distribution for a non-negative random variable $W$ such that $W=_d W^*$, where \begin{eqnarray} W^*=_d U^{1/\theta}(W+1) \qquad (1) \end{eqnarray} with $U \sim {\cal U}[0,1]$ independent of $W$, and $=_d$ denoting equality in distribution. Members of this family appear in number theory, stochastic geometry, perpetuities and the study of algorithms. We obtain bounds in Wasserstein type distances between ${\cal D}_\theta$ and \begin{eqnarray} W_n= \frac{1}{n} \sum_{i=1}^n Y_k B_k \qquad (2) \end{eqnarray} where $B_1,\ldots,B_n, Y_1, \ldots, Y_n$ are independent with $B_k \sim {\rm Ber}(1/k), E[Y_k]=k, {\rm Var}(Y_k)=\sigma_k^2$ and provide an application to the minimal directed spanning tree in $\mathbb{R}^2$, and also obtain such bounds when the Bernoulli variables in $(2)$ are replaced by Poissons. We also give simple proofs and provide rates for the Dickman convergence of the weighted sums, arising in probabilistic number theory, of the form \begin{eqnarray} S_n=\frac{1}{\log(p_n)} \sum_{k=1}^n X_k \log(p_k) \end{eqnarray} where $(p_k)_{k \ge 1}$ is an enumeration of the prime numbers in increasing order and $X_k$ is Bernoulli with success probability $1/(1+p_k)$, Geometric with parameter $(1-1/p_k)$ or Poisson with mean $\lambda_k$. In addition, we broaden the class of generalized Dickman distributions by studying the fixed points of the transformation \begin{eqnarray*} s(W^*)=_d U^{1/\theta}s(W+1) \end{eqnarray*} generalizing $(1)$, that allows the introduction of non-identity utility functions $s(\cdot)$ into Vervaat perpetuities. We obtain distributional bounds for recursive methods that can be used to simulate from this family, with special attention paid to the generalized Dickman case when $s(x)=x$.
Uncertainty Principles for Kac Algebras

In this paper, we introduce the notation of bi-shift of biprojections in subfactor theory to unimodular Kac algebras. We characterize the minimizers of Hirschman-Beckner uncertainty principle and Donoho-Stark uncertainty principle for unimodular Kac algebras with biprojections and prove Hardy's uncertainty principle in terms of minimizers.
Noble metal nanoparticles for LSPR-based optical sensing

A newly emerging field in bioanalytics based on biomolecular binding detected label-free at single metal nanoparticles is introduced. Thereby particles which show the effect of localized surface plasmon resonance (LSPR) are used as plasmonic transducers. They change their spectroscopic properties (a band in the UV-VIS range) upon binding of molecules. This effect is even observable at the single nanoparticle level using micro spectroscopy and presents the base for a new field of single particle bioanalytics with the promise of highly parallel and miniaturized sensor arrays. The paper describes this approach and shows first result from our work regarding the detection of DNA binding at single nanoparticle sensors.
Limit distributions of Studentized means

Let X,Xj,j∈N, be independent, identically distributed random variables with probability distribution F. It is shown that Student's statistic of the sample {Xj}nj=1 has a limit distribution G such that G({−1,1})≠1, if and only if: (1) X is in the domain of attraction of a stable law with some exponent 0 0 such that nx−2(1+∫(−x,x)y2F{dy})≤1. If G({−1,1})=1, then Student's statistic of the sample {Xj}nj=1 has a limit distribution if and only if \P(|X|>x),x>0, is a slowly varying function at +∞.
Yamogenin-Induced Cell Cycle Arrest, Oxidative Stress, and Apoptosis in Human Ovarian Cancer Cell Line

Steroidal saponins are a group of compounds with complex structures and biological activities. They have anti-inflammatory, antimicrobial, fungicidal, and antitumor properties. Yamogenin is one of the spirostane saponins and occurs in Trigonella foenum-graecum, Asparagus officinalis, and Dioscorea collettii. It is a stereoisomer of diosgenin—a well-known compound whose activity and mechanisms of action in cancer cells are determined. However, the antitumor effect of yamogenin is still little known, and the mechanism of action has not been determined. In this study, we evaluated the effect of yamogenin on human ovarian cancer SKOV-3 cells in vitro by determining the cellular factors that trigger cell death. The viability of the cells was assessed with a Real-Time xCELLigence system and the cell cycle arrest with flow cytometry. The activity of initiator and executioner caspases (-8, -9, and -3/7) was estimated with luminometry and flow cytometry, respectively. The mitochondrial membrane depolarization, the level of oxidative stress, and DNA damage in the yamogenin-treated cells were also evaluated by flow cytometry. Genes expression analysis at the mRNA level was conducted with Real-Time PCR. Bid activation and chromatin condensation were estimated with fluorescent microscopy. The obtained results indicate that yamogenin has cytotoxic activity in SKOV-3 cells with an IC50 value of 23.90 ± 1.48 µg/mL and strongly inhibits the cell cycle in the sub-G1 phase. The compound also triggers cell death with a significant decrease in mitochondrial membrane potential, an increase in the level of oxidative stress (over two times higher in comparison to the control), and activation of caspase-8, -9, -3/7, as well as Bid. The results of genes expression indicate that the Tumor Necrosis Factor (TNF) Receptor Superfamily Members (TNF, TNFRSF10, TNFRSF10B, TNFRSF1B, and TNFRSF25), Fas Associated via Death Domain (FADD), and Death Effector Domain Containing 2 (DEDD2) were significantly upregulated and their relative expression was at least two times higher than in the control. Our work shows that yamogenin induces apoptosis in ovarian cancer cells, and both the extrinsic and mitochondrial—intrinsic pathways are involved in this process.
Aspects of the mathematical theory of
 disordered quantum spin chains

We give an introduction into some aspects of the emerging mathematical theory of many-body localization (MBL) for disordered quantum spin chains. In particular, we discuss manifestations of MBL such as zero-velocity Lieb-Robinson bounds, quasi-locality of the time evolution of local observables, as well as exponential clustering and low entanglement of eigenstates. Explicit models where such properties have recently been verified are the XY and XXZ spin chain, in each case with disorder introduced in the form of a random exterior field. We introduce these models, state many of the available results and try to provide some general context. We discuss methods and ideas which enter the proofs and, in a few illustrative examples, include more detailed arguments. Finally, we also mention some directions for future mathematical work on MBL.
Space-efficient knot mosaics for prime knots with mosaic number 6

In 2008, Kauffman and Lomonaco introduce the concepts of a knot mosaic and the mosaic number of a knot or link, the smallest integer $n$ such that a knot or link can be represented on an $n$-mosaic. In arXiv:1702.06462, the authors explore space-efficient knot mosaics and the tile number of a knot or link, the smallest number of non-blank tiles necessary to depict the knot or link on a mosaic. They determine bounds for the tile number in terms of the mosaic number. In this paper, we focus specifically on prime knots with mosaic number 6. We determine a complete list of these knots, provide a minimal, space-efficient knot mosaic for each of them, and determine the tile number (or minimal mosaic tile number) of each of them.