Miracle-dz/UniFAR-FTUs huggingface dataset:
An Intermediate Language for General Sparse Format Customization

The inevitable trend of hardware specialization drives an increasing use of custom data formats in processing sparse workloads, which are typically memory-bound. These formats facilitate the automated generation of target-aware data layouts to improve memory access latency and bandwidth utilization. However, existing sparse tensor programming models and compilers offer little or no support for productively customizing the sparse formats. Moreover, since these frameworks adopt an attribute-based approach for format abstraction, they cannot easily be extended to support general format customization. To overcome this deficiency, we propose UniSparse, an intermediate language that provides a unified abstraction for representing and customizing sparse formats. We also develop a compiler leveraging the MLIR infrastructure, which supports adaptive customization of formats. We demonstrate the efficacy of our approach through experiments running commonly-used sparse linear algebra operations with hybrid formats on multiple different hardware targets, including an Intel CPU, an NVIDIA GPU, and a simulated processing-in-memory (PIM) device.
Comparison of two 2-D numerical models for snow avalanche simulation.

Snow avalanches are gravitational processes characterised by the rapid movement of a snow mass, threatening inhabitants and damaging infrastructure in mountain areas. Such phenomena are complex events, and for this reason, different numerical models have been developed to reproduce their dynamics over a given topography. In this study, we focus on the two-dimensional numerical simulation tools RAMMS::AVALANCHE and FLO-2D, aiming to compare their performance in predicting the deposition area of snow avalanches. We also aim to assess the employment of the FLO-2D simulation model, normally used in water flood or mud/debris flow simulations, in predicting the motion of snow avalanches. For this purpose, two well-documented avalanche events that occurred in the Province of Bolzano (IT) were analyzed (Knollgraben, Pichler Erschbaum avalanches). The deposition area of each case study was simulated with both models through back-analysis processes. The simulation results were evaluated primarily by comparing the simulated deposition area with the observed one through statistical indices. Subsequently, the maximum flow depth, velocity and deposition depth were also compared between the simulation results. The results showed that RAMMS::AVALANCHE generally reproduced the observed deposits better compared to FLO-2D simulation. FLO-2D provided suitable results for wet and dry snow avalanches after a meticulous calibration of the rheological parameters, since they are not those typically considered in avalanche rheology studies. However, the results showed that FLO-2D can be used to study the propagation of snow avalanches and could also be adopted by practitioners to define hazard areas, expanding its field of application.
Energy Efficient Multi User Spatial Modulated NOMA for mmWave Communications

In this paper, we propose a downlink multi-user (MU) generalized spatial modulation (GSM) aided non-orthogonal multiple access (MU-GSM-NOMA) transmission technique for mmWave communications. Given a set of users, a user grouping algorithm is presented to partition the users into different clusters, and each of them is served by a dedicated uniform linear array (ULA) of antennas. The ULAs generate analog beams to steer the amplitude and phase-modulated symbols by employing the principles of NOMA. Further, the analog beams carry additional spatial information bits encoded in GSM for a particular cluster user. We propose simple digital combiners for receiver processing based on matched filtering, for which we have characterized the theoretical guarantee of the perfect detection of spatially modulated analog beams. Also, the work proposes an optimization algorithm to allocate power to the users, ensuring stable successive interference cancellation for a specific case of two users in each cluster. Finally, we present the numerical results, which demonstrate the outperformance of our proposed approach over the existing ones in terms of average sum rate and energy efficiency.
Exopolysaccharides-Mediated ZnO Nanoparticles for the Treatment of Aquatic Diseases in Freshwater Fish Oreochromis mossambicus

Bacterial fish disease outbreaks are a key concern for aquaculture. Complementary feed additives such as immunostimulants can serve as an ideal solution for disease prevention. Herein, we scrutinized the efficacy of exopolysaccharides (EPSs) from probiotic Bacillus licheniformis and EPS-mediated zinc oxide nanoparticles (EPS-ZnO NPs) for a diet to evaluate growth parameters, antioxidant enzyme activities, and immune stimulation together with disease resistance against Aeromonas hydrophila and Vibrio parahaemolyticus in Mozambique tilapia Oreochromis mossambicus. Fish were separated into seven groups, with six experimental groups fed with EPS and EPS-ZnO NPs at 2, 5, and 10 mg/g and a control fed a basal diet. The fish ingesting feed supplemented with EPS and EPS-ZnO NPs at 10 mg/g showed improved growth performance. Cellular and humoral-immunological parameters were tested in serum and mucus after 15 and 30 days of feeding. These parameters were substantially enriched with a 10 mg/g diet (p < 0.05) of EPS and EPS-ZnO NPs in comparison with the control. Furthermore, the EPS and EPS-ZnO NP supplemental diet actively enhanced the antioxidant response (glutathione peroxidase, superoxide dismutase, and catalase). In addition, the supplemental diet of EPS and EPS-ZnO NPs lowered the death rate and improved the disease resistance of O. mossambicus following assessment with A. hydrophila and V. parahaemolyticus at 50 µL. Hence, the overall results suggest that the supplemental diet of EPS and EPS-ZnO NPs might be used to ensure aquaculture feed additives.
How interesting and coherent are the stories generated by a large‐scale neural language model? Comparing human and automatic evaluations of machine‐generated text

Evaluation of the narrative text generated by machines has traditionally been a challenge, particularly when attempting to evaluate subjective elements such as interest or believability. Recent improvements in narrative machine text generation have been largely driven by the emergence of transformer‐based language models, trained on massive quantities of data, resulting in higher quality text generation. In this study, a corpus of stories is generated using the pre‐trained GPT‐Neo transformer model, with human‐written prompts as inputs upon which to base the narrative text. The stories generated through this process are subsequently evaluated through both human evaluation and two automated metrics: BERTScore and BERT Next Sentence Prediction, with the aim of determining whether there is a correlation between the automatic scores and the human judgements. The results show variation in human evaluation results in comparison to modern automated metrics, suggesting further work is required to train automated metrics to identify text that is defined as interesting by humans.
Explainable and Robust Deep Forests for EMG-Force Modeling

Machine and deep learning techniques have received increasing attentions in estimating finger forces from high-density surface electromyography (HDsEMG), especially for neural interfacing. However, most machine learning models are normally employed as block-box modules. Additionally, most previous models suffer from performance degradation when dealing with noisy signals. In this work, we propose to employ a forest ensemble model for HDsEMG-force modeling. Our model is explainable and robust against noise. Additionally, we explored the effect of increasing the depth of forest models in EMG-force modeling problems. We evaluated the performance of deep forests with a finger force estimation task. Training and testing data were acquired 3–25 days apart, approximating realistic scenarios. Results showed that deep forests significantly outperformed other models. With artificial signal distortion in 20% channels, deep forests also showed a higher robustness, with the error reduced from that of the baseline by $>$50% compared with all other models. We provided explanations for the proposed model using the mean decrease impurity (MDI) metric, revealing a strong correspondence between the model and physiology.
Noise-robust face recognition algorithm based on improved local directional pattern

For obtaining more extensive sampling information,this paper presents a novel approach based on improved local directional pattern(ILDP) for face recognition,which adopts the edge gradient information of local #-shape neighborhood including 16 pixels.Specially,the local #-shape neighborhood of each pixel is equally divided into two 3×3 sub-neighborhoods,and 8 edge gradient values of each sub-neighborhood are gained by convolving it with 8 Kirsch masks respectively.The ILDP just utilizes the direction of the largest edge gradient value of each sub-neighborhood.Then,these two directions are encoded into a double-digit octal number to produce the ILDP code.Finally,the face descriptor is represented by using the global concatenated histogram based on ILDP map extracted from the face image which is divided into several sub-blocks.The experimental results demonstrate that the proposed method in this paper is more robust to random noise than other single face descriptors based on local texture information.
Development and Validation of the Life for Low Vision Questionnaire (LIFE4LVQ) Using Rasch Analysis: A Questionnaire Evaluating Ability and Independence

Low vision (LV) has a substantial impact on an individual’s daily functionality and patient-reported outcome measures (PROMs) are increasingly incorporated into the evaluation of this problem. The objective of this study was to describe the design of the new “Life for Low Vision Questionnaire (LIFE4LVQ)”, as a measure of daily functionality in LV and to explore its psychometric properties. A total of 294 participants completed the LIFE4LVQ and the data were subjected to Rasch analysis to determine the psychometric properties of the questionnaire, including response category ordering, item fit statistics, principal component analysis, precision, differential item functioning, and targeting. Test–retest reliability was evaluated with an interval of three weeks and intraclass correlation coefficients (ICC) were used. The correlation between the questionnaire score and Best Corrected Visual Acuity (BCVA) was examined using Spearman’s correlation coefficient. Rasch analysis revealed that for most items the infit and outfit mean square fit values were close to 1, both for the whole scale and its subscales (ability and independence). The separation index for person measures was 5.18 with a reliability of 0.96, indicating good discriminant ability and adequate model fit. Five response categories were found for all items. The ICC was 0.96 (p < 0.001; 95% CI, 0.93–0.98), suggesting excellent repeatability of the measure. Poorer BCVA was significantly associated with worse scores (rho = 0.559, p < 0.001), indicating excellent convergent validity. The functional, 40-item LIFE4LVQ proved to be a reliable and valid tool that effectively measures the impact of LV on ability and independence.
FlexEdge: Digital Twin-Enabled Task Offloading for UAV-Aided Vehicular Edge Computing

Integrating unmanned aerial vehicles (UAVs) into vehicular networks have shown high potentials in affording intensive computing tasks. In this paper, we study the digital twin driven vehicular edge computing networks for adaptively computing resource management where an unmanned aerial vehicle (UAV) named FlexEdge acts as a flying server. In particular, we first formulate an energy consumption minimization problem by jointly optimizing UAV trajectory and computation resource under the practical constraints. To address such a challenging problem, we then build the computation offloading process as a Markov decision process and propose a deep reinforcement learning-based proximal policy optimization algorithm to dynamically learn the computation offloading strategy and trajectory design policy. Numerical results indicate that our proposed algorithm can achieve quick convergence rate and significantly reduce the system energy consumption.
Integrals Involving a Modified Bessel Function of the Second Kind and an E-Function

The first formula to be proved is where p ≧ q + 1, | amp z | < л, R(k±n + αr)>0, r = l, 2, …, p. For other values of p and q the result is valid if the integral is convergent. A second formula is given in § 3. The following formulae are required in the proof: where R(z);>0, (1); where R(α)>0, | amp z | < л, (2); where the contour starts from -∞ on the ξ-axis, passes round the origin in the positive direction, and ends at -∞ on the ξ-axis, the initial value of amp ζ being - л, (3).
Carbon Emission Reduction Effect of Information Technology: Stata Analysis Based on City Panal Model

—Application of Information Technology such as big data, Internet, blockchain and cloud computing provides more possibilities for the low carbon economy. This paper scientifically estimates the carbon emission of 275 cities from 2011 to 2018 in China, builds a city panal model and studies the impact of Information Technology on carbon emissions with Stata software analysis, which verifies the emission reduction effect of Information Technology. Mechanism analysis shows that Information Technology curb the intensity of carbon emission by alleviating credit constraints and reducing the energy consumption intensity.
Dynamic power management of a system on chip based on AMBA AHB bus

This paper presents new dynamic voltage scaling and power management architectures for a System on Chip with an AMBA AHB bus. The Power State Machine describing the status of the core follows the recommendations of the ACPI standard. The algorithm controls the power states of each block on the basis of battery status, chip temperature and workload conditions. The DVS and DPM architectures proposed has been described at system level in SystemC. In particular, we investigated the possibility to change clock frequency and supply voltage for each master, slave and bus independently when no transfer is required. A system level analysis has been performed to evaluate the effect of different DVS and DPM algorithms, topologies and architectures on power dissipation and system performances.
Constructive model predictive control for constrained nonlinear systems

This paper develops a new model predictive control (MPC) design for stabilization of continuous‐time nonlinear systems subject to state and input constraints. The key idea is to construct an analytic form of the controller with some undetermined parameters and to calculate the parameters by minimizing online a performance index. By using the method of control Lyapunov functions (CLFs), we construct an appropriate variation on Sontag's formula, with one degree of freedom reflecting ‘decay rate’ of CLFs. Moreover, the constructed univariate control law is used to characterize the terminal region that guarantees the feasibility of the optimal control problem. Provided that the initial feasibility of the optimization problem is satisfied, the stability of the control scheme can be guaranteed. An example is given to illustrate the application of the constructive MPC design. Copyright © 2008 John Wiley & Sons, Ltd.
Evaluation and Estimation of Various Markov Models with Applications to Membrane Channel Kinetics

Hidden Markov modelling is a powerful and efficient digital signal processing strategy for extracting the maximum likelihood model from a finite length sample of noisy data. Assuming the number of states in the model is known, then the state levels, transition probabilities, initial state distribution and the noise variance can be estimated. We investigate the applicability of this technique in membrane channel kinetics not only as a parameter estimator, but also as an aid to discriminating between various model types according to their statistical likelihood. We survey three representative classes of channel dynamics, namely: aggregated Markov models, semi-Markov models (with asymptotically convergent transition probabilities), and coupled Markov models; reformulating each within a discrete-time hidden Markov model framework. We then provide numerical evidence of the effectiveness of the procedure using simulated channel data and hence show that the correct model, as well as the model parameters, can be discerned. We also demonstrate that the model likelihood can be used to indicate the approximate number of states in the model.
Variation in Embryonic Metabolic Reaction Norms and the Role of the Environment

Early developmental environments can shape how organisms respond to later environments, but despite the potential for this phenomenon to alter the evolution of phenotypes and their underlying mechanisms in variable environments, details of this process are not understood. For example, both temperature and parental age can alter offspring metabolic plasticity and growth within species, yet the extent of such effects is unknown. We measured the reaction norms of embryonic heart rate in response to egg temperature and the change in egg mass over the incubation period in wild house sparrows. Using Bayesian linear mixed models, we estimated covariation in the intercepts and slopes of these reaction norms among clutches and eggs. We found that heart rate intercepts, not slopes, varied among clutches and that neither intercepts nor slopes varied among eggs within clutches. In contrast, egg mass intercepts and slopes varied among clutches and eggs. Ambient temperature did not explain variance in reaction norms. Instead, individuals from older mothers were more metabolically sensitive to egg temperature and lost less mass over the incubation period than individuals from younger mothers. Nevertheless, heart rate reaction norms and egg mass reaction norms did not covary. Our results suggest that early environments influenced by parents may contribute to variation in embryonic reaction norms. The structure of variation in embryonic reaction norms that exists both among clutches and among eggs demonstrates a complexity in plastic phenotypes that should be explored in future work. Furthermore, the potential for the embryonic environment to shape the reaction norms of other traits has implications for the evolution of plasticity more broadly.
A novel algorithm for adaptive beamforming based on projection transformation

An improved projection transformation method for enhancing the performance of the linearly constrained minimum variance beamformer is presented. The novel algorithm uses a projection operator, which is the basis vectors for the estimated signal-plus-interference subspace obtained by searching for it in the array manifold space by means of priori knowledge. The new algorithm can further reduce the computational complexity and eliminate the signal cancellation when a desired signal is contained in the correlation matrix. Moreover, the new method has a good beam pattern with lower sidelobe level and faster convergence speed in the case that there exist amplitude and phase errors and/or colored noise. The advantages are confirmed by computer simulations.
Ship License Plate Super-Resolution in the Wild

Ship license plate (SLP) recognition plays an important role in ship supervision and harbour management. Practically, low-resolution (LR) SLP images are illegible and challenging to SLP recognition. Most existing super-resolution (SR) methods are not suitable for real-world LR SLP images with over smoothed reconstructions. To alleviate these deficiencies, in this letter, we propose a parallel enhanced SR generative adversarial network (PESRGAN) for SLP images. A novel degradation model is developed to construct a more feasible LR dataset. A parallel SR convolutional neural network (SRCNN) module based on ESRGAN is proposed for feature extraction. To characterize the difference between text foreground and background, a new gradient loss is developed in PESRGAN to sharpen the character boundary. Comparisons to many state-of-the-art (SOTA) SR methods are presented to show the effectiveness of the proposed algorithm.
Geometry of projective Hilbert space.

Geodesic equations determined by the metric in a projective Hilbert space P are found. It is shown that connections in the tangent bundle over P consistent with these geodesics can be introduced. A connection with zero torsion is proposed. Quantum-system dynamics follows the geodesics of another connection which is found for the case of the constant Hamiltonian. A set of geometrical quantities is defined, conserved by a quantum evolution
Complex Object Matching Using Polynomial Regression Technique Based on Pixels Distance

The ability to model the object and to distinguish it from a large number of database has been investigated by many researches working in the computer vision and pattern recognition field. In this paper the trademark image as our focus on complex object was modeled using boundaries pixel distance. The invariant has been resulted from the distance of each boundaries pixel to their central point. By performing polynomial regression on each set of sorted distances, a unique set of numerical features from the coefficients of this polynomial function has been produced.
Versatile Application of Fluorescent Quantum Dot Labels in Super-resolution Fluorescence Microscopy

Quantum dots (QDs) are well known as bright and photostable inorganic fluorescent probes for microscopy imaging, with many attractive features superior to those found in organic dyes. However, their broadband excitation spectrum and emission blinking property have limited the applicability of QDs in modern super-resolution microscopy techniques. In this work, we systematically investigate practical approaches to overcoming these drawbacks and provide examples of their use across many commercially available super-resolution microscopy systems now accessible to biologists, with examples across the major super-resolution techniques. This work further maps out how QDs can be further engineered to facilitate their applications in the respective super-resolution microscopy techniques.