Miracle-dz/UniFAR-FTUs huggingface dataset:
Hardware/Software Codesign for Convolutional Neural Networks Exploiting Dynamic Partial Reconfiguration on PYNQ

Convolutional Neural Networks (CNN), next to Recurrent Neural Networks (RNNs), are the most important subgroup of Deep Neural Networks (DNNs) due to their recent success in industry, especially regarding image processing. Hardware platforms, like Field Programmable Gate Arrays (FPGAs) and Application Specific Integrated Circuits (ASICs), turned out to be a viable alternative to Graphical Processing Units (GPUs) for DNN implementations, especially according to applications with strict power and performance constraints. Moreover, FPGAs supporting Dynamic Partial Reconfiguration can be reconfigured in the field multiple times. In this paper we present a toolflow designing layer specific hardware blocks and reconfiguring them during runtime. During reconfiguration parts of the inference path are executed in software. Starting at the training of CNNs with the help of the Caffe framework, we provide a parameter parser. Furthermore, layer reimplementation templates in C++ help to easily design layers in software and hardware with the help of High-Level Synthesis according to layer specific characteristics. We further demonstrate the possibilities of our hardware/software codesign approach and hardware reconfiguration on the Deep Landmark Detection application. The whole project is available on Github.
Urban Traffic Prediction from Mobility Data Using Deep Learning

Traffic information is of great importance for urban cities, and accurate prediction of urban traffics has been pursued for many years. Urban traffic prediction aims to exploit sophisticated models to capture hidden traffic characteristics from substantial historical mobility data and then makes use of trained models to predict traffic conditions in the future. Due to the powerful capabilities of representation learning and feature extraction, emerging deep learning becomes a potent alternative for such traffic modeling. In this article, we envision the potential and broard usage of deep learning in predictions of various traffic indicators, for example, traffic speed, traffic flow, and accident risk. In addition, we summarize and analyze some early attempts that have achieved notable performance. By discussing these existing advances, we propose two future research directions to improve the accuracy and efficiency of urban traffic prediction on a large scale.
Work-Stealing, Locality-Aware Actor Scheduling

The actor programming model is gaining popularity due to the prevalence of multi-core systems along with the rising need for highly scalable and distributed applications. Frameworks such as Akka, Orleans, Pony, and C++ Actor Framework (CAF) have been developed to address these application requirements. Each framework provides a runtime system to schedule and run millions of actors, potentially on multi-socket platforms with non-uniform memory access (NUMA). However, the literature provides only limited research that studies or improves the performance of actor-based applications on NUMA systems. This paper studies the performance penalties that are imposed on actors running on a NUMA system and characterizes applications based on the actor type, behavior, and communication pattern. This information is used to identify workloads that can benefit from improved locality on a NUMA system. In addition, two locality-and NUMA-aware work-stealing schedulers are proposed and their their respective execution overhead in CAF is studied on both AMD and Intel machines. The performance of the proposed work-stealing schedulers is evaluated against the default scheduler in CAF.
Microglial TREM2/DAP12 Signaling: A Double-Edged Sword in Neural Diseases

Microglia are activated after neuronal injury and in neurodegenerative diseases, and trigger neuroinflammation in the central nervous system (CNS). Microglia-derived neuroinflammation has both beneficial and detrimental effects on neurons. Because the timing and magnitude of microglial activation is thought to be a critical determinant of neuronal fate, understanding the molecular mechanisms underlying microglial activation is required to enable establishment of microglia-targeted therapies for neural diseases. Plasma membrane receptors play primary roles as activators of microglia and in this review, we focus on a receptor complex involving triggering receptor expressed on myeloid cells 2 (TREM2) and DNAX-activating protein of 12 kDa (DAP12), both of which are causative genes for Nasu-Hakola disease, a dementia with bone cysts. Recent transcriptome approaches demonstrated TREM2/DAP12 signaling as the principal regulator that transforms microglia from a homeostatic to a neural disease-associated state. Furthermore, animal model studies revealed critical roles for TREM2/DAP12 in the regulation of microglial activity, including survival, phagocytosis, and cytokine production, not only in Alzheimer's disease but also in other neural diseases, such as Parkinson's disease, demyelinating disease, ischemia, and peripheral nerve injury. Intriguingly, while TREM2/DAP12-mediated microglial activation is detrimental for some diseases, including peripheral nerve injury, it is beneficial for other diseases. As the role of activated microglia differs among disease models, TREM2/DAP12 signaling may result in different outcomes in different diseases. In this review we discuss recent perspectives on the role of TREM2/DAP12 in microglia and their contribution to neural diseases.
Synthesis and Characteristic Evaluation of Convex Metallic Reflectarray Antennas to Radiate Relatively Orthogonal Multibeams

In this paper, we present the design of metallic reflectarray antennas consisting of convex nonresonant elements to radiate multiple beams with optimum radiation gains and relatively orthogonal overlapping. In particular, an antenna is realized at 38 GHz to provide sufficient gains and beam widths for simultaneous link-budget and radio coverage satisfaction in the applications of fifth-generation mobile communications. The pattern optimization employs the steepest decent method (SDM) to optimize the phases of reflecting elements for gain tradeoff. The SDM procedure is very efficient because relatively simple formulations of analysis in closed form are developed to subsequently update the phases in each iteration. This procedure avoids the cumbersome and numerous full-wave numerical simulations of scattering fields. Design examples and their radiation characteristics are presented to demonstrate the concepts and validate the feasibility.
A Review of Distributed Algorithms for Principal Component Analysis

Principal component analysis (PCA) is a fundamental primitive of many data analysis, array processing, and machine learning methods. In applications where extremely large arrays of data are involved, particularly in distributed data acquisition systems, distributed PCA algorithms can harness local communications and network connectivity to overcome the need of communicating and accessing the entire array locally. A key feature of distributed PCA algorithm is that they defy the conventional notion that the first step toward computing the principal vectors is to form a sample covariance. This paper is a survey of the methodologies to perform distributed PCA on different data sets, their performance, and of their applications in the context of distributed data acquisition systems.
Proliferation, cancer, and aging—novel functions of the nucleolar methyltransferase fibrillarin?

Fibrillarin is an essential nucleolar protein that catalyzes the 2′‐O‐methylation of ribosomal RNAs. Recently, experimental data have begun to accumulate that suggest that fibrillarin can influence various cellular processes, development of pathological processes, and even aging. The exact mechanism by which fibrillarin can influence these processes has not been found, but some experimental data indicate that up‐ or downregulation of fibrillarin can modify the ribosome structure and, thus, causе an alteration in relative efficiency with which various mRNAs are translated. Here, we discuss recent studies on the potential roles of fibrillarin in the regulation of cell proliferation, cancer progression, and aging.
MP-STREAM: A Memory Performance Benchmark for Design Space Exploration on Heterogeneous HPC Devices

Sustained memory throughput is a key determinant of performance in HPC devices. Having an accurate estimate of this parameter is essential for manual or automated design space exploration for any HPC device. While there are benchmarks for measuring the sustained memory bandwidth for CPUs and GPUs, such a benchmark for FPGAs has been missing. We present MP-STREAM, an OpenCL-based synthetic micro-benchmark for measuring sustained memory bandwidth, optimized for FPGAs, but which can be used on multiple platforms. Our main contribution is the introduction of various generic as well as device-specific parameters that affect memory bandwidth. We present results of running our benchmark on a CPU, a GPU and two FPGA targets, and discuss our observations. The results underline the utility of our benchmark for optimizing HPC applications for FPGAs, and provide valuable optimization hints for FPGA programmers.
CompStor: An In-storage Computation Platform for Scalable Distributed Processing

The explosion of data-centric and data dependent applications requires new storage devices, interfaces, and software stacks. Big data analytics solutions such as Hadoop, MapReduce and Spark have addressed the performance challenge by using a distributed architecture based on a new paradigm that relies on moving computation closer to data. In this paper, we describe a novel approach aimed at pushing the "move computation to data" paradigm to its ultimate limit by enabling highly efficient and flexible in-storage processing capability in solid state drives (SSDs). We have designed CompStor, an FPGA-based SSD that implements computational storage through a software stack (devices, protocol, interface, software, and systems) and a dedicated hardware for in-storage processing including a quad-core ARM processor subsystem. The dedicated hardware resources provide in-storage data analytics capability without degrading the performance of common storage device functions such as read, write and trim. Experimental results show up to 3X energy saving for some applications in comparison to the host CPU. To the best of our knowledge, the 24TB CompStor SSD is the first one capable of supporting in-storage computation running an operating system, enabling all types of applications and Linux shell commands to be executed in-place with no modification.
Destination Prediction by Trajectory Distribution-Based Model

In this paper, we propose a new method to predict the final destination of vehicle trips based on their initial partial trajectories. We first review how we obtained clustering of trajectories that describes user behavior. Then, we explain how we model main traffic flow patterns by a mixture of 2-D Gaussian distributions. This yielded a density-based clustering of locations, which produces a data driven grid of similar points within each pattern. We present how this model can be used to predict the final destination of a new trajectory based on their first locations using a two-step procedure: we first assign the new trajectory to the clusters it most likely belongs. Second, we use characteristics from trajectories inside these clusters to predict the final destination. Finally, we present experimental results of our methods for classification of trajectories and final destination prediction on data sets of timestamped GPS-Location of taxi trips. We test our methods on two different data sets, to assess the capacity of our method to adapt automatically to different subsets.
4-Element Concentric Pentagonal Slot-Line-Based Ultra-Wide Tuning Frequency Reconfigurable MIMO Antenna System

In this communication, a novel, compact, ultrawideband tuning, concentric pentagonal slot-line-based frequency-reconfigurable multiple-input-multiple-output (MIMO) antenna system is presented. It consists of a four-element antenna design on a commercially available FR-4 substrate with the dimensions of $60\times 120\times 1.56$ mm3. All the antenna elements are planar in structure, which are etched out from ground plane. Frequency reconfigurability is achieved using varactor diodes by reactively loading the antennas. Each capacitance value of the varactor diode results in the triband operation. Proper capacitive reactance loading enables the antenna to resonate over a wide frequency band. The proposed MIMO antenna system is tuned over an ultrawide frequency band, and a smooth variation of the resonance frequencies is observed from 1.32 to 1.49 and 1.75 to 5.2 GHz. Moreover, the unique feature of the proposed antenna system is the effectiveness of reactively loading the slot over all the resonating bands. The proposed antenna system is compact and suitable to be used in wireless handheld devices and mobile terminals for cognitive radio applications. The envelope correlation coefficient did not exceed 0.186 in the entire operating band of the MIMO antenna operation. The maximum measured gain of the MIMO antenna is 4.5 dBi with a maximum efficiency of 81%.
Combining Convolutional Neural Network and Distance Distribution Matrix for Identification of Congestive Heart Failure

Congestive heart failure (CHF) is a serious pathophysiological condition with high morbidity and mortality, which is hard to predict and diagnose in early age. Artificial intelligence and deep learning combining with cardiac rhythms and physiological time series provide a potential to help in solving it. In this paper, we proposed a novel method that combines a convolutional neural network (CNN) and a distance distribution matrix (DDM) in entropy calculation to classify CHF patients from normal subjects, and demonstrated the effectiveness of this combination. Specifically, three entropy methods were used to generate the distribution matrixes from a 300-point RR interval (i.e., the time interval between the successive cardiac cycles) time series, which are Sample entropy, fuzzy local measure entropy, and fuzzy global measure entropy. Then, three high representative CNN models, i.e., AlexNet, DenseNet, and SE_Inception_v4 were chosen to learn the pattern of the data distributions hidden in the generated distribution matrixes. All data used in our experiments were gathered from the MIT-BIH RR Interval Databases (http://www.physionet.org). A total of 29 CHF patients and 54 normal sinus rhythm subjects were included in this paper. The results showed that the combination of FuzzyGMEn-generated DDM and Inception_v4 model yielded the highest accuracy of 81.85% out of all proposed combinations.
Guest Editor’s Introduction

The firestorm of accusations of sexual violence (SV) that swept across the United States in the latter half of 2017 represented a seismic shift in the national social landscape, bringing down a number of prominent men from the worlds of journalism, politics, and entertainment, among others. A social media campaign linked to the hashtag #MeToo, used millions of times by SV victims and their supporters in at least 85 countries, ultimately brought movement founders into the national spotlight and earned these “Silence Breakers” the Time Magazine “Persons of the Year” Award (Felsonthal, 2017). The sexually aggressive behaviors so prominently featured in news accounts of the accusations against powerful men appeared to have been facilitated by some significant power disparities between the alleged perpetrators and their victims. Although the ultimate impact of this apparent cultural shift toward finally coming to grips with SV remains to be determined, at the time these headlines had the effect of eclipsing—if, perhaps, only partly—a similar national debate concerning sexual assault (SA) on college campuses, which has featured protracted examination of the role of student alcohol use as a facilitator of the assaultive behaviors. Indeed, particularly on college campuses, it has become clear that few topics are as emotionally fraught as this one. It is with an eye on this discussion that this special issue of Violence Against Women presents the following six papers representing cutting edge research into understanding and preventing alcohol-related SV among college-aged and young adults. First, a few words about definitions are in order. As viewed broadly by the Centers for Disease Control and Prevention (CDC), SV is defined as “a sexual act that is committed or attempted by another person without freely given consent of the victim or against someone who is unable to consent or refuse” and includes forced or alcohol/drug-facilitated penetration of a victim, forced or alcohol/drug-facilitated incidents in which the victim was made to penetrate a perpetrator or someone else, nonphysically pressured unwanted penetration, intentional sexual touching, or noncontact acts of a sexual nature (Basile, Smith, Breiding, Black, & Mahendra, 2014). This definition is somewhat broader than that usually used in delineating SA. Thus, the recent Campus Climate
[Chemical Constituents from Cynomorium songaricum].

Objective To study the chemical constituents from the fleshy stems of Cynomorium songaricum. Methods The chemical constituents were isolated and purified by chromatography on MCI, silica gel, Sephadex LH-20 columns, etc. The structures of compounds were elucidated by physicochemical property and spectral analyses. Results 15 compounds were isolated and identified as choerospondin( 1), isolariciresinol-4-O-β-D-glucopyranoside( 2), lutelin-7-O-β-D-glucopyranoside( 3), 3, 4-dihydroxyphenethylacetate( 4),maslinic acid( 5),catechin ( 6),ursolic acid( 7),gentisic acid( 8),mannitol( 9), phloroglucinol( 10), β-sitosterol( 11), daucosterol( 12), rutin( 13), cetylic acid( 14) and epicatechin( 15). Conclusion Compounds 4,5,8,9 and 10 are obtained from this plant for the first time.
Towards a Testing Framework with Visual Feedback for Actor-Based Systems

We introduce a prototype testing framework as an extension of JUnit for testing actor-based systems. Our framework runs a given JUnit test in various schedules and records the execution trace for each run. In case a failure is observed during one of the test runs, the corresponding execution trace is compared with those traces associated with passed test runs. The sequence of exchanged messages that are different with respect to these runs are extracted. A UML sequence diagram is automatically generated to depict this sequence. Hence, the developer can observe the interleaving of messages that caused the failure. We illustrated the application of our framework on two sample actor-based software systems.
Use of Code Structural Features for Machine Learning to Predict Effective Optimizations

Since it is difficult to explicitly express the underlying relationship between a code and appropriate optimizations for it, this paper discusses a possibility of using machine learning to predict appropriate optimizations for a given code. In this paper, selection of appropriate compiler options is taken as an example of the prediction, because it can be seen as selection of code optimizations; use of different compiler options results in enabling different compiler optimizations. One severe problem is that it is difficult to collect a sufficient number of data for a machine learning model to well understand the underlying relationships among codes and their appropriate optimizations. Therefore, in addition to conventional features of a code, such as profiling data and parameterized code features, we directly use a code structure itself to retrieve more information from a limited number of codes. The evaluation results suggest that use of code structural features can potentially improve the prediction accuracy if the training dataset contains data of similar code structures.
Mitigating Critical Path Decompression Latency in Compressed L1 Data Caches Via Prefetching

Increasing the size of cache memory is a common approach for reducing miss rates and increasing performance in a CPU. Doing this, however, increases the static and dynamic energy consumption of the cache. Compression can be utilized to increase the effective capacity of cache memory without physically increasing its size. We can also use compression to reduce the physical size of the cache, and therefore reduce its energy consumption, while maintaining a reasonable effective cache capacity. Unfortunately, a decompression latency is experienced when accessing the compressed data. This affects the critical execution path of the processor and can have a significant impact on performance, especially when implemented in L1 cache. Previous work has used cache prefetching techniques to hide the latency of lower level memory accesses. Our work proposes the combination of data prefetching and compression techniques to reduce the impact of decompression latency and improve the feasibility of compression in L1 caches. We evaluate the performance of Last Outcome (LO), Stride (S), and Two-Level (2L) prefetching, as well as hybrid combinations of these methods (S/LO & 2L/S), in combination with Base-Delta-Immediate (B ? I) compression. The results demonstrate that using B ? I, in combination with data prefetching, provides performance improvement over B? I compression alone in L1 data cache. We find that a 4KB Hybrid S/LO prefetcher results in an average speedup of 1.7% and improvement to the energy-delay product of the CPU by 1.5% versus B ? I alone.
RDF Data Storage Techniques for Efficient SPARQL Query Processing Using Distributed Computation Engines

The rapidly growing amount of linked open data demands semantic RDF services that are efficient, scalable, and distributed along with high availability and fault tolerance. To address this concern, the Big Data processing infrastructure Hadoop has been adopted for RDF data management systems. In this paper, we introduce distributed RDF data stores, namely VPExp and 3CStore, based on the existing vertical partitioning (VP) approach. In the VPExp approach, we propose splitting of predicates based on explicit type information of an object. The 3CStore scheme is designed with a 3-column store, comprising of a subset of triples from the VP table based on different join correlations, to reduce the number of join operations while executing SPARQL queries as SQL in a distributed system. We evaluate these two RDF data storage approaches by comparing them with vertical partitioning approach and state-of-the-art RDF management system S2RDF. We also present an evaluation of query performance of these systems built upon two popular distributed computation engines namely, Spark and Drill.
Semantics-Preserving Parallelization of Stochastic Gradient Descent

Stochastic gradient descent (SGD) is a well-known method for regression and classification tasks. However, it is an inherently sequential algorithm – at each step, the processing of the current example depends on the parameters learned from previous examples. Prior approaches to parallelizing linear learners using SGD, such as Hogwild! and AllReduce, do not honor these dependencies across threads and thus can potentially suffer poor convergence rates and/or poor scalability. This paper proposes SymSGD, a parallel SGD algorithm that, to a first-order approximation, retains the sequential semantics of SGD. Each thread learns a local model in addition to a model combiner, which allows local models to be combined to produce the same result as what a sequential SGD would have produced. This paper evaluates SymSGD's accuracy and performance on 6 datasets on a shared-memory machine shows up-to 11x speedup over our heavily optimized sequential baseline on 16 cores and 2.2x, on average, faster than Hogwild!