Miracle-dz/UniFAR-FTUs huggingface dataset:
Accelerating non-power-of-2 size Fourier transforms with GPU Tensor Cores

Fourier transforms whose sizes are powers of two or have only small prime factors have been extensively studied, and optimized implementations are typically memory-bound. However, handling arbitrary transform sizes—which may be prime or have large prime factors—is difficult. Direct discrete Fourier transform (DFT) implementations involve extra computation, while fast Fourier transform (FFT)-style factorized decompositions introduce additional overheads in register use, multiprocessor occupancy, and memory traffic. Tensor Cores are hardware units included in modern GPUs which perform matrix multiply-adds at a much higher throughput than normal GPU floating-point instructions. Because of their higher throughput and better uniformity across sizes, DFT/FFT implementations using Tensor Cores can surpass the performance of existing DFT/FFT implementations for difficult sizes. We present key insights in this approach, including complex number representation, efficient mapping of odd sizes to Tensor Cores (whose dimensions are all powers of 2), and adding a size 2 or size 4 epilogue transform at very low cost. Furthermore, we describe a method for emulating FP32 precision while using lower-precision Tensor Cores to accelerate the computation. For large batch sizes, our fastest Tensor Core implementation per size is at least 10% faster than the state-of-the-art cuFFT library in 49% of supported sizes for FP64 (double) precision and 42% of supported sizes for FP32 precision. The numerical accuracy of the results matches that of cuFFT for FP64 and is degraded by only about 0.3 bits on average for emulated FP32. To our knowledge, this is the first application of Tensor Cores to FFT computation which meets the accuracy and exceeds the speed of the state of the art.
The Hitchhiker's Guide to the End-of-Life for Smart Devices

The progressing severe environmental pollution and dwindling nonrenewable resources force society to increase the reuse of electronic waste (e-waste) to ensure the availability of sufficient resources for future products and an environment worth living in. The constant growth and expansion of the Internet of Things (IoT) and related smart devices amplifies the aforementioned problem, especially since a large amount of e-waste is either not disposed of correctly or hoarded at home. Thus, a solution is required to ease and incentivize the correct disposal of e-waste. While the growing number of smart devices increases the amount of e-waste, they also offer new technical opportunities to solve the very same problem they create. Instead of relying on the smart device owner to correctly dispose them at the recycling center, smart devices could arrange their delivery to the nearby recycling center themselves in a self-organized manner once they reach their end-of-life or are not used any further by the owner. This work introduces the Hitchhiker service platform that addresses the given problem of smart device e-waste. We outline the ecosystem and its stakeholders by following the Design Science Research approach. Moreover, we introduce the Hitchhiker system architecture for a self-adaptive disposal system of smart devices and explain selected system engagement processes. Finally, we discuss extensions of the service platform to support non-smart legacy devices that are not yet capable of organizing their own disposal logistics.
Optimizing Depthwise Separable Convolution Operations on GPUs

The depthwise separable convolution is commonly seen in convolutional neural networks (CNNs), and is widely used to reduce the computation overhead of a standard multi-channel 2D convolution. Existing implementations of depthwise separable convolutions target accelerating model training with large batch sizes with a large number of samples to be processed at once. Such approaches are inadequate for small-batch-sized model training and the typical scenario of model inference where the model takes in a few samples at once. This article aims to bridge the gap of optimizing depthwise separable convolutions by targeting the GPU architecture. We achieve this by designing two novel algorithms to improve the column and row reuse of the convolution operation to reduce the number of memory operations performed on the width and the height dimensions of the 2D convolution. Our approach employs a dynamic tile size scheme to adaptively distribute the computational data across GPU threads to improve GPU utilization and to hide the memory access latency. We apply our approach on two GPU platforms: an NVIDIA RTX 2080Ti GPU and an embedded NVIDIA Jetson AGX Xavier GPU, and two data types: 32-bit floating point (FP32) and 8-bit integer (INT8). We compared our approach against cuDNN that is heavily tuned for the NVIDIA GPU architecture. Experimental results show that our approach delivers over <inline-formula><tex-math notation="LaTeX">$2\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="lu-ieq1-3084813.gif"/></alternatives></inline-formula> (up to <inline-formula><tex-math notation="LaTeX">$3\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>3</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="lu-ieq2-3084813.gif"/></alternatives></inline-formula>) performance improvement over cuDNN. We show that, when using a moderate batch size, our approach averagely reduces the end-to-end training time of MobileNet and EfficientNet by 9.7 and 7.3 percent respectively, and reduces the end-to-end inference time of MobileNet and EfficientNet by 12.2 and 11.6 percent respectively.
Emerging Computing Devices: Challenges and Opportunities for Test and Reliability*

The paper addresses some of the opportunities and challenges related to test and reliability of three major emerging computing paradigms; i.e., Quantum Computing, Computing engines based on Deep Neural Networks for AI, and Approximate Computing (AxC). We present a quantum accelerator showing that it can be done even without the presence of very good qubits. Then, we present Dependability for Artificial Intelligence (AI) oriented Hardware. Indeed, AI applications shown relevant resilience properties to faults, meaning that the testing strongly depends on the application behavior rather than on the hardware structure. We will cover AI hardware design issues due to manufacturing defects, aging faults, and soft errors. Finally, We present the use of AxC to reduce the cost of hardening a digital circuit without impacting its reliability. In other words how to go beyond usual modular redundancy scheme.
Modeling of membrane fouling in a submerged membrane reactor using support vector regression

AbstractRemoval rate of Fe2+ and Mn2+ using submerged membrane reactor for drinking water in the presence of fulvic acid and iron hydroxide is studied using the data from the experiments obtained from various concentrations of Fe2+, Mn2+, fulvic acid, and iron hydroxide. The relationship between these contaminants and membrane fouling is investigated. In the experiments, flux is kept as constant, and the pressure change with time is observed. To model the relationship, a regression analysis using the support vector regression (SVR) model is presented. Hyperparameter optimization for SVR is important, that is, wrong selection may cause underfitting/overfitting phenomena. In order to find optimal values, grid search method is performed with various parameters such as different kernel functions (radial basis functions, polynomial, linear), cost parameter (C), and scale parameters γ and e. The results obtained by SVR show that proposed method is feasible.
Detecting Random Read Faults to Reduce Test Escapes in FinFET SRAMs

Manufacturing defects in FinFET SRAMs can cause hard-to-detect faults such as Random Read Faults (RRFs). Detection of RRFs is not trivial, as they may not lead to incorrect outputs. Undetected RRFs become test escapes, which might lead to no-trouble-found devices and early in-field failures. Therefore, the detection of RRFs is of utmost importance. This paper proposes test solutions to detect RRFs and reduce test escapes. To achieve this, we first statistically analyze the failure rate due to RRFs, followed by an experimental study of stress conditions’ (SCs) impact on detecting RRFs, such as test algorithms, supply voltage, and temperature. Based on the results, we propose a new Design-For-Testability (DFT) scheme for FinFET SRAMs to detect such faults using SCs that improve the detection rate of RRFs. This scheme introduces a negligible area and test time overhead while significantly enhancing RRF detection. Hence, using the proposed DFT leads to reduced test escapes and, consequently, higher-quality FinFET SRAMs.
Finer-LRU: A Scalable Page Management Scheme for HPC Manycore Architectures

In HPC systems, the increasing need for a higher level of concurrency has led to packing more cores within a single chip. However, since multiple processes share memory space, the frequent access to resources in critical sections where only atomic operation has to be executed can result in poor performance. In this paper, we focus on reducing lock contention on the memory management system of an HPC manycore architecture. One of the critical sections causing severe lock contention in the I/O path is in the page management system, which uses multiple Least Recently Used (LRU) lists with a single lock instance. To solve this problem, we propose a Finer-LRU scheme, which optimizes the page reclamation process by splitting LRU lists into multiple sub-lists, each having its own lock instance. Our evaluation result shows that the Finer-LRU scheme can improve sequential write throughput by 57.03% and reduce latency by 98.94% compared to the baseline Linux kernel version 5.2. 8 in the Intel Knights Landing (KNL) architecture.
Writer Identification Using Microblogging Texts for Social Media Forensics

Establishing authorship of online texts is fundamental to combat cybercrimes. Unfortunately, text length is limited on some platforms, making the challenge harder. We aim at identifying the authorship of Twitter messages limited to 140 characters. We evaluate popular stylometric features, widely used in literary analysis, and specific Twitter features like URLs, hashtags, replies or quotes. We use two databases with 93 and 3957 authors, respectively. We test varying sized author sets and varying amounts of training/test texts per author. Performance is further improved by feature combination via automatic selection. With a large amount of training Tweets (>500), a good accuracy (Rank-5>80%) is achievable with only a few dozens of test Tweets, even with several thousands of authors. With smaller sample sizes (10–20 training Tweets), the search space can be diminished by 9–15% while keeping a high chance that the correct author is retrieved among the candidates. In such cases, automatic attribution can provide significant time savings to experts in suspect search. For completeness, we report verification results. With few training/test Tweets, the EER is above 20–25%, which is reduced to <15% if hundreds of training Tweets are available. We also quantify the computational complexity and time permanence of the employed features.
Optimal Repair-Scaling Trade-off in Locally Repairable Codes: Analysis and Evaluation

How to improve the repair performance of erasure-coded storage is a critical issue for maintaining high reliability of modern large-scale storage systems. Locally repairable codes (LRC) are one popular family of repair-efficient erasure codes that mitigate the repair bandwidth and are deployed in practice. To adapt to the changing demands of access efficiency and fault tolerance, modern storage systems also conduct frequent scaling operations on erasure-coded data. In this article, we analyze the optimal trade-off between the repair and scaling performance of LRC in clustered storage systems. Specifically, we focus on two optimal repair-scaling trade-offs, and design placement strategies that operate along the two optimal repair-scaling trade-off curves subject to the fault tolerance constraints. We prototype and evaluate our placement strategies on a LAN testbed, and show that they outperform the conventional placement schemes in repair and scaling operations.
An Ultralow-Power OOK/BFSK/DBPSK Wake-Up Receiver Based on Injection-Locked Oscillator

This article presents a high-sensitivity and high energy efficiency wake-up receiver (WuRx) based on an injection-locked oscillator (ILO) and an envelope detector to provide the capabilities of reliable demodulation for ON–OFF keying (OOK), binary frequency shift keying (BFSK), and differential binary phase shift keying (DBPSK) demodulation schemes. A 54-<inline-formula> <tex-math notation="LaTeX">$\mu \text{W}$ </tex-math></inline-formula> 433-MHz multimode WuRx is implemented in a 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. For a data rate of 200 kb/s and a bit error rate (BER) <0.1%, the receiver achieves sensitivities of −80/−78/−77 dBm under the OOK/BFSK/DBPSK demodulation schemes, respectively. Furthermore, incorporating an external loop antenna with a reception of an ILO, the receiver features a low-power mode of just <inline-formula> <tex-math notation="LaTeX">$11~\mu \text{W}$ </tex-math></inline-formula> with a sensitivity of at least −71 dBm.
Serpentine-Shaped Metamaterial Energy Harvester for Wearable and Implantable Medical Systems

Integration with the curvilinear, soft, and time-dynamic surfaces of the human body is critical for most implantable and wearable biomedical systems. Devices that can imitate the mechanics of the body provide opportunities to create human-machine interfaces. Additionally, wireless functionality is essential to monitor health/wellness, study disease conditions, and execute other functions. The use of metamaterials in wireless applications is becoming widespread due to its extraordinary properties such as evanescent wave amplification and negative refractive index. This paper studies a soft, flexible and stretchable Complementary Split Ring Resonator (CSRR) metamaterial energy harvester using a volume of 5.6 x 5.6 x 1 mm3 on a Polydimethylsiloxane (PDMS) substrate. The CSRR is backed by a ground plane to absorb the incident power, and a via (load) is used to maximize the power harvesting efficiency. For stretchability, a typically rigid patch of the CSRR is replaced by the serpentine mesh. From the ANSYS HFSS simulation, it is found that the serpentine structure helps to reduce the size of the CSRR due to an increase in electrical length. The structure can also achieve high-quality factor (Q-factor), thereby enabling almost unity efficiency. The CSRR metamaterials can be used in future for wireless applications to integrate with the skin, the heart, and the brain.
Diffuse alveolar haemorrhage with suspected idiopathic pulmonary hemosiderosis and decrease in lung diffusing capacity and chronic respiratory failure

Idiopathic pulmonary hemosiderosis (IPH) is a rare disease of unknown aetiology that causes recurrent episodes of diffuse alveolar haemorrhage (DAH). A male patient in his 50s had repeatedly experienced hemoptysis for the past 6 years, along with a decrease in the pulmonary diffusing capacity and chronic respiratory failure. After a 6-year follow-up, the patient experienced sudden exacerbation of hemoptysis and respiratory failure, and he was hospitalised. A CT of the chest revealed diffuse pulmonary infiltrates, whereas the bronchoalveolar lavage revealed hemosiderin-laden macrophages. Thus, the patient was diagnosed with DAH. As all diseases that cause DAH other than IPH were negative, the patient was suspected of IPH. He was treated with a combination of glucocorticoids and azathioprine, and his hemoptysis and chronic respiratory failure improved; however, the decrease in the pulmonary diffusing capacity did not improve. Treating adult-onset IPH with glucocorticoids and azathioprine might not improve pulmonary diffusing capacity.
Progressive supranuclear palsy: diagnosis and management

Treating patients with progressive supranuclear palsy (PSP) is both effective and rewarding. This review aims to share our experience in the proactive management of PSP, considering the patient, the family and the medical context in which the illness unfolds. There are many opportunities to assist your patients, ameliorate their symptoms, reduce their risks and harm, and guide them through the complex medical, social and legal minefield that characterises life with chronic neurological illness. We summarise the challenges of early diagnosis, consider PSP mimics and the role of investigations in excluding these, and discuss the available pharmacological and non-pharmacological treatment strategies to tackle the common and challenging symptoms of PSP. The best treatment will be patient centred and as part of a multidisciplinary team.
Playable Video Generation: Supplementary Material

In this Supplementary Material, we provide additional details regarding the neural network architecture used in our experiments (Sec. 1), our training procedure (Sec. 2) and the baseline selection process (Sec. 3). Then, we detail the results of our human evaluation in Sec. 4. Finally, we report more qualitative results in Sec. 6. Importantly, this document is completed by an accompanying website composed of two parts. The first (see play-bair.html, play.html and play-tennis.html) contains fully playable demos of CADDY running locally in the browser, the second (see main.html) shows qualitative results. Note that, to guarantee better compatibility across devices, our browser demo runs on CPU and may require up to ten seconds to load, while our complete model runs on GPU in real-time. Due to the limitations on the size of the submission files, it was possible to include only the model trained on the Atari Breakout dataset. Also, html format offers more possibilities in term of visualization (e.g. videos) and should be favored for qualitative evaluation.
The Lottery Ticket Hypothesis for Object Recognition: Supplementary Material

We provide additional details for some of the experiments presented in the paper. In particular, we provide comparison with a simpler ImageNet ticket transfer alternative in Section A, analyse the transfer of tickets across downstream tasks in Section B, compare the different errors made by dense and pruned models in Section C, verify the faster convergence of sparser models in Section D and finally analyze the disk space and number of compute operations in Section E.
OTT_A_253946 6351..6359

*These authors contributed equally to this work Abstract: Uveal melanoma (UM) is the most common and aggressive primary intraocular tumor in adults. UM is classified as a malignant tumor with a strong tendency of metastasis, which always leads to poor outcomes. At present, the pathogenesis of UM remains unclear and lacks effective therapies. Recent studies have shown that microRNAs (miRNAs), defined as a group of 21–23 nucleotides single-stranded noncoding RNAs, play a significant role in UM. By binding to the complementary sites within the 3ʹ untranslated region (3ʹUTR) of message RNAs (mRNAs), miRNAs regulate genes by decaying mRNAs or inhibiting their translation. Thus, miRNAs can modulate various biological behaviors of tumors, including cell proliferation, invasion and metastasis. Furthermore, miRNAs have shown clinical applications by serving as biomarkers for diagnosis and prognosis, regulating immune response, and functioning as epigenetic regulators. It is reasonable to believe that miRNAs have wide application prospects in the early diagnosis and therapy of UM.
Protective Effects of Almond Oil on Streptozotocin-Induced Diabetic Rats via Regulating Nrf2/HO-1 Pathway and Gut Microbiota

Almond oil has been used as a medicine substitution for its numerous health benefits. This study aimed to evaluate the effect of almond oil on streptozotocin- (STZ-) induced diabetic rats for 4 weeks. The results showed that the administration of almond oil could significantly increase body weight, attenuate abnormally elevated blood glucose, promote insulin secretion, and improve glucose tolerance. Almond oil treatment also suppressed oxidative stress, reduced inflammation reaction, improved liver and kidney function, upregulated the expressions of Nrf2, HO-1, and NQO1, while downregulating the expression of Keap1. Furthermore, almond oil reversed the gut microbiota change by STZ and regulated the gut microbiota associated with glucose metabolism. At the phylum level, the relative abundance of Firmicutes was decreased, while Bacteroidetes was increased by almond oil treatment. More importantly, the ratio of Firmicutes/Bacteroidetes was significantly increased. At the genus level, administration of almond oil increased the abundances of Lactobacillus, Bacteroides, and Lachnospiraceae_NK4A136_group, while decreased the abundances of Ruminococcaceae_UCG-014, Clostridium_sensu_stricto_1, and Fusicatenibacter. These results provided evidence for the regulating effect of almond oil on diabetic rats via the Nrf2/HO-1 pathway and gut microbiota.
Deep Compositional Metric Learning

In this paper, we propose a deep compositional metric learning (DCML) framework for effective and generalizable similarity measurement between images. Conventional deep metric learning methods minimize a discriminative loss to enlarge interclass distances while suppressing intraclass variations, which might lead to inferior generalization performance since samples even from the same class may present diverse characteristics. This motivates the adoption of the ensemble technique to learn a number of sub-embeddings using different and diverse subtasks. However, most subtasks impose weaker or contradictory constraints, which essentially sacrifices the discrimination ability of each sub-embedding to improve the generalization ability of their combination. To achieve a better generalization ability without compromising, we propose to separate the sub-embeddings from direct supervisions from the subtasks and apply the losses on different composites of the sub-embeddings. We employ a set of learnable compositors to combine the sub-embeddings and use a self-reinforced loss to train the compositors, which serve as relays to distribute the diverse training signals to avoid destroying the discrimination ability. Experimental results on the CUB-200-2011, Cars196, and Stanford Online Products datasets demonstrate the superior performance of our framework.1
Center-based 3D Object Detection and Tracking — Supplementary Materials

Algorithm 1: Center-based Tracking Input :T (t−1) = {(p,v, c,q, id, a) j }j=1: Tracked objects in the previous frame, with center p, ground plane velocity v, category label c, other bounding box attributes q, tracking id id, and inactive age a (active tracks will have a = 0). D̂ = {(p̂, v̂, ĉ, q̂) i }i=1: Detections in the current frame in descending confidence. Output : T (t) = {(p,v, c,q, id, a)j=1}: Tracked Objects. 1 Hyper parameters: Matching distance threshold τ ; Max inactive age A. 2 Initialization: Tracks T , and matches S are initialized as empty sets. 3 T (t) ← ∅, S ← ∅ 4 F ← Cost(D̂, T (t−1)) // Fij = ||p̂ i − v̂,p (t−1) j ||2 5 for i← 1 to N do 6 j ← argminj / ∈S Fij 7 // Class-wise distance threshold τc 8 if Fij ≤ τc then 9 // Associate with tracked object
Resting-State Large Scale Brain Networks

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