Miracle-dz/UniFAR-FTUs huggingface dataset:
Synergistic cache layout for reuse and compression

Recent advances in research on compressed caches make them an attractive design point for effective hardware implementation for last-level caches. For instance, the yet another compressed cache (YACC) layout leverages both spatial and compression factor localities to pack compressed contiguous memory blocks from a 4-block super-block in a single cache block location. YACC requires less than 2% extra storage over a conventional uncompressed cache. Performance of LLC is also highly dependent on its cache block replacement management. This includes allocation and bypass decision on a miss as well as replacement target selection which is guided by priority insertion policy on allocation and priority promotion policy on a hit. YACC uses the same cache layout as a conventional set-associative uncompressed cache Therefore the LLC cache management policies that were introduced during the past decade can be transposed to YACC. However, YACC features super-block tags instead of block tags. For uncompressed block, these super-block tags can be used to monitor the reuse behavior of blocks from the same super-block. We introduce the First In Then First Use Bypass (FITFUB) allocation policy for YACC. With FITFUB, a missing uncompressed block that belongs to a super-block that is already partially valid in the cache is not stored in the cache on its first use, but only on its first reuse if any. FITFUB can be associated with any priority insertion/promotion policy. YACC+FITFUB with compression turned off, achieves an average 6.5%/8% additional performance over a conventional LLC, for single-core/multi-core workloads, respectively. When compression is enabled, the performance benefits associated with compression and FITFUB are almost additive reaching 12.7%/17%. This leads us to call this design the Synergistic cache layout for Reuse and Compression (SRC). SRC reaches the performance benefit that would be obtained with a 4X larger cache, but with less than 2% extra storage.
Towards Modelling Multi-Arm Robots: Eccentric Arrangement of Concentric Tubes

This paper presents and experimentally evaluates a quasistatic mechanics-based model that describes the shape of concentric tube robotic (CTR) arms when they are eccentrically arranged along an also-flexible backbone. The model can estimate the shape of both the backbone and CTR arms, and can accommodate an arbitrary number of CTR arms arranged in an eccentric position with regards to the backbone's neutral axis. Experimental evaluation with a prototype system on the benchtop highlights the promise of the end-to-end proposed modelling approach, as the error between model and experiment is around 11% of the manipulator's overall length. The theory is the first step towards modeling multi-arm concentric tube robots, a class of continuum robots that is increasingly being considered for single-port surgical applications.
Optimal Power and Rate Allocation in Superposition Transmission With Successive Noise Signal Sharing Toward Zero Intercept Probability

In this letter, we show that superposition transmission (SuT) with optimal power and rate allocation achieves zero intercept probability (IP) without assuming infinite signal-to-noise power ratio and furthermore without significant sacrifice of the secrecy performance. This letter also provides tradeoff relationships between IP and outage probability of data signals and between IP and ergodic data rate, respectively. With numerical investigation, it is shown that the proposed SuT with the zero IP does not significantly degrade the performances from the information transmission that allows certain positive IP.
Hierarchical Temporal Memory Enhanced One-Shot Distance Learning for Action Recognition

One-shot action recognition is one of the most challenging tasks due to the very limited training samples. For one-shot video action recognition, randomly selected frames from cluttered frame features may result in a poor performance. To use the most valuable frames in a better feature space, this paper proposes Hierarchical Temporal Memory Enhanced One-shot Distance Learning (HED). Firstly, we introduce temporal triplet from different frames, so that the intra-class distance will be decreased while the inter-class distance will be increased. Secondly, the Hierarchical Temporal Memory (HTM), a biological plausible unsupervised model for sequence prediction, is employed to enhance the one-shot action recognition by finding the most valuable frames in a video sequence. Finally, the selected frames together with the temporal triplet trained model are used to get the corresponding category label. Extensive experiments conducted on three benchmark datasets (i.e UCF11, UCF50 and HMDB51) demonstrate that we can achieve significant improvement than the state-of-the-art methods.
Pricing for Revenue Maximization in Inter-DataCenter Networks

As more applications and businesses move to the cloud, pricing for inter-datacenter links has become an important problem. In this paper, we study revenue maximizing pricing from the perspective of a network provider in inter-datacenter networks. Designing a practical bandwidth pricing scheme requires us to jointly consider the requirements of envy-freeness and arbitrage-freeness, where envy-freeness guarantees the fairness of resource allocation and arbitrage-freeness induces users to truthfully reveal their data transfer requests. Considering the non-convexity of the revenue maximization problem and the lack of information about the users' utilities, we propose a framework for computationally efficient pricing to approximately maximize revenue in a range of environments. We first study the case of a single link accessed by many users, and design a (1 + E)-approximation pricing scheme with polynomial time complexity and information complexity. Based on dynamic programming, we then extend the pricing scheme for the tollbooth network, preserving the (1 + E) approximation ratio and the computational complexity. For the general network setting, we analyze the revenue generated by uniform pricing, which determines a single per unit price for all potential users. We show that when users have similar utilities, uniform pricing can achieve a good approximation ratio, which is independent of network topology and data transfer requests. The pricing framework can be extended to multiple time slots, enabling time-dependent pricing.
Decomposition of Landsat 8 OLI Images by Simplified Spectral Patterns for Land Cover Mapping

Decomposition of multispectral image data has recently become the focus of remote sensing scientists because it improves efficiency of land cover mapping by data reduction. The three main traditional techniques used for decomposition are principal component analysis, linear spectral mixing, and pattern decomposition. This paper presents a new method of pattern decomposition using simplified spectral patterns. Spectral characteristics of land cover are analyzed using the shape of their spectral reflectance curves, leading to decomposition of an image into component images with pixels that are characterized by spectral reflectance curves of the same shape. The author proposes a method for converting a spectral reflectance curve from an analog to a simplified digital form consisted of 15 digits of zeros, ones, and twos. This form of the spectral reflectance curve is called simplified spectral pattern (SSP). One hundred Landsat 8 OLI scenes of 14 global biomes were collected for the research. The study found that 34 dominant simplified spectral patterns represent land cover types in selected scenes, covering 96.53% of the total 4,150,671,513 pixels. SSP correlates with single land cover types such as water, ice, and snow or with multiple land cover types such as vegetation, minerals, rock, and soil.
Modified Cascaded Multilevel Inverter with Reduced Component Count

This paper introduces a basic module for symmetrical multilevel inverter. The proposed multilevel inverter is composed of cascaded basic unit with H-bridge. Multi-carrier based level-shift pulse width modulation scheme is developed to achieve the switching signals for switches. The proposed cascaded multilevel inverter results in reduction of number of components compared to other conventional and recent multilevel inverters. The performance and operation of proposed inverter is verified through simulation results through MATLAB/Simulink software and experimental implementation of single phase 11-level inverter.
Eye Movement-Related Confounds in Neural Decoding of Visual Working Memory Representations

Abstract A relatively new analysis technique, known as neural decoding or multivariate pattern analysis (MVPA), has become increasingly popular for cognitive neuroimaging studies over recent years. These techniques promise to uncover the representational contents of neural signals, as well as the underlying code and the dynamic profile thereof. A field in which these techniques have led to novel insights in particular is that of visual working memory (VWM). In the present study, we subjected human volunteers to a combined VWM/imagery task while recording their neural signals using magnetoencephalography (MEG). We applied multivariate decoding analyses to uncover the temporal profile underlying the neural representations of the memorized item. Analysis of gaze position however revealed that our results were contaminated by systematic eye movements, suggesting that the MEG decoding results from our originally planned analyses were confounded. In addition to the eye movement analyses, we also present the original analyses to highlight how these might have readily led to invalid conclusions. Finally, we demonstrate a potential remedy, whereby we train the decoders on a functional localizer that was specifically designed to target bottom-up sensory signals and as such avoids eye movements. We conclude by arguing for more awareness of the potentially pervasive and ubiquitous effects of eye movement-related confounds.
Moderate coffee consumption is inversely associated with the metabolic syndrome in the Korean adult population

Abstract Results from studies investigating the association between coffee consumption and the metabolic syndrome in Koreans have been inconsistent and remain controversial. We examined coffee intake in relation to the metabolic syndrome using data from the Korea National Health and Nutrition Examination Survey 2012–2015, with 8387 adults aged 19–64 years. Furthermore, we conducted a stratified analysis by obesity status (BMI <25 v. ≥25 kg/m2) to examine whether the association varied by obesity status. A 112-item FFQ was used to assess diet. Multivariable logistic regression models were used to determine the association of coffee consumption (<1, 1–2, 3–4 and ≥5 times/d) and the metabolic syndrome. Compared to people who consumed <1 time/d of coffee, the OR for the metabolic syndrome in those who consumed 3–4 times/d of coffee was 0·75 (95 % CI 0·58, 0·97). However, ≥5 times/d of coffee consumption was not significantly associated with the metabolic syndrome. By obesity status, in non-obese people, those consuming more coffee had lower odds of the metabolic syndrome, showing a significant linear trend (P-trend=0·0248). In obese people, the OR for the metabolic syndrome in people with 3–4 times/d of coffee consumption v. <1 time/d was 0·68 (95 % CI 0·50, 0·93), but ≥5 times/d of coffee consumption showed a non-significant positive association. Our findings suggest that moderate coffee consumption of 3–4 times/d is inversely associated with the metabolic syndrome in Korean adults. The association between heavy coffee consumption of ≥5 times/d and the metabolic syndrome varied by obesity status.
AUV-Based Underwater 3-D Point Cloud Generation Using Acoustic Lens-Based Multibeam Sonar

This study proposes a 3-D seafloor scanning method using sonar images obtained by an acoustic lens-based multibeam sonar (ALMS). ALMS is a forward-looking sonar and is generally used to implement real-time acoustic vision for autonomous underwater vehicles (AUVs). ALMS is originally not a 3-D sonar, and elevation information is eliminated when the 3-D world is mapped to the 2-D sonar image. The proposed method provides a unique analysis of sonar image geometry for extracting the missing elevation information. Different from conventional 3-D reconstruction methods, the proposed method can be continuously executed regardless of the existence of any remarkable features in the sonar image, which means that the proposed method can scan the seafloor during a full-time AUV operation. Once the 3-D seafloor data are obtained, the height of the seafloor can be used for the detection of suspected objects. Therefore, the proposed method enables long-range seafloor object detection, triggering succeeding processes involving the AUV approaching the detected object, and the start of a detailed investigation at a close range.
APRIGUARD: The Automatic and Eficient Method for Privacy Violations Detection in OSNs (Online Social Networks

The users from the social networks like Facebook, Twitter etc. expecting that social networks can able to preserve their privacy. To address this, recently PRIGUARD framework introduced in which meta model and description logic used for describing the social network commitments and domain in order to specify the privacy needs of end users. This approach is able to detect the violations of privacy in both sound and complete. However the still there is scope of improvement in PRIGUARD method and limitations. One such limitation of PRIGUARD does not supporting the proactive violations of its commitments whenever its required to pro-vide the context dependent privacy management. In this paper we are presenting the Automated PRIGUARD (APRI-GUARD) which is based on existing PRIGUARD method. APRIGUARD method not only supports the functionality of existing PRIGUARD but also enabling the approach of proactively violates its commitments. This achieves more efficiency and security to end users privacy. The proposed approach will be used before taking a particular action for checking whether it will lead violation and hence it will results in its prevention before it happens. Antithesis, it will be utilized to do sporadic checks on the system to see if any violations have occurred. )
Inverse Tone Mapping Operator Using Sequential Deep Neural Networks Based on the Human Visual System

Conventional digital displays show images with much less dynamic range than that of human visual perception. High-dynamic range (HDR) displays are being developed for viewing images with higher dynamic range than that of conventional low-dynamic range (LDR) images. However, to view existing LDR images on HDR displays, an inverse tone mapping operator (ITMO), which is a process to extend the dynamic range of LDR images, is required. In this paper, we propose an adaptive ITMO by effectively learning the differences between LDR and HDR images using a sequential learning process: dynamic range learning followed by color difference learning. Our proposed method enables visualization of colors similar to real-world colors better than conventional ITMOs by learning color differences based on the human visual system properties. For the objective comparison, seven different evaluation metrics optimized for HDR image evaluation were used. Our method resulted in 10%–25% improved HDR-visual difference predictor-2.2 values over those of other ITMOs. Other metrics also demonstrated the superior performance of our method. For the subjective comparison, eight human observers evaluated the estimated HDR images in terms of color appearance and overall preferences. Our method received an average 4.7 out of 5 score, whereas other ITMOs received below 3.5 scores in the evaluation of color appearance. The objective and subjective evaluations’ results showed that our proposed method outperformed the conventional ITMOs in estimating the dynamic range and color appearance of ground-truth HDR images.
Identification of Alkaloids from Corydalis yanhusuo W. T. Wang as Dopamine D1 Receptor Antagonists by Using CRE-Luciferase Reporter Gene Assay

Corydalis yanhusuo W. T. Wang (C. yanhusuo) has been traditionally used for drug addiction and pain relief in China. In our previous study, we showed that the extract of C. yanhusuo blocks dopamine receptors, demonstrating that its pharmacological activities are mostly due to the antagonistic effects of some of its components at dopamine receptors. As part of our ongoing project on C. yanhusuo, the aim of the present study is to establish a high-throughput and low-cost screening assay system and test the abilities of the isolated alkaloids from C. yanhusuo to inhibit dopamine-induced dopamine D1 receptor activity. By using our established cyclic adenosine monophosphate (cAMP)-response element (CRE)-luciferase reporter gene assay system, we identified eight alkaloids from C. yanhusuo with D1 receptor antagonistic activities. We next validated the activities of these compounds using fluorometric imaging plate reader (FLIPR) assay by measuring the intracellular Ca2+ change. Six out of eight compounds, including tetrahydropalmatine, corydaline, 13-methyldehydrocorydalmine, dehydrocorybubine, dehydrocorydaline, and columbamine, can be confirmed for their inhibitory activities. The dopamine-receptor-antagonistic effects of four compounds, including 13-methyldehydrocorydalmine, dehydrocorydaline, columbamine, and corydaline, are reported for the first time. The present study provides an important pharmacological basis to support the traditional use of C. yanhusuo in China.
Stencil codes on a vector length agnostic architecture

Data-level parallelism is frequently ignored or underutilized. Achieved through vector/SIMD capabilities, it can provide substantial performance improvements on top of widely used techniques such as thread-level parallelism. However, manual vectorization is a tedious and costly process that needs to be repeated for each specific instruction set or register size. In addition, automatic compiler vectorization is susceptible to code complexity, and usually limited due to data and control dependencies. To address some these issues, Arm recently released a new vector ISA, the Scalable Vector Extension (SVE), which is Vector-Length Agnostic (VLA). VLA enables the generation of binary files that run regardless of the physical vector register length. In this paper we leverage the main characteristics of SVE to implement and optimize stencil computations, ubiquitous in scientific computing. We show that SVE enables easy deployment of textbook optimizations like loop unrolling, loop fusion, load trading or data reuse. Our detailed simulations using vector lengths ranging from 128 to 2,048 bits show that these optimizations can lead to performance improvements over straight-forward vectorized code of up to 56.6% for 2,048 bit vectors. In addition, we show that certain optimizations can hurt performance due to a reduction in arithmetic intensity, and provide insight useful for compiler optimizers.
Chemical Diversity of Metal Sulfide Minerals and Its Implications for the Origin of Life

Prebiotic organic synthesis catalyzed by Earth-abundant metal sulfides is a key process for understanding the evolution of biochemistry from inorganic molecules, yet the catalytic functions of sulfides have remained poorly explored in the context of the origin of life. Past studies on prebiotic chemistry have mostly focused on a few types of metal sulfide catalysts, such as FeS or NiS, which form limited types of products with inferior activity and selectivity. To explore the potential of metal sulfides on catalyzing prebiotic chemical reactions, here, the chemical diversity (variations in chemical composition and phase structure) of 304 natural metal sulfide minerals in a mineralogy database was surveyed. Approaches to rationally predict the catalytic functions of metal sulfides are discussed based on advanced theories and analytical tools of electrocatalysis such as proton-coupled electron transfer, structural comparisons between enzymes and minerals, and in situ spectroscopy. To this end, we introduce a model of geoelectrochemistry driven prebiotic synthesis for chemical evolution, as it helps us to predict kinetics and selectivity of targeted prebiotic chemistry under “chemically messy conditions”. We expect that combining the data-mining of mineral databases with experimental methods, theories, and machine-learning approaches developed in the field of electrocatalysis will facilitate the prediction and verification of catalytic performance under a wide range of pH and Eh conditions, and will aid in the rational screening of mineral catalysts involved in the origin of life.
Score based Face Quality Assessment (FQA)

Face image quality has a key role in the performance accuracy of automatic face recognition algorithms. In video sequence multiple faces of a subject are available of which selecting the face image(s) with high quality improves the recognition performance. Face detection algorithms often encounter the problem of detecting outliers as a face. This enhances the computational load and increases the risk of face mismatch. In this work, we propose a feature based learning framework for assessing the face image quality. The proposed method is simple and easy to implement for different recognition methods. Experimental result demonstrates its effectiveness in improving the robustness of face detection and recognition.
Wallet App Credibility Analysis Based on App Content and User Reaction

Online reviews have become a valuable resource for decision making. In recent years, analysis ofreviews has attracted significant attention. Individuals and organizations in making purchase and other organizational decisions are increasingly using online reviews. Positive reviews acclaim significant financial gains, fame and prestige for businesses in market. On the other end, this gives strong enticement to fraudulent to hypocrite the system by posting disingenuous reviews to promote or to vilify some target products and services, which is known as opinion spam. Therefore, we made this research effort as an initial impetus in the direction to identify the presence of fake reviews. We performed this experiment on Wallet Apps accessible through Google Play Store. To do so, we rate applications based on features availability existence in Google App based on App content and user reactions. We computed four major scores-Description score, Positive Store, Negative Store and Review Tag Score and finally assigned a cumulative normalized score to all Wallet Apps. Further, a comparative analysis has been done between cumulative normalized score and stated Google App rating. Significant deviation indicates a strong probability that application has an opinion spam.
PAFD: Phased Array Full-Duplex

We present the design and implementation of PAFD, a design methodology that enables full-duplex (FD) in hybrid beamforming systems with constant amplitude phased array antennas. The key novelty in PAFD's design is construction of analog beamformers that maximize the beamforming gains in the desired directions while simultaneously reducing the self-interference (SI). PAFD is implemented on the WARP platform, and its performance is extensively evaluated in both indoor and outdoor environments. Our experimental results reveal that (i) PAFD sacrifices a few dB in beamfomring gain to provide large amounts of reduction in SI power; (ii) the reduction in SI is dependent on the number of phased array antennas and increases as the number of antennas increases; and (iii) finally, PAFD significantly outperforms half-duplex (HD) for small cells even in presence of high interference caused by uplink clients to the downlink clients. The gains increase with a larger array size or less multipath in the propagation environment.