Miracle-dz/UniFAR-FTUs huggingface dataset:
Hole-like Fermi surface in the overdoped non-superconducting Bi1.8 Pb0.4 Sr2CuO6+δ

In high-temperature cuprate superconductors, the anti-ferromagnetic spin fluctuations are thought to have a very important role in naturally producing an attractive interaction between the electrons in the d-wave channel. The connection between superconductivity and spin fluctuations is expected to be especially consequential at the overdoped end point of the superconducting dome. In some materials, that point seems to coincide with a Lifshitz transition, where the Fermi surface changes from the hole-like centered at to the electron-like, centered at the Γ point causing a loss of large momentum anti-ferromagnetic fluctuations. Here, we study the doping dependence of the electronic structure of Bi1.8Pb0. 4Sr2CuO in angle-resolved photoemission and find that the superconductivity vanishes at lower doping than that at which the Lifshitz transition occurs. This requires a more detailed re-examination of a spin fluctuation scenario.
Physics-based Shadow Image Decomposition for Shadow Removal

We propose a novel deep learning method for shadow removal. Inspired by physical models of shadow formation, we use a linear illumination transformation to model the shadow effects in the image that allows the shadow image to be expressed as a combination of the shadow-free image, the shadow parameters, and a matte layer. We use two deep networks, namely SP-Net and M-Net, to predict the shadow parameters and the shadow matte respectively. This system allows us to remove the shadow effects from images. We then employ an inpainting network, I-Net, to further refine the results. We train and test our framework on the most challenging shadow removal dataset (ISTD). Our method improves the state-of-the-art in terms of mean absolute error (MAE) for the shadow area by 20\%. Furthermore, this decomposition allows us to formulate a patch-based weakly-supervised shadow removal method. This model can be trained without any shadow- free images (that are cumbersome to acquire) and achieves competitive shadow removal results compared to state-of-the-art methods that are trained with fully paired shadow and shadow-free images. Last, we introduce SBU-Timelapse, a video shadow removal dataset for evaluating shadow removal methods.
Solving Mixed Integer Programs Using Neural Networks

Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics developed with decades of research to solve large-scale MIP instances encountered in practice. Machine learning offers to automatically construct better heuristics from data by exploiting shared structure among instances in the data. This paper applies learning to the two key sub-tasks of a MIP solver, generating a high-quality joint variable assignment, and bounding the gap in objective value between that assignment and an optimal one. Our approach constructs two corresponding neural network-based components, Neural Diving and Neural Branching, to use in a base MIP solver such as SCIP. Neural Diving learns a deep neural network to generate multiple partial assignments for its integer variables, and the resulting smaller MIPs for un-assigned variables are solved with SCIP to construct high quality joint assignments. Neural Branching learns a deep neural network to make variable selection decisions in branch-and-bound to bound the objective value gap with a small tree. This is done by imitating a new variant of Full Strong Branching we propose that scales to large instances using GPUs. We evaluate our approach on six diverse real-world datasets, including two Google production datasets and MIPLIB, by training separate neural networks on each. Most instances in all the datasets combined have $10^3-10^6$ variables and constraints after presolve, which is significantly larger than previous learning approaches. Comparing solvers with respect to primal-dual gap averaged over a held-out set of instances, the learning-augmented SCIP is 2x to 10x better on all datasets except one on which it is $10^5$x better, at large time limits. To the best of our knowledge, ours is the first learning approach to demonstrate such large improvements over SCIP on both large-scale real-world application datasets and MIPLIB.
Optimal voluntary and mandatory insect repellent usage and emigration strategies to control the chikungunya outbreak on Reunion Island

In 2005, a chikungunya virus outbreak devastated the tropical island of Reunion, infecting a third of the total population. Motivated by the Reunion Island case study, we investigate the theoretic potential for two intervention measures under both voluntary and mandatory protocols to control a vector-borne disease when there is risk of the disease becoming endemic. The first measure uses insect repellent to prevent mosquito bites, while the second involves emigrating to the neighboring Mauritius Island to avoid infection. There is a threshold on the cost of using repellent above which both voluntary and mandatory regimes find it optimal to forgo usage. Below that threshold, mandatory usage protocols will eradicate the disease; however, voluntary adoption leaves the disease at a small endemic level. Emigrating from the island to avoid infection results in a tragedy-of-the-commons effect: while being potentially beneficial to specific susceptible individuals, the remaining islanders paradoxically face a higher risk of infection. Mandated relocation of susceptible individuals away from the epidemic is viable only if the cost of this relocation is several magnitudes lower than the cost of infection. Since this assumption is unlikely to hold for chikungunya, it is optimal to discourage such emigration for the benefit of the entire population. An underlying assumption about the conservation of human-vector encounter rates in mosquito biting behavior informs our conclusions and may warrant additional experimental verification.
EQUILIBRIUM DISTRIBUTION OF METAL-FLUORIDE COMPLEXES

Equilibrium data for the formation of metal complexes with inorganlc ligands are compiled in the form of distribution diagrams (fraction of each species present vs ligand concentration), with the necessary calculations and plotting performed by means of a high-speed computer. Diagrams of Be, VO, UO/sub 2/, Al, Ga, In, Sc, Y, Cr, Fe, TiO, Zr, Hf, and Th fluoride complexes are illustrated. (P.C.H.)
Automatic Selection of Context Configurations for Improved (and Fast) Class-Specific Word Representations

Recent work has demonstrated that state-of-the-art word embedding models require different context types to produce high-quality representations for different word classes such as adjectives (A), verbs (V), and nouns (N). This paper is concerned with identifying contexts useful for learning A/V/N-specific representations. We introduce a simple yet effective framework for selecting class-specific context configurations that yield improved representations for each class. We propose an automatic A* style selection algorithm that effectively searches only a fraction of the large configuration space. The results on predicting similarity scores for the A, V, and N subsets of the benchmarking SimLex-999 evaluation set indicate that our method is useful for each class: the improvements are 6% (A), 6% (V), and 5% (N) over the best previously proposed context type for each class. At the same time, the model trains on only 14% (A), 26.2% (V), and 33.6% (N) of all dependency-based contexts, resulting in much shorter training time.
Elon

We present a new mechanism called Elon for enabling efficient and long-term reprogramming in wireless sensor networks. Elon reduces the transferred code size significantly by introducing the concept of replaceable component. It avoids the cost of hardware reboot with a novel software reboot mechanism. Moreover, it significantly prolongs the reprogrammable lifetime (i.e., the time period during which the sensor nodes can be reprogrammed) by avoiding flash writes for TelosB nodes. Experimental results show that Elon transfers up to 120--389 times less information than Deluge, and 18--42 times less information than Stream. The software reboot mechanism that Elon applies reduces the rebooting cost by 50.4%--53.87% in terms of beacon packets, and 56.83% in terms of unsynchronized nodes. In addition, Elon prolongs the reprogrammable lifetime by a factor of 3.3.
CNT–PVDF composite flow-through electrode for single-pass sequential reduction–oxidation

In this study, a five-layer electrochemical CNT–PVDF filter that is thin, flexible, and stable is demonstrated to be effective and efficient for single-pass nitrobenzene mineralization by sequential reduction–oxidation. Key to the technology is development of a CNT–PVDF membrane that is mechanically stable, electrically conductive, and non-Faradaic, which is used to prevent CNT release from and electrochemical degradation of the Faradaic CNT electrodes. A five-layer electrochemical filter: (1) conductive & protective CNT–PVDF, (2) Faradaic CNT electrode, (3) insulating PVDF separator, (4) Faradaic CNT electrode, and (5) conductive & protective CNT–PVDF is formed by mechanical press. At a total cell potential of 4 V, the sequential electrochemical reduction–oxidation process is able to reduce >99% of the influent nitrobenzene to aniline then oxidize >80% of the aniline to non-aromatic products (mostly carbon dioxide) in a single-pass (∼5 s hydraulic residence time). The mineralization current efficiency is ∼20%, total cell potential is completely (>99%) distributed to the two electrodes, and the energy requirement is ∼36000 kJ per mole of nitrobenzene degraded.
OverCode

In MOOCs, a single programming exercise may produce thousands of solutions from learners. Understanding solution variation is important for providing appropriate feedback to students at scale. The wide variation among these solutions can be a source of pedagogically valuable examples and can be used to refine the autograder for the exercise by exposing corner cases. We present OverCode, a system for visualizing and exploring thousands of programming solutions. OverCode uses both static and dynamic analysis to cluster similar solutions, and lets teachers further filter and cluster solutions based on different criteria. We evaluated OverCode against a nonclustering baseline in a within-subjects study with 24 teaching assistants and found that the OverCode interface allows teachers to more quickly develop a high-level view of students' understanding and misconceptions, and to provide feedback that is relevant to more students' solutions.
Gamma elements as protomental representations: Suggestions for expanding W. R. Bion’s theory of elements

ABSTRACT W. R. Bion developed various models and theories to describe the formation of mental representations. This article introduces an expansion of the model of alpha and beta elements by a new category, termed “gamma elements”. Despite being transformable, gamma elements are characterized by an excessive feeling of reality, such that the subject must evacuate them. We are dealing here with visions, pseudohallucinations, scenarios in nightmares that are as terrible as they are sensorily charged, flashbacks, or overwhelming physical feelings interspersed with hypochondriacal anxieties. Using the “theory of maternal semiotics” (Kristeva), the author traces how the child’s projectiles, i.e. beta experiences projected into the maternal container, are transformed into alpha and gamma elements. In this respect, gamma elements are more or less precise indicators of the analytic process: they indicate the extent to which the analysand is capable of enduring and transforming frustrating experiences in the here-and-now. The expansion of Bion’s model of elements, therefore, has direct clinical relevance, in that gamma elements can be understood both as imaginative creations and as indications of disorganizing processes.
Liquid/Solid Mass Transfer in Fixed Beds

The liquid/solid mass transfer behavior of a fixed bed of cylinders was studied using the diffusion-controlled dissolution of copper in acidified potassium dichromate. Variables studied were solution velocity and cylinder diameter. For a steady flow, the data were correlated for the conditions 25 < Re < 600 and Sc = 990 by the equation Sh = 2.65 Sc 0.33 Re 0.5 . A comparison between the present data and previously obtained data for a fixed bed of Raschig rings has revealed that cylinder packing produces higher rates of mass transfer than Raschig rings.
N-terminal pro-brain natriuretic peptide and sudden cardiac death in hypertrophic cardiomyopathy

Objective Elevated levels of N-terminal pro-brain natriuretic peptide (NT-proBNP) are associated with heart failure-related death in hypertrophic cardiomyopathy (HCM), but the relationship between NT-proBNP level and sudden cardiac death (SCD) in HCM remains undefined. Methods The study prospectively enrolled 977 unrelated patients with HCM with available NT-proBNP results who were prospectively enrolled and followed for 3.0±2.1 years. The Harrell’s C-statistic under the receiver operating characteristic curve was calculated to evaluate discrimination performance. A combination model was constructed by adding NT-proBNP tertiles to the HCM Risk-SCD model. The correlation between log NT-proBNP level and cardiac fibrosis as measured by late gadolinium enhancement (LGE) or Masson’s staining was analysed. Results During follow-up, 29 patients had SCD. Increased log NT-proBNP levels were associated with an increased risk of SCD events (adjusted HR 22.27, 95% CI 10.93 to 65.63, p<0.001). The C-statistic of NT-proBNP in predicting SCD events was 0.80 (p<0.001). The combined model significantly improved the predictive efficiency of the HCM Risk-SCD model from 0.72 to 0.81 (p<0.05), with a relative integrated discrimination improvement of 0.002 (p<0.001) and net reclassification improvement of 0.67 (p<0.001). Furthermore, log NT-proBNP levels were significantly correlated with cardiac fibrosis as detected either by LGE (r=0.257, p<0.001) or by Masson’s trichrome staining in the myocardium (r=0.198, p<0.05). Conclusion NT-proBNP is an independent predictor of SCD in patients with HCM and may help with risk stratification of this disease.
Use of Ores and Industrial Products As Oxygen Carriers in Chemical-Looping Combustion

A screening of Fe- and Mn-based ores and industrial products was made in order to identify suitable low-cost materials that could be used as oxygen carriers in chemical-looping combustion (CLC). A laboratory fluidized bed reactor system, simulating chemical-looping combustion by exposing the sample to alternating reducing and oxidizing conditions, was used. Fifteen grams of each material with a particle size of 125−180 μm was exposed to a flow of 450 mLn/min of either methane or syngas (50% CO, 50% H2) during reduction. During the oxidizing phase to a flow of 1000 mLn/min, 5% O2 in nitrogen was used. All materials had a high reactivity with syngas. Some materials such as the Mn-based Colormax and the Fe-based Glodskal had also a high reactivity with methane making them possible candidates for CLC with gaseous fuel. Some of the materials, especially the Mn-based ones, showed poor mechanical stability and poor fluidizing properties. Roughly half of the Fe-based materials, but only one of the Mn-based materi.
VUV to UV laser spectroscopy of atomic species in processing plasmas

Laser absorption spectroscopy in the vacuum ultraviolet range (VUV-LAS) has been applied for the quantitative measurement of light atomic species and halogen atoms, which are of importance in various plasma processing technologies. So far, measurements of F and C atoms have been made with this method. For the generation of a VUV laser, a two-photon resonance four-wave mixing method has been used in the sum frequency generation scheme or difference frequency generation scheme. Wavelength tunability is essential when the background absorption exists due to the parent molecules and other species produced in plasmas. The absolute density of target atoms can be derived from the integral over the line profile converted into the absorption coefficient. Since the VUV-LAS measurement can yield only the values averaged over the line-of-sight, some complementary methods such as two-photon absorption laser induced fluorescence (TALIF) and optical emission actinometry are used for the spatial resolution. Examples of experimental data are given for F and C (VUV-LAS) and for O, N and H (TALIF) with a brief discussion.
The Effects of Social Networking on Marital Relationships and Couples Quality of Life (Case Study: Couples of Social Networking Members in Shiraz)

Objective: The purpose of this study was to investigate the effect of social networks on marital relationship quality of life among couples of social network members in Shiraz in 2018. Methodology: For purposes of this research, this research was a applied and descriptive-correlative method. The statistical population of this study was all couples of social networking members in Shiraz. The sample was 425 individuals selected as sample size. Results: The instrument used in this research was researcher made marital relationship questionnaire and the quality of life and Sherborne to analyze the data, descriptive statistics (mean, standard deviation) and inferential (Pearson correlation coefficient) and SPSS software were used. Reliability of the questionnaires was 0.83 and 0.76, respectively, through the coefficient of kronbach's alpha coefficient. Conclusion: The findings of this study showed that the use of social networks has a significant effect on marital relationships and quality of life of couples in Shiraz.
An Open-Source Scalable Thermal and Power Controller for HPC Processors

In the last decade, high performance multi-core processor designs have followed an increase in number of cores, interfaces, heterogeneity and System-on-chip (SoC) complexity. HPC applications also require tailored chip designs with specific operating points and performance indexes. In this scenario, an advanced and configurable Power Controller System (PCS) is necessary to meet power and thermal constraints, without the necessity of static ultra-conservative margins on the operating points. In this paper, we propose an open-source PCS design, based on a parallel ultra-low power microcontroller with RISC-V cores, and an open-source software environment based on a Real-time operating system (RTOS) with a configurable Power-thermal control algorithm. Considering a 1ms control interval, the overhead of the RTOS is about 6% of the cycles in the nominal case. The control algorithm is able to limit temperature and power consumption within given bounds, while maximizing performance. The PCS is able to control up to 76 different cores/computing units with headroom for larger core counts.
A DIN Spec 91345 RAMI 4.0 Compliant Data Pipelining Model: An Approach to Support Data Understanding and Data Acquisition in Smart Manufacturing Environments

Today, data scientists in the manufacturing domain are confronted with various communication standards, protocols and technologies to save and transfer various kinds of data. These circumstances makes it hard to understand, find, access and extract data needed for use case depended applications. One solution could be a data pipelining approach enforced by a semantic model which describes smart manufacturing assets itself and the access to their data along their life-cycle. Many research contributions in smart manufacturing already came out with with reference architectures like the RAMI 4.0 or standards for meta data description or asset classification. Our research builds upon these outcomes and introduces a semantic model based DIN Spec 91345 (RAMI 4.0) compliant data pipelining approach with the smart manufacturing domain as exemplary use case. This paper has a focus on the developed semantic model used to enable an easy data exploration, finding, access and extraction of data, compatible with various used communication standards, protocols and technologies used to save and transfer data.
DeMux Controlled Sensor Based Smart Irrigation System

The increase in pollution levels in water bodies has made water scarce for agricultural needs. Technology in irrigation not only overcomes this rising problem but also increases the efficiency of the farmers, thereby increasing the Gross Domestic Product of a country. This paper aims at using IoT in indoor irrigation methods to control the wastage of water and increase the efficiency of the soil. The proposed system reads the sensor data and handles water automatically (using relay motors and sprinklers) through a Control System board. A WiFi module is used for communication between sensors and base stations. Continuous detected information dealing with and showing on the server is cultivated using graphical UI. Remote checking of the field water system framework decreases human intercession and permits remote observing and controlling on the android phone. Distributed computing is an alluring answer for the huge measure of information created by the remote sensor arrangement. This paper proposes and assesses a cloud-based remote correspondence framework to screen and control a lot of sensors and actuators to evaluate the plants’ water needs. A light source powered by solar energy is used to manage the temperature of the soil according to season/crop needs.
Analog Circuit Incipient Fault Diagnosis from Raw Signals using Multi-Layer Extreme Learning Machine

Analog circuits play important roles in modern electronic systems. Incipient fault diagnosis of analog circuits is a recognized challenging research direction due to the difficulty of fault feature extraction and identification. This paper proposes an early fault diagnosis algorithm for analog circuits based on multilayer extreme learning machine (ML-ELM). Its basic idea comes from the Auto-encoder(AE) and the Extreme Learning Machine (ELM). The proposed method which combines the characteristics of both, so it has the ability of feature extraction and fast training speed. In this method, the time domain sampling signal of the circuit can be directly used as the fault sample, and the unsupervised feature extraction is carried out layer by layer through the deep network, the diagnosis results are obtained after the supervised classification by the traditional ELM algorithm. The whole process does not rely on multi-step iterative and reverses fine-tuning. The experimental results of the Sallen-Key band-pass filter circuit and Leapfrog low-pass filter circuit show that the method has not only high diagnosis accuracy, but also fast diagnosis speed. Because this method gets rid of the tedious manual feature extraction and complex signal processing, it improves the training efficiency and makes the method more universal.
Mental Workload Estimation Using EEG

Mental workload contributes considerably to the outcome or the performance of any task. The concern of human workload increases during a human-machine collaboration task or in a multitasking environment. This paper presents a comparative study of machine learning algorithms used to estimate workload using Electroencephalography (EEG) data. An open-access EEG dataset acquired during a “simultaneous capacity (SIMKAP) experiment” and “no task” is used to create and validate models for binary classification of workload as present and absent respectively. The paper presents an implementation of various classification models that use EEG data to predict the workload. In this paper, implementation for KNN classifier (57.3%), Random Forest classifier (57.19%), MLP network classifier (58.2%), CNN+ LSTM network classifier (58.68%), and LSTM network classifier (61.08%) has been reported. The paper can be further extended to study operator workload in real-time using a brain-computer interface paradigm for any kind of task in a real-world application. The workload classification can be further used in human-machine tasks to decide task allocation between the system to achieve optimal performance in a complex critical system.