Miracle-dz/UniFAR-FTUs huggingface dataset:
SHDM-NET: Heat Map Detail Guidance with Image Matting for Industrial Weld Semantic Segmentation Network

In actual industrial production, the assessment of the steel plate welding effect is an important task, and the segmentation of the weld section is the basis of the assessment. This paper proposes an industrial weld segmentation network based on a deep learning semantic segmentation algorithm fused with heatmap detail guidance and Image Matting to solve the automatic segmentation problem of weld regions. In the existing semantic segmentation networks, the boundary information can be preserved by fusing the features of both high-level and low-level layers. However, this method can lead to insufficient expression of the spatial information in the low-level layer, resulting in inaccurate segmentation boundary positioning. We propose a detailed guidance module based on heatmaps to fully express the segmented region boundary information in the low-level network to address this problem. Specifically, the expression of boundary information can be enhanced by adding a detailed branch to predict segmented boundary and then matching it with the boundary heat map generated by mask labels to calculate the mean square error loss. In addition, although deep learning has achieved great success in the field of semantic segmentation, the precision of the segmentation boundary region is not high due to the loss of detailed information caused by the classical segmentation network in the process of encoding and decoding process. This paper introduces a matting algorithm to calibrate the boundary of the segmentation region of the semantic segmentation network to solve this problem. Through many experiments on industrial weld data sets, the effectiveness of our method is demonstrated, and the MIOU reaches 97.93%. It is worth noting that this performance is comparable to human manual segmentation ( MIOU 97.96%).
Automatic differentiation and the optimization of differential equation models in biology

A computational revolution unleashed the power of artificial neural networks. At the heart of that revolution is automatic differentiation, which calculates the derivative of a performance measure relative to a large number of parameters. Differentiation enhances the discovery of improved performance in large models, an achievement that was previously difficult or impossible. Recently, a second computational advance optimizes the temporal trajectories traced by differential equations. Optimization requires differentiating a measure of performance over a trajectory, such as the closeness of tracking the environment, with respect to the parameters of the differential equations. Because model trajectories are usually calculated numerically by multistep algorithms, such as Runge-Kutta, the automatic differentiation must be passed through the numerical algorithm. This article explains how such automatic differentiation of trajectories is achieved. It also discusses why such computational breakthroughs are likely to advance theoretical and statistical studies of biological problems, in which one can consider variables as dynamic paths over time and space. Many common problems arise between improving success in computational learning models over performance landscapes, improving evolutionary fitness over adaptive landscapes, and improving statistical fits to data over information landscapes.
Landau levels and snake states of pseudo-spin-1 Dirac-like electrons in gapped Lieb lattices

This work reports the three-band structure associated with a Lieb lattice with arbitrary nearest and next-nearest neighbors hopping interactions. For specific configurations, the system admits a flat band located between two dispersion bands, where three inequivalent Dirac valleys are identified. Furthermore, quasi-particles are effectively described by a spin-1 Dirac-type equation. Under external homogeneous magnetic fields, the Landau levels are exactly determined as the third-order polynomial equation for the energy can be solved using Cardano’s formula. It is also shown that an external anti-symmetric field promotes the existence of current-carrying states, so-called snake states, confined at the interface where the external field changes its sign.
Connectivity of random hypergraphs with a given hyperedge size distribution

This article discusses random hypergraphs with varying hyperedge sizes, admitting large hyperedges with size tending to inﬁnity, and heavy-tailed limiting hyperedge size distributions. The main result describes a threshold for the random hypergraph to be connected with high probability, and shows that the average hyperedge size suﬃces to characterise connectivity under mild regularity assumptions. Especially, the connectivity threshold is in most cases insensitive to the shape and higher moments of the hyperedge size distribution. Similar results are also provided for related random intersection graph models.
The Impact of Six Sigma and TQM on Quality Performance in Manufacturing Industries

In operation management research field, Six Sigma has been considered as infrastructural strategy. Manufacturing companies' trend is to implement this strategy in order to maintain their competitive advantage. The purpose of this study is to explore the value of Six Sigma in improving quality performance. This study then proposed a model that integrated Six Sigma practices with top management support and metric focus. This integrative model will be tested using Structural Equation Modeling. Based on the proposed model, research hypotheses are being developed. The paper culminates with suggested future research work.
Conditional space-time POD extensions for stability and prediction analysis

The correlation and extraction of coherent structures from a turbulent flow is a principle objective of data-driven modal decomposition techniques. The Conditional space-time Proper Orthogonal Decomposition (CPOD) offers insight into transient dynamics, revealing the causation of specific flow phenomenon - or events, in a customizable manner. This work exploits the temporal evolution of CPOD modes in a reduced subspace, resulting in new extensions and adaptations that meet or exceed the capabilities of other decomposition methods. Chiefly, it is demonstrated that the subsequent application of dynamic mode decomposition (DMD) to CPOD modes, provides a flexible tool to investigate targeted flow instabilities, both tonal and convective in nature. By extending the CPOD time-horizon to educe the former type, it is shown that CPOD-DMD can exactly reproduce Spectral POD modes. Regarding the latter, a multi-resolution framework (CPOD-mrDMD) yields a refined"cause and effect"stability analysis, capable of diagnosing the natural forcing mechanisms within the flow, and the resulting unstable modes. In a separate application, CPOD properties are appreciated in the context of reduced order models, with an example of real-time flow prediction of extreme events derived from an active sensor correlated to a CPOD mode. The various CPOD functions and perspectives in this work are demonstrated on: the nonlinear chaotic Lorenz system, 3D intermittent turbulent spots in supersonic boundary layer transition, Schlieren video processing of unstarted inlet buzz, the aeroacoustic feedback forcing of a resonating impinging jet, and prediction of intermittent bluff-body wake structures impinging on a channel wall.
A Lexicon and Depth-wise Separable Convolution Based Handwritten Text Recognition System

Cursive handwritten text recognition is a challenging research problem in the domain of pattern recognition. The current state-of-the-art approaches include models based on convolutional recurrent neural networks and multi-dimensional long short-term memory recurrent neural networks techniques. These methods are highly computationally extensive as well model is complex at design level. In recent studies, combination of convolutional neural network and gated convolutional neural networks based models demonstrated less number of parameters in comparison to convolutional recurrent neural networks based models. In the direction to reduced the total number of parameters to be trained, in this work, we have used depthwise convolution in place of standard convolutions with a combination of gated-convolutional neural network and bidirectional gated recurrent unit to reduce the total number of parameters to be trained. Additionally, we have also included a lexicon based word beam search decoder at testing step. It also helps in improving the the overall accuracy of the model. We have obtained 3.84% character error rate and 9.40% word error rate on IAM dataset; 4.88% character error rate and 14.56% word error rate in George Washington dataset, respectively.
Dynamic Budget Throttling in Repeated Second-Price Auctions

In today's online advertising markets, a crucial requirement for an advertiser is to control her total expenditure within a time horizon under some budget. Among various budget control methods, throttling has emerged as a popular choice, managing an advertiser's total expenditure by selecting only a subset of auctions to participate in. This paper provides a theoretical panorama of a single advertiser's dynamic budget throttling process in repeated second-price auctions. We first establish a lower bound on the regret and an upper bound on the asymptotic competitive ratio for any throttling algorithm, respectively, when the advertiser's values are stochastic and adversarial. Regarding the algorithmic side, we propose the OGD-CB algorithm, which guarantees a near-optimal expected regret with stochastic values. On the other hand, when values are adversarial, we prove that this algorithm also reaches the upper bound on the asymptotic competitive ratio. We further compare throttling with pacing, another widely adopted budget control method, in repeated second-price auctions. In the stochastic case, we demonstrate that pacing is generally superior to throttling for the advertiser, supporting the well-known result that pacing is asymptotically optimal in this scenario. However, in the adversarial case, we give an exciting result indicating that throttling is also an asymptotically optimal dynamic bidding strategy. Our results bridge the gaps in theoretical research of throttling in repeated auctions and comprehensively reveal the ability of this popular budget-smoothing strategy.
Construction of non-CSS quantum codes using measurements on cluster states

The Measurement-based quantum computation provides an alternate model for quantum computation compared to the well-known gate-based model. It uses qubits prepared in a specific entangled state followed by single-qubit measurements. The stabilizers of cluster states are well defined because of their graph structure. We exploit this graph structure extensively to design non-CSS codes using measurement in a specific basis on the cluster state. % We aim to construct $[[n,1]]$ non-CSS code from a $(n+1)$ qubit cluster state. The procedure is general and can be used specifically as an encoding technique to design any non-CSS codes with one logical qubit. We show there exists a $(n+1)$ qubit cluster state which upon measurement gives the desired $[[n,1]]$ code.
Secure UAV-to-Ground MIMO Communications: Joint Transceiver and Location Optimization

Unmanned aerial vehicles (UAVs) are foreseen to constitute promising airborne communication devices as a benefit of their superior channel quality. But UAV-to-ground (U2G) communications are vulnerable to eavesdropping. Hence, we conceive a sophisticated physical layer security solution for improving the secrecy rate of multi-antenna aided U2G systems. Explicitly, the secrecy rate of the U2G MIMO wiretap channels is derived by using random matrix theory. The resultant explicit expression is then applied in the joint optimization of the MIMO transceiver and the UAV location relying on an alternating optimization technique. Our numerical results show that the joint transceiver and location optimization conceived facilitates secure communications even in the challenging scenario, where the legitimate channel of confidential information is inferior to the eavesdropping channel.
FSHMEM: Supporting Partitioned Global Address Space on FPGAs for Large-Scale Hardware Acceleration Infrastructure

By providing highly efficient one-sided communication with globally shared memory space, Partitioned Global Address Space (PGAS) has become one of the most promising parallel computing models in high-performance computing (HPC). Meanwhile, FPGA is getting attention as an alternative compute platform for HPC systems with the benefit of custom computing and design flexibility. However, the exploration of PGAS has not been conducted on FPGAs, unlike the traditional message passing interface. This paper proposes FSHMEM, a software/hardware framework that enables the PGAS programming model on FPGAs. We implement the core functions of GASNet specification on FPGA for native PGAS integration in hardware, while its programming interface is designed to be highly compatible with legacy software. Our experiments show that FSHMEM achieves the peak bandwidth of 3813 MB/s, which is more than 95% of the theoretical maximum, outperforming the prior works by 9.5×. It records 0.35us and 0.59us latency for remote write and read operations, respectively. Finally, we conduct a case study on the two Intel D5005 FPGA nodes integrating Intel's deep learning accelerator. The two-node system programmed by FSHMEM achieves 1.94× and 1.98× speedup for matrix multiplication and convolution operation, respectively, showing its scalability notential for HPC infrastructure.
Simple and complex spiking neurons: perspectives and analysis in a simple STDP scenario

Spiking neural networks (SNNs) are largely inspired by biology and neuroscience and leverage ideas and theories to create fast and efficient learning systems. Spiking neuron models are adopted as core processing units in neuromorphic systems because they enable event-based processing. Among many neuron models, the integrate-and-fire (I&F) models are often adopted, with the simple leaky I&F (LIF) being the most used. The reason for adopting such models is their efficiency and/or biological plausibility. Nevertheless, rigorous justification for adopting LIF over other neuron models for use in artificial learning systems has not yet been studied. This work considers various neuron models in the literature and then selects computational neuron models that are single-variable, efficient, and display different types of complexities. From this selection, we make a comparative study of three simple I&F neuron models, namely the LIF, the quadratic I&F (QIF) and the exponential I&F (EIF), to understand whether the use of more complex models increases the performance of the system and whether the choice of a neuron model can be directed by the task to be completed. Neuron models are tested within an SNN trained with spike-timing dependent plasticity (STDP) on a classification task on the N-MNIST and DVS gestures datasets. Experimental results reveal that more complex neurons manifest the same ability as simpler ones to achieve high levels of accuracy on a simple dataset (N-MNIST), albeit requiring comparably more hyper-parameter tuning. However, when the data possess richer spatio-temporal features, the QIF and EIF neuron models steadily achieve better results. This suggests that accurately selecting the model based on the richness of the feature spectrum of the data could improve the whole system’s performance. Finally, the code implementing the spiking neurons in the SpykeTorch framework is made publicly available.
Optimizing Bipedal Maneuvers of Single Rigid-Body Models for Reinforcement Learning

In this work, we propose a method to generate reduced-order model reference trajectories for general classes of highly dynamic maneuvers for bipedal robots for use in sim-to-real reinforcement learning. Our approach is to utilize a single rigid-body model (SRBM) to optimize libraries of trajectories offline to be used as expert references that guide learning by regularizing behaviors when incorporated in the reward function of a learned policy. This method translates the model's dynamically rich rotational and translational behavior to a full-order robot model and successfully transfers to real hardware. The SRBM's simplicity allows for fast iteration and refinement of behaviors, while the robustness of learning-based controllers allows for highly dynamic motions to be transferred to hardware. Within this work we introduce a set of transferability constraints that amend the SRBM dynamics to actual bipedal robot hardware, our framework for creating optimal trajectories for a variety of highly dynamic maneuvers as well as our approach to integrating reference trajectories for a high-speed running reinforcement learning policy. We validate our methods on the bipedal robot Cassie on which we were successfully able to demonstrate highly dynamic grounded running gaits up to 3.0 m/s.
A Privacy-Preserving Energy Management System for Cooperative Multi-Microgrid Networks

This paper presents an Energy Management System (EMS) that considers power exchanges between a set of interconnected microgrids (MGs) and the main grid, in the context of Multi-MG (MMG) systems. The model is first formulated as a centralized optimization problem, which is then decomposed into subproblems corresponding to each MG, using Lagrangian relaxation, and solved through a distributed approach using a subgradient method. The proposed model determines the power exchanges minimizing the operation cost of each MG, considering grid constraints and preserving the privacy of each MG by not revealing their generation cost and demand information. The distributed approach is validated with respect to the centralized problem, and various case studies are presented to demonstrate the performance of the proposed approach, comparing the costs of the MGs operating individually and cooperatively. The results show that all MGs in the MMG system improve their cost as consequence of the power exchanges, thus demonstrating the advantages of interconnecting MGs.
Progressively-connected Light Field Network for Efficient View Synthesis

This paper presents a Progressively-connected Light Field network (ProLiF), for the novel view synthesis of complex forward-facing scenes. ProLiF encodes a 4D light field, which allows rendering a large batch of rays in one training step for image- or patch-level losses. Directly learning a neural light field from images has difficulty in rendering multi-view consistent images due to its unawareness of the underlying 3D geometry. To address this problem, we propose a progressive training scheme and regularization losses to infer the underlying geometry during training, both of which enforce the multi-view consistency and thus greatly improves the rendering quality. Experiments demonstrate that our method is able to achieve significantly better rendering quality than the vanilla neural light fields and comparable results to NeRF-like rendering methods on the challenging LLFF dataset and Shiny Object dataset. Moreover, we demonstrate better compatibility with LPIPS loss to achieve robustness to varying light conditions and CLIP loss to control the rendering style of the scene. Project page: https://totoro97.github. io/projects/prolif.
An X-Ray Study of Coronally Connected Active Eclipsing Binaries

We present a detailed X-ray analysis and imaging of stellar coronae of five coronally connected eclipsing binaries, namely, 44 Boo, DV Psc, ER Vul, XY UMa, and TX Cnc. Both components of these binaries are found to be active. The X-ray light curves of detached and semidetached type systems show eclipsed-like features, whereas no evidence for coronal eclipsing is shown by the contact type systems. The X-ray light curve of DV Psc shows the O’Connell-like effect where the first maximum is found to be brighter than that of the second. Results of the coronal imaging using three-dimensional deconvolution of X-ray light curves show the coronae of all these binaries are either in the contact or over-contact configuration, with the primary being 1.7–4 times X-ray brighter than its companion. In the current sample, a minimum of 30%–50% of total UV emission is found to originate from the photosphere and positively correlated with the X-ray emission. X-ray spectra of these systems are well explained by two-temperature plasma models. The temperature corresponding to cool and hot components of plasma are found to be in the ranges of 0.25–0.64 and 0.9–1.1 keV, respectively. For the majority of binaries in the sample, the phase-resolved X-ray spectral analysis shows the orbital modulation in X-ray luminosity and emission measure corresponding to the hot component. A total of seven flaring events are also detected in the four systems with the flare energy in the range of (1.95–27.0) × 1033 erg and loop length of the order of 109–11 cm.
Emergence of highly bond-dependent anisotropic magnetic interactions in Sr 4 RhO 6 : a theoretical study

The quantum spin liquid states as a natural ground state of the Kitaev model have led to a quest for new materials candidates hosting Kitaev physics. Yet, there are very few materials hosting Kitaev physics. Using a combination of ab initio and model Hamiltonian methods, we propose that Sr 4 RhO 6 belongs to this category. With a tight-binding model and exact diagonalization approach, we show that despite substantial trigonal-like distortion, the electronic and magnetic properties of Sr 4 RhO 6 can be well described in terms of pseudo-spin = 1/2 states. Magnetic interactions among this pseudo-spins, estimated using the second-order perturbation method are highly bond-dependent anisotropic in nature with two particularly noticeable features, antiferromagnetic Kitaev and Dzyaloshinskii-Moriya interactions. The gaped spin-wave spectra of Sr 4 RhO 6 obtained with linear spin-wave theory are consistent with the underlying magnetic frustration. Further spectra analysis reveals a crucial role of these interactions in Sr 4 RhO 6 . In the end, we brieﬂy discuss the enhanced magnetic interactions in a similar material Ca 4 RhO 6 and propose it be another magnetic candidate belonging to the 4 d class.
Mn composition dependence of phase stability of Ga1−xMnxAs alloys from first principles

The Ga1−xMnxAs alloy is one of the most intensively studied spintronics materials, but its best known Curie temperature TC is still low for practical application. The phase instability of the zincblende Ga1−xMnxAs is known to be one of the main issues hindering the increase of the Mn incorporation, which is crucial in realizing the high TC at room temperature level. While the substitutional dilute Mn incorporation in this alloy indicates a zincblende structure (alpha phase), a single phase bulk alpha-MnAs has not yet been grown—instead, the bulk MnAs exists in a NiAs structure (beta phase) in nature. Here, we determine the phase stability of the alpha- and beta-phase Ga1−xMnxAs alloys for various Mn compositions through the fully optimized total energy calculations using the all-electron full-potential linearized augmented plane-wave method. The alpha phase is found to be energetically more stable over the beta phase for the Mn composition of less than 10% in agreement with experiment. We also find that t.