Miracle-dz/UniFAR-FTUs huggingface dataset:
Clustering Scientific Document Based on an Extended Citation Model

With the number of published scientific paper increasing exponentially, scientific document clustering is becoming a challenging task. Therefore, a scientific document clustering model with high quality is needed. In this paper, we propose an extended citation model for scientific document clustering. On the one hand, the proposed model considers that 1) the high frequency and the wide distribution of a scientific document cited in other documents will result in the high similarity between the citing and the cited documents; and 2) the close location of two scientific documents cited in a scientific document will also result in the high similarity between these two documents. On the other hand, the proposed model combines a citation networks and textual similarity network to enhance the performance of scientific document clustering. To evaluate the performance of our proposed model, we collect scientific documents from PMC and PubMed databases in the field of oncology as a case study. It is proved that our proposed model can obtain reasonably clustering results by comparing it with traditional scientific documents clustering models, such as traditional bibliographic coupling model and textual similarity model, according to the indices of precision, recall, and F1-score.
The Good, the Bad and the Ugly: A Study of Security Decisions in a Cyber-Physical Systems Game

Stakeholders’ security decisions play a fundamental role in determining security requirements, yet, little is currently understood about how different stakeholder groups within an organisation approach security and the drivers and tacit biases underpinning their decisions. We studied and contrasted the security decisions of three demographics—security experts, computer scientists and managers—when playing a tabletop game that we designed and developed. The game tasks players with managing the security of a cyber-physical environment while facing various threats. Analysis of 12 groups of players (4 groups in each of our demographics) reveals strategies that repeat in particular demographics, e.g., managers and security experts generally favoring technological solutions over personnel training, which computer scientists preferred. Surprisingly, security experts were not ipso facto better players—in some cases, they made very questionable decisions—yet they showed a higher level of confidence in themselves. We classified players’ decision-making processes, i.e., procedure-, experience-, scenario- or intuition-driven. We identified decision patterns, both good practices and typical errors and pitfalls. Our game provides a requirements sandbox in which players can experiment with security risks, learn about decision-making and its consequences, and reflect on their own perception of security.
Factors Which Affect the Frequency of Sporulation and Tetrad Formation in Saccharomyces cerevisiae Baker's Yeasts

Vol. 61, no. 2, p. 635, legend to Fig. 3, line 1: "VS ( )" should read "VS ((symbl)). " Legend to Fig. 4, line 3: "DS81-D ( )" should read "DS81-D ((symbl)). " [This corrects the article on p. 630 in vol. 61. ].
Automated CAD and Modeling of Origami Structures for Reconfigurable Antenna Applications

Electromagnetic (EM) simulation of origami-inspired antenna structures has been a challenge due to its complex and sometimes counter-intuitive 3-D designs. In this work, we show the development of a unified automated tool that constructs the CAD models for origami type structures for conducting full-wave simulations. In the proposed toolset, we have used the well known Mathematica Notebook called ‘Tessellatica’, for obtaining origami geometries and subsequently generated CAD models for full-wave simulations in ANSYS High Frequency Structure Simulator (HFSS). The developed toolset can be used to build HFSS 3-D models and conduct full-wave simulations for an arbitrary origami type structure.
A compact photonic horizontal spot-size converter realized in silicon-on-insulator

We present a compact planar coupler connecting two optical waveguides with highly different widths. The coupler consists of various nonperiodic waveguide sections, whose dimensions are determined using a genetic optimization algorithm. Efficiencies that exceed those of the more conventional designs with similar lengths, like gradual linear tapers, were obtained in silicon-on-insulator using 248-nm-deep ultraviolet lithography.
Risk factors for anastomotic leakage after anterior resection for rectal cancer.

BACKGROUND The risk factors for anastomotic leak (AL) after anterior resection have been evaluated in several studies and remain controversial as the findings are often inconsistent or inconclusive. OBJECTIVE To analyze the risk factors for AL after anterior resection in patients with rectal cancer. DESIGN Retrospective analysis. SETTING The Nationwide Inpatient Sample 2006 to 2009. PATIENTS A total of 72 055 patients with rectal cancer who underwent elective anterior resection. MAIN OUTCOME MEASURES To build a predictive model for AL using demographic characteristics and preadmission comorbidities, the lasso algorithm for logistic regression was used to select variables most predictive of AL. RESULTS The AL rate was 13.68%. The AL group had higher mortality vs the non-AL group (1.78% vs 0.74%). Hospital length of stay and cost were significantly higher in the AL group. Laparoscopic and open resections with a diverting stoma had a higher incidence of AL than those without a stoma (15.97% vs 13.25%). Multivariate analysis revealed that weight loss and malnutrition, fluid and electrolyte disorders, male sex, and stoma placement were associated with a higher risk of AL. The use of laparoscopy was associated with a lower risk of AL. Postoperative ileus, wound infection, respiratory/renal failure, urinary tract infection, pneumonia, deep vein thrombosis, and myocardial infarction were independently associated with AL. CONCLUSIONS Anastomotic leak after anterior resection increased mortality rates and health care costs. Weight loss and malnutrition, fluid and electrolyte disorders, male sex, and stoma placement independently increased the risk of leak. Laparoscopy independently decreased the risk of leak. Further studies are needed to delineate the significance of these findings.
On Reduced-Cost Design Optimization of Antennas Using Trust-Region Gradient Search

A reduced-cost gradient search with numerical derivatives for design optimization of antenna structures is presented. Our approach involves management of the antenna response Jacobians based on relative relocation of the design during subsequent algorithm iterations, as well as the size of the trust region (i.e., optimization domain at current iteration). Consistent computational savings are demonstrated (40 percent on average as compared to the reference algorithm) for a set of benchmark antennas, along the algorithm robustness.
Transient Key-based Obfuscation for HLS in an Untrusted Cloud Environment

Recent advances in cloud computing have led to the advent of Business-to-Business Software as a Service (SaaS) solutions, opening new opportunities for EDA. High-Level Synthesis (HLS) in the cloud is likely to offer great opportunities to hardware design companies. However, these companies are still reluctant to make such a transition, due to the new risks of Behavioral Intellectual Property (BIP) theft that a cloud-based solution presents. In this paper, we introduce a key-based obfuscation approach to protect BIPs during cloud-based HLS. The source-to-source transformations we propose hide functionality and make normal behavior dependent on a series of input keys. In our process, the obfuscation is transient: once an obfuscated BIP is synthesized through HLS by a service provider in the cloud, the obfuscation code can only be removed at Register Transfer Level (RTL) by the design company that owns the correct obfuscation keys. Original functionality is thus restored and design overhead is kept at a minimum. Our method significantly increases the level of security of cloud-based HLS at low performance overhead. The average area overhead after obfuscation and subsequent de-obfuscation with tests performed on ASIC and FPGA is 0.39%, and over 95% of our tests had an area overhead under 5%.
Analyzing fault tolerance mechanism of Hadoop Mapreduce under different type of failures

MapReduce is the most popular distributed paradigm thanks to its features such as fault tolerance for processing of large-scale data. Hadoop is considred as a widely used implementation of MapReduce, it provides an open-source solution for processing with big data. By using Hadoop, enterprises can process enormous volumes of data on a large clusters. However, when the size of clusters used for processing data increases, the system will experience more failures during execution of Mapreduce applications. The scheduler in Hadoop is responsible for scheduling and monitoring the jobs and tasks. In case of a task fails, Hadoop reschedule it. In addition, MapReduce introduces a novel data replication and task re-execution strategies for fault tolerance in order to meet the end users requirements. MapReduce's tasks are independent of each other which isolates the impact of failures to the single task. Furthermore, MapReduce replicates each data block and re-executes the failed tasks, which potentially avoids the data transfer and checkpointing overhead during the execution of tasks. This paper intends to lead a better understanding of fault tolerance mechanism of Hadoop Mapreduce despite failures. The paper focuses on evaluation of the performance of many representative Hadoop MapReduce applications, with different execution parameters as well as under different failure scenarios. We will also present different options to inject failures into MapReduce applications to simulate real world failures. To trigger failures in applications or systems, we use failure injection technique. It has been long used in computer design to test and evaluate error correction and failure management schemes. Finally, we will present the cause of failures and Hadoop MapReduce behaviors during the failed of job processing.
Novel adaptive control scheme for plant with separable nonlinearity

In many practical systems containing separable nonlinearity, an undesirable sustained oscillation is observed at the plant output. This is due to the existence of stable limit cycles. A general approach to eliminate the occurence of limit cycle, an inverse characteristic of the nonlinearity is implemented in closed loop. In this paper, a method is proposed to eliminate the effects of unknown nonlinearity by incorporating an adaptive inverse nonlinearity as a compensator. This unknown nonlinearity is identified through the general nonlinearity structure. A Recursive Least Square identification algorithm is used to identify the parameters of the unknown nonlinearity. The identified parameters are used to implement an adaptive inverse nonlinearity through general inverse nonlinearity which reduces the limit cycle oscillation considerably. The numerical simulation is carried out to validate the proposed method for a plant with backlash nonlinearity.
Approximate Logic Synthesis by Symmetrization

Approximate synthesis is a recent trend in logic synthesis that changes some outputs of a logic specification to take advantage of error tolerance of some applications and reduce complexity and consumption of the final implementation. We propose a new approach to approximate synthesis of combinational logic where we derive its closest symmetric approximation, i.e., the symmetric function obtained by injecting the minimum number of errors in the original function. Since BDDs of totally symmetric functions are quite compact, this approach is particularly convenient for BDD-based implementations, such as networks of MUXes directly mapped from BDDs. Our contribution is twofold: first we propose a polynomial algorithm for computing the closest symmetric approximation of an incompletely specified Boolean function with an unbounded number of errors; then we discuss strategies to achieve partial symmetrization of the original specification while satisfying given error bounds. Experimental results on classical and new benchmarks confirm the efficacy of the proposed approach.
Prognostic impact of multimorbidity in patients with type 2 diabetes and ST-elevation myocardial infarction

Introduction There is an increasing body of evidence on the clinical importance of multimorbidity, which is defined as the coexistence of two or more chronic conditions. Type 2 diabetes (T2DM) is one of the most frequent chronic conditions. Most adults with type 2 diabetes have at least 1 coexisting chronic condition and approximately 40% have 3 or more. Prior studies have suggested that cardiovascular (CVD) and non-CVD comorbid conditions yield worse outcomes in patients hospitalized with ST-elevation myocardial infarction (STEMI). It is unclear, however, the extent to which multimorbidity has a cumulative effect on long-term risk. Therefore we have set out to determine the prognostic value of multiple comorbidity on long-term outcomes in this population of patients. Methods A total of 277 patients with T2DM and STEMI undergoing primary percutaneous coronary intervention (PCI) were enrolled. Based on the number of comorbidities the study population was divided into two groups: group 1 (N=58) with ≤ 1 comorbidity and group 2 (N=219) with ≥ 2 comorbidities. Results Comorbid conditions were prevalent among study participants (Figure 1). The median number of comorbidities was three. 15.9% of patients had one comorbidity and 22.0%, 34.3%, and 22.7% of patients had two, three or at least four comorbid conditions respectively. A majority of patients had at least one CVD comorbidity (6.1% of patients had none), whereas 53.1% of patients did not have any non-CVD comorbidity. During hospitalization 3 out of 58 patients (5.2%) died in group 1 and 25 of 219 patients (11.4%) died in group 2. The number of comorbid conditions was not an independent predictor of in-hospital death. During 12-month follow-up, 5 of 58 patients (8.6%) and 42 of 219 patients (19.9%) died, respectively in group 1 and 2 (P=0.05). The number of comorbid conditions proved in ROC analysis that for 12-month mortality, the prognostic value was modest, but for 12-month acute coronary syndromes the prognostic value was good. Increase in the number of comorbid conditions by one was associated with a 15% increase in the relative risk of 12-month mortality and a 41% increase in the relative risk of 12-month acute coronary syndromes (ACS). Conclusions Comorbid conditions are highly prevalent among these groups of patients. Majority of patients have at least 2 other cardiovascular comorbidities and one or two non-cardiovascular comorbidities. In terms of long-term follow-up, multimorbidity was associated with worse outcomes. The risk of both long-term mortality and ACS increased with the increasing number of comorbidities. In summary, our findings highlight the importance of indentifying patients with multimorbidity. This, in turn, could allow for provision of better care to these high-risk and complex group of patients.
Advanced Online Volt/var(Q) Control System for Optimal Voltage Profile

The situation, grid integration of variable renewable energy and electricity sector reforming in Japan, will increase uncertainties in power system operation.To handle these uncertainties, this paper introduces the sophisticated voltage and reactive power management technology based on AC-OPF (Optimal Power Flow) using online data, advanced online volt/var(Q) control system (OPENVQ: Optimized Performance Enabling Network for Volt/var(Q)), which can reduce operating expenditure (OPEX) by transmission loss reduction and optimized control of shunt devices and transformer taps, and/or capital expenditure (CAPEX) by optimized management of voltage control assets. Its architecture consists of a security assessment, a look-ahead operational condition predictor, optimal power flow calculation, and scenario analyzer.To validate the performance of OPENVQ system, a simulator has been developed that can handle multi-period operational conditions considering time series. Simulation results based on the TEPCO Power Grid 1500-bus model show that multiple control procedures created by OPENVQ system can optimize the KPIs, and new logic to reduce the number of control of shunt devices and transformer taps for managing grid voltage could be confirmed.Furthermore the feasibility study of OPENVQ system in Thailand has been conducted by NEDO (New Energy and Industrial Technology Development Organization)’s study scheme in Japan.
UMEM based 1/f noise model for amorphous ESL IGZO TFTs

This work presents a physics-based model for the flicker (1/f) noise behavior of bottom-gated amorphous InGaZnO (a-IGZO) thin-film transistors (TFTs) with an Edge-Stop Layer (ESL). The model is derived based on the so-called Unified Method and Extraction Procedure (UMEM), a technique previously used in accurate device parameter extraction and simulation of current-voltage (I-V) characteristics of the amorphous hydrogenated Silicon (a-Si:H), polycrystalline Silicon (poly-Si), and Organic TFT (OTFT) devices. It has been reported that 1/f noise in the bottom-gated a-IGZO TFT with ESL and with a very thin film IGZO layer obeys the carrier number fluctuation theory. The unified 1/f noise model is, therefore, adapted here to take into account the correlated bulk mobility fluctuation as well. Excellent agreements of the model with measured DC and flicker noise characteristics have shown the model validation.
Investigation of the influence of global stresses and strains on the magnetic properties of electrical steels with varying alloying content and grain size

The process of manufacturing iron cores for electric machines out of electrical steel can drastically influence the magnetic properties of the material. It is well known that the local soft magnetic properties are degraded by the cutting process. In the production process the material also experiences tensile and compressive stress, i.e. when pressing the stator core into motor housing. In this work, the influence of these stresses on the soft magnetic properties of six different 0.35 mm thick fully processed non-oriented electrical steels with varying alloy content and varying grain size is studied on 30 mm wide and 300 mm long strip samples. A single strip testing setup with force in field direction applied to the specimen is used. This field metric method allows for quantitative analysis of the magnetic properties and ac iron losses as a function of the applied stress. For the specific case of 1.0 T and 400 Hz measured in rolling direction on one material the application of tensile stress first leads to a decrease in the losses, up to a stress of 30 MPa. For higher stresses the losses increase again by 11 % at an applied strain of 100 MPa. The results for compressive stress look significantly different: the application of stress always leads to an increase of the losses. At -40 MPa, the iron losses increase by 64 %. These effects are stronger the lower the polarization or frequency of the applied magnetic field is chosen. The Bertotti model is used to separate the static (hysteresis) and dynamic (eddy current and excess) losses. We discuss the observed deteriorations as an effect of the inverse magnetostriction, which leads to changes in the magnetoelastic energy.
Crystal structure of a protein repair methyltransferase from Pyrococcus furiosus with its L-isoaspartyl peptide substrate.

Protein L-isoaspartyl (D-aspartyl) methyltransferases (EC 2.1.1.77) are found in almost all organisms. These enzymes catalyze the S-adenosylmethionine (AdoMet)-dependent methylation of isomerized and racemized aspartyl residues in age-damaged proteins as part of an essential protein repair process. Here, we report crystal structures of the repair methyltransferase at resolutions up to 1.2 A from the hyperthermophilic archaeon Pyrococcus furiosus. Refined structures include binary complexes with the active cofactor AdoMet, its reaction product S-adenosylhomocysteine (AdoHcy), and adenosine. The enzyme places the methyl-donating cofactor in a deep, electrostatically negative pocket that is shielded from solvent. Across the multiple crystal structures visualized, the presence or absence of the methyl group on the cofactor correlates with a significant conformational change in the enzyme in a loop bordering the active site, suggesting a role for motion in catalysis or cofactor exchange. We also report the structure of a ternary complex of the enzyme with adenosine and the methyl-accepting polypeptide substrate VYP(L-isoAsp)HA at 2.1 A. The substrate binds in a narrow active site cleft with three of its residues in an extended conformation, suggesting that damaged proteins may be locally denatured during the repair process in cells. Manual and computer-based docking studies on different isomers help explain how the enzyme uses steric effects to make the critical distinction between normal L-aspartyl and age-damaged L-isoaspartyl and D-aspartyl residues.
Macro-Cell Capacity Enhancement with Dynamic Full-Duplex Cellular System

FDC (Full Duplex Cellular) system has the potential to ideally double the communication capacity without additional spectral resources. Meanwhile, recently reported studies related to FDC system have focused on micro-cells since they have a wide consensus that the large transmission power of BSs (Base Stations) in macro-cells increases self-interference and degrades performance. In this paper, we propose a DDC (Dynamic-Duplex Cellular) system that enables FD in the current LTE macro-cell and enhances communication capacity by a PFS (Proportional Fairness Scheduling)-based scheme with appropriate power control of BS and UE. Performance of the proposed DDC system is evaluated by system level simulation for dynamically adding UL (Uplink) communications to DL (Downlink) subframes under the macro-cell environment defined in the 3GPP documents. The simulation results demonstrate that the proposed DDC scheme can increase UL user throughput without degrading DL user throughput while maintaining the fairness with regard to individual UE throughput.
DHCNN for Visibility Estimation in Foggy Weather Conditions

This paper proposes a new method to estimate visibility range in strong foggy weather conditions on a basis of the Deep Hybrid Convolutional Neural Network (DHCNN). Our method is designed to estimate visibility distance from a digital camera in real-time but by way of using deep networks, it becomes a more challenging task to achieve outcomes quickly. In addition to this, prior to making any prediction, the model needs to pre-process each input so it will produce the desired results. As a consequence, our implemented prototype consists of two main stage: pre-processing inputs and classifier. Each of those stages concatenated sequentially. From the outer perspective, this demonstrates our model's architecture very deep and computationally costly. However, these two stages make our model more robust and help to learn only useful features from inputs. Since the first pre-processing stage identifies Region of Interest (ROI) and removes redundant parts from a high-resolution image and sends forward to classifier just ROI part in lower resolution. We witnessed great accuracy in estimating visibility on not only heavy foggy images but also the classification of hazy images fulfilled very accurately.