Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatio-Temporal Defect Generation Process in Irradiated HfO2 MOS Stacks: Correlated Versus Uncorrelated Mechanisms

In this paper, we analyze the dependence of the Weibull slope (ß) extracted from TDDB tests on HfO2 MOS capacitors (MOSCAPs) on the initial density of defects artificially induced by carefully tuned micro beam irradiation experiments with different carbon dosages. The consistent experimental trend of reducing $p$ with increasing defect density was reproducible only with physics-based breakdown simulations that considered correlated defect generation in HfO2 and localized damage (partial percolation paths) traces created by the impinging ions. Scenarios of spatially random initial defect distribution and random stress-induced defect generation (in space and time) could not explain the experimental trends, confirming that correlated defect generation does exist in HfO2 thereby altering the conventional understanding of TDDB by quite a bit.
Power-Aware Allocation of Fault-Tolerant Multirate AUTOSAR Applications

Software-to-hardware allocation plays an important role in the development of resource-constrained automotive embedded systems that are required to meet timing, reliability and power requirements. This paper proposes an Integer Linear Programming optimization approach for the allocation of fault-tolerant embedded software applications that are developed using the AUTOSAR standard. The allocation takes into account the timing and reliability requirements of the multirate software applications and the heterogeneity of their execution platforms. The optimization objective is to minimize the total power consumption of the applications that are distributed over multiple computing units. The proposed approach is evaluated using a range of different software applications from the automotive domain, which are generated using the real-world automotive benchmark. The evaluation results indicate that our proposed allocation approach is effective while meeting the timing, reliability, and power requirements of the considered automotive software applications.
Low-Frequency Noise Measurements to Characterize Cu-Electromigration Down to 44nm Metal Pitch

This paper demonstrates the deteriorating electromigration (EM) reliability of Cu interconnects when scaling the metal pitch from 54 to 44nm. The study is carried out using a new EM test method based on low-frequency noise. Both EM lifetime and activation energy are found to decrease when scaling the line-width from 32 to 22nm. The decreasing activation energy is attributed to an increased number of grain boundaries and therefore enhanced grain boundary diffusion in narrow lines. The paper also shows that EM lifetimes can be qualitatively predicted based on the data obtained from LFN measurements.
Reliability Limiting Defects in MOS Gate Oxides: Mechanisms and Modeling Implications

Oxygen vacancies in SiO2 and dangling silicon bonds at the Si/SiOΣ2 interface are the most significant gate-dielectric defects that limit the performance, reliability, and radiation response of MOS devices. This paper reviews experimental and computational results that provide insight into the microstructure and formation of defects in SiO2. These results are critical to models of MOS total-ionizing-dose radiation response, low-frequency (1/$f$) noise, and bias-temperature instabilities. Complex responses are illustrated for MOS devices with SiO2and high-K dielectrics, as well as AlGaN/GaN HEMTs, during simultaneous or sequential irradiation and high-field stress, emphasizing the universality and significance of this issue.
Analysis of Output Current Ripple rms in Multiphase Drives Using Space Vector Approach

Multiphase variable-speed drives, supplied from two-level voltage source inverters (VSIs), are nowadays considered for various industrial applications. Although numerous pulsewidth modulation (PWM) schemes for multiphase VSIs, aimed at sinusoidal output voltage generation, have been developed, no detailed analysis of the impact of these modulation schemes on the output current ripple has ever been reported. This paper presents a comprehensive analytical analysis and comparison of the output current ripple caused by the application of three different continuous PWM schemes, using a five-phase VSI as an example. Main properties of sinusoidal PWM, fifth harmonic injection PWM, and space vector PWM are elaborated and analyzed using the harmonic flux concept. Space vector theory is applied in the analysis. As a result, harmonic distortion factors are obtained for each PWM scheme. Theoretical considerations are verified by simulations and experimental investigation using a custom-designed five-phase VSI-fed induction motor drive.
Iron-Induced Turnover of the Arabidopsis IRON-REGULATED TRANSPORTER1 Metal Transporter Requires Lysine Residues1[W][OA]

Iron is an essential micronutrient but is toxic if accumulated at high levels. Thus, iron uptake and distribution in plants are controlled by precise regulatory mechanisms. IRON-REGULATED TRANSPORTER1 (IRT1) is the major high affinity iron transporter responsible for iron uptake from the soil in Arabidopsis (Arabidopsis thaliana). Previously, we showed that IRT1 is subject to posttranscriptional regulation; when expressed from the constitutive cauliflower mosaic virus 35S promoter, IRT1 protein accumulates only in iron-deficient roots. IRT1 contains an intracellular loop that may be critical for posttranslational regulation by metals. Of particular interest are a histidine (His) motif (HGHGHGH) that might bind metals and two lysine residues that could serve as attachment sites for ubiquitin. We constructed a set of mutant IRT1 alleles: IRT1H154Q, IRT1H156Q, IRT1H158Q, IRT1H160Q, IRT14HQ (quadruple His mutant), IRT1K146R, IRT1K171R, and a double mutant (IRT1K146R,K171R). Mutation of the His or lysine residues did not eliminate the ability of IRT1 to transport iron or zinc. Expression of each of the IRT1 variants and an IRT1intact construct in plants from the 35S promoter revealed that either K146 or K171 is required for iron-induced protein turnover, and 35S-IRT1K146R,K171R plants contain higher levels of iron as compared to 35S-IRT1 and wild type. Furthermore, accumulation of metals in 35S-IRT1K146R,K171R plants was not associated with an increase in ferric chelate reductase activity; this result indicates that, at least under conditions when iron is abundant, reduction of ferric iron may not be the rate-limiting step in iron uptake by strategy I plants such as Arabidopsis.
BCFinder: A Lightweight and Platform-Independent Tool to Find Third-Party Components in Binaries

Open source movement boosts several open source communities and millions of open source repositories (repos) are available on these communities. Consequently, component-based development and code reuse greatly improve the efficiency of software development. However, they can also bring some problems, such as license violation and security weaknesses. While code reuse detection has been extensively studied in source form, third-party components detection for software in binary form especially based on large scale database like Github has been less researched. In this paper, we take a series of data cleaning processes to get filtered 22K C/C++ repos on Github. We extend the code reuse detection for binaries against such a large-scale data set and design a system called BCFinder as an assistant tool for binary analysis. BCFinder finds third-party components in binaries automatically by feature matching. We evaluate BCFinder with a number of real-word binary programs across platform and compiling configurations. Experiments show that BCFinder is an effective supplementary tool for binary analysis. BCFinder is, by far, the first lightweight, rapid and platform-independent tool to detect component reuse in binaries against a large-scale data base like Github.
Understanding the Reputation Differences between Women and Men on Stack Overflow

Women are underrepresented in the software development workforce which provides many well-paying jobs and career advancements. Many women utilize various online communities to gain access to technical fields. Among many such online communities, online programming Q&A communities are particularly important by serving as knowledge bases for self-directed informal learning and providing opportunities for individuals to exhibit their credible, verifiable expertise for career opportunities and advancements. However, these Q&A communities are often male-oriented, hence may create barriers for women to participate and earn reputations. In this paper, we aim to develop an understanding towards how male and female developers participate in the online Q&A communities and how their participation impacts their successfulness in terms of reputations. Using the data from the Stack Overflow, we quantitative examine the differences in behavior and outcomes between men and women. Our analyses reveal significant differences in how men and women participate in the online Q&A community and how successful they are. For example, women have much lower reputations on average which are mostly resulting from women and men's differences in their activities. We further discuss the potential design implications.
Contextual Interference Effect: Elaborative Processing or Forgetting—Reconstruction? A Post Hoc Analysis of Transcranial Magnetic Stimulation—Induced Effects on Motor Learning

The elaborative-processing and forgetting-reconstruction hypotheses are the 2 principal explanations for the contextual interference (CI) effect. The present authors' purpose was to identify which of these 2 hypotheses better accounts for the CI effect. They synchronized single transcranial magnetic stimulation (TMS) pulses to each intertrial interval to modulate information processing during Blocked and Random Practice conditions. Participants practiced 3 arm tasks with either a Blocked or Random Practice order. The 3 stimulation conditions (No TMS, TMS, Sham TMS) by 2-practice order (Blocked, Random) between-participant design resulted in 6 experimental groups. Without TMS, motor learning increased under Random Practice. With TMS, this learning benefit diminished. These results support the elaborative-processing hypothesis by showing that perturbing information processing, evoked by Random Practice, deteriorates the learning benefit. Unlike the prediction of the forgetting—reconstruction hypothesis, adding perturbation during Blocked Practice did not significantly enhance motor learning.
Feature-Enhanced Surfaces from Incomplete Point Cloud with Segmentation and Curve Skeleton Information

Raw data of point cloud is often noisy and with topological defects (such as holes), which cause problems including faulty connection and inaccurate structure. As a result, the surface reconstruction of point cloud data is a highly challenging problem. This work proposes a novel method, which improves the surface quality compared with existing methods. Our method combines both the local detailed features and the global topological information during the reconstruction process. To facilitate the feature refinement, we first pre-process the point cloud data by relocating each point, upsampling the point data, and optimizing normals to enhance the features and geometric details. We then identify the topological information by segmenting the geometry and constructing curve skeletons for each part and guide the surface reconstruction with the skeletons by minimal user interaction. We demonstrate the effectiveness of our methods with various examples, where our reconstruction can fill out missing data and preserve sharp features.
Simulation and Characterization of Genetic Algorithm Implemented on MPPT for PV System under Partial Shading Condition

On the field of photovoltaic powered system study, there is maximum power point tracking (MPPT) keeps the energy transfer on its peak condition. This condition is on a P-V curve where the characteristic depends on temperature of PV surface and irradiation level. Many algorithms have been established to this MPPT technique including algorithm based on artificial intelligence. One of the algorithms is genetic algorithm (GA) as heuristic algorithm that ran by emulate the process of natural selection. This algorithm application is conducted for MPPT technique. This research was conducted by making model to compare GA to the conventional one, perturb and observe (PO) being used as analytical method. Moreover, correlation analysis was conducted to analyze the characteristics of the GA to MPPT technique. The results of this research presented that the genetic algorithm applied to MPPT had worthy met accuracy on tracking and power output. Whilst correlation test displayed that parameter of individual quantity and generation quantity influenced importantly to the escalation of tracking accuracy and power output.
Applying Gamification and Posing to Software Development

Background: The influence of each developer's performance on a project's results (e.g., total software development effort) cannot be ignored. To enhance the performance of each developer, there are various approaches such as improving software engineering education and creating development support tools. In this study, we selected gamification and posing (power posing) and evaluated the effects of these approaches on coding. They are expected to improve the mental state and the motivation of developers. Aim: Select which method (gamification or posing) should be used to enhance developers' work efficiency. Method: Subjects created programs based on given specifications. Group A (Gamification was applied to a task) and group B (Posing was applied to a task) were made. We evaluated the coding time of each group. Results: Gamification was more effective than posing for reducing coding time.
The association between gestational diabetes mellitus and first trimester aneuploidy screening markers

Objective To establish whether maternal serum first trimester concentrations of PAPP-A and free hCGβ are altered in pregnancies that subsequently are diagnosed by an oral glucose tolerance test (OGTT) with gestational diabetes mellitus (GDM). Methods Over the period 2009 and 2011, the results for women who had first trimester screening for aneuploidy were matched with those having an oral glucose tolerance test at 22–26 weeks for suspected GDM. Free hCGβ, PAPP-A and NT MoMs were compared amongst the group having an OGTT with confirmed GDM and those in which GDM was not confirmed. A second comparison group consisted of all non-aneuploidy singleton pregnancies in which no OGTT was performed. Results During the three-year period, 27,660 singleton pregnancies were screened of which 7429 cases had an OGTT of which 870 cases were classed as GDM by WHO criteria. There was a significant 7–9% reduction in both PAPP-A and free hCGβ MoM in the GDM group compared with either the OGTT non-GDM group or the remaining pregnancies with no known risk factors for evidence of GDM. There was no difference in the NT measurements. Conclusions First trimester concentrations of PAPP-A and free hCGβ are reduced in pregnancies that subsequently are diagnosed with GDM and may be useful in further screening algorithms for this disorder although the sensitivity alone is quite poor.
Why Did This Reviewed Code Crash? An Empirical Study of Mozilla Firefox

Code review, i.e., the practice of having other team members critique changes to a software system, is a pillar of modern software quality assurance approaches. Although this activity aims at improving software quality, some high-impact defects, such as crash-related defects, can elude the inspection of reviewers and escape to the field, affecting user satisfaction and increasing maintenance overhead. In this research, we investigate the characteristics of crash-prone code, observing that such code tends to have high complexity and depend on many other classes. In the code review process, developers often spend a long time on and have long discussions about crash-prone code. We manually classify a sample of reviewed crash-prone patches according to their purposes and root causes. We observe that most crash-prone patches aim to improve performance, refactor code, add functionality, or fix previous crashes. Memory and semantic errors are identified as major root causes of the crashes. Our results suggest that software organizations should apply more scrutiny to these types of patches, and provide better support for reviewers to focus their inspection effort by using static analysis tools.
A Novel optimization Technique for FOPIλDμ Controller Applied in MIMO System

This study demonstrates a non integer order proportional integral derivative controller based on parameter design tuning method to achieve the best optimized response. The methodology was to decouple a multiple input multiple output twin rotor system into single input single output and apply fractional order PID (FOPID) to the bifurcated models. The objective of the system was to fulfill the response characteristics based on classical PID controller. A method named as Simulink Response optimization (SRO) is introduced for tuning the desired output result which is shown in this paper. λ and μ are the two new fractional operators added to the PID controller to get a better performance.
Comprehensive Diagnosis of Bacterial Infection Associated with Acute Cholecystitis Using Metagenomic Approach

Acute cholecystitis (AC), which is strongly associated with retrograde bacterial infection, is an inflammatory disease that can be fatal if inappropriately treated. Currently, bacterial culture testing, which is basically recommended to detect the etiological agent, is a time-consuming (4–6 days), non-comprehensive approach. To rapidly detect a potential pathogen and predict its antimicrobial susceptibility, we undertook a metagenomic approach to characterize the bacterial infection associated with AC. Six patients (P1–P6) who underwent cholecystectomy for AC were enrolled in this study. Metagenome analysis demonstrated possible single or multiple bacterial infections in four patients (P1, P2, P3, and P4) with 24-h experimental procedures; in addition, the CTX-M extended-spectrum ß-lactamase (ESBL) gene was identified in two bile samples (P1 and P4). Further whole genome sequencing of Escherichia coli isolates suggested that CTX-M-27-producing ST131 and CTX-M-14-producing novel-ST were identified in P1 and P4, respectively. Metagenome analysis of feces and saliva also suggested some imbalance in the microbiota for more comprehensive assessment of patients with AC. In conclusion, metagenome analysis was useful for rapid bacterial diagnostics, including assessing potential antimicrobial susceptibility, in patients with AC.
Hyperdimensional Computing With Local Binary Patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings

<italic>Objective:</italic> We develop a fast learning algorithm combining symbolic dynamics and brain-inspired hyperdimensional computing for both seizure onset detection and identification of ictogenic (seizure generating) brain regions from intracranial electroencephalography (iEEG). <italic>Methods:</italic> Our algorithm first transforms iEEG time series from each electrode into symbolic local binary pattern codes, from which a <italic>holographic distributed representation</italic> of the brain state of interest is constructed across all the electrodes and over time in a hyperdimensional space. The representation is used to quickly learn from few seizures, detect their onset, and identify the spatial brain regions that generated them. <italic>Results:</italic> We assess our algorithm on our dataset that contains 99 short-time iEEG recordings from 16 drug-resistant epilepsy patients being implanted with 36–100 electrodes. For the majority of the patients (ten out of 16), our algorithm quickly learns from one or two seizures and perfectly (100%) generalizes on novel seizures using <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-fold cross-validation. For the remaining six patients, the algorithm requires three to six seizures for learning. Our algorithm surpasses the state-of-the-art including deep learning algorithms by achieving higher specificity (94.84% versus 94.77%) and macroaveraging accuracy (95.42% versus 94.96%), and 74× lower memory footprint, but slightly higher average latency in detection (15.9 s versus 14.7 s). Moreover, the algorithm can reliably identify (with a <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>-value <inline-formula><tex-math notation="LaTeX">$< 0.01$</tex-math></inline-formula>) the relevant electrodes covering an ictogenic brain region at two levels of granularity: cerebral hemispheres and lobes. <italic>Conclusion and significance:</italic> Our algorithm provides: 1) a unified method for both learning and classification tasks with end-to-end binary operations; 2) one-shot learning from seizure examples; 3) linear computational scalability for increasing number of electrodes; and 4) generation of transparent codes that enables post-translational support for clinical decision making. Our source code and anonymized iEEG dataset are freely available at <uri>http://ieeg-swez.ethz. ch</uri>.
Effects of barium on stimulus‐induced rises of [K+]o in human epileptic non‐sclerotic and sclerotic hippocampal area CA1

In the hippocampus of patients with therapy‐refractory temporal lobe epilepsy, glial cells of area CA1 might be less able to take up potassium ions via barium‐sensitive inwardly rectifying and voltage‐independent potassium channels. Using ion‐selective microelectrodes we investigated the effects of barium on rises in [K+]o induced by repetitive alvear stimulation in slices from surgically removed hippocampi with and without Ammon's horn sclerosis (AHS and non‐AHS). In non‐AHS tissue, barium augmented rises in [K+]o by 147% and prolonged the half time of recovery by 90%. The barium effect was reversible, concentration dependent, and persisted in the presence of α‐amino‐3‐hydroxy‐5‐methyl‐4‐isoxazolepropionate (AMPA), N‐methyl‐d‐aspartate (NMDA) and γ‐aminobutyric acid [GABA(A)] receptor antagonists. In AHS tissue, barium caused a decrease in the baseline level of [K+]o. In contrast to non‐AHS slices, in AHS slices with intact synaptic transmission, barium had no effect on the stimulus‐induced rises of [K+]o, and the half time of recovery from the rise was less prolonged (by 57%). Under conditions of blocked synaptic transmission, barium augmented stimulus‐induced rises in [K+]o, but only by 40%. In both tissues, barium significantly reduced negative slow‐field potentials following repetitive stimulation but did not alter the mean population spike amplitude. The findings suggest a significant contribution of glial barium‐sensitive K+‐channels to K+‐buffering in non‐AHS tissue and an impairment of glial barium‐sensitive K+‐uptake in AHS tissue.