Miracle-dz/UniFAR-FTUs huggingface dataset:
Circadian Rhythm Dysfunction Accelerates Disease Progression in a Mouse Model With Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease caused by interactions between environmental factors and genetic susceptibility. Circadian rhythm dysfunction (CRD) is a significant contributor to neurodegenerative conditions such as Alzheimer’s disease and Parkinson’s disease. However, whether CRD contributes to the progression of ALS remains little known. We performed behavioral and physiological tests on SOD1G93A ALS model mice with and without artificially induced CRD, and on wild-type controls; we also analyzed spinal cord samples histologically for differences between groups. We found that CRD accelerated the disease onset and progression of ALS in model mice, as demonstrated by aggravated functional deficits and weight loss, as well as increased motor neuron loss, activated gliosis, and nuclear factor κB-mediated inflammation in the spinal cord. We also found an increasing abundance of enteric cyanobacteria in the ALS model mice shortly after disease onset that was further enhanced by CRD. Our study provides initial evidence on the CRD as a risk factor for ALS, and intestinal cyanobacteria may be involved.
Tackling the Fidelity-Energy Trade-Off in Wireless Body Sensor Networks

Wearable and connected health is a dominant field in the era of the Internet of Things (IoT). Indeed, Body Sensor Networks (BSNs) have been widely used for enabling many connected health applications in diverse areas including: activity recognition, elder care, sports, and rehabilitation. Although the number of transistors in an integrated circuit follows Moore's law, it does not apply to batteries. Thus, in BSNs, the sensor node's compact size means it has a strict energy constraint. At the same time, body sensor nodes have sensing requirements that should provide an acceptable level of data fidelity in order to accurately infer the amount and type of activity. This paper investigates the energy challenges associated with combining MEMS-based Inertial Measurement Units (IMU) and ball-tube motion detectors for BSNs. We examine multiple scenarios using these sensor types for body motion characterization. We then investigate the feasibility of these technologies along with collaborative sensing with the aim of reducing overall energy consumption. We conducted experiments examining sedentary (i.e. no movement) and non-sedentary activities. Our results show the possibility of 67 % reduction in energy consumption during the sedentary periods only, while maintaining the required level of data fidelity for inferring motion types.
Energy-performance design exploration of a low-power microprogrammed deep-learning accelerator

This paper presents the design space exploration of a novel microprogrammable accelerator in which PEs are connected with a Network-on-Chip and benefit from low-power features enabled through a practical implementation of a Dual-Vdd assignment scheme. An analytical model, fitted with postlayout data obtained with a 28nm FDSOI design kit, returns implementations with optimal energy-performance tradeoff by taking into consideration all the key design-space variables. The obtained Pareto analysis helps us infer optimization rules aimed at improving quality of design.
Infiltration of Alternatively Activated Macrophages in Cancer Tissue Is Associated with MDSC and Th2 Polarization in Patients with Esophageal Cancer

Myeloid derived suppressor cells (MDSCs) expand in cancer bearing hosts and contribute to tumor immune evasion. M2 macrophages constitute a major cellular component of cancer-related inflammation. However, the correlation between circulating MDSCs and infiltrating M2 macrophages in tumor tissues from patients with esophageal cancer (ECA), and its potential relationship with the polarization of Th2 cells remain unclear. In the present study, we showed the level of MDSCs in PBMC and Arg1 in plasma were significantly elevated in ECA patients, and the increased ratio of MDSC in PBMC was closely related to the expression of CD163 in cancer tissues. In addition, the ECA patients exhibited remarkable increases in the mRNA levels of IL-4 and GATA3, as well as the protein levels of IL-13 and IL-6, but IFN-γ and IL-12 in peripheral blood were decreased. Our data indicate that the increased Th2 cytokines are associated with MDSCs and M2 macrophages polarization, and foster the infiltration of CD163+M2 macrophages in cancer tissues, which promote the formation of immunosuppressive microenvironment in ECA patients.
Nanoporous Superalloy Membranes: A Review

The ability to produce metallic membrane materials with porosity on the nanoscale from Ni‐based superalloys, hitherto used exclusively for high temperature applications, has been discovered 15 years ago. The basic principle is to first convert the initial γ/γ′ microstructure, containing isolated γ′‐precipitates, into a bi‐continuous network where both phases are in themselves continuous and interpenetrate each other. Then, one of the two phases is selectively removed, so that a rigid structure consisting of the remaining phase with pores on the location of the removed phase results. This article reviews the progress made so far. In that time period, a number of ways to fabricate these unique materials have emerged, utilizing 1) single crystals and polycrystals as precursor materials as well as 2) coarsening of coherent and incoherent γ′‐precipitates to realize bi‐continuity of the microstructure. Consequently, a family of superalloy membranes has emerged with specific microstructures, properties, advantages, and limitations. It is the intention of this article to give an overview on these various manufacturing routes, as well as on resulting microstructures and properties. Finally, possible fields of applications are outlined. It is demonstrated that the particular manufacturing process from a solid to the porous material leads to certain advantages, such as the ability to structure the material in porous and solid areas as required by the application.
Exploratory relation extraction in large multilingual data

Database Systems and Information Management Group Institute of Software Engineering and Theoretical Computer Science Doctor of Engineering Exploratory Relation Extraction in Large Multilingual Data by Alan AKBIK The task of Relation Extraction (RE) is concerned with creating extractors that automatically find structured, relational information in unstructured data such as natural language text. Motivated by an explosion of sources of readily available text data such as the web, RE offers intriguing possibilities for querying, organizing, and analyzing information by drawing upon the clean semantics of structured databases and the abundance of unstructured data. However, practical applications of RE are often characterized by vague and shifting information needs on the one hand and large multilingual datasets of unknown content on the other. Classical RE approaches are unable to handle such scenarios since they require a careful, upfront definition of extraction tasks before extractors can be created in an effort-intensive, time-consuming process. With this thesis, I propose the paradigm of Exploratory Relation Extraction (ERE), a user-driven but data-guided process of exploration for relations of interest in unknown data. I show how distributional evidence and an informed linguistic abstraction can be employed to allow users to openly explore a dataset for relations of interest and rapidly prototype extractors for discovered relations at minimal effort. Furthermore, I propose the use of a language-neutral representation of shallow semantics to address the issue of multilingual data. This representation enables a shared feature space for different languages against which extractors can be developed. I present a method that expands English-language Semantic Role Labeling (SRL) to other languages and use it to generate multilingual SRL resources for seven distinct languages from different language groups, namely Arabic, Chinese, French, German, Hindi, Russian and Spanish in order to bootstrap semantic parsers for these languages. Together, the researched approaches represent a novel way for data scientists to work with large multilingual datasets of unknown content. TECHNISCHE UNIVERSITÄT BERLIN
Reverse Engineering Convolutional Neural Networks Through Side-channel Information Leaks

A convolutional neural network (CNN) model represents a crucial piece of intellectual property in many applications. Revealing its structure or weights would leak confidential information. In this paper we present novel reverse-engineering attacks on CNNs running on a hardware accelerator, where an adversary can feed inputs to the accelerator and observe the resulting off-chip memory accesses. Our study shows that even with data encryption, the adversary can infer the underlying network structure by exploiting the memory and timing side-channels. We further identify the information leakage on the values of weights when a CNN accelerator performs dynamic zero pruning for off-chip memory accesses. Overall, this work reveals the importance of hiding off-chip memory access pattern to truly protect confidential CNN models.
Theoretical foundation of relevance frequency for text categorization

In text mining finding the significance of each term is a text is an important problem. In a two-class setting, relevance frequency is used to compute the discriminating power of a term for text categorization. Relevance frequency has shown better performance than other term weighting methods on number of datasets. However, relevance frequency is based on intuitive considerations. In this paper, we develop a probabilistic model to explain relevance frequency. The model provides a theoretical foundation for relevance frequency.
Monocular Vision-based Vehicle Localization Aided by Fine-grained Classification

Monocular camera systems are prevailing in intelligent transportation systems, but by far they have rarely been used for dimensional purposes such as to accurately estimate the localization information of a vehicle. In this paper, we show that this capability can be realized. By integrating a series of advanced computer vision techniques including foreground extraction, edge and line detection, etc., and by utilizing deep learning networks for fine-grained vehicle model classification, we developed an algorithm which can estimate vehicles location (position, orientation and boundaries) within the environment down to 3.79 percent position accuracy and 2.5 degrees orientation accuracy. With this enhancement, current massive surveillance camera systems can potentially play the role of e-traffic police and trigger many new intelligent transportation applications, for example, to guide vehicles for parking or even for autonomous driving.
3TAAV: A three-tier architecture for pseudonym-based anonymous authentication in VANETs

Vehicular Ad-hoc Network (VANET) is an emerging communication framework to abet in day-to-day management of vehicular traffic and safety of vehicles. Anonymous authentication is one of the key requirements in VANET ensures the privacy of the origin of the message. Existing security protocols in VANETs, that provide anonymous authentication based on the two-tier architecture, consists of two VANET elements, namely vehicles and road side units (RSUs) working as the key generating server (KGS). These protocols rely heavily on RSU to provide anonymous identity to vehicles. In these protocols, with the movement of the vehicle from one RSU to another RSU, a fresh anonymous identity needs to be generated for every vehicle with additional handing over and taking over procedures. In this paper, we propose a novel security protocol for anonymous authentication based on three-tier architecture involving three VANET elements, viz., vehicle, RSU and pseudonym server (PSS) for achieving multiple levels of anonymity in VANETs. The proposed protocol reduces the overhead incurred by the RSU for certificate management and eliminates the requirement of handing over and taking over of vehicles while changing from one RSU to another. Considering the overhead of RSU in certificate management, the number of pseudonyms cached by each vehicle and blackout-time (duration for which vehicle is not part of VANET due to non-acquisition of pseudo-identity) as performance metrics for simulation, the simulation results demonstrate the efficacy of the proposed approach.
ADAM Genomics Schema - Extension for Precision Medicine Research*

High-throughput sequencing technologies have made research on precision medicine possible. Precision medicine treatments will be effective for individual patients based on their genomic, environmental, and lifestyle factors. This requires integrating this data to find one, or a combination of, single nucleotide polymorphisms (SNPs) linked to a disease or treatment [1]. In 2013, the University of California Berkeley's AmpLab created the ADAM genomic format that allows the transformation, analysis and querying of large amounts of genomics data by using a columnar file format. However, while ADAM addresses the issue of processing large genomics data; it lacks the ability to link the patients' clinical and demographical data, which is crucial in precision medicine research. This paper presents an ADAM genomic schema extension to support clinical and demographical data by automating the addition of data items to the currently available ADAM schema. This extension allows for clinical, demographical and epidemiological analysis at large scale as initially intended by the AmpLab.
Design and development of multifunction voltage relay on FPGA

Relay is one of protective device that detects any abnormal operation of the circuit and gives signal to circuit breaker to remove the faulty part from the healthy circuit as early as possible so that minimum or no damage to the healthy system. Initially Voltage Relay are developed with solid state devices, later on solid state devices are replaced with embedded systems viz. microprocessors and microcontrollers due to their programming flexibility but both have the sequential architecture which increases the time delay in sense-process and communicate cycles. In this paper the pipelined architecture based Multi-Functional voltage relay is implemented on Field Programmable Gate Arrays (FPGA). In the proposed relay sense-process and communicate cycles are works concurrently. The multifunction voltage relay is implemented on GENESYS XILINX VIRTEX 5 XC5VLX50T FPGA board with communication facility. Results are displayed on hyper terminal with help of Universal Asynchronous Receiver and Transmitter (UART) interface. Different values of over voltage, normal voltage and under voltage is communicated to hyper terminal after converting to ASCII.
Genetic and environmental determination of tracking in subcutaneous fat distribution during adolescence 1 3

Background: The distribution of fat and adipose tissue is an important predictor of disease risk. Variation in fat distribution during adolescence is correlated with fat distribution in adulthood. Objective: The objective was to gain insight into the relative contribution of genes and environment to the stability of subcutaneous fat distribution from early adolescence into young adulthood. Design: Ratio of trunk to extremity skinfold thickness (TER) data from the Leuven Longitudinal Twin Study (n 105 Belgian twin pairs followed from 10 to 18 y of age) was entered into a longitudinal path analysis. Results: The best-fitting model included additive genetic sources of variance and nonshared environment. Heritabilities ranged between 84.3% (95% CI: 63.9–92.3%) and 88.6% (95% CI: 76.5–94.1%) in boys and between 78.4% (95% CI: 59.3–88.3%) and 88.3% (95% CI: 77.0–93.8%) in girls. The majority of the phenotypic tracking (boys: 0.40–0.78; girls: 0.38–0.72) could be attributed to the moderate-to-high genetic correlations (rG) (between 0.27–0.84 and 0.38–0.80 for the various age intervals in boys and girls, respectively). This rG could be attributed to both genetic sources of variance, which are the same throughout adolescence, as well as genetic sources of variance that are “switched-on” at a certain age, the effect of which is then transmitted to subsequent observations. Environmental correlations (rE) in boys ranged between 0.51 and 0.70 but contributed relatively little to phenotypic tracking because the amount of variance explained by the environment was low (11.4– 15.7%). In girls rE was low to moderate at best (0.09–0.48). Conclusion: Phenotypic tracking in subcutaneous fat distribution during adolescence is predominantly explained by additive genetic sources of variance. Am J Clin Nutr 2007;86:652–60.
VirtualSpace - Overloading Physical Space with Multiple Virtual Reality Users

Although virtual reality hardware is now widely available, the uptake of real walking is hindered by the fact that it requires often impractically large amounts of physical space. To address this, we present VirtualSpace, a novel system that allows overloading multiple users immersed in different VR experiences into the same physical space. VirtualSpace accomplishes this by containing each user in a subset of the physical space at all times, which we call tiles; app-invoked maneuvers then shuffle tiles and users across the entire physical space. This allows apps to move their users to where their narrative requires them to be while hiding from users that they are confined to a tile. We show how this enables VirtualSpace to pack four users into 16m2. In our study we found that VirtualSpace allowed participants to use more space and to feel less confined than in a control condition with static, pre-allocated space.
Come Together: Facilitating Collocated Multilingual Group Discussion with a Language Support Tool

For global organizations, bringing individuals with diverse background together stimulates idea generation as well as extends existing knowledge base, which is beneficial for group productivity. However, native speakers (NS) may dominate cross-lingual teamwork communicating solely in a common language, which may reduce diversity and harm productivity of the whole group. As a solution, we consider it necessary to support non-native speakers' (NNS) contribution for equalizing NS' and NNS' participation. In this study, we examine how recent language support technologies might affect the group dynamics of collocated multilingual teams consisting of multiple NSs and NNSs. In our experiment, quads of two NSs and two NNSs were provided with a language support tool that integrates machine translation, automatic speech recognition and shared display to enable NNSs participating in multilingual teamwork without requiring them to use a common language. Automatic language detection allowed them to dynamically decide what language to use in their group discussions. We found that using the tool increased the variety of languages used (e.g., Chinese, English and Japanese) and had the potential to equalize NS' and NNS' participation.
Analysis of IoT security at network layer

The new future for the connectivity to the world are the IoT devices which interconnect heterogeneous object with each other. These IoT devices will make an autonomous world which will have the ability to exchange information and make decisions at the real time. But since these devices are very small therefore they have constrained to memory, processing, battery powered and resources due to which chances of an attack on IoT devices are more than normal devices. To overcome this these IoT devices use lightweight protocols and standards at each layer. At the network layer, 6LoWPAN and RPL are the two main protocols which transfer the data from one device to another. This paper focuses on analyzing different types of attack on the 6LoWPAN and RPL and a new solution for the increased rank attack which helps to stop the loop and resource depletion.
Research on security situation prediction of equipment support information network based on Bayesian network

According to the characteristics of equipment support information network, this paper proposes a network security situation prediction model based on attack graph and Bayesian network. This paper proposes a global attack graph generation algorithm based on the forward search algorithm, and proposes an optimization scheme to solve the attack loop problem. A method for calculating the node probability of attack graph is proposed by using Bayesian network. The experimental results show that the model can accurately predict the security situation of the equipment support information network. The calculation results are more objective and practical because of adding the equipment weight information.
Identifying Speech Input Errors Through Audio-Only Interaction

Speech has become an increasingly common means of text input, from smartphones and smartwatches to voice-based intelligent personal assistants. However, reviewing the recognized text to identify and correct errors is a challenge when no visual feedback is available. In this paper, we first quantify and describe the speech recognition errors that users are prone to miss, and investigate how to better support this error identification task by manipulating pauses between words, speech rate, and speech repetition. To achieve these goals, we conducted a series of four studies. Study 1, an in-lab study, showed that participants missed identifying over 50% of speech recognition errors when listening to audio output of the recognized text. Building on this result, Studies 2 to 4 were conducted using an online crowdsourcing platform and showed that adding a pause between words improves error identification compared to no pause, the ability to identify errors degrades with higher speech rates (300 WPM), and repeating the speech output does not improve error identification. We derive implications for the design of audio-only speech dictation.
Inaccuracy Blindness in Collaboration Persists, even with an Evaluation Prompt

The tendency to believe and act on others' misinformation is documented in much prior work. This paper focuses on inaccuracy blindness, the tendency to take a collaborator's poor information at face value, which reduces problem-solving success. We draw on social psychological research from the 1970s showing that evaluative rating scales can prompt a change in perspective. In a series of studies, we prototyped and tested an evaluation prompt meant to encourage skepticism in participant detectives trying to identify a serial killer. In tests of the prototype, the prompt was partially successful in inducing skepticism (Exp. 1), but a larger study (Exp. 2) showed that, despite the evaluation prompt, participants' inaccuracy blindness persisted. This work, and the literature more generally, shows that the tendency to be misled by collaborators' inaccurate information remains a strong phenomenon that is hard to counteract and remains a significant challenge for the CHI community.
Effects of low- to medium-angle grain boundaries on creep properties of superalloy

The creep properties of Ni-based bicrystal specimens with low- to medium-angle grain boundaries are investigated at 1100°C and 130 MPa with two misorientation angles prepared by double-seed solidification. Experimental results show that the angle of the grain boundary with respect to the stretching direction has a strong effect on creep life. Molecular dynamics simulations show that the effect of the grain boundary on the neighbouring dislocation density is related to the grain boundary angle. A creep constitutive model with the grain boundary angle and the initial damage rate can be adopted to describe the creep damage evolution. The model is then modified according to the molecular dynamics simulation and experimental results to give more accurate predictions of creep life.