Miracle-dz/UniFAR-FTUs huggingface dataset:
How Trans-Inclusive Are Hackathons?

Hackathons can be fun! However, for the transgender community and other minorities, hackathons can have an uncomfortable atmosphere. In this article, we surveyed 44 LGBTQIA+ experienced in hackathons and interviewed seven transgender participants. By understanding their needs and challenges, we introduce five recommendations to make hackathons more inclusive.
Optical spin control and coherence properties of acceptor bound holes in strained GaAs

Hole spins in semiconductors are a potential qubit alternative to electron spins. In nuclear-spin-rich host crystals like GaAs, the hyperfine interaction of hole spins with nuclei is considerably weaker than that for electrons, leading to potentially longer coherence times. Here we demonstrate optical pumping and coherent population trapping for acceptor-bound holes in a strained GaAs epitaxial layer. We find $\mu$s-scale longitudinal spin relaxation time T$_1$ and an inhomogeneous dephasing time T$_2^*$ of $\sim$7~ns. We attribute the spin relaxation mechanism to a combination effect of a hole-phonon interaction through the deformation potentials and a heavy-hole light-hole mixing in an in-plane magnetic field. We attribute the short T$_2^*$ to g-factor broadening due to strain inhomogeneity. T$_1$ and T$_2^*$ are quantitatively calculated based on these mechanisms and compared with the experimental results. While the hyperfine-mediated decoherence is mitigated, our results highlight the important contribution of strain to relaxation and dephasing of acceptor-bound hole spins.
The Michigan infrared test thermal ELT N-band (MITTEN) cryostat

We introduce the Michigan Infrared Test Thermal ELT N-band (MITTEN) Cryostat, a new facility for testing infrared detectors with a focus on mid-infrared (MIR) wavelengths (8-13 microns). New generations of large format, deep well, fast readout MIR detectors are now becoming available to the astronomical community. As one example, Teledyne Imaging Sensors (TIS) has introduced a long-wave Mercury-Cadmium-Telluride (MCT) array, GeoSnap, with high quantum efficiency (< 65 %) and improved noise properties compared to previous generation Si:As blocked impurity band (BIB) detectors. GeoSnap promises improved sensitivities, and efficiencies, for future background-limited MIR instruments, in particular with future extremely large telescopes (ELTs). We describe our new test facility suitable for measuring characteristics of these detectors, such as read noise, dark current, linearity, gain, pixel operability, quantum efficiency, and point source imaging performance relative to a background scene, as well as multiple point sources of differing contrast. MITTEN has an internal light source, and soon an accompanying filter wheel and aperture plate, reimaged onto the detector using an Offner relay. The baseline temperature of the cryostat interior is maintained is < 40 K and the optical bench maintains a temperature of 16 K using a two-stage pulse-tube cryocooler package from Cryomech. No measurable background radiation from the cryostat interior has yet been detected.
Intrusion Detection System from Machine Learning Perspective

: The excessive growth of internet and the rise of cyber security threats have led to easily vulnerable network environment. Cyber security threat ranges from simple password hacking to advanced persistent threats. To provide a secured network environment, firewall, intrusion detection system, intrusion prevention system etc., can be utilized. Intrusion Detection System (IDS) is used to monitor packets, network traffic, malicious activity etc., IDS can be used to scan ports and it can be used to generate alerts when a suspicious activity occurs in the network. This paper deals with different types of intrusion detection system attacks, datasets and tools. An overview of machine learning algorithms was performed. Machine learning life cycle from data acquisition, exploratory data analysis, data preprocessing, model generation and visualization was discussed.
GAN-based Recommendation with Positive-Unlabeled Sampling

Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products. In this work, we propose a Generative Adversarial Network based recommendation framework using a positive-unlabeled sampling strategy. Specifically, we utilize the generator to learn the continuous distribution of user-item tuples and design the discriminator to be a binary classifier that outputs the relevance score between each user and each item. Meanwhile, positive-unlabeled sampling is applied in the learning procedure of the discriminator. Theoretical bounds regarding positive-unlabeled sampling and optimalities of convergence for the discriminators and the generators are provided. We show the effectiveness and efficiency of our framework on three publicly accessible data sets with eight ranking-based evaluation metrics in comparison with thirteen popular baselines.
Brownian dynamics simulation study of self-diffusion of a charged particle in swollen counter-charged hydrogel modeled as cubic lattice

The tracer diffusion of a charged particle in an oppositely charged cubic lattice as a simplest model of charged hydrogel was studied by a Brownian dynamics simulation. The effect of the electrostatic attractive interaction between the tracer particle and the cubic lattice on the self-diffusion coefficient was mainly discussed, when the mesh size of the cubic lattice was sufficiently larger than the diameter of the particle. The charge density of the cubic lattice structure and the electrolyte concentration in the solvent were varied. The electrostatic force acting on the tracer particle was calculated using the potential screened by an electric double layer. The self-diffusion coefficient of the tracer particle significantly decreases as the charge density of the cubic lattice increases, while the increase in the electrolyte concentration in the solvent induces the disappearance of the effect of electrostatic attractive interaction on the self-diffusion coefficient, resulting into the asymptotical behavior to those observed in the non-charged cubic lattice. 2-Order reduction in the self-diffusion coefficient was obtained in this simulation and this shows the applicability of the control of diffusivity of ionic solutes in the swollen hydrogel by means of introducing ionic species covalently into the hydrogels. In the spatial distribution of the probability density of the charged tracer particle, the stochastic path along which the tracer particle tends to move was observed in the charged cubic lattice, which is caused by electrostatic potentials. The reduction of the self-diffusion coefficient in the charged cubic lattice is due to the transient entrapment at the point of local minimum of potential energy inside the stochastic paths.
Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers

The analysis of source code through machine learning techniques is an increasingly explored research topic aiming at increasing smartness in the software toolchain to exploit modern architectures in the best possible way. In the case of low-power, parallel embedded architectures, this means finding the configuration, for instance in terms of the number of cores, leading to minimum energy consumption. Depending on the kernel to be executed, the energy optimal scaling configuration is not trivial. While recent work has focused on general-purpose systems to learn and predict the best execution target in terms of the execution time of a snippet of code or kernel (e.g. offload OpenCL kernel on multicore CPU or GPU), in this work we focus on static compile-time features to assess if they can be successfully used to predict the minimum energy configuration on PULP, an ultra-low-power architecture featuring an on-chip cluster of RISC-V processors. Experiments show that using machine learning models on the source code to select the best energy scaling configuration automatically is viable and has the potential to be used in the context of automatic system configuration for energy minimisation.
Translational and Reorientational Dynamics of an Imidazolium-Based Ionic Liquid

We present results of parallel quasielastic neutron scattering (QENS) experiments and molecular dynamics numerical simulations for the dynamics of a prototype ionic liquid, 1-ethyl-3-methyl-imidazolium bromide. Differences and similarities with those from the crystal phase are also discussed. Both experiment and simulation demonstrate that, in the length and time scales being probed here (fractions of a nm and a few ps), the dynamics are dominated by activated translational diffusion in the liquid phase and reorientations of the ethyl groups in both solid and liquid.
Do Mass Media Shape Public Opinion toward China? Quantitative Evidence on New York Times with Deep Learning

Do mass media influence people's opinion of other countries? Using BERT, a deep neural network-based natural language processing model, we analyze a large corpus of 267,907 China-related articles published by The New York Times since 1970. We then compare our output from The New York Times to a longitudinal data set constructed from 101 cross-sectional surveys of the American public's views on China. We find that the reporting of The New York Times on China in one year explains 54% of the variance in American public opinion on China in the next. Our result confirms hypothesized links between media and public opinion and helps shed light on how mass media can influence public opinion of foreign countries.
A perturbation approach to optimal investment, liability ratio, and dividend strategies

We study an optimal dividend problem for an insurer who simultaneously controls investment weights in a financial market, liability ratio in the insurance business, and dividend payout rate. The insurer seeks an optimal strategy to maximize her expected utility of dividend payments over an infinite horizon. By applying a perturbation approach, we obtain the optimal strategy and the value function in closed form for log and power utility. We conduct an economic analysis to investigate the impact of various model parameters and risk aversion on the insurer's optimal strategy.
Comparing the costs of abstraction for DL frameworks

High level abstractions for implementing, training, and testing Deep Learning (DL) models abound. Such frameworks function primarily by abstracting away the implementation details of arbitrary neural architectures, thereby enabling researchers and engineers to focus on design. In principle, such frameworks could be "zero-cost abstractions"; in practice, they incur translation and indirection overheads. We study at which points exactly in the engineering life-cycle of a DL model the highest costs are paid and whether they can be mitigated. We train, test, and evaluate a representative DL model using PyTorch, LibTorch, TorchScript, and cuDNN on representative datasets, comparing accuracy, execution time and memory efficiency.
Electrochemical studies of the adsorption behavior of serum proteins on titanium

Cyclic voltammetry and electrochemical impedance spectroscopy (EIS) were used to examine the adsorption behavior of bovine serum albumin (BSA) and bovine fibrinogen on titanium in phosphate buffer pH 7.4, over the temperature range 295−343 K. It was shown that the surface charge density is directly proportional to the amount of the adsorbed protein (surface concentration), thus indicating that the adsorption is accompanied by the transfer of charge, i.e. chemisorption. On the other hand, the resulting adsorption pseudocapacitance obtained under the potentiostatic conditions not only depends on the protein surface concentration but also is a very complex function of parameters that are, in turn, dependent on structural, physical, and chemical properties of the proteins. Both techniques were shown to be very sensitive to the conformational behavior of the proteins. The adsorption of BSA onto a Ti surface resulted in a bimodal isotherm at all the temperatures studied, while the adsorption of fibrinogen resul.
Quantum teleportation and entanglement swapping with long baseline in outer space

Quantum information (QI) experiments applying quantum optics in outer space with a very long baseline may have advantages over the current Earth-bound experiments or the Earth-to-satellite experiments, because they can minimize the loss in light transmission and maximize the gain in time resolution. This future class of experiments, among them quantum teleportation and entanglement swapping, can shed light on many fundamental theoretical issues in gravitational quantum physics and relativistic QI. Regarding relativity theory, these experiments in an outer-space setting can involve observations at spacelike and timelike separations and explicate intriguing phenomena from different choices of time-slicing. Regarding QI, they may be able to ensure the causal independence of the expectation values in the Bell test. These issues are addressed in this paper with analysis and explanations.
Learning Multi-Arm Manipulation Through Collaborative Teleoperation

Imitation Learning (IL) is a powerful paradigm to teach robots to perform manipulation tasks by allowing them to learn from human demonstrations collected via teleoperation, but has mostly been limited to single-arm manipulation. However, many real-world tasks require multiple arms, such as lifting a heavy object or assembling a desk. Unfortunately, applying IL to multi-arm manipulation tasks has been challenging –asking a human to control more than one robotic arm can impose significant cognitive burden and is often only possible for a maximum of two robot arms. To address these challenges, we present MULTI-ARM ROBOTURK (MART), a multi-user data collection platform that allows multiple remote users to simultaneously teleoperate a set of robotic arms and collect demonstrations for multi-arm tasks. Using MART, we collected demonstrations for five novel two and three-arm tasks from several geographically separated users. From our data we arrived at a critical insight: most multi-arm tasks do not require global coordination throughout its full duration, but only during specific moments. We show that learning from such data consequently presents challenges for centralized agents that directly attempt to model all robot actions simultaneously, and perform a comprehensive study of different policy architectures with varying levels of centralization on our tasks. Finally, we propose and evaluate a base-residual policy framework that allows trained policies to better adapt to the mixed coordination setting common in multi-arm manipulation, and show that a centralized policy augmented with a decentralized residual model outperforms all other models on our set of benchmark tasks. Additional results and videos at https://roboturk.stanford. edu/multiarm
PePScenes: A Novel Dataset and Baseline for Pedestrian Action Prediction in 3D

Predicting the behavior of road users, particularly pedestrians, is vital for safe motion planning in the context of autonomous driving systems. Traditionally, pedestrian behavior prediction has been realized in terms of forecasting future trajectories. However, recent evidence suggests that predicting higher-level actions, such as crossing the road, can help improve trajectory forecasting and planning tasks accordingly. There are a number of existing datasets that cater to the development of pedestrian action prediction algorithms, however, they lack certain characteristics, such as bird's eye view semantic map information, 3D locations of objects in the scene, etc., which are crucial in the autonomous driving context. To this end, we propose a new pedestrian action prediction dataset created by adding per-frame 2D/3D bounding box and behavioral annotations to the popular autonomous driving dataset, nuScenes. In addition, we propose a hybrid neural network architecture that incorporates various data modalities for predicting pedestrian crossing action. By evaluating our model on the newly proposed dataset, the contribution of different data modalities to the prediction task is revealed. The dataset is available at this https URL.
Hiding in the Particles: When Return-Oriented Programming Meets Program Obfuscation

Largely known for attack scenarios, code reuse techniques at a closer look reveal properties that are appealing also for program obfuscation. We explore the popular return-oriented programming paradigm under this light, transforming program functions into ROP chains that coexist seamlessly with the surrounding software stack. We show how to build chains that can withstand popular static and dynamic deobfuscation approaches, evaluating the robustness and overheads of the design over common programs. The results suggest a significant amount of computational resources would be required to carry a deobfuscation attack for secret finding and code coverage goals.
Detection of chaotic behavior in time series.

Deterministic chaos is phenomenon from nonlinear dynamics and it belongs to greatest advances of twentieth-century science. Chaotic behavior appears apart of mathematical equations also in wide range in observable nature, so as in there originating time series. Chaos in time series resembles stochastic behavior, but apart of randomness it is totally deterministic and therefore chaotic data can provide us useful information. Therefore it is essential to have methods, which are able to detect chaos in time series, moreover to distinguish chaotic data from stochastic one. Here we present and discuss the performance of standard and machine learning methods for chaos detection and its implementation on two well known simple chaotic discrete dynamical systems - Logistic map and Tent map, which fit to the most of the definitions of chaos.
Beet curly top resistance in USDA-ARS Kimberly germplasm, 2019

Curly top caused by Beet curly top virus (BCTV) is a widespread disease problem vectored by the beet leafhopper in semiarid sugar beet production areas. Host resistance is the primary defense against this problem, but resistance in commercial cultivars is only low to intermediate. In order to identify novel sources of curly top resistance, 8 sugar beet lines produced by the USDA-ARS Kimberly sugar beet program were screened in a disease nursery in 2019. The lines were arranged in a randomized complete block design with six replications. A curly top epiphytotic was created by releasing six viruliferous beet leafhoppers per plant at the four- to six-leaf growth stage on 3 Jul. Foliar symptoms were evaluated on 22 July using a scale of 0-9 (0 = healthy and 9 = dead) in a continuous manner. Curly top symptom development was uniform and no other disease problems were evident in the plot area. The disease pressure in the test was moderately severe with good symptom development in the susceptible checks. Based on the visual rating, four entries (segregating populations) and two genetic stock line entries were not significantly different from the resistant checks. The resistant segregating populations will be suitable for single trait selection and advancement to breeding lines to enrich the BCTV resistance gene pool in the USDA-ARS sugar beet germplasm.
Human Visual Search Follows Suboptimal Bayesian Strategy Revealed by a Spatiotemporal Computational Model

Humans perform sequences of eye movements to search for a target in complex environment, but the efficiency of human search strategy is still controversial. Previous studies showed that humans can optimally integrate information across fixations and determine the next fixation location. However, their models ignored the temporal control of eye movement, ignored the limited human memory capacity, and the model prediction did not agree with details of human eye movement metrics well. Here, we measured the temporal course of human visibility map and recorded the eye movements of human subjects performing a visual search task. We further built a continuous-time eye movement model which considered saccadic inaccuracy, saccadic bias, and memory constraints in the visual system. This model agreed with many spatial and temporal properties of human eye movements, and showed several similar statistical dependencies between successive eye movements. In addition, our model also predicted that the human saccade decision is shaped by a memory capacity of around 8 recent fixations. These results suggest that human visual search strategy is not strictly optimal in the sense of fully utilizing the visibility map, but instead tries to balance between search performance and the costs to perform the task.During visual search, how do humans determine when and where to make eye movement is an important unsolved issue. Previous studies suggested that human can optimally use the visibility map to determine fixation locations, but we found that such model didn’t agree with details of human eye movement metrics because it ignored several realistic biological limitations of human brain functions, and couldn’t explain the temporal control of eye movements. Instead, we showed that considering the temporal course of visual processing and several constrains of the visual system could greatly improve the prediction on the spatiotemporal properties of human eye movement while only slightly affected the search performance in terms of median fixation numbers. Therefore, humans may not use the visibility map in a strictly optimal sense, but tried to balance between search performance and the costs to perform the task.
METODE QUANTUM LEARNING UNTUK MENINGKATKAN EFIKASI DIRI DAN HASIL BELAJAR IPS SISWA KELAS V SEKOLAH DASAR

Metode pembelajaran Quantum Learning merupakan metode belajar yang mengajak siswa belajar dalam suasana yang lebih nyaman dan menyenangkan, sehingga siswa akan lebih bebas dalam menemukan berbagai pengalaman baru dalam belajarnya. Metode pembelajaran Quantum Learning diharapkan dapat menumbuhkan efikasi diri yang tinggi pada setiap siswa. Jika setiap siswa memiliki efikasi diri yang tinggi maka siswa yang nilainya di bawah kriteria ketuntasan minimal pembelajaran IPS akan berkurang. Efikasi diri merupakan kepercayaan diri yang dimiliki oleh setiap orang untuk memotivasi dirinya sendiri agar tetap konsisten dalam belajar untuk mencapai apa yang diinginkan. Penelitian ini merupakan penelitian tindakan kelas yang dilaksanakan sebanyak 2 siklus. Pada siklus pertama, belum tampak Perubahan yang signifikan terhadap hasil pembelajaran, siswa juga belum terbiasa dengan metode quantum learning, setelah dilanjutkan ke siklus dua tampaklah peningkatan hasil belajar siswa dan efikasi diri siswa meningkat, dibuktikan dengan adanya motivasi diri siswa yang tinggi untuk menyelesaikan tugas-tugas dari guru.