Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting the Error Resilience of the Preconditioned Conjugate Gradient Method for Energy and Delay Optimization

The Preconditioned Conjugate Gradient (PCG) method is well-established for solving linear equations. Running the PCG method on a hardware accelerator ensures fast and efficient computation. At the same time, each hardware accelerator may be slightly different due to process variability or aging. To handle the variability, a rather pessimistic frequency selection for the whole population of accelerators is often utilized. Increasing the frequency may improve the performance but may also increase the risk of computational errors, affect the convergence of PCG or even corrupt the PCG results. In this paper, we present a method to determine the frequency for each hardware accelerator instance which optimizes the execution time and the energy efficiency of the PCG method. First, a technique is presented to analyze the error resilience of a PCG algorithm to overclocking. Based on the analysis results, we increase the frequency to speed up the convergence while keeping the error rate below the required threshold.
An Efficient Deep Learning Approach for Arrhythmia Classification using 3D Temporal SVCG

The classification of heart arrhythmias through recordings of its electrical activity (ECGs) represent a coveted non-invasive diagnostic tool for the detection of life threatening conditions. Nevertheless, the design of fast and effective automatic deep learning procedures solving this task is not trivial. In this work, we developed an automatic pipeline for heart rhythm classification relying on a lighter temporal representation based on a scalar vectorcardiogram (SVCG). The signal is preliminarly downsampled using a Fast Fourier Transform (FFT) retaining high-level features. Afterwards, each ECG is converted to a 3D compact representation through Inverse Dower’s transform and fed through a specffically designed convolutional neural network. The reliability of the proposed method is validated and compared to standard 12-lead ECGs from PhysioNet Computing in Cardiology Challenge (2020). We obtained a competitive test categorical accuracy for the largest dataset (SVCG 90.6-ECG S7.1 on PTBXL) and comparable results for the remaining sources. Compared with the existing methods, we propose an efficient method for the classification of heart arrhythmias through vectorcardiography, avoiding the need of hand-crafted features also having a lower computational and timing effort.
Comprehensive transcriptional analysis reveals salt stress-regulated key pathways, hub genes and time-speci�c response categories in common bermudagrass (Cynodon dactylon (L.) Pers.) roots

Background: Despite its good salt-tolerance level, key genes and pathways that are involved with temporal salt response of common bermudagrass (Cynodon dactylon (L.) Pers. ) have not been explored. Therefore, in this study, to understand the underlying regulatory mechanism following the different period of salt exposure, a comprehensive transcriptome analysis of the bermudagrass roots was conducted. Results: The transcripts regulated after 1 h, 6 h, or 24 h of hydroponic exposure to 200 mM NaCl in the roots of bermudagrass were investigated. Dataset series analysis revealed 16 distinct salt-responsive temporal transcripts. Enrichment analysis identi�ed common and distinct stress response themes such as hormonal metabolism, secondary metabolism, misc, cell wall, transcription factors and genes encoded a series of transporters. Weighted gene co-expression network analysis (WGCNA) revealed that lavenderblush2 and brown4 modules were signi�cantly positively correlated with the proline content and peroxidase activity and hub genes within these two modules were further determined. Besides, after 1 h of salt treatment, categories such as signalling receptor kinase, transcription factors, tetrapyrrole synthesis and lipid metabolism were immediately and exclusively up-enriched compared to the subsequent time points, which indicated fast-acting and immediate physiological responses. Other speci�c categories involved in secondary metabolite biosynthesis such as simple phenols, glucosinolates, iso�avones and tocopherol biosynthesis were exclusively up-regulated after 24 h of salt treatment, suggesting a slightly slower reaction of metabolic adjustment. Conclusion: Here, we revealed salt response themes that were commonly or differentially expressed in short-term salt stress, suggesting possible adaptive salt response mechanisms in the roots. Also, the distinctive salt-response pathways and potential salt-tolerant hub genes investigated can provide useful future references to explore the molecular mechanisms of bermudagrass. S-locus glycoprotein like and wall associated receptor kinase; XTH: Xyloglucan endotransglucosylase.
EMERGY ANALYSIS OF THE COBSCOOK BAY ECOSYSTEM

Energy Systems Theory and its derivative methods, Emergy Analysis and Environmental Accounting using emergy, are particularly suited for evaluation of the ecological, economic and social consequences of environmental policy decisions. For these methods to be broadly applied, scientists must document the emergy basis for the products and services provided by ecosystems and economies. In this study, the emergy basis for the Cobscook Bay estuarine ecosystem was documented. Cobscook Bay is a macrotidal estuary that is naturally eutrophic due to new nitrogen supplied from the sea. Plant production is stabilized by benthic macrofauna grazing and phytoplankton production is light limited due to the intense vertical mixing regime. The fucoid algae, kelp, red algae and benthic microalgae are best adapted to use the rich emergy signature of the Bay. Emergy Analysis indicated that primary producers are unable to use the estuary’s emergy sources as efficiently as in other estuaries. The additional emergy goes into creating rare physical, geological, and biological structures. Empower density in Cobscook Bay (7.4 E12 sej m –2 ) is three times the minimum estimate for salmon culture; therefore, salmon aquaculture may be a good human use of the Bay’s rich emergy signature. Energy transfer and observations in the Bay indicate that the system is productive and healthy overall, but not without local disturbance in space and time.
Evaluation of electroacupuncture as a non-pharmacological therapy for astrocytic structural aberrations and behavioral deficits in a post-ischemic depression model in mice

Background Ascending clinical evidence supports that electroacupuncture (EA) is effective in treating post-ischemic depression (PID), but little is known about how it works at the cellular level. Astrocytes are exquisitely sensitive to their extracellular environment, and under stressful conditions, they may experience aberrant structural remodeling that can potentially cause neuroplastic disturbances and contribute to subsequent changes in mood or behavior. Objectives This study aimed to investigate the effect of EA on behavioral deficits associated with PID in mice and verify the hypothesis that astrocytic morphology may be involved in this impact. Methods We established a PID animal model induced by transient bilateral common carotid artery occlusion (BCCAO, 20 min) and chronic restraint stress (CRS, 21 days). EA treatment (GV20 + ST36) was performed for 3 weeks, from Monday to Friday each week. Depressive- and anxiety-like behaviors and sociability were evaluated using SPT, FST, EPM, and SIT. Immunohistochemistry combined with Sholl and cell morphological analysis was utilized to assess the process morphology of GFAP+ astrocytes in mood-related regions. The potential relationship between morphological changes in astrocytes and behavioral output was detected by correlation analysis. Results Behavioral assays demonstrated that EA treatment induced an overall reduction in behavioral deficits, as measured by the behavioral Z-score. Sholl and morphological analyses revealed that EA prevented the decline in cell complexity of astrocytes in the prefrontal cortex (PFC) and the CA1 region of the hippocampus, where astrocytes displayed evident deramification and atrophy of the branches. Eventually, the correlation analysis showed there was a relationship between behavioral emotionality and morphological changes. Conclusion Our findings imply that EA prevents both behavioral deficits and structural abnormalities in astrocytes in the PID model. The strong correlation between behavioral Z-scores and the observed morphological changes confirms the notion that the weakening of astrocytic processes may play a crucial role in depressive symptoms, and astrocytes could be a potential target of EA in the treatment of PID.
MODSC: Many-Objective-Optimization-Driven Data-Balancing Strategy in Cross-Architectural Malware Classification for Extreme IoT

Stable operation of IoT systems in extreme environments is critical for infrastructures, including energy, aerospace, and healthcare. But resource-constrained devices are vulnerable to malware exploiting known weaknesses due to complex operating conditions and low-frequency device maintenance, and systems are at serious risk of failure or information leakage. However, automated security defense based on machine learning faces the challenges of low cross-architectural generalization and multidimensional data imbalance. We propose a many-objective-optimization-driven data balancing strategy for cross-architectural malware classification (MODSC) to address the above issues. MODSC constructs the optimization problem model for data balancing strategy search based on data set information, which focuses on rebalancing the data space in different dimensions, including category distribution and architectural distribution. The many-objective evolutionary algorithm (MaOEA-PBF), guided by the performance balance function, is then designed to solve the above model. The MODSC framework was tested on a group of IoT malware data sets with stepped imbalance rates containing six common architectures. The experimental results show that MODSC can both steadily and effectively improve cross-architectural generalization while maintaining a high level of confidence compared to popular data processing methods.
Tracking Multiple Objects Outside the Line of Sight using Speckle Imaging Supplementary Technical Report

As discussed in Section 4 of the main paper, tracking objects around the corner (non-line-of-sight imaging) is analogous to direct line of sight imaging, where the target objects are illuminated directly by a diffuse light source, and imaged with a bare sensor. Here, we provide details of the relationship between object motion and motion of the speckle pattern recorded by a bare sensor. The object motion is defined in 3D space (3 degrees of freedom for translation, and 3 degrees of freedom for rotation) and speckle motion is defined in the 2D image space. Consider the right-hand side of Figure 5 in the main paper. Let the speckle patterns observed by the camera before and after motion be S(x, y) and S′(x, y), respectively, where (x, y) are image coordinates. A well known result in optics states that small object motions [∆X,∆Y,∆Z] result only in local displacements (∆x,∆y) in the observed speckle pattern [6], i.e., S(x, y) = S′(x + ∆x, y + ∆y). Furthermore, the local speckle displacement (∆x,∆y) is related to object motion via a simple, linear equation: [ ∆x ∆y ] = Mtrans ∆X ∆Y ∆Z + Mrot ΘX ΘY ΘZ  , (1)
The efficacy of aerosol–cloud radiative perturbations from near-surface emissions in deep open-cell stratocumuli

Abstract. Aerosol–cloud radiative effects are determined and quantified in simulations of deep open-cell stratocumuli observed during the VAMOS Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) campaign off the west coast of Chile. The cloud deck forms in a boundary layer 1.5 km deep, with cell sizes reaching 50 km in diameter. Global databases of ship tracks suggest that these linear structures are seldom found in boundary layers this deep. Here, we quantify the changes in cloud radiative properties to a continuous aerosol point source moving along a fixed emission line releasing 1017 particles per second. We show that a spatially coherent cloud perturbation is not evident along the emission line. Yet our model simulates an increase in domain-mean all-sky albedo of 0.05, corresponding to a diurnally averaged cloud radiative effect of 20 W m−2, given the annual mean solar insolation at the VOCALS-REx site. Therefore, marked changes in cloud radiative properties in precipitating deep open cells may be driven by anthropogenic near-surface aerosol perturbations, such as those generated by ships. Furthermore, we demonstrate that these changes in cloud radiative properties are masked by the naturally occurring variability within the organised cloud field. A clear detection and attribution of cloud radiative effects to a perturbation in aerosol concentrations becomes possible when sub-filtering of the cloud field is applied, using the spatio-temporal distribution of the aerosol perturbation. Therefore, this work has implications for the detection and attribution of effective cloud radiative forcing in marine stratocumuli, which constitutes one of the major physical uncertainties within the climate system. Our results suggest that ships may sometimes have a substantial radiative effect on marine clouds and albedo, even when ship tracks are not readily visible.
Efficacy and Safety of Novel Thiazolidinedione Rivoglitazone in Type-2 Diabetes a Meta-Analysis

No meta-analysis has analyzed the safety and efficacy of rivoglitazone in type-2 diabetes (T2DM). We undertook this meta-analysis to address this knowledge gap. Electronic databases were searched for RCTs involving T2DM patients receiving rivoglitazone in the intervention arm, and placebo/active comparator in the control arm. The primary outcome was to evaluate changes in HbA1c. Secondary outcomes were to evaluate alterations in glucose, lipids, and adverse events. From initially screened 24 articles, data from 3 RCTs (3591 patients) that fulfilled all criteria was analzsed. HbA1c was significantly lower with standard-dose (1 mg/d) [MD-0.86% (95%CI:-1.11–-0.61); P < 0.01; I2 = 87%] and high-dose (1.5-2 mg/d) [MD-0.97%(95%CI:-1.03–-0.90); P < 0.01; I2 = 19%] rivoglitazone compared to placebo. When compared to pioglitazone (30-45 mg/d), HbA1c lowering was comparable with standard-dose [MD 0.05%(95%CI:-0.01 – 0.11); P = 0.08; I2 = 11%], but superior with high-dose [MD -0.11%(95%CI:-0.18– -0.04); P < 0.01; I2 = 0%] rivoglitazone. Triglycerides were significantly lower with standard-dose [MD-17.95 mg/dl (95%CI:-34.23–-1.66); P = 0.03; I2 = 0%] and high-dose [MD-40.41 mg/dl (95%CI:-72.90– -7.93);P = 0.01;I2 = 71%] rivoglitazone compared to placebo. Adiponectin significantly improved with standard-dose [MD 7.94 ng/ml (95%CI: 5.48–10.39); P < 0.01;I2 = 98%] and high-dose [MD 13.82 ng/ml (95%CI: 8.16–19.48); P < 0.01; I2 = 100%] rivoglitazone compared to placebo. hsCRP was significantly lower with standard-dose [MD -1.00 mg/L (95% CI: -1.20 – -0.80); P < 0.01; I2 = 6%] and high-dose [MD -1.50 mg/L (95%CI:-1.59– -1.40); P < 0.01; I2 = 0%] rivoglitazone compared to placebo. Treatment-emergent adverse events with standard-dose [Risk ratio (RR) 1.16 (95%CI: 0.84 –1.60); P = 0.38; I2 = 0%] and high-dose [RR1.34 (95%CI: 0.99–1.83); P = 0.06; I2 = 0%] rivoglitazone was comparable to placebo. Severe adverse events with standard-dose [RR1.88 (95%CI: 0.69–5.12);P = 0.22;I2 = 0%] and high-dose [RR 1.27 (95% CI: 0.45 – 3.59); P = 0.68; I2 = 0%] rivoglitazone was comparable to placebo. This meta-analysis highlights the good glycaemic efficacy and safety of both standard and high-dose rivoglitazone, and appears to be better than lobeglitazone in T2DM.
Hierarchical Structure of Glucosamine Hydrochloride Crystals in Antisolvent Crystallization

The crystal morphology of glucosamine hydrochloride (GAH) during antisolvent crystallization was investigated in this work. Particles of different shapes, such as plate-like crystals, leaflike clusters, fan-like dendrites, flower-like aggregates, and spherulites, were produced by tuning the type of antisolvents and crystallization operating conditions. The hierarchical structures of GAH crystals tended to be formed in a water + isopropanol mixture. The effects of operation parameters on the polycrystalline morphology were studied, including crystallization temperature, solute concentration, feeding rate of GAH aqueous solution, solvent-to-antisolvent mass ratio, and stirring rate. The evolution process of GAH spherulites was monitored using SEM, indicating a crystallographic branching mode. The crystal habit was predicted to identify the dominant faces. Molecular dynamics simulations were performed and the interaction energy of solute or solvent molecules on crystal surfaces was calculated. The experimental and simulation studies help to understand the branching mechanism and design a desired particle morphology.
Reconfigurable Compute in Memory Using SRAM and Bit Serial Algorithm

In the past conventional von Neumann computer architecture, the data that are stored in the memory unit and the computing core are separated which leads to the increase in power consumption and latency as data size being increased In-memory computing, or compute in memory, aims to perform computer computations entirely within computer memory to eliminate these bottlenecks. One area of exploration for in-memory computing is deep neural network (DNN) acceleration through processing-in-memory platforms (PIM). Using bitwise digital multiplication, the digital CIM macro is designed with reconfigurable 1-8-bit input/weight and single sign bit. A column of bit cells makes up a column MAC, which performs a multiplication and accumulation operation, while dot product is computed by using a row of MACs functions as a single neuron. The bit cell includes three primary components: a user-defined 6T SRAM cell to store weight, for computing bitwise multiplication an XNOR gate is being used, and a carry look adder to accumulate partial sums. A digital CIM memory is designed using $64\times 64$ SRAM bit cells for digital bit-serial computing was designed in 45-nm technology with 1-8-bit reconfigurable weight/input bits.
Polygenic Risk Score and Rare Variant Burden Identified by Targeted Sequencing in a Group of Patients with Pigment Epithelial Detachment in Age-Related Macular Degeneration

A subset of ophthalmic imaging examination results from 334 patients were subjected to reanalysis to identify a specific group of patients with pigment epithelial detachment (PED) in at least one eye. Overall, we found a subgroup of 47 patients manifesting PED and studied their genotypes in comparison to those of patients with age-related macular degeneration without PED and healthy controls. We established a polygenic risk score that allowed the explanation of 16.3% of the variation within the disease. The highest predictive value was achieved for a model consisting of six non-coding variants: rs760306 (BEST1), rs148662546 (BEST1), rs11569560 (C3), rs74600252 (GUCA1B), rs2240688 (PROM1), and rs185507582 (TCF4). The risk of PED occurrence was found to be the highest in the first tercile, showing a 7.89-fold higher risk compared to the third tercile for AMD without PED (95% CI: 2.87; 21.71, p < 0.001) and a 7.22-fold higher risk compared to the healthy controls (95% CI: 2.60; 20.06, p < 0.001). In addition, we focused on rare variants in targeted genes. The rare variants’ burden was compared among the groups, but no statistical significance was observed in the number of rare variants, predicted functional effects, or pathogenicity classification.
Data Augmentation With CycleGAN to Build a Classifier for Novel Defects From the Dicing Stage of Semiconductor Package Assembly

Industry 4.0, a concept first proposed by Germany, has resulted in an increasing number of companies adopting a mass customization strategy. This strategy is widely used across various industries, enabling the production of small batches of diversified products to meet the diverse needs of customers. It encompasses the business process of providing customized goods that best fulfill individual customer requirements, thereby necessitating small-scale production of multiple products. Therefore, the product life cycle of mass customization is much shorter than other production strategies. When product line changes are frequent and customized products have high yield rates, accurately detecting potential defects from a limited number of images is a daunting challenge. If the defect identification classification model needs to maintain a certain level of identification accuracy and the model needs to be deployed quickly, it is impossible to wait until a large number of defect images are collected before deploying an accurate model for new defects. Obtaining a high-precision defect identification classification model is crucial. In this study, we employed the style transfer method of CycleGAN, which takes advantage of the unmatched training images, to successfully transfer the style of defective images from old products to defect-free images of new products. However, CycleGAN requires a large number of images for training, so this study primarily focuses on rare sample categories. We first obtained the defect mask through a semantic segmentation model and then separated the foreground defect from the wafer background using digital image processing techniques. We then copied and pasted the separated defect onto a new wafer background to generate fake defect images. Finally, a generative adversarial network architecture was used to perform image blending to make the fake defect images more natural and realistic. The effectiveness of the data augmentation method was verified through a convolutional neural network model. Through the proposed method in this study, the number of defect images in new products was successfully increased, which helps to deploy a defect identification classification model for new products quickly.