Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiological Differences Between Low Versus High Skeletal Muscle Hypertrophic Responders to Resistance Exercise Training: Current Perspectives and Future Research Directions

Numerous reports suggest there are low and high skeletal muscle hypertrophic responders following weeks to months of structured resistance exercise training (referred to as low and high responders herein). Specifically, divergent alterations in muscle fiber cross sectional area (fCSA), vastus lateralis thickness, and whole body lean tissue mass have been shown to occur in high versus low responders. Differential responses in ribosome biogenesis and subsequent protein synthetic rates during training seemingly explain some of this individual variation in humans, and mechanistic in vitro and rodent studies provide further evidence that ribosome biogenesis is critical for muscle hypertrophy. High responders may experience a greater increase in satellite cell proliferation during training versus low responders. This phenomenon could serve to maintain an adequate myonuclear domain size or assist in extracellular remodeling to support myofiber growth. High responders may also express a muscle microRNA profile during training that enhances insulin-like growth factor-1 (IGF-1) mRNA expression, although more studies are needed to better validate this mechanism. Higher intramuscular androgen receptor protein content has been reported in high versus low responders following training, and this mechanism may enhance the hypertrophic effects of testosterone during training. While high responders likely possess “good genetics,” such evidence has been confined to single gene candidates which typically share marginal variance with hypertrophic outcomes following training (e.g., different myostatin and IGF-1 alleles). Limited evidence also suggests pre-training muscle fiber type composition and self-reported dietary habits (e.g., calorie and protein intake) do not differ between high versus low responders. Only a handful of studies have examined muscle biomarkers that are differentially expressed between low versus high responders. Thus, other molecular and physiological variables which could potentially affect the skeletal muscle hypertrophic response to resistance exercise training are also discussed including rDNA copy number, extracellular matrix and connective tissue properties, the inflammatory response to training, and mitochondrial as well as vascular characteristics.
Changing or keeping solutions in dynamic optimization problems with switching costs

Dynamic optimization problems (DOPs) are problems that change over time. However, most investigations in this domain are focused on tracking moving optima (TMO) without considering the cost of switching from one solution to another when the environment changes. Robust optimization over time (ROOT) tries to address this shortcoming by finding solutions which remain acceptable for several environments. However, ROOT methods change solutions only when they become unacceptable. Indeed, TMO and ROOT are two extreme cases in the sense that in the former, the switching cost is considered zero and in the latter, it is considered very large. In this paper, we propose a new semi ROOT algorithm based on a new approach to switching cost. This algorithm changes solutions when: 1) the current solution is not acceptable and 2) the current solution is still acceptable but algorithm has found a better solution and switching is preferable despite the cost. The main objective of the proposed algorithm is to maximize the performance based on the fitness of solutions and their switching cost. The experiments are done on modified moving peaks benchmark (mMPB) and the performance of the proposed algorithm alongside state-of-the-art ROOT and TMO methods is investigated.
RT-ByzCast: Byzantine-Resilient Real-Time Reliable Broadcast

Today's cyber-physical systems face various impediments to achieving their intended goals, namely, communication uncertainties and faults, relative to the increased integration of networked and wireless devices, hinder the synchronism needed to meet real-time deadlines. Moreover, being critical, these systems are also exposed to significant security threats. This threat combination increases the risk of physical damage. This paper addresses these problems by studying how to build the first real-time Byzantine reliable broadcast protocol (RTBRB) tolerating network uncertainties, faults, and attacks. Previous literature describes either real-time reliable broadcast protocols, or asynchronous (non real-time) Byzantine ones. We first prove that it is impossible to implement RTBRB using traditional distributed computing paradigms, e.g., where the error/failure detection mechanisms of processes are decoupled from the broadcast algorithm itself, even with the help of the most powerful failure detectors. We circumvent this impossibility by proposing RT-ByzCast, an algorithm based on aggregating digital signatures in a sliding time-window and on empowering processes with self-crashing capabilities to mask and bound losses. We show that RT-ByzCast (i) operates in real-time by proving that messages broadcast by correct processes are delivered within a known bounded delay, and (ii) is reliable by demonstrating that correct processes using our algorithm crash themselves with a negligible probability, even with message loss rates as high as 60 percent.
Combining hardware nodes and software components ordering-based heuristics for optimizing the placement of distributed IoT applications in the fog

As fog computing brings compute and storage resources to the edge of the network, there is an increasing need for automated placement (i.e., selection of hosting devices) to deploy distributed applications. Such a placement must conform to applications' resource requirements in a heterogeneous fog infrastructure. The placement decision-making is further complicated by Internet of Things (IoT) applications that are tied to geographical locations of physical objects/things. This paper presents a model, an objective function, and a mechanism to address the problem of placing distributed IoT applications in the fog. Based on a backtrack search algorithm and accompanied heuristics, the proposed mechanism is able to deal with large scale problems, and to efficiently make placement decisions that fit the objective---to lower placed applications' response time. The proposed approach is validated through comparative simulations of different combinations of the algorithms and heuristics on varying sizes of infrastructures and applications.
Minimizing the Dimensionality Problem in mm-Wave with Large Number of Antenna Elements Using Fast CUR-Decomposition

Millimeter wave (mm-Wave) communications is a key technology to meet the increasing demand for high data rate. Meanwhile, the mm-Wave base station (BS) needs to employ a large number of antenna elements to increase the gain as well as to serve a huge number of users. However, the integration of a vast number of antenna elements will cause dimensionality problem due to large dimension of channel correlation matrix. Therefore, we propose a novel codebook construction design based on Fast CUR-decomposition to minimise this problem. The proposed method represents the original correlation matrix as the product of three very low dimension matrices compared to the original. Then the new rotated codebook is constructed by the new rotation matrix. Moreover, we evaluate our method with the original matrix regarding compression ratio (CR) and mismatch error between the two matrices. Additionally, we provide the ergodic sum-rate capacities for the conventional methods such as singular value decomposition (SVD) technique, zero-forcing (ZF), and a matched filter (MF) to compare with our proposed method. Furthermore, we analyse the enhancement of the system capacity regarding the number of antenna elements and the required feedback bits. The simulation results show that the proposed method can achieve good results after minimising dimension problem. This work it can be extended in the future to be applied to a 5G system with over 100 antenna elements.
Strategic orientation in evaluation of supply chain activities

Article history: Received October 18, 2011 Received in Revised form February, 2, 2012 Accepted 8 April 2012 Available online April 11 2012 Employment of supply chain management could impact the entire functional and practical aspects of industry and services positively and change the manager’s attitude toward the implementation of supply chain management systems from a cost-oriented attitude to an enabling attitude towards better activities with other chain members. Yet still many organizations do not have the required vision within the identification of strategic appropriate measure in implementation, evaluation and usage of proper benchmarks to attain an integrated chain. Constantly supply chain management activity can be introduced as a tied up with strategic level of organization. Hence, it is a necessary with an overall strategic approach to identify the criteria, which enable us to include the whole process level of chain, financial matter of chain activities, visible and invisible properties, human activity and turn all these activities into an integrated approach to resolve supply chain difficulties. In this paper, we present an integrated balanced score card combined with DEMATEL technique to prioritize different alternatives for supply chain implementation. © 2012 Growing Science Ltd. All rights reserved.
Age-Related Declines in Prefrontal Cortical Expression of Metabotropic Glutamate Receptors that Support Working Memory

Abstract Glutamate signaling is essential for the persistent neural activity in prefrontal cortex (PFC) that enables working memory. Metabotropic glutamate receptors (mGluRs) are a diverse class of proteins that modulate excitatory neurotransmission via both presynaptic regulation of extracellular glutamate levels and postsynaptic modulation of ion channels on dendritic spines. This receptor class is of significant therapeutic interest for treatment of cognitive disorders associated with glutamate dysregulation. Working memory impairment and cortical hypoexcitability are both associated with advanced aging. Whether aging modifies PFC mGluR expression, and the extent to which any such alterations are regionally or subtype specific, however, is unknown. Moreover, it is unclear whether specific mGluRs in PFC are critical for working memory, and thus, whether altered mGluR expression in aging or disease is sufficient to play a causative role in working memory decline. Experiments in the current study first evaluated the effects of age on medial PFC (mPFC) mGluR expression using biochemical and molecular approaches in rats. Of the eight mGluRs examined, only mGluR5, mGluR3, and mGluR4 were significantly reduced in the aged PFC. The reductions in mGluR3 and mGluR5 (but not mGluR4) were observed in both mRNA and protein and were selectively localized to the prelimbic (PrL), but not infralimbic (IL), subregion of mPFC. Finally, pharmacological blockade of mGluR5 or mGluR2/3 using selective antagonists directed to PrL significantly impaired working memory without influencing non-mnemonic aspects of task performance. Together, these data implicate attenuated expression of PFC mGluR5 and mGluR3 in the impaired working memory associated with advanced ages.
Accelerated exhaustive eye glints localization method for infrared video oculography

Human eye glints localization could significantly and directly improve accuracy of gaze tracking, especially when several glints are localized precisely at the same time. However, traditional algorithms have not ensured the accuracy of glints localization to be used for gaze tracking. Aiming at the problem in infrared video oculography that those glints reflected on the surface of eye iris could be hard to identify and localize, we propose an algorithm for precisely and exhaustively locating eye glints of high accuracy with accelerated process. Our contributions are two-folded. (1). We propose an exhaustive eye glints localization algorithm, which could guarantee both 94.9% recognition rate and < 1 pixel positional accuracy under various brightness conditions. (2). We propose methods to accelerate our algorithm, including a modified quadratic ellipse difference algorithm and video frame difference examination, which guarantee the real-time video processing requirement for gaze tracking system.
Interpretable CNV-based tumour classification using fuzzy rule based classifiers

DNA copy number variations (CNVs) are widespread structural variants in the genome that are believed to play an important role in tumour evolution. Although CNVs affect a greater fraction of the genome than single nucleotide polymorphism (SNPs), so far they have received much less attention, and their contribution in cancer genomics is not still fully understood. In this work we propose a novel pipeline to support tumour type classification and rule extraction based on somatic CNV data. The pipeline outputs an interpretable Fuzzy Rule Based Classifier (FRBC), on which inference can be made. The pipeline benchmarking is performed over a set of samples of kidney cancer from TCGA. The results show the potential application of the approach: The method is able to classify between three kidney tumour types, with an accuracy of ∼ 93%, using a compact set of ~ 50 interpretable rules.
Customising LOD views: a declarative approach

This paper is about web applications to browse and efficiently visualise large Linked Open Dataset (LOD). The focus is on the customisation of LOD views over semantic datasets also for non expert users. The paper presents the motivation and the details of a visual data format and a chain of tools to easily produce and customize such visualisations. Two proofs of concepts are also presented in order to demonstrate the feasibility and flexibility of our approach.
Towards Dynamically Monitoring Android Applications on Non-rooted Devices in the Wild

Dynamic analysis is an important technique to reveal sensitive behavior of Android apps. Current works require access to the code-level and system-level events (e.g., API calls and system calls) triggered by the running apps and consequently they can only be conducted on in-lab running environments (e.g., emulators and modified OS). The strict requirement of running environment hinders their deployment in scale and makes them vulnerable to anti-analysis techniques. Furthermore, current dynamic analysis of Android apps exploits input generators to invoke app behavior, which, however, cannot provide sufficient code coverage. We propose to dynamically analyze app behavior on non-rooted devices used by the public so that it is possible to analyze dynamically in scale without input generators. By doing so, we also maximize the code coverage since the app behavior is invoked by real users of the apps. To achieve such a goal, we build UpDroid, a system for detecting sensitive behavior without modifying Android OS, rooting the device, or leveraging emulators. UpDroid detects sensitive events by monitoring the changing of public resources on the device, instead of accessing low-level events that require rooting or system modification. To identify the apps that trigger the detected events, UpDroid formulates the identification as a ranking problem and adopts learning to rank technique to solve it. Our experimental results demonstrate that UpDroid can successfully detect the use of 15 out of 26 permissions that are labeled dangerous in the official Android documentation. We also compare UpDroid with API hooking which can theoretically capture all sensitive behavior but requires root permission and system modifications. Results show that UpDroid can still achieve 70% coverage of API hooking even without root permission or any system modifications.
Genotypic Variations in Phenolic, Flavonoids and Their Antioxidant Activities in Maize Plants Treated with Zn (II) HEDTA Grown in Salinized Media

Zinc (Zn (II) HEDTA) was used to determine their effect on salt-induced damages in maize plants. The aim of this study was to investigate the antioxidant capacity and the levels of enhanced total phenolic (TPC), total flavonoid (TFC) contents and their antioxidant activity in leaves of two maize cultivars Single cross 10 (SC10) and Single cross 162 (SC162) grown in two levels of salinity 0.00 and 100 mmol in response to 20 μmol Zn (II) HEDTA foliar spray treatments. Significant differences (P ≤ 0.05) in amounts of TPC ranged from (2.55 to 4.62 mg/gdw as Gallic) in Single cross 10 (SC10) and from (2.53 to 4.38 mg/gdw as Gallic) in Single cross 162 (SC162), TFC (ranged 1.53 to 2.41 mg/gdw as qurestien) in Single cross 10 (SC10) and from (1.28 to 2.41 mg/gdw as qurestien) in Single cross 162 (SC162) among all treated plants were observed. The levels of their compounds increase related to foliar spraying of Zn (II) HEDTA. A significant positive correlation between TPC, TFC and DPPH scavenging activity and iron chelating activity was observed which shows that phenolic compounds were involved in the mechanism of salt tolerance of the two cultivars by showing enhanced antioxidant activity which resulted in reduced membrane damage and hence improved growth. According to the results obtained, the adverse effects of salt stress on maize plants can partly be alleviated with application of Zn (II)-HEDTA chelates. It is concluded that the application of Zn (II) HEDTA to maize plants grown in salt conditions leads to the increase of antioxidant compounds and maize tolerance.
Resembled Generative Adversarial Networks: Two Domains with Similar Attributes

We propose a novel algorithm, namely Resembled Generative Adversarial Networks (GAN), that generates two different domain data simultaneously where they resemble each other. Although recent GAN algorithms achieve the great success in learning the cross-domain relationship, their application is limited to domain transfers, which requires the input image. The first attempt to tackle the data generation of two domains was proposed by CoGAN. However, their solution is inherently vulnerable for various levels of domain similarities. Unlike CoGAN, our Resembled GAN implicitly induces two generators to match feature covariance from both domains, thus leading to share semantic attributes. Hence, we effectively handle a wide range of structural and semantic similarities between various two domains. Based on experimental analysis on various datasets, we verify that the proposed algorithm is effective for generating two domains with similar attributes.
Are People Aware of Their Inattention ? Evidence from Credit Card Repayment *

Early repayment of credit card bills is a major and suboptimal choice that has been largely ignored in the literature. Our study analyzes the distribution of the credit card repayment dates from a large commercial bank in Asia, and we find that 70% of consumers pay credit card bills in advance. We explain this finding by identifying two types of inattentive agents: sophisticated inattentive agents who are aware of their inattention problems and thus choose to pay in advance, and naïve inattentive agents who are unaware of the problem and thus, often make late repayments. We show that a temporary increase in attention level leads to earlier repayments. The early repayments are sophisticated choices affected by revolving interest, investment return, and amount payable rather than random mistakes. Learning also leads to earlier repayments and switching to automatic payment.
Visualized and Interacted Life: Personal Analytics and Engagements with Data Doubles

A field of personal analytics has emerged around self-monitoring practices, which includes the visualization and interpretation of the data produced. This paper explores personal analytics from the perspective of self-optimization, arguing that the ways in which people confront and engage with visualized personal data are as significant as the technology itself. The paper leans on the concept of the “data double”: the conversion of human bodies and minds into data flows that can be figuratively reassembled for the purposes of personal reflection and interaction. Based on an empirical study focusing on heart-rate variability measurement, the discussion underlines that a distanced theorizing of personal analytics is not sufficient if one wants to capture affective encounters between humans and their data doubles. Research outcomes suggest that these explanations can produce permanence and stability while also profoundly changing ways in which people reflect on themselves, on others and on their daily lives.
Low power driven loop tiling for RRAM crossbar-based CNN

Convolutional neural networks (CNNs) have been proposed to be widely adopted to make predictions on a large amount of data in modern embedded systems. Multiply and accumulate (MAC) operations serve as the most computationally expensive portion in CNN. Compared to the manner of executing MAC operations in GPU and FPGA, CNN implementation in the RRAM crossbar-based computing system (RCS) demonstrates the outstanding advantages of high performance and low power. However, the current design presents a very high overhead on peripheral circuits and memory accesses, limiting the gains of RCS. Addressing the problem, recently a Multi-CLP (Convolutional Layer Processor) structure has been proposed, where the FPGA controlling resources can be shared by multiple computation units. Exploiting this idea, the Peripheral Circuit Unit (PeriCU)-Reuse scheme has been proposed, with the underlying idea is to put the expensive AD/DAs onto spotlight and arrange multiple convolution layers to be sequentially served by the same PeriCU. This paper adopts the above structures. It is further observed that memory accesses can be bypassed if two adjacent layers are assigned in different CLPs. A loop tiling technique is proposed to enable memory accesses bypassing and further improve the energy of RCS. And to guarantee correct data dependency between layers, the safe starting time for a layer is discussed if its previous layer is tiled in a different CLP. The experiments of two convolutional applications validate that the loop tiling technique integrated with the Multi-CLP structure can efficiently meet power budgets and further reduce energy consumption by 61.7%.
Evolutionary expectation maximization

We establish a link between evolutionary algorithms (EAs) and learning of probabilistic generative models with binary hidden variables. Learning is formulated as approximate maximum likelihood optimization using variational expectation maximization. When choosing truncated posteriors as variational distributions, the variational parameters take the form of sets of latent states. By (A) identifying these sets with populations of genomes, and by (B) defining the fitness of individuals as the joint probability of the chosen generative model, the sets of latent states can be optimized using EAs. We obtain scalable learning algorithms that effectively improve the tractable free energy objective of truncated posteriors. While this novel approach is applicable to any model with binary latents and tractable joint probability, as a proof of concept, we here apply it to the optimization of parameters of noisy-OR Bayes Nets (modeling binary data) and Binary Sparse Coding (modelling continuous data). We find that the data likelihood is efficiently improved by employing genetic algorithms with point mutations and single-point cross-over as EAs. In general we believe that, with the novel link established here, standard as well as recent results in evolutionary optimization can be leveraged to address the difficult problem of parameter optimization in generative models.