Miracle-dz/UniFAR-FTUs huggingface dataset:
SpChar: Characterizing the Sparse Puzzle via Decision Trees

Sparse matrix computation is crucial in various modern applications, including large-scale graph analytics, deep learning, and recommendation systems. The performance of these algorithms varies greatly depending on the structure of the input matrix, making it difficult to gain a comprehensive understanding of sparse computation and its relationship to inputs, algorithms, and target machine architecture. Despite extensive research on certain sparse algorithms, such as SpMV, the overall family of sparse algorithms has yet to be investigated as a whole. In this paper, we introduce SpChar, a workload characterization methodology for general sparse computation. SpChar employs tree-based models to identify the most relevant hardware and input characteristics, starting from hardware and input-related metrics gathered from Performance Monitoring Counters and matrices. Our analysis enables the creation of a characterization loop that facilitates the optimization of sparse computation by mapping the impact of architectural features to inputs and algorithmic choices. We apply SpChar to more than 600 matrices from the SuiteSparse Matrix collection and three state-of-the-art Arm CPUs to determine the critical hardware and software characteristics that affect sparse computation. In our analysis, we determine that the biggest limiting factors for high-performance sparse computation are (1) the latency of the memory system, (2) the pipeline flush overhead resulting from branch misprediction, and (3) the poor reuse of cached elements. However, the degree to which those impact the performance of a CPU greatly depends on the algorithm and the input data.
Phenomenology of the Dark Matter sector in the 2HDM extended with Complex Scalar Singlet

The Two Higgs Doublet model augmented with a complex scalar singlet (2HDMS) is a well-motivated candidate for Beyond Standard Model (BSM) Physics. We investigate the dark matter phenomenology of the 2HDMS with the complex scalar singlet as the dark matter candidate. We perform a study of the parameter space allowed by existing experimental constraints from dark matter, flavour physics and collider searches. The distinction between real and complex scalar dark matter in the context of the 2HDMS is also performed. Furthermore, we discuss the discovery potential for the 2HDMS at the HL-LHC and at future high-energy $e^+e^-$ colliders.
Weak pattern convergence for SLOPE and its robust versions

The Sorted L-One Estimator (SLOPE) is a popular regularization method in regression, which induces clustering of the estimated coefficients. That is, the estimator can have coefficients of identical magnitude. In this paper, we derive an asymptotic distribution of SLOPE for the ordinary least squares, Huber, and Quantile loss functions, and use it to study the clustering behavior in the limit. This requires a stronger type of convergence since clustering properties do not follow merely from the classical weak convergence. For this aim, we utilize the Hausdorff distance, which provides a suitable notion of convergence for the penalty subdifferentials and a bridge toward weak convergence of the clustering pattern. We establish asymptotic control of the false discovery rate for the asymptotic orthogonal design of the regressor. We also show how to extend the framework to a broader class of regularizers other than SLOPE.
FireFly: A High-Throughput Hardware Accelerator for Spiking Neural Networks With Efficient DSP and Memory Optimization

Spiking neural networks (SNNs) have been widely used due to their strong biological interpretability and high-energy efficiency. With the introduction of the backpropagation algorithm and surrogate gradient, the structure of SNNs has become more complex, and the performance gap with artificial neural networks (ANNs) has gradually decreased. However, most SNN hardware implementations for field-programmable gate arrays (FPGAs) cannot meet arithmetic or memory efficiency requirements, which significantly restricts the development of SNNs. They do not delve into the arithmetic operations between the binary spikes and synaptic weights or assume unlimited on-chip RAM resources using overly expensive devices on small tasks. To improve arithmetic efficiency, we analyze the neural dynamics of spiking neurons, generalize the SNN arithmetic operation to the multiplex-accumulate operation, and propose a high-performance implementation of such operation by utilizing the DSP48E2 hard block in Xilinx Ultrascale FPGAs. To improve memory efficiency, we design a memory system to enable efficient synaptic weights and membrane voltage memory access with reasonable on-chip RAM consumption. Combining the above two improvements, we propose an FPGA accelerator that can process spikes generated by the firing neurons on-the-fly (FireFly). FireFly is the first SNN accelerator that incorporates DSP optimization techniques into SNN synaptic operations. FireFly is implemented on several FPGA edge devices with limited resources but still guarantees a peak performance of 5.53 TOP/s at 300 MHz. As a lightweight accelerator, FireFly achieves the highest computational density efficiency compared with existing research using large FPGA devices.
Predicting Short Term Energy Demand in Smart Grid: A Deep Learning Approach for Integrating Renewable Energy Sources in Line with SDGs 7, 9, and 13

Integrating renewable energy sources into the power grid is becoming increasingly important as the world moves towards a more sustainable energy future in line with SDG 7. However, the intermittent nature of renewable energy sources can make it challenging to manage the power grid and ensure a stable supply of electricity, which is crucial for achieving SDG 9. In this paper, we propose a deep learning-based approach for predicting energy demand in a smart power grid, which can improve the integration of renewable energy sources by providing accurate predictions of energy demand. Our approach aligns with SDG 13 on climate action, enabling more efficient management of renewable energy resources. We use long short-term memory networks, well-suited for time series data, to capture complex patterns and dependencies in energy demand data. The proposed approach is evaluated using four historical short-term energy demand data datasets from different energy distribution companies, including American Electric Power, Commonwealth Edison, Dayton Power and Light, and Pennsylvania-New Jersey-Maryland Interconnection. The proposed model is also compared with three other state-of-the-art forecasting algorithms: Facebook Prophet, Support Vector Regression, and Random Forest Regression. The experimental results show that the proposed REDf model can accurately predict energy demand with a mean absolute error of 1.4%, indicating its potential to enhance the stability and efficiency of the power grid and contribute to achieving SDGs 7, 9, and 13. The proposed model also has the potential to manage the integration of renewable energy sources in an effective manner.
NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras

We present NeuriCam, a novel deep learning-based system to achieve video capture from low-power dual-mode IoT camera systems. Our idea is to design a dual-mode camera system where the first mode is low power (1.1 mW) but only outputs grey-scale, low resolution and noisy video and the second mode consumes much higher power (100 mW) but outputs color and higher resolution images. To reduce total energy consumption, we heavily duty cycle the high power mode to output an image only once every second. The data for this camera system is then wirelessly sent to a nearby plugged-in gateway, where we run our real-time neural network decoder to reconstruct a higher-resolution color video. To achieve this, we introduce an attention feature filter mechanism that assigns different weights to different features, based on the correlation between the feature map and the contents of the input frame at each spatial location. We design a wireless hardware prototype using off-the-shelf cameras and address practical issues including packet loss and perspective mismatch. Our evaluations show that our dual-camera approach reduces energy consumption by 7x compared to existing systems. Further, our model achieves an average greyscale PSNR gain of 3.7 dB over prior single and dual-camera video super-resolution methods and 5.6 dB RGB gain over prior color propagation methods. Open-source code: https://github.com/vb000/NeuriCam.
Theory of magnetoresistance due to lattice dislocations in face-centred cubic metals

A theoretical model to describe the low temperature magneto-resistivity of high purity copper single and polycrystals containing different density and distribution of dislocations has been developed. In the model, magnetoresistivity tensor is evaluated numerically using the effective medium approximation. The anisotropy of dislocation-induced relaxation time is considered by incorporating two independent energy bands with different relaxation times and the spherical and cylindrical Fermi surfaces representing open, extended and closed electron orbits. The effect of dislocation microstructure is introduced by means of two adjustable parameters corresponding to the length and direction of electron orbits in the momentum space, which permits prediction of magnetoresistance of FCC metals containing different density and distribution of dislocations. The results reveal that dislocation microstructure influences the character of the field-dependent magnetoresistivity. In the orientation of the open orbits, the quadratic variation in magnetoresistivity changes to quasi-linear as the density of dislocations increases. In the closed orbit orientation, dislocations delay the onset of magnetoresistivity saturation. The results indicate that in the open orbit orientations of the crystals, the anisotropic relaxation time due to small-angle dislocation scattering induces the upward deviation from Kohler’s rule. In the closed orbit orientations Kohler’s rule holds, independent of the density of dislocations. The results obtained with the model show good agreement with the experimental measurements of transverse magnetoresistivity in deformed single and polycrystal samples of copper at 2 K.
Deflation acceleration of lattice QCD simulations

Close to the chiral limit, many calculations in numerical lattice QCD can potentially be accelerated using low-mode deflation techniques. In this paper it is shown that the recently introduced domain-decomposed deflation subspaces can be propagated along the field trajectories generated by the Hybrid Monte Carlo (HMC) algorithm with a modest effort. The quark forces that drive the simulation may then be computed using a deflation-accelerated solver for the lattice Dirac equation. As a consequence, the computer time required for the simulations is significantly reduced and an improved scaling behaviour of the simulation algorithm with respect to the quark mass is achieved.
A GENERAL PROCEDURE FOR ESTIMATING THE POPULATION PARAMETER IN THE PRESENCE OF RANDOM NON-RESPONSE

In this paper we have considered the problem of estimating the general population parameter in the presence of random non-response using an auxiliary variable in three different situations. A general family of estimators of the parameter has been suggested and its properties are studied. In particular, the problem of estimating the population coefficient of variation has been considered.
LOW-COST MODIFIED ADAPTIVE CONTROL (PHASE I), STATE-BASED MODELING: AN ANALYSIS OF ERROR IN ORDER APPROXIMATION

When building discrete time, discrete-state data-driven models, transition probabilities are stored in Markov transition matrices. Augmenting the history or "order" of the associated Markov chains allows for present states to be predicated upon states in previous time periods without contradicting the Markovian property. However, when faced with real data, the "true" order of the model is not always known. This project details the inadequacies of the present method of evaluating order, and outlines and evaluates new methods to approximate the model order including simulation, the likelihood-based Bayesian Information Criterion, Schwarz Information Criterion, and Akaike Information Criterion as well as using comparison vectors, an ad-hoc method. In addition, this project suggests an approach for using these techniques to approximate order, and discusses the error associated with using that prediction model.
Singular Point Detection of Fingerprint Based on Direction

It is very important to detect singular point(core and delta) accurately and reliably for classification and matching of fingerprint.In this paper,introduce a good method for singularity detection in fingerprint to improve accuracy of the position and reliability of the singular point.Core and delta are detected by the Poincare Index method.The falsehood singular points are eliminated in basis of correlation rule.At last,experiment results show that the method detect accurately and reliably the singular points.Experimental results on fingerprint images and comparison with other methods show less computational complexity and more robustness.
Efficacy and safety of low- and high-dose slow oral egg immunotherapy for hen's egg allergy: Single-center non-inferiority randomized trial.

BACKGROUND Low-dose oral immunotherapy (OIT) is a safe treatment for hen's egg allergy; however, comparison of its therapeutic effects with those of high-dose OIT has not been reported. OBJECTIVE To compare the efficacy of low- and high-dose boiled egg-white (EW) OIT for hen's egg allergy. METHODS Patients with hen's egg allergy were randomly assigned to two groups: OIT using hard-boiled EW with a maximum maintenance dose of 2 and 20 g in the low-dose (L-D) and high-dose (H-D) groups, respectively. The intake dose was ingested twice a week, increased by approximately 20% per week until reaching the target maintenance dose (2 or 20 g hard-boiled EW), and maintained thereafter according to the schedule. The threshold was confirmed via oral food challenge (OFC) after 6 months, and the difference in the proportion of subjects passing the exit OFC between groups was evaluated. RESULTS Fifty-two patients (L-D, n = 23; H-D, n = 29) were enrolled. Thirty-three patients (L-D, n = 17; H-D, n = 16) completed the 6-month OIT and underwent an exit OFC. In total, three (L-D, 3/17; H-D, 3/16) patients in each group tested negative for an exit OFC with a 20-g reactive dose (p = 1.000). EW-specific IgE levels in both groups decreased significantly after OIT (L-D, p < 0.001; H-D, p = 0.002). CONCLUSIONS A threshold-elevating effect was observed in the L-D group, not inferior to that in the H-D group. Low-dose OIT may be appropriate to treat hen's egg allergy for the first 6 months.
Ensemble species distribution modeling of Culex tarsalis (Diptera: Culicidae) in the continental United States

West Nile virus (WNV) is the primary mosquito-borne disease in the United States and has had case reports every year since its introduction in 1999. As such, it is critical that we characterize the distribution of WNV vectors. Estimates of Culex tarsalis Coquillett species distribution, a major WNV vector, are scarce. We used ensemble distribution modeling to estimate habitat suitability for this species across the contiguous United States by consolidating presence data from four publicly available mosquito trapping data servers.The central plains region and much of the western US were estimated to have high habitat suitability. We identified multiple metrics of temperature and precipitation to be important in predicting the occurrence of Cx. tarsalis in a given geographic area. Furthermore, we observed habitat suitability for Cx. tarsalis to be significantly higher in areas with a high incidence of West Nile neuroinvasive disease compared to areas with low WN disease incidence, suggesting that Cx. tarsalis is present in regions with a high incidence of disease.
A note on model uncertainty in linear regression

Summary. We consider model selection uncertainty in linear regression. We study theoretically and by simulation the approach of Buckland and co-workers, who proposed estimating a parameter common to all models under study by taking a weighted average over the models, using weights obtained from information criteria or the bootstrap. This approach is compared with the usual approach in which the 'best' model is used, and with Bayesian model averaging. The weighted predictor behaves similarly to model averaging, with generally more realistic mean-squared errors than the usual model-selection-based estimator.
Composition dependence of band alignments in GaxIn1−xAsySb1−y heterojunctions lattice matched to GaSb and InAs

A theoretical model utilizing a universal tight binding method and a correlated function expansion technique is presented to calculate the valence band maximum (VBM) and the conduction band minimum (CBM) of the binary (GaAs, InAS, GaSb, and InSb) and quaternary alloy GaxIn1−xAsySb1−y systems. By organizing the relative positions of the VBM and CBM between semiconductors, the band alignments and band types in the heterojunctions are determined. A straddling (type-I) band alignment in InAs/GaAs, InSb/GaAs, and GaSb/InSb, staggered (type-II) band alignment in GaSb/GaAs, and broken (type-III) band alignment in InSb/InAs and InAs/GaSb are found respectively. In addition, the compositional variations of VBM, CBM, valence band offset, conduction band offset, and band type for the alloy GaxIn1−xAsySb1−y lattice matched on GaSb and InAs are obtained as increasing the composition x. A pronounced upward bowing for the VBM and a very slight upward bowing (almost linear) for CBM are found, respectively. By controlling.
Identifying the social context of single- and mixed-species group formation in large African herbivores

Despite continued interest in mixed-species groups, we still lack a unified understanding of how ecological and social processes work across scales to influence group formation. Recent work has revealed ecological correlates of mixed-species group formation, but the mechanisms by which concomitant social dynamics produce these patterns, if at all, is unknown. Here, we use camera trap data for six mammalian grazer species in Serengeti National Park. Building on previous work, we found that ecological variables, and especially forage quality, influenced the chances of species overlap over small spatio-temporal scales (i.e. on the scales of several metres and hours). Migratory species (gazelle, wildebeest and zebra) were more likely to have heterospecific partners available in sites with higher forage quality, but the opposite was true for resident species (buffalo, hartebeest and topi). These findings illuminate the circumstances under which mixed-species group formation is even possible. Next, we found that greater heterospecific availability was associated with an increased probability of mixed-species group formation in gazelle, hartebeest, wildebeest and zebra, but ecological variables did not further shape these patterns. Overall, our results are consistent with a model whereby ecological and social drivers of group formation are species-specific and operate on different spatio-temporal scales. This article is part of the theme issue ‘Mixed-species groups and aggregations: shaping ecological and behavioural patterns and processes’.
Automated Emotion Recognition in the Workplace: How Proposed Technologies Reveal Potential Futures of Work

Emotion recognition technologies, while critiqued for bias, validity, and privacy invasion, continue to be developed and applied in a range of domains including in high-stakes settings like the workplace. We set out to examine emotion recognition technologies proposed for use in the workplace, describing the input data and training, outputs, and actions that these systems take or prompt. We use these design features to reflect on these technologies' implications using the ethical speculation lens. We analyzed patent applications that developed emotion recognition technologies to be used in the workplace (N=86). We found that these technologies scope data collection broadly; claim to reveal not only targets' emotional expressions, but also their internal states; and take or prompt a wide range of actions, many of which impact workers' employment and livelihoods. Technologies described in patent applications frequently violated existing guidelines for ethical automated emotion recognition technology. We demonstrate the utility of using patent applications for ethical speculation. In doing so, we suggest that 1) increasing the visibility of claimed emotional states has the potential to create additional emotional labor for workers (a burden that is disproportionately distributed to low-power and marginalized workers) and contribute to a larger pattern of blurring boundaries between expectations of the workplace and a worker's autonomy, and more broadly to the data colonialism regime; 2) Emotion recognition technology's failures can be invisible, may inappropriately influence high-stakes workplace decisions and can exacerbate inequity. We discuss the implications of making emotions and emotional data visible in the workplace and submit for consideration implications for designers of emotion recognition, employers who use them, and policymakers.
Methodological Middle Spaces: Addressing the Need for Methodological Innovation to Achieve Simultaneous Realism, Control, and Scalability in Experimental Studies of AI-Mediated Communication

As AI-mediated communication (AI-MC) becomes more prevalent in everyday interactions, it becomes increasingly important to develop a rigorous understanding of its effects on interpersonal relationships and on society at large. Controlled experimental studies offer a key means of developing such an understanding, but various complexities make it difficult for experimental AI-MC research to simultaneously achieve the criteria of experimental realism, experimental control, and scalability. After outlining these methodological challenges, this paper offers the concept of methodological middle spaces as a means to address these challenges. This concept suggests that the key to simultaneously achieving all three of these criteria is to abandon the perfect attainment of any single criterion. This concept's utility is demonstrated via its use to guide the design of a platform for conducting text-based AI-MC experiments. Through a series of three example studies, the paper illustrates how the concept of methodological middle spaces can inform the design of specific experimental methods. Doing so enabled these studies to examine research questions that would have been either difficult or impossible to investigate using existing approaches. The paper concludes by describing how future research could similarly apply the concept of methodological middle spaces to expand methodological possibilities for AI-MC research in ways that enable contributions not currently possible.