Miracle-dz/UniFAR-FTUs huggingface dataset:
A Rapid Verification Framework for Developing Multi-core Processor

A multi-core processor is widely used to achieve both high performance and low energy consumption. However, verification of a multi-core processor is more difficult than that of single-core processor. Because multi-core processor has large and complex circuits, and special mechanisms such as a cache coherency mechanism also increases complexity. In general, design flow of a processor include such as the following steps; functional verification by high level language, cycle accurate verification by RTL simulation and timing analysis considered gate delay, wiring delay and others. In particular, co-simulation framework for single-core processor has been proposed to reduce time of cycle accurate verification with RTL simulation. However, if the conventional framework is applied to verify a multi-core processor, it causes three problems; failure of the co-simulation framework by a mismatch of load and store operation, failure of the system call emulation by cache coherency mechanism and requirement of task scheduling by execution multi-threaded program. These problems makes verification of a multi-core processor difficult seriously and increases simulation time dramatically. Therefore, this paper proposes a rapid verification framework to support execution of a multi-threaded program for multi-core processors. The proposed method makes it possible to verify both a homogeneous and heterogeneous multi-core processors with the cache coherency mechanism, and to execute multithreaded programs without full system simulation. The proposed framework extends conventional co-simulation framework for a single-core processor. The proposed framework is composed of the following three extensions; bypassing loaded value from the verified processor to virtual processor, a cache access mechanism for system call emulation, and an internal task scheduler. As evaluation results, our framework verifies two-core processor correctly. Furthermore, the proposed method achieves to reduce the number of execution cycles by 71% in maximum and 46% in average compared with full system simulation.
Snowflake: A Model Agnostic Accelerator for Deep Convolutional Neural Networks

Deep convolutional neural networks (CNNs) are the deep learning model of choice for performing object detection, classification, semantic segmentation and natural language processing tasks. CNNs require billions of operations to process a frame. This computational complexity, combined with the inherent parallelism of the convolution operation make CNNs an excellent target for custom accelerators. However, when optimizing for different CNN hierarchies and data access patterns, it is difficult for custom accelerators to achieve close to 100% computational efficiency. In this work, we present Snowflake, a scalable and efficient accelerator that is agnostic to CNN workloads, and was designed to always perform at near-peak hardware utilization. Snowflake is able to achieve a computational efficiency of over 91% on modern CNN models. Snowflake, implemented on a Xilinx Zynq XC7Z045 SoC is capable of achieving a peak throughput of 128G-ops/s and a measured throughput of 100 frames per second and 120 G-ops/s on the AlexNet CNN model, 36 frames per second and 116G- ops/s on the GoogLeNet CNN model and 17 frames per second and 122 G-ops/s on the ResNet-50 CNN model. To the best of our knowledge, Snowflake is the only implemented system capable of achieving over 91% efficiency on modern CNNs and the only implemented system with GoogLeNet and ResNet as part of the benchmark suite.
Membranes for Redox Flow Battery Applications

The need for large scale energy storage has become a priority to integrate renewable energy sources into the electricity grid. Redox flow batteries are considered the best option to store electricity from medium to large scale applications. However, the current high cost of redox flow batteries impedes the wide spread adoption of this technology. The membrane is a critical component of redox flow batteries as it determines the performance as well as the economic viability of the batteries. The membrane acts as a separator to prevent cross-mixing of the positive and negative electrolytes, while still allowing the transport of ions to complete the circuit during the passage of current. An ideal membrane should have high ionic conductivity, low water intake and excellent chemical and thermal stability as well as good ionic exchange capacity. Developing a low cost, chemically stable membrane for redox flow cell batteries has been a major focus for many groups around the world in recent years. This paper reviews the research work on membranes for redox flow batteries, in particular for the all-vanadium redox flow battery which has received the most attention.
Statistical analysis and simulation of design models evolution

Tools, algorithms and methods in the context of Model-Driven Engineering (MDE) have to be assessed, evaluated and tested with regard to different aspects such as correctness, quality, scalability and efficiency. Unfortunately, appropriate test models are scarcely available and those which are accessible often lack desired properties. Therefore, one needs to resort to artificially generated test models in practice. Many services and features of model versioning systems are motivated from the collaborative development paradigm. Testing such services does not require single models, but rather pairs of models, one being derived from the other one by applying a known sequence of edit steps. The edit operations used to modify the models should be the same as in usual development environments, e.g. adding, deleting and changing of model elements in visual model editors. Existing model generators are motivated from the testing of model transformation engines, they do not consider the true nature of evolution in which models are evolved through iterative editing steps. They provide no or very little control over the generation process and they can generate only single models rather than model histories. Moreover, the generation of stochastic and other properties of interest also are not supported in the existing approaches. Furthermore, blindly generating models through random application of edit operations does not yield useful models, since the generated models are not (stochastically) realistic and do not reflect true properties of evolution in real software systems. Unfortunately, little is known about how models of real software systems evolve over time, what are the properties and characteristics of evolution, how one can mathematically formulate the evolution and simulate it. To address the previous problems, we introduce a new general approach which facilitates generating (stochastically) realistic test models for model differencing tools and tools for analyzing model histories. We propose a model generator which addresses the above deficiencies and generates or modifies models by applying proper edit operations. Fine control mechanisms for the generation process are devised and the generator supports stochastic and other properties of interest in the generated models. It also can generate histories, i.e. related sequences, of models. Moreover, in our approach we provide a methodological framework for capturing, mathematically representing and simulating the evolution of real design models. The proposed framework is able to capture the evolution in terms of edit operations applied between revisions. Mathematically, the representation of evolution is based on different statistical distributions as well as different time series models. Forecasting, simulation and generation of stochastically
Image Phylogeny Forests Reconstruction

Today, a simple search for an image on the Web can return thousands of related images. Some results are exact copies, some are variants (or near-duplicates) of the same digital image, and others are unrelated. Although we can recognize some of these images as being semantically similar, it is not as straightforward to find which image is the original. It is not easy either to find the chain of transformations used to create each modified version. There are several approaches in the literature to identify near-duplicate images, as well as to reconstruct their relational structure. For the latter, a common representation uses the parent-child relationship, allowing us to visualize the evolution of modifications as a phylogeny tree. However, most of the approaches are restricted to the case of finding the tree of evolution of the near-duplicates, with few works dealing with sets of trees. Since one set of near-duplicates can contain n independent subsets, it is necessary to reconstruct not only one phylogeny tree, but several trees that will compose a phylogeny forest. In this paper, through the analysis of the state-of-the-art image phylogeny algorithms, we introduce a novel approach to deal with phylogeny forests, based on different combinations of these algorithms, aiming at improving their reconstruction accuracy. We analyze the effectiveness of each combination and evaluate our method with more than 40 000 testing cases, using quantitative metrics.
Hibernus++: A Self-Calibrating and Adaptive System for Transiently-Powered Embedded Devices

Energy harvesters are being used to power autonomous systems, but their output power is variable and intermittent. To sustain computation, these systems integrate batteries or supercapacitors to smooth out rapid changes in harvester output. Energy storage devices require time for charging and increase the size, mass, and cost of systems. The field of transient computing moves away from this approach, by powering the system directly from the harvester output. To prevent an application from having to restart computation after a power outage, approaches such as Hibernus allow these systems to hibernate when supply failure is imminent. When the supply reaches the operating threshold, the last saved state is restored and the operation is continued from the point it was interrupted. This paper proposes Hibernus++ to intelligently adapt the hibernate and restore thresholds in response to source dynamics and system load properties. Specifically, capabilities are built into the system to autonomously characterize the hardware platform and its performance during hibernation in order to set the hibernation threshold at a point which minimizes wasted energy and maximizes computation time. Similarly, the system auto-calibrates the restore threshold depending on the balance of energy supply and consumption in order to maximize computation time. Hibernus++ is validated both theoretically and experimentally on microcontroller hardware using both synthesized and real energy harvesters. Results show that Hibernus++ provides an average 16% reduction in energy consumption and an improvement of 17% in application execution time over state-of-the-art approaches.
Using decision lists to construct interpretable and parsimonious treatment regimes

A treatment regime formalizes personalized medicine as a function from individual patient characteristics to a recommended treatment. A high‐quality treatment regime can improve patient outcomes while reducing cost, resource consumption, and treatment burden. Thus, there is tremendous interest in estimating treatment regimes from observational and randomized studies. However, the development of treatment regimes for application in clinical practice requires the long‐term, joint effort of statisticians and clinical scientists. In this collaborative process, the statistician must integrate clinical science into the statistical models underlying a treatment regime and the clinician must scrutinize the estimated treatment regime for scientific validity. To facilitate meaningful information exchange, it is important that estimated treatment regimes be interpretable in a subject‐matter context. We propose a simple, yet flexible class of treatment regimes whose members are representable as a short list of if–then statements. Regimes in this class are immediately interpretable and are therefore an appealing choice for broad application in practice. We derive a robust estimator of the optimal regime within this class and demonstrate its finite sample performance using simulation experiments. The proposed method is illustrated with data from two clinical trials.
An Avian Basal Ganglia-forebrain Circuit Contributes Differentially to Syllable Woolley and E. Tumer for Critical Reading of the Manuscript, and E. Tumer for Assistance

27 28 Behavioral variability is important for motor skill learning but continues to be 29 present and actively regulated even in well-learned adult behaviors. In adult songbirds, 30 two types of song variability can persist and are modulated by social context: variability 31 in syllable structure and variability in syllable sequencing. The degree to which the 32 control of both types of adult variability is shared or distinct remains unknown. The 33 output of a basal ganglia-forebrain circuit, LMAN (the lateral magnocellular nucleus of 34 the anterior nidopallium), has been implicated in song variability. For example, in adult 35 zebra finches, neurons in LMAN actively control the variability of syllable structure. It is 36 unclear, however, whether LMAN contributes to variability in adult syllable sequencing 37 because sequence variability in adult zebra finch song is minimal. In contrast, 38 Bengalese finches retain variability in both syllable structure and syllable sequencing 39 into adulthood. We analyzed the effects of LMAN lesions on the variability of syllable 40 structure and sequencing and on the social modulation of these forms of variability in 41 adult Bengalese finches. We found that lesions of LMAN significantly reduced the 42 variability of syllable structure but not of syllable sequencing. We also found that LMAN 43 lesions eliminated the social modulation of the variability of syllable structure but did not 44 detect significant effects on the modulation of sequence variability. These results 45 demonstrate that LMAN contributes differentially to syllable versus sequence variability 46 of adult song and suggest that these forms of variability are regulated by distinct neural 47 pathways. 48 49
Efficient signature matching with multiple alphabet compression tables

Signature matching is a performance critical operation in intrusion prevention systems. Modern systems express signatures as regular expressions and use Deterministic Finite Automata (DFAs) to efficiently match them against the input. In principle, DFAs can be combined so that all signatures can be examined in a single pass over the input. In practice, however, combining DFAs corresponding to intrusion prevention signatures results in memory requirements that far exceed feasible sizes. We observe for such signatures that distinct input symbols often have identical behavior in the DFA. In these cases, an Alphabet Compression Table (ACT) can be used to map such groups of symbols to a single symbol to reduce the memory requirements. In this paper, we explore the use of multiple alphabet compression tables as a lightweight method for reducing the memory requirements of DFAs. We evaluate this method on signature sets used in Cisco IPS and Snort. Compared to uncompressed DFAs, multiple ACTs achieve memory savings between a factor of 4 and a factor of 70 at the cost of an increase in run time that is typically between 35% and 85%. Compared to another recent compression technique, D2FAs, ACTs are between 2 and 3.5 times faster in software, and in some cases use less than one tenth of the memory used by D2FAs. Overall, for all signature sets and compression methods evaluated, multiple ACTs offer the best memory versus run-time trade-offs.
Latent Text Mining for Cybercrime Forensics

Recent research reveals that the number of cyber-attacks has been doubled in the past three years. This is a devastating growth of the number of cyber-attacks, and it reveals a serious business problem around the world. Existing intrusion detection systems (IDSs), intrusion prevention systems (IPSs), and anti-malware systems mainly rely on low-level network traffic features or program code signatures to detect cyber-attacks. However, since hackers can constantly change their attack tactics by, it is extremely difficult for existing security solutions to detect cyber-attacks. There are increasing more evidences showing that cybercriminals tend to exchange cybercrime knowledge and transact via online social media. Accordingly, it presents unprecedented opportunities for security intelligence experts to tap into online social media to extract the vital security intelligence for cyber-attack forensics. The main contributions of this paper are the design, development, and evaluation of a Latent Dirichlet Allocation (LDA)-based latent text mining model for cyber-attack forensics. Our preliminary evaluation of the proposed latent text mining model based on a real-world data set crawled from Twitter and Blog sites shows that it significantly outperforms the probabilistic latent semantic indexing (pLSI) method in terms of extracting more relevant and richer concepts describing real-world cyber-attack incidents.
Metabolic programming of a beige adipocyte phenotype by genistein

Scope Promoting the development of brown or beige adipose tissue may protect against obesity and related metabolic features, and potentially underlies protective effects of genistein in mice. Methods and results We observed that application of genistein to 3T3‐L1 adipocytes changed the lipid distribution from large droplets to a multilocular distribution, reduced mRNAs indicative of white adipocytes (ACC, Fasn, Fabp4, HSL, chemerin, and resistin) and increased mRNAs that are a characteristic feature of brown/beige adipocytes (CD‐137 and UCP1). Transcripts with a role in adipocyte differentiation (Cebpβ, Pgc1α, Sirt1) peaked at different times after application of genistein. These responses were not affected by the estrogen receptor (ER) antagonist fulvestrant, revealing that this action of genistein is not through the classical ER pathway. The Sirt1 inhibitor Ex‐527 curtailed the genistein‐mediated increase in UCP1 and Cebpβ mRNA, revealing a role for Sirt1 in mediating the effect. Baseline oxygen consumption and the proportional contribution of proton leak to maximal respiratory capacity was greater for cells exposed to genistein, demonstrating greater mitochondrial uncoupling. Conclusions We conclude that genistein acts directly on adipocytes or on adipocyte progenitor cells to programme the cells metabolically to adopt features of beige adipocytes. Thus, this natural dietary agent may protect against obesity and related metabolic disease.
The 2 nd International Workshop on Inductive Reasoning and Machine Learning for the Semantic Web Proceedings

I will describe a novel meta-learning approach to optimizing the knowledge discovery or data mining (DM) process. This approach has three features that distinguish it from its predecessors. First, previous meta-learning research has focused exclusively on improving the learning phase of the DM process. More specifically, the goal of meta-learning has typically been to select the most appropriate algorithm and/or parameter settings for a given learning task. We adopt a more process-oriented approach whereby meta-learning is applied to design choices at different stages of the complete data mining process or workflow (hence the term meta-mining). Second, meta-learning for algorithm or model selection has consisted mainly in mapping dataset properties to the observed performance of algorithms viewed as black boxes. While several generations of researchers have worked intensively on characterizing datasets, little has been done to understand the internal mechanisms of the algorithms used. At best, a few have considered perceptible features of algorithms like their ease of implementation or their robustness to noise, or the interpretability of the models they produce. In contrast, our meta-learning approach complements dataset descriptions with an in-depth analysis and characterization of algorithms their underlying assumptions, optimization goals and strategies, together with the structure and complexity of the models and patterns they generate. Third, previous meta-learning approaches have been strictly (meta) data-driven. To make sense of the intricate relationships between tasks, data and algorithms at different stages of the data mining process, our meta-miner relies on extensive background knowledge concerning knowledge discovery itself. For this reason we have developed a data mining ontology, which defines the essential concepts and relations needed to represent and analyse data mining objects and processes. In addition, a DM knowledge base gathers assertions concerning data preprocessing and machine learning algorithms as well as their implementations in several open-source software packages. The DM ontology and knowledge base are domain-independent; they can be exploited in any application area to build databases describing domain-specific data analysis tasks, datasets and experiments. Aside from their direct utility in their respective target domains, such databases are the indispensable source of training and evaluation data for the meta-miner. These three features together lay the groundwork for semantic meta-mining, the process of mining DM meta-data on the basis of data mining expertise distilled in an ontology and knowledge base.
View-invariant Fall Detection for Elderly in Real Home Environment

We propose a novel context based human fall detection mechanism in real home environment. Fall incidents are detected using head and floor information. The centroid location of the head and feet from each frame are used to learn a context model consisting of normal head and floor blocks. Every floor block has its associated Gaussian distribution, representing a set of head blocks. This Gaussian distribution defines standard vertical distance as average height of an object at that specific floor block. The classification of blocks and average height is later used to detect a fall. Fall detection methods often detect bending situations as fall. This method is able to distinguish bending and sitting from falling. Furthermore, a fall into any direction and at any distance from camera can be detected. Evaluation results show the robustness and high accuracy of the proposed approach.