Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Sorting Circuits for Short Keys

A long-standing open question in the algorithms and complexity literature is whether there exist sorting circuits of size o(n log n). A recent work by Asharov, Lin, and Shi (SODA’21) showed that if the elements to be sorted have short keys whose length k = o(log n), then one can indeed overcome the n log n barrier for sorting circuits, by leveraging non-comparison-based techniques. More specifically, Asharov et al. showed that there exist O(n) · min(k, log n)-sized sorting circuits for k-bit keys, ignoring poly log∗ factors. Interestingly, the recent works by Farhadi et al. (STOC’19) and Asharov et al. (SODA’21) also showed that the above result is essentially optimal for every key length k, assuming that the famous Li-Li network coding conjecture holds. Note also that proving any unconditional super-linear circuit lower bound for a wide class of problems is beyond the reach of current techniques. Unfortunately, the approach taken by Asharov et al. to achieve optimality in size somewhat crucially relies on sacrificing the depth: specifically, their circuit is super-polylogarithmic in depth even for 1-bit keys. Asharov et al. phrase it as an open question how to achieve optimality both in size and depth. In this paper, we close this important gap in our understanding. We construct a sorting circuit of size O(n)·min(k, log n) (ignoring poly log∗ terms) and depthO(log n). To achieve this, our approach departs significantly from the prior works. Our result can be viewed as a generalization of the landmark result by Ajtai, Komlós, and Szemerédi (STOC’83), simultaneously in terms of size and depth. Specifically, for k = o(log n), we achieve asymptotical improvements in size over the AKS sorting circuit, while preserving optimality in depth. *Author ordering is randomly generated. ar X iv :2 10 2. 11 48 9v 1 [ cs . D S] 2 3 Fe b 20 21
Barrier-Free Large-Scale Sparse Tensor Accelerator (BARISTA) For Convolutional Neural Networks

Convolutional neural networks (CNNs) are emerging as powerful tools for visual recognition. Recent architecture proposals for sparse CNNs exploit zeros in the feature maps and filters for performance and energy without losing accuracy. Sparse architectures that exploit two-sided sparsity in both feature maps and filters have been studied only at small scales (e.g., 1K multiply-accumulate(MAC) units). However, to realize their advantages in full, the sparse architectures have to be scaled up to levels of the dense architectures (e.g., 32K MACs in the TPU). Such scaling is challenging since achieving reuse through broadcasts incurs implicit barrier cost raises the inter-related issues of load imbalance, buffering, and on-chip bandwidth demand. SparTen, a previous scheme, addresses one aspect of load balancing but not other aspects, nor the other issues of buffering and bandwidth. To that end, we propose the barrier-free large-scale sparse tensor accelerator (BARISTA). BARISTA (1) is the first architecture for scaling up sparse CNN accelerators; (2) reduces on-chip bandwidth demand by telescoping request-combining the input map requests and snarfing the filter requests; (3) reduces buffering via basic buffer sharing and avoids the ensuing barriers between consecutive input maps by coloring the output buffers; (4) load balances intra-filter work via dynamic round-robin work assignment; and (5) employs hierarchical buffering which achieves high cache bandwidth via a few, wide, shared buffers and low buffering via narrower, private buffers at the compute. Our simulations show that, on average, barista performs 5.4x, 2.2x, 1.7x, 2.5x better than a dense, a one-sided, a naively-scaled two-sided, and an iso-area two-sided architecture, respectively. Using 45-nm technology, ASIC synthesis of our RTL design for four clusters of 8K MACs at 1 GHz clock speed, reports 213 mm$^2$ area and 170 W power.
Going deeper: magnification-invariant approach for breast cancer classification using histopathological images

Sinan Alkasaar, Computer and Information Engineering Department, Ninevah University, Mosul, Iraq. Email: sinan.alkassar@uoninevah.edu. iq Abstract Breast cancer has the highest fatality for women compared with other types of cancer. Generally, early diagnosis of cancer is crucial to increase the chances of successful treatment. Early diagnosis is possible through physical examination, screening, and obtaining a biopsy of the dubious area. In essence, utilizing histopathology slides of biopsies is more efficient than using typical screening methods. Nevertheless, the diagnosing process is still tiresome and is prone to human error during slide preparation, such as when dyeing and imaging. Therefore, a novel method is proposed for diagnosing breast cancer into benign or malignant in a magnification‐specific binary (MSB) classification. Besides, the introduced method classifies each type into four subclasses in a magnification‐specific multi‐ category (MSM) fashion. The proposed method involves normalizing the hematoxylin and eosin stains to enhance colour separation and contrast. Then, two types of novel features —deep and shallow features—are extracted using two deep structure networks based on DenseNet and Xception. Finally, a multi‐classifier method based on the maximum value is utilized to achieve the best performance. The proposed method is evaluated using the BreakHis histopathology data set, and the results in terms of diagnostic accuracy are promising, achieving 99% and 92% in terms of MSB and MSM, respectively, compared with recent state‐of‐the‐art methods reported in the survey conducted by Benhammou on the BreakHis data set using deep learning and texture‐based models.
Performance Evaluation of IEEE 802.15.6 MAC Under Varying Health Emergency Conditions

Wireless Body Area Network (WBAN) consists of health and environmental sensing nodes and a dedicated hub. The hub has more processing power and capabilities than the sensor nodes. It collects sensing data and forwards them to corresponding destinations for further processing and actions. In case of emergencies, the hub delivers the received data to the emergency department to take quick action. The end-to-end link between the sensor node and the emergency department mainly depends on the sensor node's bottleneck link and the hub. WBAN nodes communicate with the hub by accessing the physical communication channel in a randomly distributed fashion. Random channel access may cause retransmission of corrupted frames due to frame collisions. This causes performance degradation of the deployed WBAN. The IEEE 802.15. 6 standard specified eight different user priorities for different traffic types. Among these priorities, priority seven is dedicated to reporting emergency and medical implant events. If several nodes report at this priority, this causes lower priority nodes to starve. In this work, we allow nodes to switch from lower priorities to priority seven in emergencies. We conducted several experiments to study the effects of priority seven on other nodes in normal operations and emergencies. The study results showed that the packet delivery rate is severely affected when some nodes switch from normal user priority to priority seven. More studies and investigations need to be conducted to determine the optimum operation of WBAN, especially in the case of an emergency.
Plasmid-determined resistance to antimicrobial drugs and toxic metal ions in bacteria

INTRODUCTION. 362 INACTIVATION MECHANISMS. 363 Resistance to ,B-Lactam Antibiotics . 363 Plasmid-determined ,B-lactamases in gram-negative bacteria . 363 Plasmid-specified FI,anctamases in gram-positive bacteria . 365 Mechanism of hydrolysis of -lactams . 365 Factors affecting expression of 3-lactam antibiotic resistance in gram-positive and gram-negative bacteria . 365 Do some plasmils determine a barrier to the penetration of j-lactam antibiotics? 367 Regulation of f-lactamase exRression . 367 Resistance to Chloramphenicol. 367 CATs in gram-negative bacteria. 367 CATs in gram-positive bacteria . 368 Mechanism of Cm inactivation. 368 Catalytic properties of CAT . 368 Type I CAT confers resistnce to fusidic acid . 368 Regulation of CAT synthesis in E. coli. 369 Regulation of CAT synthesis in S. aureus. 370 Resistance to Aminoglycoside-Aminocyclitol Antibiotics . 370 Transport of aminoglycosides in sensitive and resistant bacteria . 371 Location of aminoglycoside-modifying enzymes in the bacterial cell . 371 Classification of aminoglycoside-aminocyclitol antibiotics and their modifying enzymes . 372 Resistance to Mercuric Ions and Organomercurials . 373 Sensitivity and resistance to mercurial compounds. 373 Spectrum of resistance to organomercurials in gram-positive and gram-negative bacteria. 375 Mercuric reductase and mechanism of reduction of mercuric ions . 380 Detoxification of organomercurial compounds. 380 Evidence for an Hg2+-specific transport system and its role in resistance . 380 Expression of resistance by multicopy plasmids . 381 Genetic analysis of the mer region. 381 Regulation of expression of mer genes. 382 BYPASS MECHANISMS. 382 Sulfonamide Resistance . 382 Inhibitory effect of sulfonamides . 382 Plasmid-specified Sur DHPS. 382 Regulation of plasmid-specified DHPS . 383 Trimethoprim Resistance. 384 Inhibitory effects of Tp . 384 Plasmid-specified Tp-resistant DHFR . 384 DECREASED ACCUMULATION INVOLVING EFFLUX MECHANISMS. 384 Resistance to Tetracyclines . 384 Uptake of Tc by susceptible cells . 384 Naturafly occurring Tc resistance determinants . 385 Mechanism of Tc resistance. 385 Structure and function of the TnlO Tc resistance region. 386 Regulation of tet gene expression . 387 Resistance to Cadmium . 388 Resistance to Arsenate. 388
Simple Entity-Centric Questions Challenge Dense Retrievers

Open-domain question answering has exploded in popularity recently due to the success of dense retrieval models, which have surpassed sparse models using only a few supervised training examples. However, in this paper, we demonstrate current dense models are not yet the holy grail of retrieval. We first construct EntityQuestions, a set of simple, entity-rich questions based on facts from Wikidata (e.g., “Where was Arve Furset born? ”), and observe that dense retrievers drastically under-perform sparse methods. We investigate this issue and uncover that dense retrievers can only generalize to common entities unless the question pattern is explicitly observed during training. We discuss two simple solutions towards addressing this critical problem. First, we demonstrate that data augmentation is unable to fix the generalization problem. Second, we argue a more robust passage encoder helps facilitate better question adaptation using specialized question encoders. We hope our work can shed light on the challenges in creating a robust, universal dense retriever that works well across different input distributions.
Circulation control improvements to rotor lift asymmetry due to reverse flow

Circulation Control Improvements to Rotor Lift Asymmetry Due to Reverse Flow Michael J. Lyons West Virginia University, Morgantown, WV, 26506 Circulation control has been applied to airfoils since the late 1960’s, and has been proven to change the aerodynamic performance by altering the interaction of the streamlines without changing the physical characteristics of the airfoil itself. This has many applications in fluid dynamics; the focus of this application is for the replacement of the conventional helicopter rotor blade system with a fly-by-wire, active circulation controlled system. Conventional helicopters use a swashplate and a series of mechanical linkages, bearings, and dampers to create a fully articulated rotor hub system. This system is required to achieve the blade characteristics required for stable flight. The need for such a system stems from the asymmetric lift developed in maneuvering flight conditions, which requires the angle of attack of the blades to be changed based on the rotational position within the rotor plane, also known as the azimuth angle. By alternatively activating blowing slots along the leading and trailing edges of the airfoil, the aerodynamic parameters (i.e. lift and drag) can be changed, effectively changing the angle of attack through streamline alteration thus eliminating the need for physical blade pitch changes. Mathematical models/codes are used to model and simulate the complex blade dynamics of a full-scale rotorcraft. Many of these codes use a blade element method that separates the rotor into small segments and evaluates the aerodynamic characteristics of these elements as two-dimensional airfoils at different local conditions. These local conditions include, but are not limited to, extreme angles of attack, reverse flow, compressibility effects, dynamic effects, and other aerodynamic phenomena. This research investigated the reverse flow aerodynamic characteristics of a 10:1 elliptical airfoil with various leading and trailing edge blowing pressures. The testing conditions for the aerodynamic investigation were: angle of attack [154 to 196 degrees]; blowing coefficient [0 to 0.009] and [0 to 0.014] for the leading and trailing edge blowing slots, respectively; and Mach number [0.073 and 0.109]. It is concluded that the potential exists for increasing forward flight speeds for helicopters using circulation control in the reverse flow regions of the helicopter rotor environment. Specifically, it is concluded that positively altering the aerodynamic characteristics, primarily the lift coefficient, in reverse flow, is possible through circulation control. Through this investigation, the general trends were found for the aerodynamic characteristics of a 10:1 circulation controlled elliptical airfoil in reverse flow. These trends led to the selection of blowing configurations to decrease the asymmetric loading condition based on the condition of the local blade environment.
Fish Skin Mucus as Putative Bio-resource for the Development of Next-Generation Antibiotics

The resistance of bacterial infections to current antibiotics continues rising. The World Health Organization has warned that this issue will become more serious. A recent study anticipates that by 2050 drug-resistant infections will affect more people than cancer. Thus, it is highly required to discover an alternative antimicrobial agent, especially in the current situation where most infectious disease pathogens are developing their resistance to the conventional drugs. Hence, it is critically important to find the next generation of antibiotics. Perhaps surprisingly, over 70 percent of currently used anti-infective are derived from naturally occurring chemicals. Plants and microbes produce a diverse array of complex chemicals, some of which have antibiotic or antiviral properties, or even are toxic to cells. Consequently, there is an urgent need to produce more effective and safe antibiotics from animals as a therapy termed as Zoo therapy or animal therapy, and in this context, fishes occupy very important place. Fishes are not only the largest and diverse group of vertebrates, but also a major component of aquatic fauna and known for their benefits to human in many ways. Besides being a good and healthy food source, several compounds from fishes have been extracted and employed as remedies (Hamada et al., 1995). According to Eraldo Mand CostaNeto (2005), „a total of 66 raw materials including scales, spur, shell, fat, skin, globe of the eye, tentacles and otolith are used in the elaboration of remedies to treat locally diagnosed ailments ARTICLE INFO ABSTRACT
Does revegetation cause soil microbiota recovery? Evidence from revisiting a revegetation chronosequence 6 years after initial sampling

The global biodiversity and land degradation crises have brought about an urgent need and great demand for restoration actions. However, restoration outcomes are often less than ideal, indicating a need for improved restoration practices. Soil microbiota are extremely diverse and functionally important and should be further considered in restoration. However, despite their importance, there remains a gap in understanding of how soil microbiota respond following native plant revegetation. Several studies have used cross‐sectional study designs of restoration chronosequences to infer that revegetation causes the recovery of soil microbiota, but it is near‐impossible to determine cause and effect relationships with cross‐sectional study designs. Here we used high‐throughput amplicon sequencing of the bacterial 16s rRNA gene from soil samples collected at two timepoints, 6 years apart, at a revegetation chronosequence in South Australia. Our results show some indications of recovery but not the additional recovery in bacterial community composition toward the reference sites as expected after this 6‐year period—a result that appears at odds to the expected patterns of revegetation causing recovery of soil microbiota. Spatially dependent factors (e.g. soil chemistry), biotic and abiotic barriers, seasonal differences in sampling, and variability among the ecological reference sites could each help explain this apparent lack of additional microbial recovery. More detailed longitudinal and/or experimental manipulation work is required to further examine the cause‐effect relationships. Our study contributes important new information and highlights knowledge gaps in how soil microbiota respond to revegetation, and we urge caution when attempting to infer causation from cross‐sectional chronosequence studies.
Survey on Self-supervised Representation Learning Using Image Transformations

Deep neural networks need huge amount of training data, while in real world there is a scarcity of data available for training purposes. To resolve these issues, self-supervised learning (SSL) methods are used. SSL using geometric transformations (GT) is a simple yet powerful technique used in unsupervised representation learning. Although multiple survey papers have reviewed SSL techniques, there is none that only focuses on those that use geometric transformations. Furthermore, such methods have not been covered in depth in papers where they are reviewed. Our motivation to present this work is that geometric transformations have shown to be powerful supervisory signals in unsupervised representation learning. Moreover, many such works have found tremendous success, but have not gained much attention. We present a concise survey of SSL approaches that use geometric transformations. We shortlist six representative models that use image transformations including those based on predicting and autoencoding transformations. We review their architecture as well as learning methodologies. We also compare the performance of these models in the object recognition task on CIFAR-10 and ImageNet datasets. Our analysis indicates the AETv2 performs the best in most settings. Rotation with feature decoupling also performed well in some settings. We then derive insights from the observed results. Finally, we conclude with a summary of the results and insights as well as highlighting open problems to be addressed and indicating various future directions.
Gaze Estimation via Modulation-Based Adaptive Network With Auxiliary Self-Learning

Given a face image, most of previous works in gaze estimation infer the gaze via a well-trained model with supervised training. However, the distribution of test data may be very different compared to that of training data since samples might be corrupted in real-world scenarios (e.g., taking a photo in strong light). This will lead to a gap between source domain (i.e., training data) and target domain (i.e., test data). In this paper, we first introduce self-supervised learning into our method for addressing challenging situations in gaze estimation. Moreover, existing appearance-based gaze estimation methods focus on directing towards the development of powerful regressors, which mainly utilize face and eye images simultaneously or face (eye) images only. However, the problem of inter cues between face and eye features has been largely overlooked. To this end, we propose a novel Modulation-based Adaptive Network (MANet) for gaze estimation, which uses high-level knowledge to filter the distractive information and bridges the intrinsic relationship between face and eye features. Further, we combine self-supervised learning and MANet to learn to adapt to challenging cases, such as abnormal lighting conditions and poor-quality images, by minimizing a self-supervised loss and a supervised loss jointly. The experimental results on several datasets demonstrate the effectiveness of our proposed approach with a real-time speed of 900 fps on a PC with an NVIDIA Titan RTX GPU.
On the utility of leg distal compliance for buffering landing impact of legged robots

Many legged robots have compliant mechanisms in the distal segments of their legs called distal compliance. One important function of such characteristic is to buffer landing impact at touchdown. However, there is still no general design strategy for it. In particular, nonlinear compliance behaviors are supposed to be more beneficial than linear ones, yet it is open what type of nonlinearity is a good fit. From this perspective, we used a simple spring–mass model performing free drop to investigate the design principles of distal compliance. The model includes damping and preload in spring and realistic limitations on spring compression, therefore gives a straightforward correspondence with actual hardware systems. We confirmed the benefits of using distal compliance over purely stiff structures, in terms of landing impact buffering. By assessing the relative influences of a variety of compliance configurations through numerical simulations, we found that for compliance behaviors of the same average stiffness, nonlinearities had little effect on the impact magnitude (<1 N), but stiffening compliance behaviors were able to provide better buffering performance by extending the impact time. It was also noticed that stiffening compliance behaviors were inevitably accompanied by a larger amplitude of spring compression, indicating that necessary trade-off has to be made for those systems concerning torso stationarity. The experimental data with our hexapod robotic platform agreed well with the results found with the proposed model, confirming that the spring–mass model could be a template to provide a useful guide for the design of distal compliance in legged robots.
Are Transformers Effective for Time Series Forecasting?

Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the elements in a long sequence. However, in time series modeling, we are to extract the temporal relations in an ordered set of continuous points. While employing positional encoding and using tokens to embed sub-series in Transformers facilitate preserving some ordering information, the nature of the permutation-invariant self-attention mechanism inevitably results in temporal information loss. To validate our claim, we introduce a set of embarrassingly simple one-layer linear models named LTSF-Linear for comparison. Experimental results on nine real-life datasets show that LTSF-Linear surprisingly outperforms existing sophisticated Transformer-based LTSF models in all cases, and often by a large margin. Moreover, we conduct comprehensive empirical studies to explore the impacts of various design elements of LTSF models on their temporal relation extraction capability. We hope this surprising finding opens up new research directions for the LTSF task. We also advocate revisiting the validity of Transformer-based solutions for other time series analysis tasks (e.g., anomaly detection) in the future.