Miracle-dz/UniFAR-FTUs huggingface dataset:
Firefly algorithm combined with Newton method to identify boundary conditions for transient heat conduction problems

ABSTRACT Firefly algorithm combined with Newton method (FA–NM) is proposed for identifying time-dependent boundary conditions of two-dimensional transient heat conduction problems with a heat source. The dual reciprocity boundary element method (DRBEM) is applied to solve the direct problem. The improved firefly algorithm has not only a good global search ability, but also a good local search ability. FA–NM can acquire accurate results with much less iterations. Furthermore, different measurement points and noises are also considered. A small number of measurement points cannot obtain precise results. With the increase in the measurement points to a certain value, the results are more accurate. When the measurement noises are not very large, FA–NM can get desirable results. With the decrease in measurement noises, the results are in better agreement with the exact solutions. Numerical results also indicate that the more front the time substep is, the more accurate the estimated boundary conditions are.
Ocular myasthenia gravis

Purpose of review To review ocular myasthenia gravis (OMG), a localized form of myasthenia gravis clinically involving only the extraocular, levator palpebrae superioris, and orbicularis oculi muscles. Recent findings Ocular manifestations can masquerade as a variety of ocular motility disorders, including central nervous system disorders and peripheral cranial nerve palsies. While sparing the pupils, the diagnosis and management can be challenging. Summary Because several diagnostic and treatment options are available for OMG, clinicians must decide the sequence and combination based on the level of disease activity and patient disability.
A Circumbinary Debris Disk in a Polluted White Dwarf System

Planetary systems commonly survive the evolution of single stars, as evidenced by terrestrial-like planetesimal debris observed orbiting and polluting the surfaces of white dwarfs. This letter reports the identification of a circumbinary dust disk surrounding a white dwarf with a substellar companion in a 2.27 hr orbit. The system bears the dual hallmarks of atmospheric metal pollution and infrared excess, however the standard (flat and opaque) disk configuration is dynamically precluded by the binary. Instead, the detected reservoir of debris must lie well beyond the Roche limit in an optically thin configuration, where erosion by stellar irradiation is relatively rapid. This finding demonstrates that rocky planetesimal formation is robust around close binaries, even those with low mass ratios.
Steganalysis feature extractor Triplet Loss SVM Two stream Fusion Face Classification Stream Patch Triplet Stream

We propose a two-stream network for face tampering detection. We train GoogLeNet to detect tampering artifacts in a face classification stream, and train a patch based triplet network to leverage features capturing local noise residuals and camera characteristics as a second stream. In addition, we use two different online face swapping applications to create a new dataset that consists of 2010 tampered images, each of which contains a tampered face. We evaluate the proposed two-stream network on our newly collected dataset. Experimental results demonstrate the effectiveness of our method.
Recursive Regression with Neural Networks : Approximating Hamilton-Jacobi-Isaacs PDE Solutions ∗

The majority of methods used to compute approximations to the Hamilton-Jacobi-Isaacs partial differential equation (HJI PDE) rely on the discretization of the state space to perform dynamic programming updates. This type of approach is known to suffer from the curse of dimensionality due to the exponential growth in grid points with the state dimension. In this work we present an approximate dynamic programming algorithm that computes an approximation of the solution of the HJI PDE by alternating between solving a regression problem and solving a minimax problem using a feedforward neural network as the function approximator. We find that this method requires less memory to run and to store the approximation than traditional gridding methods, and we test it on a few systems of two, three and six dimensions.
Label Denoising Adversarial Network ( LDAN ) for Inverse Lighting of Faces Supplementary Material

In this section, we show the quantitative evaluation of LDAN under different lighting conditions. We take the lighting condition shown in Figure 4 in our paper as example. In classification we expect extreme lighting to be differentiated more easily than normal lighting because equal changes in the angle of lighting directions affect frontal lighting less than side lighting under the Lambertian model. Table 1 shows the top-1 classification results of LDAN and SIRFS for faces under four illumination conditions corresponding to Figure 4 (a) to (d) in the original submission. As expected, under extreme side lighting (i.e., (b) and (d)) or dark lighting (i.e., (a)), LDAN predicts very consistent lightings that lead to good performance. Classifications in normal lighting (c) are harder for both methods, but LDAN is far superior to SIRFS.
Factors associated with the mental health of adolescent university students during COVID-19 quarantine in Japan

Background: University students in Japan have been quarantined during the current COVID-19 pandemic. The resulting social avoidance may increase students’ anxiety about the pandemic and adversely affect their mental health. To obtain basic data for a university health policy, we investigated factors associated with the mental health status of 570 students of a national university in Japan during quarantine. Methods: Participants completed an online questionnaire assessing respondent characteristics (gender, age, department, domicile status), subjective sense of anxiety about COVID-19, and number of social supports. The General Health Questionnaire (GHQ)-12 was used as a marker for mental health status, and sense of coherence (SOC) was used as a marker for stress resistance. Chi-square tests, t-tests, Pearson’s correlation coefficients, and multiple regression analysis were conducted to identify factors associated with GHQ score. Two-way analysis of variance (ANOVA) was conducted to identify the interaction between anxiety score and SOC scores. Results: Females showed significantly more adverse mental health than males. Regardless of gender, the strongest factor associated with GHQ score was anxiety about COVID-19, which impaired mental health status, followed by SOC score, which improved mental health status. The ANOVA results showed a significant interaction effect on GHQ scores between anxiety and SOC score in males. High SOC scores mitigated the negative effect of anxiety on mental health more than did low SOC scores, especially in males with low to medium levels of anxiety.Conclusions: The results indicate that fear of COVID-19 impaired mental health status, but SOC worked as a buffer to mitigate the negative effect of anxiety on mental health, especially among male students. Given the health risks associated with gender, universities should provide psychological care to encourage students to maintain a confident daily routine and to foster higher SOC, especially for male students.
Security in Automotive Networks: Lightweight Authentication and Authorization

Copyright and reuse: The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.
The long-term postpartum anxiety and depression outcomes for mothers with COVID-19 during pregnancy

The COVID-19 pandemic is associated with negative mental outcomes in the early postpartum period. Objective: To assess the long-term postpartum mental health of women infected with COVID-19 during pregnancy. Methods: Cross-sectional study in 101 pregnant women who gave birth in a tertiary center during the COVID-19 pandemic, between March 31, 2020, and November 30, 2021. The pregnant women were classified into 2 groups as COVID-19 positive (study group, n=52) and COVID-19 negative (control group, n=49). Sociodemographic and obstetric data were collected by questionnaire in the early (≤6 months) and late (6-18 months) postpartum periods. Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI) scores were calculated by analysis of the participants' data. Results: The mean BDI score and the rate of depression (BDI score >13) in COVID-19 positive patients were higher in the early postpartum period than in the late postpartum period. According to multivariate linear regression analysis, there was a significant correlation between the BDI score of COVID-19 patients and educational level and employment status. According to the same analysis, there was a significant correlation between the BAI score of COVID-19 patients and spousal support, marital relationship, and birth-related diseases. We found that COVID-19 positive and COVID-19 negative patients had similar BDI and BAI scores in the early (≤6 months) and late (6-18 months) postpartum periods. In addition, rates of anxiety and depression were similar in both groups at the same postpartum periods. Conclusion: In our study, COVID-19 infection in pregnancy had no significant additional impact on long-term postpartum maternal mental health.
Progress and Gravity: Overcoming Divisions between General Relativity and Particle Physics and between Physics and HPS

Reflective equilibrium between physics and philosophy, and between GR and particle physics, is fruitful and rational. I consider the virtues of simplicity, conservatism, and conceptual coherence, along with perturbative expansions. There are too many theories to consider. Simplicity supplies initial guidance, after which evidence increasingly dominates. One should start with scalar gravity; evidence required spin 2. Good beliefs are scarce, so don't change without reason. But does conservatism prevent conceptual innovation? No: considering all serious possibilities (Feynman, Weinberg, etc.) could lead to Einstein's equations. (The rehabilitation of massive gravity shows that 'progress' isn't unidirectional.) GR is surprisingly intelligible. Energy localization makes sense if one believes Noether mathematics: an infinity of symmetries shouldn't produce just one energy. Hamiltonian change results from Lagrangian-equivalence. Causality poses conceptual questions. For GR, what are canonical 'equal-time' commutators? For massive spin 2, background causality exists but is violated. Both might be cured by engineering a background null cone respected by a gauge groupoid. Perturbative expansions can enlighten. They diagnose Einstein's 1917 'mass'-Lambda analogy. Ogievetsky-Polubarinov (1965) invented an infinity of massive spin 2 gravities---including ghost-free de Rham-Gabadadze-Tolley (2010) theories! ---perturbatively, and achieved the impossible (c.f. Weyl, Cartan): spinors in coordinates.
Supplementary Information (si)

Fig. S1. The 11 factor ASC was completed at the end of scanning days and is presented here as a radar plot with mean total values (0-1) for the LSD (blue) and placebo conditions (gray). Ten of the 11 factors were rated significantly higher under LSD than placebo (p < 0.05/11, Bonferonni corrected), with " anxiety " as the exception. See Figures S2 and S3 for additional subjective ratings.
ASAP: Accelerated Short-Read Alignment on Programmable Hardware

The proliferation of high-throughput sequencing machines ensures rapid generation of up to billions of short nucleotide fragments in a short period of time. This massive amount of sequence data can quickly overwhelm today's storage and compute infrastructure. This paper explores the use of hardware acceleration to significantly improve the runtime of short-read alignment, a crucial step in preprocessing sequenced genomes. We focus on the Levenshtein distance (edit-distance) computation kernel and propose the ASAP accelerator, which utilizes the intrinsic delay of circuits for edit-distance computation elements as a proxy for computation. Our design is implemented on an Xilinx Virtex 7 FPGA in an IBM POWER8 system that uses the CAPI interface for cache coherence across the CPU and FPGA. Our design is <inline-formula><tex-math notation="LaTeX">$200\times$</tex-math><alternatives><inline-graphic xlink:href="banerjee-ieq1-2875733.gif"/></alternatives></inline-formula> faster than an equivalent Smith-Waterman-C implementation of the kernel running on the host processor, <inline-formula><tex-math notation="LaTeX">$40-60\times$</tex-math><alternatives><inline-graphic xlink:href="banerjee-ieq2-2875733.gif"/></alternatives></inline-formula> faster than an equivalent Landau-Vishkin-C++ implementation of the kernel running on the IBM Power8 host processor, and <inline-formula><tex-math notation="LaTeX">$2\times$</tex-math><alternatives><inline-graphic xlink:href="banerjee-ieq3-2875733.gif"/></alternatives></inline-formula> faster for an end-to-end alignment tool for 120–150 base-pair short-read sequences. Further the design represents a <inline-formula><tex-math notation="LaTeX">$3760\times$</tex-math><alternatives><inline-graphic xlink:href="banerjee-ieq4-2875733.gif"/></alternatives></inline-formula> improvement over the CPU in performance/Watt terms.
Impulse-Based Control of Joints and Muscles

We propose a novel approach to proportional derivative (PD) control exploiting the fact that these equations can be solved analytically for a single degree of freedom. The analytic solution indicates what the PD controller would accomplish in isolation without interference from neighboring joints, gravity and external forces, outboard limbs, etc. Our approach to time integration includes an inverse dynamics formulation that automatically incorporates global feedback so that the per joint predictions are achieved. This effectively decouples stiffness from control so that we obtain the desired target regardless of the stiffness of the joint, which merely determines when we get there. We start with simple examples to illustrate our method and then move on to more complex examples including PD control of line segment muscle actuators.
Predictive Entropy Search for Multi-objective Bayesian Optimization

We present PESMO, a Bayesian method for identifying the Pareto set of multi-objective optimization problems, when the functions are expensive to evaluate. PESMO chooses the evaluation points to maximally reduce the entropy of the posterior distribution over the Pareto set. The PESMO acquisition function is decomposed as a sum of objective-specific acquisition functions, which makes it possible to use the algorithm in decoupled scenarios in which the objectives can be evaluated separately and perhaps with different costs. This decoupling capability is useful to identify difficult objectives that require more evaluations. PESMO also offers gains in efficiency, as its cost scales linearly with the number of objectives, in comparison to the exponential cost of other methods. We compare PESMO with other methods on synthetic and real-world problems. The results show that PESMO produces better recommendations with a smaller number of evaluations, and that a decoupled evaluation can lead to improvements in performance, particularly when the number of objectives is large.
Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization

Nowadays, algorithms with fast convergence, small memory footprints, and low per-iteration complexity are particularly favorable for artificial intelligence applications. In this paper, we propose a doubly stochastic algorithm with a novel accelerating multi-momentum technique to solve large scale empirical risk minimization problem for learning tasks. While enjoying a provably superior convergence rate, in each iteration, such algorithm only accesses a mini batch of samples and meanwhile updates a small block of variable coordinates, which substantially reduces the amount of memory reference when both the massive sample size and ultra-high dimensionality are involved. Empirical studies on huge scale datasets are conducted to illustrate the efficiency of our method in practice.
Three-dimensional numerical simulation of a supersonic under-expanded rectangular jet

ii ACKNOWLEDGMENTS . iv VITA . v LIST OF TABLES . x LIST OF FIGURES. xi LIST OF SYMBOLS. xv CHAPTER 1: INTRODUCTION . 1 1.1 Motivation . 1 1.2 Background . 3 1.2.1 Physics of supersonic jets flow . 3 1.2.2 Numerical simulation of supersonic jets flow . 8 1.3 Objectives . 12 CHAPTER 2: GOVERNING EQUATIONS . 15 2.1 The Navier-Stokes equations . 15 2.2 Non-dimensional form of the governing equations . 19 2.3 Transformations to generalized curvilinear coordinates . 21 CHAPTER 3: SOLUTION APPROACH. 25 3.1 Background . 25 3.2 Discretization scheme. 27 3.2.1 Spatial discretization. 27 3.2.2 Temporal discretization. 30 viii 3.2.3 Properties of the combined time-space discretization scheme . 31 3.3 Numerical errors and artificial dissipation . 33 3.3.1 Compact filtering . 35 3.3.2 Nonlinear characteristic-based filter. 39 CHAPTER 4: BENCHMARK PROBLEMS. 44 4.1 Linear convection of a gaussian pulse . 44 4.2 Nonlinear convection of a gaussian pulse. 47 4.3 Shock tube problem . 50 CHAPTER 5: GRID GENENRATION . 55 5.1 Computational domain . 55 5.2 Grid generation. 57 CHAPTER 6: BOUNDARY CONDITIONS . 62 6.1 Jet inflow boundary . 62 6.2 Jet solid wall boundary. 63 6.3 Upstream and far-field boundaries. 64 6.4 Outflow boundary . 65 CHAPTER 7: CODE DESIGN AND DEVELOPMENT . 67 7.1 Serial code design . 67 7.2 Parallel programming. 70 7.2.1 Background . 71 7.2.2 OpenMP implementation . 75 CHAPTER 8: RESLUTS AND ANALYSIS. 81 8.1 Initial conditions. 81 8.2 Performing computations and data acquisition . 82 8.3 Data analysis . 84 8.4 Time averaged results . 88 8.5 Unsteady flow-field visualization. 93 8.6 Turbulent flow-field analysis . 98 8.7 Acoustic near-field analysis. 108 ix 8.8 Comparison with two-dimensional simulation. 115 CHAPTER 9: CONCLUSIONS. 119 9.1 Summary and conclusions. 119 9.2 Future work and extensions. 123 APPENDIX A: HYPERBOLIC GRID GENERATION . 125 APPENDIX B: CHARACTERISTIC BOUNDARY CONDITIONS. 130 BIBLIOGRAPHY . 140
Online Degree-Bounded Steiner Network Design∗

The problem of satisfying connectivity demands on a graph while respecting given constraints has been a pillar of the area of network design since the early seventies [45, 14, 13, 12, 41]. The problem of degree-bounded spanning tree, introduced in Garey and Johnson’s Black Book of NP-Completeness [37], was first investigated in the pioneering work of Fürer and Raghavachari [17] (Allerton’90). In the degree-bounded spanning tree problem, the goal is to construct a spanning tree for a graph G = (V,E) with n vertices whose maximal degree is the smallest among all spanning trees. Let b∗ denote the maximal degree of an optimal spanning tree. Fürer and Raghavachari [17] give a parallel approximation algorithm which produces a spanning tree of degree at most O(log(n)b∗). Agrawal, Klein, and Ravi ([1]) consider the following generalizations of the problem. In the degree-bounded Steiner tree problem we are only required to connect a given subset T ⊆ V . In the even more general degree-bounded Steiner forest problem the demands consist of vertex pairs, and the goal is to output a subgraph in which for every demand there is a path connecting the pair. They design an algorithm that obtains a multiplicative approximation factor of O(log(n)). Their main technique is to reduce the problem to minimizing congestion under integral concurrent flow restrictions and to then use the randomized rounding approach due to Raghavan and Thompson ([43]). Shortly after the work of Agrawal et al., Fürer and Raghavachari [18] significantly improved the result for degree-bounded Steiner forest by presenting an algorithm which produces a Steiner forest with maximum degree at most b∗ + 1. They show that the same guarantee carries over to the directed variant of the problem as well. Their result is based on reducing the problem to that of computing a sequence of maximal matchings on certain auxiliary graphs. This result settles the approximability of the problem, as computing an optimal solution is NP-hard even in the spanning tree case. In this paper, we initiate the study of degree-bounded network design problems in an online setting, where connectivity demands appear over time and must be immediately satisfied. We first design a deterministic algorithm for online degree-bounded Steiner forest with a logarithmic competitive ratio. Then we show that this competitive ratio is asymptotically best possible by proving a matching lower bound for randomized algorithms that already holds for the Steiner tree variant of the problem. In the offline scenario, the results of Fürer, Raghavachari [17, 18] and Agrawal, Klein, Ravi [1] were the starting point of a very popular line of work on various degree-bounded network design problems [34, 20, 40, 31, 27, 15]. We refer the reader to the next sections for a brief summary. One particular variant that has been extensively studied is the edge-weighted degree-bounded spanning tree. Initiated by Marathe et al. ([34]), in this version, we are given a weight function over the edges and a bound b on the maximum degree of a vertex. The goal
Formal Worst-Case Analysis of Crosstalk Noise in Mesh-Based Optical Networks-on-Chip

Crosstalk noise is an intrinsic characteristic as well as a potential issue of photonic devices. In large scale optical networks-on-chips (ONoCs), crosstalk noise could cause severe performance degradation and prevent ONoC from communicating properly. The novel contribution of this paper is the systematical modeling and analysis of the crosstalk noise and the signal-to-noise ratio (SNR) of optical routers and mesh-based ONoCs using a formal method. Formal analytical models for the worst-case crosstalk noise and minimum SNR in mesh-based ONoCs are presented. The crosstalk analysis is performed at device, router, and network levels. A general 5 × 5 optical router model is proposed for router level analysis. The minimum SNR optical link candidates, which constrain the scalability of mesh-based ONoCs, are identified. It is also shown that symmetric mesh-based ONoCs have the best SNR performance. The presented formal analyses can be easily applied to other optical routers and mesh-based ONoCs. Finally, we present case studies of mesh-based ONoCs using the optimized crossbar and Crux optical routers to evaluate the proposed formal method. We find that crosstalk noise can significantly limit the scalability of mesh-based ONoCs. For example, when the mesh-based ONoC size, using optimized crossbar, is larger than 8 × 8, the optical signal power is smaller than the crosstalk noise power; when the network size is 16 × 16 and the input power is 0 dBm, in the worst-case, the signal power is -24.9 dBm and the crosstalk noise power is -11 dBm.