Miracle-dz/UniFAR-FTUs huggingface dataset:
Operator Shifting for General Noisy Matrix Systems

In the computational sciences, one must often estimate model parameters from data subject to noise and uncertainty, leading to inaccurate results. In order to improve the accuracy of models with noisy parameters, we consider the problem of reducing error in a linear system with the operator corrupted by noise. Our contribution in this paper is to extend the elliptic operator shifting framework from Etter, Ying '20 to the general nonsymmetric matrix case. Roughly, the operator shifting technique is a matrix analogue of the James-Stein estimator. The key insight is that a shift of the matrix inverse estimate in an appropriately chosen direction will reduce average error. In our extension, we interrogate a number of questions -- namely, whether or not shifting towards the origin for general matrix inverses always reduces error as it does in the elliptic case. We show that this is usually the case, but that there are three key features of the general nonsingular matrices that allow for adversarial examples not possible in the symmetric case. We prove that when these adversarial possibilities are eliminated by the assumption of noise symmetry and the use of the residual norm as the error metric, the optimal shift is always towards the origin, mirroring results from Etter, Ying '20. We also investigate behavior in the small noise regime and other scenarios. We conclude by presenting numerical experiments (with accompanying source code) inspired by Reinforcement Learning to demonstrate that operator shifting can yield substantial reductions in error.
Magneto-optical detection of topological contributions to the anomalous Hall effect in a kagome ferromagnet

A single ferromagnetic kagome layer is predicted to realize a Chern insulator with quantized Hall conductance, which upon stacking can become a Weyl-semimetal with large anomalous Hall effect (AHE) and magneto-optical activity. Indeed, in the kagome bilayer material Fe 3 Sn 2 , a large AHE was detected, however, it still awaits the direct probing of the responsible band structure features by bulk sensitive methods. We measure the optical, both diagonal and Hall, conductivity spectra over a broad spectral range and identify the origin of the intrinsic AHE with the help of momentum- and band-decomposed ﬁrst principles calculations. We ﬁnd that low-energy transitions, tracing "helical volumes" in momentum space reminiscent of the formerly predicted helical nodal lines, substantially contribute to the AHE, which is further increased by contributions from multiple higher-energy interband transitions. Our study also reveals that local Coulomb interactions lead to band reconstructions near the Fermi level.
The effects of plasma exchange in patients with ANCA-associated vasculitis: an updated systematic review and meta-analysis

Abstract Objective To assess the effects of plasma exchange on important outcomes in anti-neutrophil cytoplasm antibody (ANCA)-associated vasculitis (AAV). Design Systematic review and meta-analysis of randomised controlled trials. Eligibility criteria Randomised controlled trials investigating effects of plasma exchange in patients with AAV or pauci-immune rapidly progressive glomerulonephritis and at least 12 months’ follow-up. Information sources Prior systematic reviews, updated by searching Medline, Embase, and CENTRAL to July 2020. Risk of bias Reviewers independently identified studies, extracted data, and assessed the risk of bias using the Cochrane Risk of Bias tool. Synthesis of results Meta-analyses were conducted using random effects models to calculate risk ratios and 95% confidence intervals. Quality of evidence was summarised in accordance with GRADE methods. Outcomes were assessed after at least12 months of follow-up and included all-cause mortality, end stage kidney disease (ESKD), serious infections, disease relapse, serious adverse events, and quality of life. Results Nine trials including 1060 participants met eligibility criteria. There were no important effects of plasma exchange on all-cause mortality (relative risk 0.90 (95% CI 0.64 to 1.27), moderate certainty). Data from seven trials including 999 participants that reported ESKD demonstrated that plasma exchange reduced the risk of ESKD at 12 months (relative risk 0.62 (0.39 to 0.98), moderate certainty) with no evidence of subgroup effects. Data from four trials including 908 participants showed that plasma exchange increased the risk of serious infections at 12 months (relative risk 1.27 (1.08 to 1.49), moderate certainty). The effects of plasma exchange on other outcomes were uncertain or considered unimportant to patients. Limitations of evidence There is a relative sparsity of events, and treatment effect estimates are therefore imprecise. Subgroup effects at the participant level could not be evaluated. Interpretation For the treatment of AAV, plasma exchange has no important effect on mortality, reduces the 12 month risk of ESKD, but increases the risk of serious infections. Funding No funding was received. Registration This is an update of a previously unregistered systematic review and meta-analysis published in 2014.
Sexual Dimorphism and Geographic Variation of the White-lipped Pit Viper (Trimeresurus albolabris) in China

Abstract: Sexual dimorphism and geographic variation are common in snakes. The white-lipped pitviper (Trimeresurus albolabris) is a common and widely distributed venomous snake species throughout the Southeast Asia. We report on the sexual dimorphism and geographic variation of T. albolabris in China. Ninety-nine specimens (57 females and 42 males) were examined by using analysis of variance, analysis of covariance, principal component analysis, and other statistical methods for 68 morphological characters. The result showed that T. albolabris displays significant sexual dimorphism in several external characters. Females have a longer snout-vent length, wider anal scales, and larger head size than males, whilst males exhibit a longer tail length and larger number of subcaudal scales. No significant geographic variation was detected, suggesting the absence of obvious morphological differentiation among local populations in China.
In vitro propagation of Vaccinium floribundum Kunth from seeds: promissory technology for mortiño accelerated production

Abstract: The Andean mortiño (Vaccinium floribundum Kunth) grows wild in the northern paramos of South América. The berries present potential opportunities for agribusiness, but an efficient technology for the production of plants and berries is required. On the other hand, the development of plant production methods from mortiño seeds will allow the plants’ accessibility and its potential use in breeding programs. The objective of this study was to develop an efficient in vitro protocol for accelerated production using seeds. We carried out the research in four phases: seed germination, plant multiplication, rooting, and acclimatization in the greenhouse. For in vitro seed germination, we studied the effect of two culture media [woody plant medium (WPM) and Murashige and Skoog medium (MS], two photoperiods (16 and 24 hours of light), and two temperatures (18 and 28 °C). The best treatment was found to be WPM + 24 h light + 18 °C. In the micropropagation of seedlings, the effect of two concentrations of three cytokinines [(trans-zeatin riboside (TZR), zeatin (ZEA), and 2-isopentenyl adenine (2iP)], and two photoperiods (16 and 0 h light) was evaluated, whereby the concentration of 0.5 mg L−1 of TZR was the best treatment. For rooting, two doses of three auxins [indole-3-butyric acid (IBA), 1-naphthaleneacetic acid (NAA), and indole-3-acetic acid (IAA)] were evaluated, resulting in the 2 mg·L−1 concentration of IBA giving the best root induction. Finally, the in vitro rooted plants were acclimatized in a greenhouse. We found that peat was the best substrate. These results show that the technology developed here is useful for in vitro production of V. floribundum using seeds.
Two Genetically Distinct yet Morphologically Indistinct Bungarus Species (Squamata, Elapidae) in Hong Kong

Abstract: Kraits (genus Bungarus) are a group of highly venomous elapid snakes found across tropical Asia. Although they are notorious for causing many, often deadly, envenomations throughout the continent, their taxonomy and species nomenclature is subject to regular revisions. The boundaries demarcating B. candidus, B. multicinctus, and B. wanghaotingi, in Southern China and Northern Myanmar are particularly obscure. Despite the extensively overlapping distributions for the three species, only B. multicinctus has been documented in Hong Kong. Following genetic analyses, we suggest that both B. multincinctus and B. wanghaotingi occur in Hong Kong. However, our morphological examinations are overall inconclusive in distinguishing the two species. With potential interspecific antivenin differences, the new identification of B. wanghaotingi in Hong Kong is of important medical interest. We recommend further ecological, taxonomic, and behavioural studies be conducted to properly distinguish B. multincinctus and B. wanghaotingi in the region.
Seasonal and ontogenic variation in the trophic spectrum of Anatolian water frogs, Pelophylax ridibundus group, in the Eastern Mediterranean (Amphibia: Anura)

Seasonal and ontogenic variation of food composition in Anatolian water frogs (Pelophylax ridibundus group) were analysed in the Seyhan and Ceyhan Deltas, southern Turkey. A total of 1,226 prey items were retrieved from the stomachs of 335 individuals (75 juveniles, 148 males, 112 females) in spring, summer and autumn. The frogs fed mainly upon invertebrates, especially insects (80.4%), and the prey spectrum was strongly influenced by prey availability in the environment. The most frequently consumed prey items were Hemiptera (31.6%), Coleoptera (29.0%), Hymenoptera (23.6%), and Diptera (21.5%). While adult frogs consume various invertebrate preys such as annelids, gastropods, arachnids, and insects, the diet of juveniles is mostly confined insects. No significant differences were found in the seasonal variation of prey types between sexes. Prey diversity and volume increased in spring and summer, and decreased in autumn with the decrease of the air temperature and reduced activity of the frogs.
Two sides of the same coin: ADHD affects reactive but not proactive inhibition in children

ABSTRACT Children with attention-deficit/hyperactivity disorder (ADHD) present a deficit in inhibitory control. Still, it remains unclear whether it comes from a deficit in reactive inhibition (ability to stop the action in progress), proactive inhibition (ability to exert preparatory control), or both. We compared the performance of 39 children with ADHD and 42 typically developing children performing a Simon choice reaction time task. The Simon task is a conflict task that is well-adapted to dissociate proactive and reactive inhibition. Beyond classical global measures (mean reaction time, accuracy rate, and interference effect), we used more sophisticated dynamic analyses of the interference effect and accuracy rate to investigate reactive inhibition. We studied proactive inhibition through the congruency sequence effect (CSE). Our results showed that children with ADHD had impaired reactive but not proactive inhibition. Moreover, the deficit found in reactive inhibition seems to be due to both a stronger impulse capture and more difficulties in inhibiting impulsive responses. These findings contribute to a better understanding of how ADHD affects inhibitory control in children.
A Weight-Based Channel Pruning Algorithm for Depth-Wise Separable Convolution Unit

Deep learning has become the hotspot of academia and industry, and has found its wide applications in many fields, especially computer vision. However, the computational and storage cost of the CNN model is now a headache to researchers. The famous model structures such as AlexNet, VGG and ResNet, are all going wider and deeper. Then it becomes hard to deploy these models on mobile and embedded devices. Accordingly, the model lightweight method is desiderated. This paper adapts the channel pruning method to features of the depth-wise separable convolution unit and its variant. By using the united prune ratio setting, the additional sparsity restraint, and the certain dataset preprocessing, our experiments show higher accuracy on the CIFAR-10 dataset than related work in some cases.
Epileptic Seizure Prediction by Synthesizing EEG Signals through GPT

Epileptic seizures have a serious impact on the normal life of patients with epilepsy. Epileptic seizure prediction can effectively reduce the mental pressure of patients with epilepsy and improve patients' quality of life. It is a key and challenging task to predict seizures from Electroencephalogram (EEG) data collected by EEG acquisition devices. To improve the accuracy of epileptic prediction, this paper proposes a method of epileptic seizure prediction based on synthetic EEG signals by Generative Pre-Training (GPT). The EEG data generated by this method is added into the actual EEG data, which effectively improves the accuracy of epilepsy prediction. In the experiments on MIT Public scalp EEG data set, through GPT EEG data enhancement, the average prediction accuracy of 10 minutes before seizure increased from 95.26% to 97.15%. The experimental results show that the proposed method is effective.
Implication of Optimizing NPU Dataflows on Neural Architecture Search for Mobile Devices

Recent advances in deep learning have made it possible to implement artificial intelligence in mobile devices. Many studies have put a lot of effort into developing lightweight deep learning models optimized for mobile devices. To overcome the performance limitations of manually designed deep learning models, an automated search algorithm, called neural architecture search (NAS), has been proposed. However, studies on the effect of hardware architecture of the mobile device on the performance of NAS have been less explored. In this article, we show the importance of optimizing a hardware architecture, namely, NPU dataflow, when searching for a more accurate yet fast deep learning model. To do so, we first implement an optimization framework, named FlowOptimizer, for generating a best possible NPU dataflow for a given deep learning operator. Then, we utilize this framework during the latency-aware NAS to find the model with the highest accuracy satisfying the latency constraint. As a result, we show that the searched model with FlowOptimizer outperforms the performance by 87.1% and 92.3% on average compared to the searched model with NVDLA and Eyeriss, respectively, with better accuracy on a proxy dataset. We also show that the searched model can be transferred to a larger model to classify a more complex image dataset, i.e., ImageNet, achieving 0.2%/5.4% higher Top-1/Top-5 accuracy compared to MobileNetV2-1.0 with 3.6 \( \times \) lower latency.
MVP: An Efficient CNN Accelerator with Matrix, Vector, and Processing-Near-Memory Units

Mobile and edge devices become common platforms for inferring convolutional neural networks (CNNs) due to superior privacy and service quality. To reduce the computational costs of convolution (CONV), recent CNN models adopt depth-wise CONV (DW-CONV) and Squeeze-and-Excitation (SE). However, existing area-efficient CNN accelerators are sub-optimal for these latest CNN models because they were mainly optimized for compute-intensive standard CONV layers with abundant data reuse that can be pipelined with activation and normalization operations. In contrast, DW-CONV and SE are memory-intensive with limited data reuse. The latter also strongly depends on the nearby CONV layers, making an effective pipelining a daunting task. Therefore, DW-CONV and SE only occupy 10% of entire operations but become memory bandwidth bound, spending more than 60% of the processing time in systolic-array-based accelerators. We propose a CNN acceleration architecture called MVP, which efficiently processes both compute- and memory-intensive operations with a small area overhead on top of the baseline systolic-array-based architecture. We suggest a specialized vector unit tailored for processing DW-CONV, including multipliers, adder trees, and multi-banked buffers to meet the high memory bandwidth requirement. We augment the unified buffer with tiny processing elements to smoothly pipeline SE with the subsequent CONV, enabling concurrent processing of DW-CONV with standard CONV, thereby achieving the maximum utilization of arithmetic units. Our evaluation shows that MVP improves performance by 2.6 \( \times \) and reduces energy by 47% on average for EfficientNet-B0/B4/B7, MnasNet, and MobileNet-V1/V2 with only a 9% area overhead compared to the baseline.
Non-contrast CT Image Automatic Diagnosis of Large Hemispheric Infarction in Hyper-acute Phase Based on Convolutional Neural Network

Hyper-acute large hemispheric infarction (LHI) is a serious type of cerebral infarction, characterized by sudden onset and rapid worsening. Without prompt diagnosis and treatment, it can severely endanger patient health and life. Therefore, early diagnosis of hyper-acute cerebral infarction and active treatment are very important. Clinically, non-contrast computed tomography (NCCT) is the preferred modality to quickly diagnose and evaluate suspected stroke patients. In this work, we proposed and evaluated an automatic deep learning method for segmenting ischemia and recognizing LHI in the hyper-acute phase from NCCT images. The method was validated against 135 NCCT volumes to attain an accuracy of 91.67%, precision of 90.95%, recall of 91.67%, and area under the receiver operating curve of 0.89 in detecting LHI at patient level. The proposed method could be potentially employed clinically to identify LHI in the hyper-acute phase quickly and accurately to aid decision making on effective clinical treatment for ischemic stroke.
Energy Eficient LSTM Inference Accelerator for Real-Time Causal Prediction

Ever-growing edge applications often require short processing latency and high energy eiciency to meet strict timing and power budget. In this work, we propose that the compact long short-term memory (LSTM) model can approximate conventional acausal algorithms with reduced latency and improved eiciency for real-time causal prediction, especially for the neural signal processing in closed-loop feedback applications. We design an LSTM inference accelerator by taking advantage of the ine-grained parallelism and pipelined feedforward and recurrent updates. We also propose a bit-sparse quantization method that can reduce the circuit area and power consumption by replacing the multipliers with the bit-shift operators. We explore diferent combinations of pruning and quantization methods for energy eicient LSTM inference on datasets collected from the electroencephalogram (EEG) and the calcium image processing applications. Evaluation results show that our proposed LSTM inference accelerator can achieve 1.19 GOPS/mW energy eiciency. The LSTM accelerator with 2-sbit/16 bit-sparse quantization and 60%-sparsity can reduce the circuit area and the power consumption by 54.1% and 56.3%, respectively, compared to a 16-bit baseline implementation.
Automated Assessment in Computer Science Education: A State-of-the-Art Review

Practical programming competencies are critical to the success in computer science (CS) education and go-to-market of fresh graduates. Acquiring the required level of skills is a long journey of discovery, trial and error, and optimization seeking through a broad range of programming activities that learners must perform themselves. It is not reasonable to consider that teachers could evaluate all attempts that the average learner should develop multiplied by the number of students enrolled in a course, much less in a timely, deep, and fair fashion. Unsurprisingly, exploring the formal structure of programs to automate the assessment of certain features has long been a hot topic among CS education practitioners. Assessing a program is considerably more complex than asserting its functional correctness, as the proliferation of tools and techniques in the literature over the past decades indicates. Program efficiency, behavior, and readability, among many other features, assessed either statically or dynamically, are now also relevant for automatic evaluation. The outcome of an evaluation evolved from the primordial Boolean values to information about errors and tips on how to advance, possibly taking into account similar solutions. This work surveys the state of the art in the automated assessment of CS assignments, focusing on the supported types of exercises, security measures adopted, testing techniques used, type of feedback produced, and the information they offer the teacher to understand and optimize learning. A new era of automated assessment, capitalizing on static analysis techniques and containerization, has been identified. Furthermore, this review presents several other findings from the conducted review, discusses the current challenges of the field, and proposes some future research directions.
Where to Hide a Stolen Elephant: Leaps in Creative Writing with Multimodal Machine Intelligence

While developing a story, novices and published writers alike have had to look outside themselves for inspiration. Language models have recently been able to generate text fluently, producing new stochastic narratives upon request. However, effectively integrating such capabilities with human cognitive faculties and creative processes remains challenging. We propose to investigate this integration with a multimodal writing support interface that offers writing suggestions textually, visually, and aurally. We conduct an extensive study that combines elicitation of prior expectations before writing, observation and semi-structured interviews during writing, and outcome evaluations after writing. Our results illustrate the individual and situational variation in machine-in-the-loop writing approaches, suggestion acceptance, and ways the system is helpful. Centrally, we report how participants perform integrative leaps, by which they do cognitive work to integrate suggestions of varying semantic relevance into their developing stories. We interpret these findings, offering modeling and design recommendations for future creative writing support technologies.