Miracle-dz/UniFAR-FTUs huggingface dataset:
Extending a 65nm CMOS process design kit for high total ionizing dose effects

Standard CMOS Process Design Kits (PDKs) do not address degradation the technology incurs when exposed to high Total Ionizing Dose (TID). Front-end electronics for the High-Luminosity Large Hadron Collider are expected to be exposed up to ten-fold doses. Bulk CMOS at 65 nm is a strong contender for such electronics due to a favorable trade-off among cost, performance, and TID-sensitivity. The present paper presents the extension of a foundry-provided PDK to cover also high TID effects. TID experiments have been carried out up to 500 Mrad. The PDK is based on binned BSIM4 models, which are adapted to different TID levels. Hence, designers may choose among different TID levels for their designs, contributing importantly to radiation-hard design practice.
Smartwatch-based sitting detection with human activity recognition for office workers syndrome

The main cause of Office Workers Syndrome (OWS) is habitual unhealthy behavior. One such example is the habit of siting for long periods at a computer. It has recently become simple to study this kind of human activity by using smartwatch sensors, and as a result, HAR (human activity recognition) has become a field attracting considerable interest. Smartwatch accelerometers have been especially useful in HAR, while gyroscopes are also helpful, especially when combined with the accelerometer. This study therefore suggests the use of HAR to detect sitting in order to identify the threat of OWS through the use of data from the accelerometer and gyroscope in a smartwatch. Two ensemble learning approaches have therefore been examined in order to establish their capability in recognizing periods of sitting. The results reveal that the accelerometer and gyroscope are complementary in their application and using them in combination thus improves the accuracy of recognition. The ensemble learning-based techniques were able to achieve an accuracy level of 93.57% for activity recognition when detecting sitting.
Electricity theft detecting based on density-clustering method

Nowadays, the problem of electricity theft and tampered smart meter data is causing widespread concern. Customer load profiles collected from smart meters can help detect abnormal electricity users and identify electricity theft. In this paper, a density-based electricity theft detection method is proposed to find out abnormal electricity patterns. Several malicious types are used to test the validation of the proposed method. Comparisons with k-means clustering, Gaussian mixture model (GMM) clustering and density-based spatial clustering of applications with noise (DBSCAN) are also con ducted. Numerical experiments show that the proposed method outperforms other methods in almost all the theft types.
BCTrust: A decentralized authentication blockchain-based mechanism

Internet of Things becomes a major part of our lives, billions of autonomous devices are connected and communicate with each other. This revolutionary paradigm creates a new dimension that removes the boundaries between the real and the virtual worlds. The Wireless Sensor Networks are a masterpiece of the success of this technology, using limited capacity sensors and actuators, industrial, medical, agricultural and many other environments can be covered and managed automatically. This autonomous interacting things should authenticate each other, and communicate securely. Otherwise malicious users can cause serious damages on such systems. In this paper we propose a robust, transparent, flexible and energy efficient blockchain-based authentication mechanism called BCTrust, which is designed especially for devices with computational, storage and energy consumption constraints. In order to evaluate our approach, we realized a real implementation with C programming language, and Ethereum Blockchain.
Dysfunctional Autism Risk Genes Cause Circuit-Specific Connectivity Deficits With Distinct Developmental Trajectories

Abstract Autism spectrum disorders (ASD) are a set of complex neurodevelopmental disorders for which there is currently no targeted therapeutic approach. It is thought that alterations of genes regulating migration and synapse formation during development affect neural circuit formation and result in aberrant connectivity within distinct circuits that underlie abnormal behaviors. However, it is unknown whether deviant developmental trajectories are circuit-specific for a given autism risk-gene. We used MRI to probe changes in functional and structural connectivity from childhood to adulthood in Fragile-X (Fmr1−/y) and contactin-associated (CNTNAP2−/−) knockout mice. Young Fmr1−/y mice (30 days postnatal) presented with a robust hypoconnectivity phenotype in corticocortico and corticostriatal circuits in areas associated with sensory information processing, which was maintained until adulthood. Conversely, only small differences in hippocampal and striatal areas were present during early postnatal development in CNTNAP2−/− mice, while major connectivity deficits in prefrontal and limbic pathways developed between adolescence and adulthood. These findings are supported by viral tracing and electron micrograph approaches and define 2 clearly distinct connectivity endophenotypes within the autism spectrum. We conclude that the genetic background of ASD strongly influences which circuits are most affected, the nature of the phenotype, and the developmental time course of the associated changes.
Cryogenic Temperature Characterization of a 28-nm FD-SOI Dedicated Structure for Advanced CMOS and Quantum Technologies Co-Integration

Silicon co-integration offers compelling scale-up opportunities for quantum computing. In this framework, cryogenic temperature is required for the coherence of solid-state quantum devices. This paper reports the characterization of an nMOS quantum-dot dedicated structure below 100 mK. The device under test is built in thin silicon film fabricated with 28 nm high- $k$ metal gate ultra-thin body and ultra-thin buried oxide advanced CMOS technology. The MOS structure is functional with improved performances at cryogenic temperature. The results open new research avenues in CMOS co-integration for quantum computing applications within the FD-SOI platform.
New Genetically Manipulated Mice Provide Insights Into the Development and Physiological Functions of Invariant Natural Killer T Cells

Invariant natural killer T (iNKT) cells are a unique T cell subset that exhibits characteristics of both innate immune cells and T cells. They express Vα14-Jα18 (Trav11-Traj18) as an invariant chain of the T cell receptor (TCR) and are restricted to the MHC class I-like monomorphic antigen presenting molecule CD1d. iNKT cells are known as immune regulators that bridge the innate and acquired immune systems by rapid and massive production of a wide range of cytokines, which could enable them to participate in immune responses during various disease states. Thus, Traj18-deficient mice, Cd1d-deficient mice, or iNKT cell-overexpressing mice such as iNKT TCRα transgenic mice and iNKT cell cloned mice which contain a Vα14-Jα18 rearrangement in the TCRα locus are useful experimental models for the analysis of iNKT cells in vivo and in vitro. In this review, we describe the pros and cons of the various available genetically manipulated mice and summarize the insights gained from their study, including the possible roles of iNKT cells in obesity and diabetes.
Disruption of a −35 kb Enhancer Impairs CTCF Binding and MLH1 Expression in Colorectal Cells

Purpose: MLH1 is a major tumor suppressor gene involved in the pathogenesis of Lynch syndrome and various sporadic cancers. Despite their potential pathogenic importance, genomic regions capable of regulating MLH1 expression over long distances have yet to be identified. Experimental Design: Here, we use chromosome conformation capture (3C) to screen a 650-kb region flanking the MLH1 locus to identify interactions between the MLH1 promoter and distal regions in MLH1-expressing and nonexpressing cells. Putative enhancers were functionally validated using luciferase reporter assays, chromatin immunoprecipitation, and CRISPR-Cas9–mediated deletion of endogenous regions. To evaluate whether germline variants in the enhancer might contribute to impaired MLH1 expression in patients with suspected Lynch syndrome, we also screened germline DNA from a cohort of 74 patients with no known coding mutations or epimutations at the MLH1 promoter. Results: A 1.8-kb DNA fragment, 35 kb upstream of the MLH1 transcription start site enhances MLH1 gene expression in colorectal cells. The enhancer was bound by CTCF and CRISPR-Cas9–mediated deletion of a core binding region impairs endogenous MLH1 expression. A total of 5.4% of suspected Lynch syndrome patients have a rare single-nucleotide variant (G > A; rs143969848; 2.5% in gnomAD European, non-Finnish) within a highly conserved CTCF-binding motif, which disrupts enhancer activity in SW620 colorectal carcinoma cells. Conclusions: A CTCF-bound region within the MLH1-35 enhancer regulates MLH1 expression in colorectal cells and is worthy of scrutiny in future genetic screening strategies for suspected Lynch syndrome associated with loss of MLH1 expression. Clin Cancer Res; 24(18); 4602–11. ©2018 AACR.
Decentralized Beam Pair Selection in Multi-Beam Millimeter-Wave Networks

Multi-beam concurrent transmission is one of promising solutions for a millimeter-wave (mmWave) network to provide seamless handover, robustness to blockage, and continuous connectivity. Nevertheless, one of the major obstacles in multi-beam concurrent transmissions is the optimization of beam pair selection, which is essential to improve the mmWave network performance. Therefore, in this paper, we propose a novel heterogeneous multi-beam cloud radio access network (HMBCRAN) architecture which provides seamless mobility and coverage for mmWave networks. We also design a novel acquirement method for candidate beam pair links (BPLs) in HMBCRANs architecture, which reduces user power consumption, signaling overhead, and overall time consumption. Based on HMBCRANs architecture and the resulted candidate BPLs for each user equipment, a beam pair selection optimization problem aiming at maximizing network sum rate is formulated. To find the solution efficiently, the considered problem is reformulated as a non-operative game with local interaction, which only needs local information exchanging among players. A decentralized algorithm based on HMBCRANs architecture and binary log-linear learning is proposed to obtain the optimal pure strategy Nash equilibrium of the proposed game, in which a concurrent multi-player selection scheme and an information exchanging protocol among players are developed to reduce the complexity and signal overheads. The stability, optimality, and complexity of the proposed algorithm are analyzed via theoretical and simulation method. The results prove that the proposed scheme has better convergence speed and sum rate against the state-of-the-art schemes.
Fooling a neural network with common adversarial noise

These days deep Neural Networks (NN) show exceptional performance on speech and visual recognition tasks. These systems are still considered a black box without deep understanding why they perform in such a manner. This lack of understanding makes NNs vulnerable to specially crafted adversarial examples — inputs with small perturbations that make the model misclassify. In this paper, we generated adversarial examples that will fool a NN used for classifying handwritten digits. We start by generating additive adversarial noise for each image. Afterwards, we propose an algorithm for crafting a single adversarial noise for misclassifying different members of the same class.
First Year Course-Based Undergraduate Research Experience (CURE) Using the CRISPR/Cas9 Genome Engineering Technology in Zebrafish †

Genetic analysis in model systems can provide a rich context for conceptual understanding of gene structure, regulation, and function. With an intent to create a rich learning experience in molecular genetics, we developed a semester-long course-based undergraduate research experience (CURE) using the CRISPR-Cas9 gene editing system to disrupt specific genes in the zebrafish. The course was offered to freshman students; nine students worked in four groups (two to three members per group) to design, synthesize, and test the nuclease activity of the CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)/sgRNAs for targeted disruption of specific genes in the zebrafish. Each group worked with a gene with an already known mutant phenotype that can be visually scored and a gene that had not been studied in zebrafish previously. Embedded in the course were a series of workshop-styled units or tutorials, including tours to core facilities. The focus was on introducing and developing skills that could be accommodated within the span of a semester. Each group successfully cloned at least one plasmid-encoding CRISPR/sgRNA template, visually analyzed injected embryos, and performed genotyping assays to detect CRISPR-Cas9 activity. In-class discussions, a final end-of-semester written test, and group oral presentations were assessed for an understanding of the CRISPR-Cas9 system, application of the CRISPR-Cas9 system as a gene manipulation tool, and experimental methods used to create plasmid vectors and synthesize sgRNA. In addition, poster presentations were evaluated by faculty, graduate students, and senior undergraduate students at a University research exposition. Self-reflections in the form of group conversations were video recorded. All students (9/9) distinctly showed learning gains after completing the activity, but the extent of the gains was variable, as seen from results of a written test and poster presentation assessment. Qualitative analysis of evaluations and self-reporting data indicated several gains, suggesting that all students found many aspects of the CURE valuable and gained project-specific (conceptual) and transferrable skills (science process and science identity).
Privacy-preserving blockchain based IoT ecosystem using attribute-based encryption

The Internet of Things (IoT) has penetrated deeply into our lives and the number of IoT devices per person is expected to increase substantially over the next few years. Due to the characteristics of IoT devices (i.e., low power and low battery), usage of these devices in critical applications requires sophisticated security measures. Researchers from academia and industry now increasingly exploit the concept of blockchains to achieve security in IoT applications. The basic idea of the blockchain is that the data generated by users or devices in the past are verified for correctness and cannot be tampered once it is updated on the blockchain. Even though the blockchain supports integrity and non-repudiation to some extent, confidentiality and privacy of the data or the devices are not preserved. The content of the data can be seen by anyone in the network for verification and mining purposes. In order to address these privacy issues, we propose a new privacy-preserving blockchain architecture for IoT applications based on attribute-based encryption (ABE) techniques. Security, privacy, and numerical analyses are presented to validate the proposed model.
Alcohol industry involvement in science: A systematic review of the perspectives of the alcohol research community

Abstract Issues Alcohol companies have recently invested large sums of money in answering research questions to which they have clear vested interests in the outcomes. There have been extensive concerns about corporate influence on public health sciences, following the experience with the tobacco industry. Approach This systematic review aims to investigate the perspectives of researchers on the activities of alcohol industry actors in relation to science, in order to guide future research. All data published in peer‐reviewed journals (including commentaries, opinion pieces, editorials and letters as well as research reports) were eligible for inclusion. This analysis focuses on the manifest rather than latent content of the articulated views, and accordingly adopts a thematic analysis using an inductive approach to the generation of themes. Key Findings There are serious concerns identified in three main areas, principally defined by where the impacts of industry scientific activities occur; on evidence informed policy making (instrumental uses of research by industry actors), on the content of the scientific evidence base itself (industry funding as a source of bias); and on the processes of undertaking research (transgressions of basic scientific norms). There are also opposing views which provide a useful critique. The evidence‐base on the validity of all concerns has been slow to develop. Implications The concerns are extensive, longstanding and unresolved and high quality investigations are needed. Conclusion This study informs the detailed content of the research needed to address the concerns identified here.
Stoichiometry of carbon, nitrogen, and phosphorus through the freshwater pipe

The “freshwater pipe” concept has improved our understanding of freshwater carbon (C) cycling, however, it has rarely been applied to macronutrients such as nitrogen (N) or phosphorus (P). Here, we synthesize knowledge of the processing of C, N, and P together in freshwaters from land to the ocean. We compared flux estimates into and out of the N and P “pipes” and showed the net removal rates of N and P by inland waters were less than those for C. The C : N : P stoichiometry of inland water inputs vs. exports differed due to large respiratory C and N losses, and efficient P burial in inland waters. Residence time plays a critical role in the processing of these elements through the pipe, where higher water residence times from streams to lakes results in substantial increases in C : N, C : P, and N : P ratios.
Double Path Networks for Sequence to Sequence Learning

Encoder-decoder based Sequence to Sequence learning (S2S) has made remarkable progress in recent years. Different network architectures have been used in the encoder/decoder. Among them, Convolutional Neural Networks (CNN) and Self Attention Networks (SAN) are the prominent ones. The two architectures achieve similar performances but use very different ways to encode and decode context: CNN use convolutional layers to focus on the local connectivity of the sequence, while SAN uses self-attention layers to focus on global semantics. In this work we propose Double Path Networks for Sequence to Sequence learning (DPN-S2S), which leverage the advantages of both models by using double path information fusion. During the encoding step, we develop a double path architecture to maintain the information coming from different paths with convolutional layers and self-attention layers separately. To effectively use the encoded context, we develop a gated attention fusion module and use it to automatically pick up the information needed during the decoding step, which is also a double path network. By deeply integrating the two paths, both types of information are combined and well exploited. Experiments show that our proposed method can significantly improve the performance of sequence to sequence learning over state-of-the-art systems.
Sentence representation and classification using attention and additional language information

Sentence representation is one of the foundational tasks in natural language processing, and long short term memory (LSTM) is a widely used tool to deal with the variable-length sentence. In this paper, a new LSTM-based sentence representation model is proposed for sentence classification task. By introducing a self-supervised method in the process of learning the hidden representation of the sentence, the proposed model automatically capture the syntactic and semantic information from the context and used as additional language information to learn better contextual hidden representation. Moreover, instead of using the final hidden representation of LSTM or the max (or average) pooling of the hidden representations over all the time step, we propose to generate the global representation of the sentence by combining all contextual hidden representations in an element-wise attention manner. We evaluate our model on three sentence classification tasks: sentiment classification, question type classification, and subjectivity classification. Experimental results show that the proposed model improves the accuracy of sentence classification compared to other sentence representation methods in all of the three tasks.
Serratia marcescens Outbreak in a Neonatal Intensive Care Unit: New Insights from Next-Generation Sequencing Applications

Serratia marcescens is an environmental bacterium that is commonly associated with outbreaks in neonatal intensive care units (NICUs). Investigations of S. marcescens outbreaks require efficient recovery and typing of clinical and environmental isolates. ABSTRACT Serratia marcescens is an environmental bacterium that is commonly associated with outbreaks in neonatal intensive care units (NICUs). Investigations of S. marcescens outbreaks require efficient recovery and typing of clinical and environmental isolates. In this study, we investigated how the use of next-generation sequencing applications, such as bacterial whole-genome sequencing (WGS) and bacterial community profiling, could improve S. marcescens outbreak investigations. Phylogenomic links and potential antibiotic resistance genes and plasmids in S. marcescens isolates were investigated using WGS, while bacterial communities and relative abundances of Serratia in environmental samples were assessed using sequencing of bacterial phylogenetic marker genes (16S rRNA and gyrB genes). Typing results obtained using WGS for the 10 S. marcescens isolates recovered during a NICU outbreak investigation were highly consistent with those obtained using pulsed-field gel electrophoresis (PFGE), the current standard typing method for this bacterium. WGS also allowed the identification of genes associated with antibiotic resistance in all isolates, while no plasmids were detected. Sequencing of the 16S rRNA and gyrB genes both showed greater relative abundances of Serratia at environmental sampling sites that were in close contact with infected babies. Much lower relative abundances of Serratia were observed following disinfection of a room, indicating that the protocol used was efficient. Variations in the bacterial community composition and structure following room disinfection and among sampling sites were also identified through 16S rRNA gene sequencing. Together, results from this study highlight the potential for next-generation sequencing tools to improve and to facilitate outbreak investigations.
Constitutive regulation of mitochondrial morphology by Aurora A kinase depends on a predicted cryptic targeting sequence at the N-terminus

Aurora A kinase (AURKA) is a major regulator of mitosis and an important driver of cancer progression. The roles of AURKA outside of mitosis, and how these might contribute to cancer progression, are not well understood. Here, we show that a fraction of cytoplasmic AURKA is associated with mitochondria, co-fractionating in cell extracts and interacting with mitochondrial proteins by reciprocal co-immunoprecipitation. We have also found that the dynamics of the mitochondrial network are sensitive to AURKA inhibition, depletion or overexpression. This can account for the different mitochondrial morphologies observed in RPE-1 and U2OS cell lines, which show very different levels of expression of AURKA. We identify the mitochondrial fraction of AURKA as influencing mitochondrial morphology, because an N-terminally truncated version of the kinase that does not localize to mitochondria does not affect the mitochondrial network. We identify a cryptic mitochondrial targeting sequence in the AURKA N-terminus and discuss how alternative conformations of the protein may influence its cytoplasmic fate.