Miracle-dz/UniFAR-FTUs huggingface dataset:
CHAMELEON: A Dynamically Reconfigurable Heterogeneous Memory System

Modern computing systems and applications have growing demand for memories with higher bandwidth. This demand can be alleviated using fast, large on-die or die-stacked memories. They are typically used with traditional DRAM as part of a heterogeneous memory system and used either as a DRAM cache or as a hardware-or OS-managed part of memory (PoM). Caches adapt rapidly to application needs and typically provide higher performance but reduce the total OS-visible memory capacity. PoM architectures increase the total OS-visible memory capacity but exhibit additional overheads due to swapping large blocks of data between fast and slow memory. In this paper, we propose Chameleon, a hybrid architecture that bridges the gap between cache and PoM architectures. When applications need a large memory, Chameleon uses both fast and slow memories as PoM, maximizing the available space for the application. When the application's footprint is smaller than the total physical memory capacity, Chameleon opportunistically uses free space in the system as a hardware-managed cache. Chameleon is a hardware-software co-designed system where the OS notifies the hardware of pages that are allocated or freed, and hardware decides on switching memory regions between PoM-and cache-modes dynamically. Based on our evaluation of multi-programmed workloads on a system with 4GB fast memory and 20GB slow memory, Chameleon improves the average performance by 11.6% over PoM and 24.2% over a latency-optimized cache.
Projected-Gradient-Descent in Rakeness-Based Compressed Sensing with Disturbance Rejection

Compressed Sensing (CS) has recently emerged as an effective tool to simultaneously acquire and compress analog waveforms in low-resource sensing devices. Its mechanisms have been also extended by both adapting the sensing stage to the actual class of input signals, and granting it the ability to reject disturbances. Regrettably, the resulting design flow entails the solution of two optimization problems with a potentially huge number of variables. This work overcomes this impasse by proposing a Project-Gradient-Descend method algorithm that drastically reduces the required CPU time to obtain a solution.
Contextual Anomaly Detection in Spatio-Temporal Data Using Locally Dense Regions

With the advancements in computing and location-acquisition technologies, large volumes of spatio-temporal tra-jectory data are being generated and stored. Anomaly detection in trajectory data is significant for several applications. Using a data-driven spatio-temporal context in the form of geographical sub-regions and different time-periods can enhance the relevance of detected anomalies. We propose a novel scalable contextual anomaly detection method for trajectory data using the regional density information. The effectiveness and scalability of the proposed method is shown through the empirical analysis and benchmarking with the state-of-the-art method.
End-Fire Substrate Integrated Waveguide Beam-Forming System for 5G Applications

This paper presents the simulated and measured results of a substrate integrated beam-forming network that combines the steering capabilities of a 4x4 Butler matrix and the horizontal polarization of a corrugated Vivaldi antenna array for 5G end-fire applications. The system demonstrates a ± 25° sweep of the main beams as well as a gain that ranges between 8.7 dB and 11.7 dB at a center frequency of 25 GHz. Good agreement between experimental and simulated results validates the design approach.
An Improved User Identification Method Across Social Networks Via Tagging Behaviors

User Identification problem is concerned with identifying the same person with multiple virtual identities across social network sites(SNSs). Most of the existing approaches pays close attention to the similarity of profile attributes, generate-contents and linkages of friends or simply combination of these features. Only one method analyzes the feasibility of user tags in User Identification problems, but does not analyze the particularity and the inconsistency of tags belong to users among different social networks. In this paper, an improved user identification method across social networks via tagging behaviors is proposed that a new symmetric variant of BM25 (BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document, regardless of the inter-relationship between the query terms within a document) using the semantic relationships between inconsistent tags among different social networks. By using extracted features from the inconsistent tagging behaviors, profile attributes and SVM supervised learning techniques, a classifier is developed for performing user identity matching between two social network sites. Evaluation on Douban and Weibo real world data-set showed that the accuracy of the proposed method is 30% higher than that of the common tag-based approach.
A Review on Autonomous Rescue Navigation System Using Wireless Sensor Networks

A navigation system can be defined as a device which will help the user to reach the destination without any obstruction or which will prevent a user from moving into a dangerous area. So in an emergency situation a navigation system or service can also be used to navigate the people to safe exits by keeping them away from the hazardous area and reduce the casualty. This type of navigation systems are mainly useful when an emergency situation arises in buildings (like shopping malls, residential flats, multi-storeyed buildings etc.) where the evacuation procedure is a complex task. In such situations the evacuation has to be donned as earlier as possible by showing the safest and shortest exist path to the usersto keep them away from hazard. These systems are a combination of wireless sensor nodes and computer based system used for route optimization, alert notification and automatic decision making. This paper is a detailed study about the existing navigation systems which works under various environmental dynamics.
Power Quality Improvement by Using UPQC: A Review

This paper presents literature review for improving the power quality by using the unified power quality conditioner (UPQC). There is a more complex network in the power system, in which generating station is connected with the load center through long transmission and distribution line. For improving the performance of distribution network the new concept came into existence known as custom devices. One of the custom power device is unified power quality conditioner is one of the custom power devices. The different configurations of UPQC system for single phase and three phases are well discussed in this paper. The various researchers have given different names to the UPQC according to their function, topology and application. The various topologies of UPQC explained are, left shunt (UPQC-L), right shunt (UPQC-R), open UPQC, Interlined UPQC (UPQC-I), Modular UPQC (UPQC-ML).
An Iterative Instance Selection Based Framework for Multiple-Instance Learning

The instance selection based model is an effective multiple-instance learning (MIL) framework, which solves the MIL problems by embedding examples (bags of instances) into a new feature space formed by some concepts (represented by some selected instances). Most previous studies use single-point concepts for the instance selection, where every possible concept is represented by only a single instance. In this paper, we apply multiple-point concepts for choosing instances, in which each possible concept is jointly represented by a group of similar instances. Furthermore, we establish an iterative instance selection based MIL framework based on multiple-point concepts, which is guaranteed to automatically converge to the needed number of concepts for a given problem. The experimental results demonstrate that the proposed framework can better handle not only common MIL problems but also hybrid ones compared to state-of-the-art MIL algorithms.
Dualizing Projected Model Counting

In many recent applications of model counting not all variables are relevant for a specific problem. For instance redundant variables are added during formula transformation. In projected model counting these redundant variables are ignored by projecting models onto relevant variables. Inspired by dual propagation which has its origin in solving quantified Boolean formulae and jointly works on both the original formula and its negation, we present a novel calculus for dual projected model counting. It allows to capture existing techniques such as blocking clauses, chronological as well as non-chronological backtracking, but also introduces new concepts including discounting and dual conflict analysis to obtain partial models. Experiments demonstrate the benefit of our approach.
Classification of three categories from prefrontal cortex using LSTM networks: fNIRS study

In the conventional neuroscience studies using functional near infrared spectroscopy (fNIRS), the region of interest (ROI) needs to be investigated. In this paper, to reduce the classification and processing time without classification accuracy loss, we have investigated the use of a long short-term memory (LSTM) networks in the fNIRS data analysis. Three mental tasks including mental arithmetic, mental counting, and puzzle solving were performed to obtain fNIRS data from the prefrontal cortex. To validate our proposed method, we have compared our method with the conventional classifiers, that is, linear discriminant analysis (LDA) and support vector machine (SVM), and feature extraction methods. Using the conventional methods, the averaged classification accuracies were 37.50% with LDA and 37.96% with SVM. However, in the case of LSTM, the maximum classification accuracy was 83.3%. The results show that LSTM networks can be used as a fNIRS classifier. Also, our results represent the possibility of online classification in fNIRS studies, which does not need the t-test process using the expected hemodynamic response.
Low-Level Control-Flow Manipulation Techniques

Contrary to interacting with software remotely at runtime, commercial software may be altered by an attacker that is able to to change the meaning of the program at will by decompiling and recompiling the program. An attacker is able to coerce control-flow, manipulate predicates in order to lead the program into a favourable state. The aim of this paper is to present a strictly limited set of low-level attack patterns and to draw a parallel to exploits carried out against system software that cannot be tampered with.
Management Algorithms for Cascaded H-Bridge Multilevel Inverters

Multilevel inverters like the cascaded H-bridge topology provide multiple benefits compared to traditional B6-bridge inverters. Due to the modular structure of the inverter and the modulation techniques it is necessary to utilize management algorithms to achieve even energy distribution between all battery modules. This paper presents algorithms for state of charge balancing, fault tolerance, and their correlation.
Tracking of Multiple Targets using GM-PHD Filter with Nonlinear Measurement Model

In this paper, a method of multi-target tracking based on Gaussian mixture probability hypothesis density(GM-PHD) filter is investigated against targets that have drastic velocity change during their maneuvering. GM-PHD filter, a recursive algorithm to track a number of targets, applies the probability hypothesis density recursion to propagate the posterior intensity. To consider practical environments, nonlinearity of an observation model is considered and techniques of extended Kalman filter(EKF) and unscented Kalman filter(UKF) are applied. In a multi-target environment when targets may appear and disappear, performance of multiple-target tracking problem is shown by the simulation.
Metabolomics Research Reveals the Mechanism of Action of Astragalus Polysaccharide in Rats with Digestive System Disorders

With the diversity of modern dietary lifestyles, digestive system disorders (DSD) have become a frequently occurring disease in recent years. Astragalus polysaccharide (APS) is a homogeneous polysaccharide extracted from Astragalus, which might ameliorate the digestive and absorptive functions. However, the treatment mechanisms remain unclear. In this study, rats with DSD were fed a high-fat–low-protein diet and subjected to weight-bearing swimming until exhaustion. When body weight and autonomous activities of the rats decreased, they were administered APS. After two weeks, serum metabolomics analysis based on LC-MS was performed to validate the therapeutic effect of APS and explore its mechanism. APS pharmacodynamics was determined in this study, and serum metabolomics analysis discovered and identified 16 significant, differentially produced metabolites involved in energy, amino acid, and lipid metabolism, including citric acid, lactic acid, alanine, phosphatidylcholine, phenylalanine. After treatment with APS, the levels of the above small-molecule metabolites were reversed. Our results show the efficacy of APS in DSD treatment through the regulation of perturbed metabolic pathways related to energy, amino acid, and lipid metabolism.
An unexpected noncarpellate epigynous flower from the Jurassic of China

The origin of angiosperms has been a long-standing botanical debate. The great diversity of angiosperms in the Early Cretaceous makes the Jurassic a promising period in which to anticipate the origins of the angiosperms. Here, based on observations of 264 specimens of 198 individual flowers preserved on 34 slabs in various states and orientations, from the South Xiangshan Formation (Early Jurassic) of China, we describe a fossil flower, Nanjinganthus dendrostyla gen. et sp. nov. The large number of specimens and various preservations allow for an evidence-based reconstruction of the flower. From the evidence of the combination of an invaginated receptacle and ovarian roof, we infer that the seeds of Nanjinganthus were completely enclosed. Evidence of an actinomorphic flower with a dendroid style, cup-form receptacle, and angiospermy, is consistent with Nanjinganthus being a bona fide angiosperm from the Jurassic, an inference that we hope will re-invigorate research into angiosperm origins.
Prospective associations between cardiac stress, glucose dysregulation and executive cognitive function in Black men: The Sympathetic activity and Ambulatory Blood Pressure in Africans study

Objective: Glucose dysregulation is an independent risk factor for cardiovascular and neurodegenerative disease development through synaptic dysfunction resulting in cognitive decline. The aim of this study was to study the interplay between impaired glycaemic metabolism (hyperglycaemia and insulin resistance), cardiac stress (cardiac troponin T and N-terminal brain natriuretic peptide) and executive cognitive function prospectively, in a bi-ethnic sex cohort. Methods: Black and White teachers (N = 338, aged 20–63 years) from the Sympathetic activity and Ambulatory Blood Pressure in Africans study were monitored over a 3-year period. Fasting blood samples were obtained for cardiac troponin T, N-terminal brain natriuretic peptide, glycated haemoglobin and the homeostatic model assessment-insulin resistance for insulin resistance. The Stroop colour-word conflict test was applied to assess executive cognitive function at baseline. Results: Over the 3-year period, Black men revealed constant high levels of cardiac troponin T (⩾4.2 ng/L), pre-diabetes (glycated haemoglobin > 5.7%) and insulin resistance (homeostatic model assessment-insulin resistance >3). %Δ Glycated haemoglobin was associated with %Δ insulin resistance (p < 0.001) and increases in %ΔN-terminal brain natriuretic peptide (p = 0.02) in Black men only. In the latter, baseline Stroop colour-word conflict test was inversely associated with %Δ cardiac troponin T (p = 0.001) and %Δ insulin resistance levels (p = 0.01). Conclusion: Progressive myocyte stretch and chronic myocyte injury, coupled with glucose dysregulation, may interfere with processes related to interference control in Black men.
Catalyst discovery through megalibraries of nanomaterials

Significance Discovering new materials is traditionally a slow process, as the materials genome encompasses a massive parameter space, particularly on the nanoscale where synthesis methods are less universal. To overcome this challenge, a cantilever-free lithography method has been used to deposit millions of discrete nanoreactors on a substrate, which can be transformed into millions of nanoparticles spatially encoded in terms of composition and size. These compositions were screened for their ability to catalytically grow carbon nanotubes, and a composition (Au3Cu) was identified as optimum for this application. More generally, this combinatorial method of synthesizing nanoparticle megalibraries offers researchers a platform for identifying structures with desired properties at a rate not previously possible. The nanomaterial landscape is so vast that a high-throughput combinatorial approach is required to understand structure–function relationships. To address this challenge, an approach for the synthesis and screening of megalibraries of unique nanoscale features (>10,000,000) with tailorable location, size, and composition has been developed. Polymer pen lithography, a parallel lithographic technique, is combined with an ink spray-coating method to create pen arrays, where each pen has a different but deliberately chosen quantity and composition of ink. With this technique, gradients of Au-Cu bimetallic nanoparticles have been synthesized and then screened for activity by in situ Raman spectroscopy with respect to single-walled carbon nanotube (SWNT) growth. Au3Cu, a composition not previously known to catalyze SWNT growth, has been identified as the most active composition.
Mini-UAV-Borne Hyperspectral Remote Sensing: From Observation and Processing to Applications

In recent years, with the rapid development of unmanned aerial vehicles (UAVs) and lightweight hyperspectral imaging (HSI) sensors, mini-UAV-borne hyperspectral remote sensing (HRS) systems have been developed and demonstrate great value and application potential. Compared to spaceborne and airborne HSI systems, mini-UAV-borne HSI systems come with relatively low manufacturing and running costs and have thus become a new research focus in the field of HRS.
Does the Presence of Diabetes Mellitus Make a Difference in Pharmacological Stress Echocardiography Outcome Results?

BACKGROUND: Coronary artery disease (CAD) is the leading cause of mortality and morbidity in patients with diabetes (DM). AIM: The aim of our study was to analyse the ability of pharmacological SE to risk stratify patients with DM using qualitative and quantitative assessment of LV function. METHODS: We prospectively assessed 105 consecutive patients (58.7 ± 9.5 y, 39 male) with known or suspected CAD who underwent dipyridamole or dobutamine SE. RESULTS: Change of systolic LV function at maximal SE was less pronounced in patients with DM, while parameters of the diastolic function and its change with stress were almost insignificant. WMSI in comparison to GLS% didn’t make a difference in SE outcome regarding DM presence. WMSI was almost unchanged at maximal stress in diabetic patients. Conversely, GLS% showed significant worsening at maximal stress in diabetic patients. However, only WMSI at maximal stress along with DM presence appeared as independent predictors of the presence of new and worsening CAD during SE. Longitudinal strain assessed using speckle tracking during pharmacological stress echocardiography was superior to conventional echocardiography expressed by wall motion analysis in making a difference regarding DM presence. CONCLUSION: We confirmed the usefulness of stress echocardiography using qualitative and/or quantitative parameters in the detection of CAD in patients with DM.
Oxidative Stress in the Newborn Period: Useful Biomarkers in the Clinical Setting

Aerobic metabolism is highly efficient in providing energy for multicellular organisms. However, even under physiological conditions, an incomplete reduction of oxygen produces reactive oxygen species and, subsequently, oxidative stress. Some of these chemical species are highly reactive free radicals capable of causing functional and structural damage to cell components (protein, lipids, or nucleotides). Oxygen is the most used drug in ill-adapted patients during the newborn period. The use of oxygen may cause oxidative stress-related diseases that increase mortality and cause morbidity with adverse long-term outcomes. Conditions such as prematurity or birth asphyxia are frequently treated with oxygen supplementation. Both pathophysiological situations of hypoxia–reoxygenation in asphyxia and hyperoxia in premature infants cause a burst of reactive oxygen species and oxidative stress. Recently developed analytical assays using mass spectrometry have allowed us to determine highly specific biomarkers with minimal samples. The detection of these metabolites will help improve the diagnosis, evolution, and response to therapy in oxidative stress-related conditions during the newborn period.
Clinical and molecular characteristics of bladder urothelial carcinoma subtypes

Bladder urothelial carcinoma (BLCA) is a common malignancy with high heterogeneity. A reasonable molecular subtyping can facilitate biological study and personalized therapy of BLCA. In this study, unsupervised consensus clustering was used to acquire the molecular subtypes of BLCA based on messenger RNA (mRNA) and microRNA (miRNA) data. Gene signature markers and canonical signaling pathways were compared between different subtypes. The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for the functional annotation of overexpressed genes in different subtypes for Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Three molecular subtypes were identified including C1 (luminal‐P53 like), C2 (luminal‐other), and C3 (basal‐immune‐squamous). C2 was different from C1 and C3 in clinical characteristics, including younger, better prognosis, and a higher proportion of papillary, Asian, low‐grade, early‐stage, lymph node negative, and complete remission patients (P < 0.05). Three molecular subtypes also showed distinct mRNA and miRNA expression patterns. luminal and P53‐like markers were highly expressed in subtype C1, luminal markers were highly expressed in subtype C2, and basal, EMT/claudin‐low, immune and squamous‐differentiation markers were highly expressed in subtype C3. In addition, highly expressed genes in C1 were involved in extracellular signal transduction and cell‐cell interaction, highly expressed genes in C2 were associated with oxygen transport, energy and steroid metabolism, and highly expressed genes in C3 were related with inflammatory, immune, cytokine, and signal transduction. BLCA in different molecular subtypes showed different clinical and molecular characteristics and personalized therapy needed to be adopted according to the molecular subtypes.