Miracle-dz/UniFAR-FTUs huggingface dataset:
Breadth-First Pipeline Parallelism

We introduce Breadth-First Pipeline Parallelism, a novel training schedule which optimizes the combination of pipeline and data parallelism. Breadth-First Pipeline Parallelism lowers training time, cost and memory usage by combining a high GPU utilization with a small batch size per GPU, and by making use of fully sharded data parallelism. Experimentally, we observed an increase of up to 43% in training throughput for a 52 billion-parameter model using a small batch size per GPU compared to Megatron-LM, which would reduce the training time and cost by the same amount on a large GPU cluster.
Compensation for state‐dependent nonlinearity in a modified repetitive control system

This paper presents an estimation and compensation of state‐dependent nonlinearity for a modified repetitive control system. It is based on the equivalent‐input‐disturbance (EID) approach. The nonlinearity is estimated by an EID estimator and compensated by incorporation of the estimate into the repetitive control input. A two‐dimensional model of the EID‐based modified repetitive control system is established that enables the preferential adjustment of control and learning actions by means of 2 tuning parameters. The singular‐value‐decomposition technique and Lyapunov stability theory are used to derive a linear‐matrix‐inequality–based asymptotic stability condition. Exploiting the stability condition and an overall performance evaluation index, a design algorithm is developed. Simulation results for the tracking control of a chuck‐workpiece system show that the method not only compensates state‐dependent nonlinearity but also improves the tracking performance for the periodic reference input, thereby demonstrating the validity of the method.
Existence of periodic solutions for the discrete-time counterpart of a neutral-type cellular neural network with time-varying delays and impulses

From the mathematical point of view, a cellular neural network (CNN) can be characterized by an array of identical nonlinear dynamical systems called cells (neurons) that are locally interconnected. Using the semi-discretization method, in the present talk we construct a discrete-time counterpart of a neutral-type CNN with time-varying delays and impulses. Sufficient conditions for the existence of periodic solutions of the discrete-time system thus obtained are found by using the continuation theorem of coincidence degree theory.
Key Predistribution for Heterogeneous Group of Sensor Nodes using Combinatorial Design

Objective: To design a key pre-distribution scheme for a heterogeneous group of sensor nodes with the help of combinatorial design for high resilience against node capture. Methods/Statistical analysis: Methodology used here is based on theory of balanced incomplete block design where a group of BIBD is used for each of the sensor groups and keys are distributed based on the orientation of these groups. Findings: Connectivity, Storage limitation and resilience are evaluated for the proposed scheme. Application/Improvements: Combinatorial design based scheme is proven to be most suitable method among various key pre-distribution schemes. Combinatorial design based scheme distributes keys to the sensor nodes according to Balanced Incomplete Block Design (BIBD). Use of combinatorial design improves the overall performance of the network.
Transient stress evolution of the long-term slow slip cycle initiates the 2014 M w 7.3 Guerrero earthquake

Transients of aseismic fault slip, slow-slip events (SSEs), have been observed in spatial and temporal proximity to the initiation of megathrust earthquakes. However, the underlying physics potentially connecting a preceding SSE to earthquake rupture remains to be determined. Here, we link 3D observation-driven slow-slip cycle models with dynamic rupture simulations of the 2014 M w 7.3 Guerrero, Mexico earthquake across the geometrically complex flat-slab Cocos plate. Our physics-based models reproduce key regional geodetic and teleseismic observations on timescales ranging from decades to seconds. We find that accelerating SSE fronts transiently increase shear stress at the down-dip end of the seismogenic portion of the megathrust. The stresses cast by the 2014 M w 7.6 SSE are significantly larger than those during the three previous episodic SSEs, and can dynamically initiate earthquake rupture. We show that in addition to the transient stresses caused by SSEs, megathrust asperities explain the observed complexities in the coseismic energy release and static surface displacements. We conclude that it is crucial to jointly analyze the long- and short-term interactions of SSEs and megathrust earthquakes across several (a)seismic cycles. Our study has important implications for identifying earthquake precursors and understanding megathrust faulting processes.
COUNTING OF FLOWERS BASED ON K-MEANS CLUSTERING AND WATERSHED SEGMENTATION

A BSTRACT . This paper proposes a hybrid algorithm combining K-means clustering and watershed algorithms for flower segmentation and counting. We use the K-means clustering al-gorithm to obtain the main colors in a complex background according to the cluster centers and then take a color space transformation to extract pixel values for the hue, saturation, and value of flower color. Next, we apply the threshold segmentation technique to segment flowers precisely and obtain the binary image of flowers. Based on this, we take the Euclidean distance transformation to obtain the distance map and apply it to find the local maxima of the connected components. Afterward, the proposed algorithm adaptively determines a minimum distance between each peak and apply it to label connected components using the watershed segmentation with eight-connectivity. On a dataset of 30 images, the test results reveal that the proposed method is more efficient and precise for the counting of overlapped flowers ignoring the degree of overlap, number of overlap, and relatively irregular shape.
Double spiral galaxies and the extratropical cyclone in South Georgia and the South Sandwich Islands

—The work is focused on the comparative analysis of the shape of spiral galaxies and the subtropical cyclone that formed north of Georgia Island and passed north of the South Sandwich Islands, in the South Atlantic Ocean. Subtropical cyclones with double spirals appear to be common in these areas of the South Atlantic. A subtropical cyclone is a weather system that has some characteristics of a tropical cyclone and some characteristics of an extratropical cyclone. They can form between the equator and the 50th parallel. In mathematics, a spiral is a curve which emanates from a point, moving farther away as it revolves around the point. The characteristic shape of hurricanes, cyclones, typhoons is a spiral. The characteristic equation of which spiral the Extratropical Cyclone (EC) Its double spiral shape, whose mathematical equation has already been deﬁned as Cote’s spiral, Gobato et al. (2022) and similarly Lindblad (1964) show shape of double spiral galaxies, already studied among others is discussed here.
Proving Unlinkability Using ProVerif Through Desynchronised Bi-Processes

Unlinkability is a privacy property of crucial importance for several systems such as mobile phones or RFID chips. Analysing this security property is very complex, and highly error-prone. Therefore, formal verification with machine support is desirable. Unfortunately, existing techniques are not sufficient to directly apply verification tools to automatically prove unlinkability. In this paper, we overcome this limitation by defining a simple transformation that will exploit some specific features of ProVerif. This transformation, together with some generic axioms, allows the tool to successfully conclude on several case studies. We have implemented our approach, effectively obtaining direct proofs of unlinkability on several protocols that were, until now, out of reach of automatic verification tools.
ENSEMBLED LSTM WITH WALK FORWARD OPTIMIZATION IN ALGORITHMIC TRADING

:: This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ensembled with XGBoost and MAC). All models were compared to Buy and Hold benchmark and evaluated using Performance Metrics, that is Annualized Return Compounded, Maximum Drawdown, Maximum Loss Duration, and three types of Information Ratio. This research uses daily S&P 500 index data ranging from 2000 to 2023. Every strategy was optimized with novel walk forward approach consisting of numerous in sample and out of sample periods. MAC and best performing ML methods were subjected to sensitivity analysis. The results show that LSTM ensembled with XGBoost and MAC yields the most promising results in terms of risk-adjusted returns which suggest further research focused on ensembling of individual ML strategies. Finally, we show that classical methods of technical analysis (that is, MAC) are much less robust and indifferent to change in hyperparameters than machine learning based algorithms, especially LSTM.
Reconstruction of finite-valued sparse signals

The need of reconstructing discrete-valued sparse signals from few measurements, that is solving an undetermined system of linear equations, appears frequently in science and engineering. Those signals appear, for example, in error correcting codes as well as massive Multiple-Input Multiple-Output (MIMO) channel and wideband spectrum sensing. A particular example is given by wireless communications, where the transmitted signals are sequences of bits, i.e., with entries in f0; 1g. Whereas classical compressed sensing algorithms do not incorporate the additional knowledge of the discrete nature of the signal, classical lattice decoding approaches do not utilize sparsity constraints. In this talk, we present an approach that incorporates a discrete values prior into basis pursuit. In particular, we address finite-valued sparse signals, i.e., sparse signals with entries in a finite alphabet. We will introduce an equivalent null space characterization and show that phase transition takes place earlier than when using the classical basis pursuit approach. We will further discuss robustness of the algorithm and show that the nonnegative case is very different from the bipolar one. One of our findings is that the positioning of the zero in the alphabet - i.e., whether it is a boundary element or not - is crucial.
Batch Policy Iteration Algorithms for Continuous Domains

This paper establishes the link between an adaptation of the policy iteration method for Markov decision processes with continuous state and action spaces and the policy gradient method when the differentiation of the mean value is directly done over the policy without parameterization. This approach allows deriving sound and practical batch Reinforcement Learning algorithms for continuous state and action spaces.
Understanding the effects of comorbidity severity among high-need, high-cost alcohol-related frequent attenders: A cross-sectional study

Objectives: Alcohol dependence (AD) imposes a significant economic burden and frequently cooccurs with other mental disorders. This study aimed to determine the impact of comorbidity severity among high-need, high-cost (HNHC) alcohol-related frequent attendees. (ARFAs). Methods: In a cross-sectional study, 128 participants filled out questionnaires. Assessing patients with the TLFB, APQ, SF-12, and Adult Service Use Schedule. Results: a) Psychiatric comorbidities are common in patients with AD. b) Comorbidity severity significantly predicts alcohol-related problems (APQ), quality of life (QoL), and emergency department (ED) attendance and admission after controlling for the influence of age and gender. c) Daily alcohol intake significantly modifies the relationship between comorbidity severity and emergency department attendance. Conclusions: When treating patients with AD, evaluating and considering the severity of comorbidity is essential.
Dynamic Analysis of a Lü Model in Six Dimensions and Its Projections

Abstract In this article, extended complex Lü models (ECLMs) are proposed. They are obtained by substituting the real variables of the classical Lü model by complex variables. These projections, spanning from five dimensions (5D) and six dimensions (6D), are studied in their dynamics, which include phase spaces, calculations of eigenvalues and Lyapunov’s exponents, Poincaré maps, bifurcation diagrams, and related analyses. It is shown that in the case of a 5D extension, we have obtained chaotic trajectories; meanwhile the 6D extension shows quasiperiodic and hyperchaotic behaviors and it exhibits strange nonchaotic attractor (SNA) features.
Study of Band Structure Characteristics of Periodic Two-Dimensional of Photonic Crystals with Different Geometric Forms

In this paper, new structures of two-dimensional periodic photonic crystals in the form of dielectric rods with different geometric shapes, are presented. All photonic crystals presented in this paper are in a square lattice. Band structure and photonic band gap limitation graph of these structures are obtained by using numerical methods of plane wave expansion (PWE) for TE and TM modes. The simulation results for these structures show different photonic band gap widths and places for different structures and modes.
A Review of Research on Intelligence and High-Quality Development of Enterprises

The development of intelligence in China is in full swing, and it is at the world's leading level in many fields. Guided by the scientific judgment of the government work report ' promoting the deep integration of the Internet, big data, artificial intelligence and the real economy ', the deep integration with intelligence is an important starting point for achieving high-quality economic development in China. The realization of high-quality development of micro-enterprises is an important prerequisite and basic guarantee for high-quality development at the macro level. It is undoubtedly an important issue to study intelligent technology and high-quality development of micro-enterprises in the new era. In recent years, scholars at home and abroad have made extensive explorations in the economic effects of artificial intelligence, the influencing factors of high-quality development of enterprises, and intelligent ' enabling ' high-quality development, which have contributed important results with reference value and significance to the study of intelligent and high-quality development of enterprises.This paper reviews and summarizes this, in order to provide some help for the high quality development of enterprises.
INTRODUCTION TO THE CHIP-FIRING GAME

The chip-ﬁring game is a dynamic process on a graph in which vertices weighed by their “chip values” (which together compose a chip con- ﬁguration on the graph) ﬁre chips to neighboring vertices under a set of rules determining when a vertex is “ready to ﬁre” or, conversely, when a conﬁg- uration is “stable” in the sense that no vertex is ready to ﬁre. This paper introduces fundamental deﬁnitions and properties of this combinatorial sub- ject. Using the graph Laplacian, we deﬁne two types of ﬁrings (ﬁring at an individual vertex and cluster-ﬁring of multiple vertices) and two types of games (with or without a sink , through which chips are consumed without returning to the system). From there, we will study basic properties of the game, includ- ing the fact that order does not matter in sequences of ﬁrings at individual vertices (conﬂuence), criteria for when a conﬁguration will eventually cease to ﬁre, and the structures and duality of critical and superstable conﬁgurations. We are concerned with two basic motivating questions throughout this paper: 1) Given the rules of ﬁring and whether a sink exists, since the behavior of a chip-ﬁring process is entirely determined by its initial chip conﬁguration, can we eﬀectively categorize these conﬁgurations from a combinatorial perspective to study their properties? 2) If the chip-ﬁring game starting from some initial chip conﬁguration can either eventually stabilize or go on inﬁnitely, to what extent do we need to modify the chip values on diﬀerent types of conﬁgurations so that they will behave otherwise? What do we make of conﬁgurations that remain stable even after a substantial change to its chip values?
Load Frequency Control of Multi-Area Multi-Source Power System by Gray Wolf Optimization Technique

: Now a day’s power system is expanding due to increasing load demand. It is also typical task to maintain load demand on behalf of stability limits. To meet load demand energy generator plays important role which uses automatic generation control process to maintain stability limits and load demand. In this presented work we designed PID controller for multi area power system for AGC. MAMS power system is considered for this purpose. For this purpose, MAMS power system is considered. PID controller optimization parameters are achieved using GWO technique. Superiority of results of developed optimization technique is compared with various optimization methods. Proposed system model includes Boiler Dynamics, Generation Rate Constraints and non-linearity of Governor Dead Band. Obtained simulation results indicates the effectiveness of derived approach which gives better response by comparing its results with other techniques. Sensitivity analysis can also done by changing parameters of system and various loading conditions within limit of ±25% for its rated values to check the robustness of presented work. Results of proposed approach is compared with some recent algorithms like TLBO and DE for same interconnected power system. To obtain optimized parameters of controller gain GWO technique is used. Minimization of an objective function provides better stability of the system.
Molecular and phenotypic analysis of the

The Saccharomyces cerevisiae mnnlO mutant is defective in the synthesis of A'-linked oligosaccharides (Ballou et al., 1989). This mutation has no effect on O-linked sugars, but results in the accumulation of glycoproteins that contain severely truncated /V-linked outer-chain oligosaccharides. We have cloned the MNN10 gene by complementation of the hygromycin B sensitivity conferred by the mutant phenotype. Sequence analysis predicts that MnnlOp is a 46.7 kDa type II membrane protein with structural features characteristic of a glycosyltransferase. Subcellular fractionation data indicate that most of the MnnlO protein cofracti-onates with Golgi markers and away from markers for the endoplasmic reticulum (ER), suggesting MnnlOp is localized to the Golgi complex. A comparison of the MnnlO protein sequence to proteins in the two different databases identified five proteins that are homologous to MnnlOp, including a well characterized Schizosaccharomyces pombe al,2 galactosyltransferase that resides in the Golgi complex. Taken together, these results suggest that MNN10 encodes a novel Golgi-localized mannosyltransferase contained in this previously unrecognized family of related sugar transferases.
Anomaly Detection in Brain MRI: From Supervised to Unsupervised Deep Learning

Anatomical imaging of the human brain and central nervous system is a fundamental component of todays diagnosis and therapy of various neurological diseases. To relate symptoms to neurological causes, radiologists analyze the high resolution visualizations provided by modalities such as Magnetic Resonance Imaging (MRI) and try to identify abnormal structures. This manual process is not only cumbersome, time-consuming and costly, but also prone to human error: According to numerous studies, in up to 5-10% of images pathologies remain unnoticed. Lately, breakthroughs in the automatic, computer-assisted analysis of brain MRI achieved through advances in the field of machine learning—so-called supervised Deep Learning methods—have shown performances on par with or even outperforming that of human experts. However, these methods come at a cost: Such deep artificial neural networks need to be trained from vast amounts of carefully annotated examples of benign and diseased cases, for which manual curation and thus precious expert human resources are required. Moreover, these methods do not provide guarantees of being capable of identifying pathologies not present in the training data. The research presented in this thesis focuses on overcoming these burdens and highlights a path from the paradigm of supervised Deep Learning towards annotation-free, unsupervised methods. This includes i) semi-supervised concepts which can leverage both annotated and unlabeled data to improve generalization of Deep Learning-based approaches, and ii) unsupervised anomaly detection frameworks which do not require manual markings of pathologies at all. The latter contributions employ deep representation learning, generative modeling and image-to-image translation techniques to build a model of normal anatomy, which allows to identify anomalies in brain MRI as distributional outliers. This way, opposed to supervised methods, the resulting models are not pathology-specific. A major emphasis is further put on modeling healthy brain distributions at high resolution to be able to detect and delineate particularly small brain lesions. Ultimately, it is shown that the fusion of aforementioned supervised and unsupervised techniques yields an effective self-teaching framework for brain lesion segmentation. This framework also outlines a potential, inexpensive way of integrating Deep-Learning-based anomaly detection into everyday clinical routine. Although validated on brain MRI, the concepts can certainly be translated to other imaging modalities and parts of the anatomy (e.g. CT, X-Ray), opening up great opportunities for integration of Deep Learning into radiology.
Typing time PWI: A scalable paradigm for studying lexical production

Lexical production research has relied extensively on in-person naming experiments such as Picture Word Interference (PWI). While the interference effects observed in the standard paradigm have been well-attested, PWI experiments have traditionally been underpowered, owing to limitations associated with large-scale data collection. In this study, we validate a scalable, typing time-based PWI paradigm that can be deployed on the internet, and enables large-scale replications of the interference effects observed in the spoken modality. We also propose an automated response coding process that incorporates production errors such as incorrect responses and restarts, and can be leveraged to illuminate aspects of response conflict and correction processes in incremental production.
Local Analysis of Entropy-Regularized Stochastic Soft-Max Policy Gradient Methods

Entropy regularization is an efficient technique for encouraging exploration and preventing a premature convergence of (vanilla) policy gradient methods in reinforcement learning (RL). However, the theoretical understanding of entropy-regularized RL algorithms has been limited by the assumption of exact gradient oracles. To go beyond this limitation, we study the convergence of stochastic soft-max vanilla policy gradient with entropy regularization and prove how to utilize the curvature information around the optimal policy to guarantee that the action probabilities will still remain uniformly bounded with high probability. Moreover, we develop the “last iterate” convergence and sample complexity result for the proposed algorithm given a good initialization.