Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Multi-Filter Framework for Texture Image Representation Improvement Using Set of Pattern Descriptors to Fingerprint Liveness Detection

The use of user recognition and authentication systems has become very common and is part of everyday routines for many people, guaranteeing access to the automatic teller machines, entrance to the gym or even to smartphones. Among all the biometrics that can be analyzed in this type of system, the fingerprint is the most considered due to the ease of collection, the uniqueness of each user, and the large amount of solid theories and computational libraries available in the scientific literature. However, in recent years, the falsification of these biometrics with synthetic materials, known as spoofing, has become a real threat to these systems. To circumvent these effects without the addition of hardware devices, techniques based on the analysis of texture pattern descriptors were developed. In this work, we propose a new framework based on steps of data augmentation, image processing and replication, and feature fusion and reduction. The method has as main objective to improve the ability of classifiers, or sets of classifiers, to recognize life in fingerprints. Furthermore, it is proposed a generalization of vector representation of patterns described in matrix form from the systematic use of sets of mapping functions. All the proposed material was analyzed on the well-established benchmark of the Liveness Detection competition of the 2009, 2011, 2013 and 2015 editions, presenting an average accuracy of 97.77% and being a competitive strategy in relation to the other techniques that make up the state of the art of specialized literature.
The Effectiveness of Cognitive Behavior Therapy on Anxiety, Physical Symptoms, Worry, and Attention Deficit in Women with Generalized Anxiety Disorder

Generalized Anxiety Disorder (GAD) causes a person's life to be full of worries by involving cognitive processes and not tolerating uncertainty and increasing worry and affecting the quality of sleep and attention of these people, and disrupting life functions. This study investigated the effectiveness of cognitive-behavioral therapy (CBT) on anxiety, physical symptoms, worry, and attention deficits in people with GAD. It was a quasi-experimental pre-test and post-test study with an experimental group and a control group. In this study, 30 women with GAD were selected using a voluntary sampling method. After matching, participants were randomly divided into experimental and control groups. The experimental group received CBT for 10 sessions, but the control group did not receive any treatment. The research instruments included the attention skills Questionnaire by Savari and Oraki and the Pennsylvania State Worry Scale. There was a significant difference in physical symptoms in the experimental group compared to the control group (P< 0.01, F = 65.28), while in the experimental group, there was a significant difference in worry compared to the control group. Moreover, there was a significant difference in attention deficit in the experimental group compared to the control group. The results showed that CBT improved attention deficit and worry in women with GAD.
A Measurement Method of the Shortest Distance Between Ultrahigh Ships and Transmission Lines Based on Binocular Vision

High-voltage overhead transmission lines inevitably cross waterways such as rivers. The insufficient air-gap distance between some parts of ultrahigh ships, such as floating crane ships, and transmission lines has caused many transmission lines tripping and even transmission lines to be broken accidents. This article proposes a measurement method of the shortest distance between ultrahigh ships and transmission lines based on binocular vision for coordinate transformation. The method can measure the shortest distance when the ultrahigh ship will pass under the transmission line in advance. A small simulated experimental platform for measuring the shortest distance between ultrahigh ships and transmission lines based on binocular vision is established. The influence factors on the measurement error of the shortest distance are analyzed on the experimental platform. Based on the simulated small experiment, some suggestions are proposed to extend to the practical application. The results show that the relative error of the shortest distance obtained by the method proposed in this article is less than 10% in the small simulated experiment, which verifies the method’s effectiveness. This article’s research will help avoid accidents such as overhead transmission lines being scratched by ultrahigh ships when the overhead transmission lines cross the waterway.
Existence of nontrivial solutions for asymptotically linear periodic Schrödinger equations

We study the Schrödinger equation:Section.Display where is periodic and is periodic in the -variables, is in a gap of the spectrum of the operator and is asymptotically linear as We prove that under some asymptotically linear assumptions for , this equation has a nontrivial solution. Our assumptions for are different from the classical assumptions raised by Li and Szulkin.
Anisotropic scaling of the two-dimensional Ising model I: the torus

We present detailed calculations for the partition function and the free energy of the finite two-dimensional square lattice Ising model with periodic and antiperiodic boundary conditions, variable aspect ratio, and anisotropic couplings, as well as for the corresponding universal free energy finite-size scaling functions. Therefore, we review the dimer mapping, as well as the interplay between its topology and the different types of boundary conditions. As a central result, we show how both the finite system as well as the scaling form decay into contributions for the bulk, a characteristic finite-size part, and – if present – the surface tension, which emerges due to at least one antiperiodic boundary in the system. For the scaling limit we extend the proper finite-size scaling theory to the anisotropic case and show how this anisotropy can be absorbed into suitable scaling variables.
Entropy-Driven Inclusion of Natural Protoberberine Alkaloids in Sulfobutylether-β-Cyclodextrin

The understanding of the relationship between molecular structure and the thermodynamics of host-guest binding is essential for the rational design of the applications of inclusion complexes. To obtain insight into the factors governing the driving force of complex formation in aqueous solutions, the encapsulation of five pharmaceutically important protoberberine alkaloids was studied in sulfobutylether-β-cyclodextrin having on average 6.4 degrees of substitution (SBE6.4βCD). Spectrophotometric, fluorescence spectroscopic, and isothermal calorimetric measurements showed 1:1 complexation in dilute solutions. From 1.92 × 104 M−1, about an eight-fold decrease of the association constant was observed in the series of berberine ≈ coptisine >> palmatine > epiberberine > dehydrocorydaline. The embedment of these alkaloids in the SBE6.4βCD cavity was entropy-controlled with mildly negative enthalpy contributions. These findings suggest that the stabilization of the examined complexes arises primarily from the hydrophobic interaction between the constituents. The more than three orders of magnitude smaller association constants of protoberberine alkaloids with SBE6.4βCD than with cucurbit[7]uril, a host having similar cavity size, originates from the much smaller exothermicity of the confinement in the former macrocycle.
Simplified Compressor and Encoder Designs for Low-Cost Approximate Radix-4 Booth Multiplier

In this brief, we present a novel design methodology of cost-effective approximate radix-4 Booth multipliers, which can significantly reduce the power consumption of error-resilient signal processing tasks. In contrast that the prior studies only focus on the approximation of either the partial product generation with encoders or the partial product reductions with compressors, the proposed method considers two major processing steps jointly by forcing the generated error directions to be opposite to each other. As the internal errors are naturally balanced to have zero mean, as a result, the proposed approximate Booth multiplier can minimize the required processing energy under the same number of approximate bits compared to the previous designs. Simulation results on FIR filtering and image classification applications reveal that the proposed approximate Booth multiplier shows the most attractive energy-performance trade-offs, achieving 28% and 34% of energy reduction compared to the exact Booth multiplier, respectively, with negligible accuracy loss.
Latitudinal shifts of soil microbial biomass seasonality

Abstract Soil microbes ultimately drive the mineralization of soil organic carbon and thus ecosystem functions. We compiled a dataset of the seasonality of microbial biomass carbon (MBC) and developed a semi-mechanistic model to map monthly MBC across the globe. MBC exhibits an equatorially symmetric seasonality between the Northern and Southern Hemispheres. In the Northern Hemisphere, MBC peaks in autumn and is minimal in spring at low latitudes (<25°N), peaks in the spring and is minimal in autumn at mid-latitudes (25°N to 50°N), while peaks in autumn and is minimal in spring at high latitudes (>50°N). This latitudinal shift of MBC seasonality is attributed to an interaction of soil temperature, soil moisture, and substrate availability. The MBC seasonality is inconsistent with patterns of heterotrophic respiration, indicating that MBC as a proxy for microbial activity is inappropriate at this resolution. This study highlights the need to explicitly represent microbial physiology in microbial models. The interactive controls of environments and substrate on microbial seasonality provide insights for better representing microbial mechanisms in simulating ecosystem functions at the seasonal scale.
Distributed Average Tracking of Heterogeneous Physical Second-order Agents With No Input Signals Constraint

This paper addresses distributed average tracking of physical second-order agents with heterogeneous nonlinear dynamics, where there is no constraint on input signals. The nonlinear terms in agents' dynamics are heterogeneous, satisfying a Lipschitz-like condition that will be defined later and is more general than the Lipschitz condition. In the proposed algorithm, a control input and a filter are designed for each agent. Each agent's filter has two outputs and the idea is that the first output estimates the average of the input signals and the second output estimates the average of the input velocities asymptotically. In parallel, each agent's position and velocity are driven to track, respectively, the first and the second outputs. Having heterogeneous nonlinear terms in agents' dynamics necessitates designing the filters for agents. Since the nonlinear terms in agents' dynamics can be unbounded and the input signals are arbitrary, novel state-dependent time-varying gains are employed in agents' filters and control inputs to overcome these unboundedness effects. Finally the results are improved to achieve the distributed average tracking for a group of double-integrator agents, where there is no constraint on input signals and the filter is not required anymore. Numerical simulations are also presented to illustrate the theoretical results.
G2-manifolds and associative submanifolds via semi-Fano 3-folds

We construct many new topological types of compact G_2-manifolds, i.e. Riemannian 7-manifolds with holonomy group G_2. To achieve this we extend the twisted connected sum construction first developed by Kovalev and apply it to the large class of asymptotically cylindrical Calabi–Yau 3-folds built from semi-Fano 3-folds constructed previously by the authors. In many cases we determine the diffeomorphism type of the underlying smooth 7-manifolds completely; we find that many 2-connected 7-manifolds can be realised as twisted connected sums in a variety of ways, raising questions about the global structure of the moduli space of G_2-metrics. Many of the G_2-manifolds we construct contain compact rigid associative 3-folds, which play an important role in the the higher-dimensional enumerative geometry (gauge theory/calibrated submanifolds) approach to defining deformation invariants of G_2-metrics. By varying the semi-Fanos used to build different G_2-metrics on the same 7-manifold we can change the number of rigid associative 3-folds we produce.
Universal Linear Optics for Ultra-Fast Neuromorphic Silicon Photonics Towards Fj/MAC and TMAC/sec/mm2 Engines

The field of neuromorphic photonics has been projected to comprise the next-generation Neural Network platform, expected to lead to remarkable advances in compute energy- and area-efficiency metrics. Herein, we review the performance of state-of-the-art neuromorphic photonic demonstrators, summarizing the impact of the circuit architecture and employed weight technology on the system credentials in terms of scalability, energy- and footprint-efficiency. We provide an overview of a recently demonstrated photonic crossbar multi-port interferometer, holding significant insertion loss, technology versatility and robustness advantages over all state-of-the-art linear optical layouts. This novel linear optical circuit architecture is then transferred onto an integrated silicon photonic (SiPho) platform, realizing a single-column crossbar and selecting SiGe electro-absorption modulators (EAM) for both its fan in and weighting stages. This single-neuron coherent SiPho prototype is then experimentally benchmarked on the MNIST dataset, allowing for record-high compute-rates up to 50 GHz/axon. The classification accuracy was studied for compute rates ranging between 16-50 GHz, revealing 99.03% accuracy at 16 GHz that reduces by only 3.79% at 50 GHz. Finally, we investigate the interdependence of compute rate, bit resolution and energy efficiency, in photonic accelerator layouts and benchmark our proposed Xbar architecture against state-of-the-art photonic and electronic accelerators. The analysis reveals an energy- and footprint-efficiency of 54 fJ/MAC and 1.54 TMAC/s/mm2, respectively, shaping in this way a promising roadmap for next-generation neuromorphic accelerators.
EGFR T751_I759delinsN Mutation in Exon19 Detected by NGS but Not by Real-Time PCR in a Heavily-Treated Patient with NSCLC

The detection of driver gene mutations can determine appropriate treatment strategies for non-small cell lung cancer (NSCLC) by identifying the presence of an effective druggable target. Mutations in the gene encoding the epidermal growth factor receptor (EGFR) are common driver mutations in NSCLC that can be effectively targeted by the use of EGFR tyrosine kinase inhibitors (EGFR-TKIs). However, without the detection of driver mutations, appropriate therapeutic decisions cannot be made. The most commonly applied methods for detecting driver gene mutations are assays based on polymerase chain reaction (PCR). However, the underlying mechanism of PCR-based assays limits the detection of rare mutations. Therefore, patients harboring rare mutations may not receive optimal treatment. We report a heavily-treated patient with NSCLC who harbored a T751_I759delinsN mutation in exon 19 of EGFR that was not detected by real-time PCR but was successfully detected by next-generation sequencing (NGS). The detection of a driver mutation using NGS resulted in the administration of targeted therapy, leading to favorable progression-free survival for the patient. Our report highlights the importance and potential of routine NGS testing among NSCLC patients for whom traditional assays fail to detect driver mutations when determining treatment options.
The projected Newton-Kleinman Method for the Algebraic Riccati Equation

The numerical solution of the algebraic Riccati equation is a challenging task especially for very large problem dimensions. In this paper we present a new algorithm that combines the very appealing computational features of projection methods with the convergence properties of the inexact Newton-Kleinman procedure equipped with a line search. In particular, the Newton scheme is completely merged in a projection framework with a single approximation space so that the Newton-Kleinman iteration is only implicitly performed. Moreover, the line search turns out to be exact in our setting, i.e., the existence of a local minimum of the Riccati residual norm along the current search direction is guaranteed and the corresponding minimizer is chosen as step-size. This property determines a monotone decrease of the Riccati residual norm under some mild assumptions. Several numerical results are reported to illustrate the potential of our novel approach.
A sharpening of a problem on Bernstein polynomials and convex function and related results

We present a short proof of a conjecture proposed by I. Ra\c{s}a (2017), which is an inequality involving basic Bernstein polynomials and convex functions. This proof was given in the letter to I. Ra\c{s}a (2017). The methods of our proof allow us to obtain some extended versions of this inequality as well as other inequalities given by I. Ra\c{s}a. As a tool we use stochastic convex ordering relations. We propose also some generalizations of the binomial convex concentration inequality. We use it to insert some additional expressions between left and right sides of the Ra\c{s}a inequalities.
Communicating through a geometrically frustrated channel

We propose an intuitively appealing formulation of the zero-temperature triangular lattice Ising antiferromagnet (TIAFM) on a cylinder as a model of noninteracting fermions hopping on a ring and evolving in imaginary time with pair annihilation events. Among the features of the model which can then be related to local semi-conservation of particle number are: infinite-range influence of boundary conditions, multiple “zero temperature pure phases” in the infinite-length limit differing in entropy density, sensitivity of the asymptotic rate of decay (with respect to length) of mutual information between end configurations to circumference modulo 3, and even the known powerlaw falloff of the spin-spin correlator on an infinite plane. The ability of boundary conditions to determine the bulk entropy density enables communication between the two far-separated ends of such a cylinder even when all pair-wise spin-spin correlations between the ends are vanishingly small. In the fermionic language, breakdown of these zero-temperature phenomena at positive temperature is understood as passage of the system into a superconducting phase.
M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity

Emotion recognition, or the ability of computers to interpret people’s emotional states, is a very active research area with vast applications to improve people’s lives. However, most image-based emotion recognition techniques are flawed, as humans can intentionally hide their emotions by changing facial expressions. Consequently, brain signals are being used to detect human emotions with improved accuracy, but most proposed systems demonstrate poor performance as EEG signals are difficult to classify using standard machine learning and deep learning techniques. This paper proposes two convolutional neural network (CNN) models (M1: heavily parameterized CNN model and M2: lightly parameterized CNN model) coupled with elegant feature extraction methods for effective recognition. In this study, the most popular EEG benchmark dataset, the DEAP, is utilized with two of its labels, valence, and arousal, for binary classification. We use Fast Fourier Transformation to extract the frequency domain features, convolutional layers for deep features, and complementary features to represent the dataset. The M1 and M2 CNN models achieve nearly perfect accuracy of 99.89% and 99.22%, respectively, which outperform every previous state-of-the-art model. We empirically demonstrate that the M2 model requires only 2 seconds of EEG signal for 99.22% accuracy, and it can achieve over 96% accuracy with only 125 milliseconds of EEG data for valence classification. Moreover, the proposed M2 model achieves 96.8% accuracy on valence using only 10% of the training dataset, demonstrating our proposed system’s effectiveness. Documented implementation codes for every experiment are published for reproducibility.
Material Transformations – Making as a Catalyst of Experience

This paper examines my personal process of materializing lived experiences into a material installation, and how this process affected how I perceive those lived experiences. The process is investigated through to the image schematic structure of experience, in relation to the metaphoric nature of material substances, across a specific practice led research process, the Edges of the Existent installation. In this paper, materializing experience is seen as a form of transference, a catalyst through which lived experience is turned into tangible material artefacts, which perform, express and interpret the embodied, emotional and reflected content related to experiences. Through this transference, the artist is able to access their preconscious levels of experience and articulate them in a manner that is both embodied and experiential. The tangibility of materiality creates a multidimensional layer to conscious experience, through which the preconscious structures of experience can be reached. These dimensions add to the experiential value of the work of art, as embodied experiences evolve into more metaphoric and abstract processes of thought.
Families of Markov chains with compatible symmetric-group actions

For each $n,r \geq 0$, let $KG(n,r)$ denote the Kneser Graph; that whose vertices are labeled by $r$-element subsets of $n$, and whose edges indicate that the corresponding subsets are disjoint. Fixing $r$ and allowing $n$ to vary, one obtains a family of nested graphs, each equipped with a natural action by a symmetric group $S_n$, such that these actions are compatible. Collections of graphs of this type are common in algebraic combinatorics and include families such as the Johnson Graphs, Crown Graphs and Rook Graphs. In previous work, the authors systematically studied families of this type using the language of representation stability and FI-modules. In that work, it is shown that such families of graphs exhibit a large variety of asymptotic regular behaviors. The present work applies the theory developed in that previous work, later refined in work of the authors and Speyer, to study random walks on the graphs of such families. We show that the moments of hitting times exhibit rational function behavior asymptotically. By consequence we conclude similar facts about the entries of the discrete Green's functions, as defined by Chung and Yau. Finally, we illustrate how the algebro-combinatorial structure of the graphs in these families give bounds on the mixing times of random walks on those graphs. We suggest some possible directions for future study, including of the appearance, or not, of the cut-off phenomenon, originally presented by Diaconis.