Miracle-dz/UniFAR-FTUs huggingface dataset:
Ultraviolet‐Induced Transient and Stable Color Centers in Self‐Q‐Switching Laser Glass

The optical absorption spectrum of ultraviolet‐ (uv‐) induced room‐temperature stable color centers and the uv spectrum for their generation in a laser self‐Q‐switching glass were obtained. The optical absorption spectrum is similar to that for x‐ray‐irradiated silicate glasses of simpler composition. The uv generation spectrum was found to consist of a narrow Gaussian line of width 2250 cm−1 with peak at 45 800 cm−1. The optical absorption spectrum of the uv‐induced transient color centers was also obtained and appears to consist of the superposition of a broad asymmetric absorption band peaked near 740 nm and a Gaussian‐shaped annihilation band peaked near 674 nm. The lifetime of the 740‐nm band was found to be about 200 msec and that of the 674‐nm band about 400 msec. The 740‐nm transient absorption band is identified with the E1,2− band which Mackey, Smith, and Halperin attribute to a trapped electron polaron. The line shape of this transient absorption is interpreted in terms of a large‐polaron stron.
Synthesis of New Pyrazolo[1,5‐a]pyrimidine Derivatives via CT‐Complexation

Pyrazolo[ 1 ,5-a]pyrimidinesi) and indanopyrazolo[ 1,5-a]pyrimidines2) have been synthesized from 3,5-diamino-4-arylazopyrazoles with TCNE and CNIND, respectively, via CT-complexation. Due to interesting biological properties of pyrazolopyrimidine~~,~) and in an expansion of our interest on this type of compounds we have utilized 5-substituted-3-amino-2H-pyrazole-4-carbonitriles 1-3 as pyrazole derivatives to act as donors in complexation with x-acceptors. The objectives of the present work are to ascertain the effective donating and cyclizing centers in 3,5-diamino-4-arylazopyrazoles on complexation with the electron acceptors5), and interpretation of the effect of the polyfunctional groups in la-e, 3 on the interaction with TCNE and CNIND as x-acceptors.
IVERMECTIN REPROPOSING FOR COVID-19 TREATMENT OUTPATIENTS IN MILD STAGE IN PRIMARY HEALTH CARE CENTERS

Background: The emergence of COVID-19 requires alternative treatments based on the reuse of drugs as a strategy to prevent the progression of the disease in patients infected with SARS-COV-2. The goal was to evaluate the use of ivermectin in mild stage outpatients to heal and / or reverse the progression of COVID-19 disease towards the development of moderate or severe stages. Methods: Cluster Assigned Clinical Trial (2:1) in outpatients, n = 234. The subjects were divided into experimental (EG: n = 110) and control groups (CG: n = 62). The EG received ivermectin orally 4 drops of 6 mg = 24 mg every 7 days for 4 weeks. All participants were diagnosed by positive RT-PCR for COVID-19 and were evaluated by clinical examination, at the beginning and the end of protocol. Data analyzed were applied the proportion, bivariate, and logical regression tests with level significance p < 0.05. This study was registered at ClinicalTrials.gov Identifier NCT04784481. Findings: Both groups were similar in age, sex, and comorbidities (EG: 56F, median age= 40.0, range: 18.0 - 75.0; CG: 34F, median age = 37.5, range: 18.0 - 71.0). A significant reduction in the symptom numbers was observed in the EG when the medical examination was performed from 5th to 9th days, after starting treatment (p = 0.0026). Although, medical examination from 10th to 14th day, showed a progressive reduction of the percentage symptom numbers, these were not significative in both groups. A higher proportion of medical release was observed in EG (98.2%) vs CG (87.1%) (p = 0.003). EG showed 8 times more chance of receiving medical release than CG (OR 7.99, 95% CI: 1.64 -38.97, p = 0.003). The treatment effect with ivermectin to obtain medical release was analyzed by the logistic regression model based in the following control variables: sex, age, and comorbidities. Then, the chance to obtain medical release was maintained in EG (OR 10.37, 95% CI: 2.05 - 52.04, p = 0.005). Interpretation: Treatment with ivermectin in outpatients with mild stage COVID-19 disease managed to slightly reduce the symptom numbers. Also, this treatment improved the clinical state to obtain medical release, even in the presence of comorbidities. The treatment with ivermectin could significantly prevent the evolution to serious stages since the EG did not present any patient with referral to critical hospitalization.
Causal Hidden Markov Model for Time Series Disease Forecasting

We propose a causal hidden Markov model to achieve robust prediction of irreversible disease at an early stage, which is safety-critical and vital for medical treatment in early stages. Specifically, we introduce the hidden variables which propagate to generate medical data at each time step. To avoid learning spurious correlation (e.g., confounding bias), we explicitly separate these hidden variables into three parts: a) the disease (clinical)-related part; b) the disease (non-clinical)-related part; c) others, with only a),b) causally related to the disease however c) may contain spurious correlations (with the disease) inherited from the data provided. With personal attributes and disease label respectively provided as side information and supervision, we prove that these disease-related hidden variables can be disentangled from others, implying the avoidance of spurious correlation for generalization to medical data from other (out-of-) distributions. Guaranteed by this result, we propose a sequential variational auto-encoder with a reformulated objective function. We apply our model to the early prediction of peripapillary atrophy and achieve promising results on out-of-distribution test data. Further, the ablation study empirically shows the effectiveness of each component in our method. And the visualization shows the accurate identification of lesion regions from others.1
Automatic Graph Partitioning for Very Large-scale Deep Learning

This work proposes RaNNC (Rapid Neural Network Connector) as middleware for automatic hybrid parallelism. In recent deep learning research, as exemplified by T5 and GPT-3, the size of neural network models continues to grow. Since such models do not fit into the memory of accelerator devices, they need to be partitioned by model parallelism techniques. Moreover, to accelerate training for huge training data, we need a combination of model and data parallelisms, i.e., hybrid parallelism. Given a model description for PyTorch without any specification for model parallelism, RaNNC automatically partitions the model into a set of subcomponents so that (1) each subcomponent fits a device memory and (2) a high training throughput for pipeline parallelism is achieved by balancing the computation times of the subcomponents. Since the search space for partitioning models can be extremely large, RaNNC partitions a model through the following three phases. First, it identifies atomic subcomponents using simple heuristic rules. Next it groups them into coarser-grained blocks while balancing their computation times. Finally, it uses a novel dynamic programming-based algorithm to efficiently search for combinations of blocks to determine the final partitions. In our experiments, we compared RaNNC with two popular frameworks, Megatron-LM (hybrid parallelism) and GPipe (originally proposed for model parallelism, but a version allowing hybrid parallelism also exists), for training models with increasingly greater numbers of parameters. In the pre-training of enlarged BERT models, RaNNC successfully trained models five times larger than those Megatron-LM could, and RaNNC’s training throughputs were comparable to Megatron-LM’s when pre-training the same models. RaNNC also achieved better training throughputs than GPipe on both the enlarged BERT model pre-training (GPipe with hybrid parallelism) and the enlarged ResNet models (GPipe with model parallelism) in all of the settings we tried. These results are remarkable, since RaNNC automatically partitions models without any modification to their descriptions; Megatron-LM and GPipe require users to manually rewrite the models’ descriptions.
Neural Transformation Learning for Deep Anomaly Detection Beyond Images

Data transformations (e.g. rotations, reflections, and cropping) play an important role in self-supervised learning. Typically, images are transformed into different views, and neural networks trained on tasks involving these views produce useful feature representations for downstream tasks, including anomaly detection. However, for anomaly detection beyond image data, it is often unclear which transformations to use. Here we present a simple end-to-end procedure for anomaly detection with learnable transformations. The key idea is to embed the transformed data into a semantic space such that the transformed data still resemble their untransformed form, while different transformations are easily distinguishable. Extensive experiments on time series demonstrate that our proposed method outperforms existing approaches in the one-vs.-rest setting and is competitive in the more challenging n-vs.-rest anomaly detection task. On tabular datasets from the medical and cyber-security domains, our method learns domain-specific transformations and detects anomalies more accurately than previous work.
Fast Cluster Detection in Networks by First Order Optimization

Cluster detection plays a fundamental role in the analysis of data. In this paper, we focus on the use of s-defective clique models for network-based cluster detection and propose a nonlinear optimization approach that efficiently handles those models in practice. In particular, we introduce an equivalent continuous formulation for the problem under analysis, and we analyze some tailored variants of the Frank-Wolfe algorithm that enable us to quickly find maximal s-defective cliques. The good practical behavior of those algorithmic tools, which is closely connected to their support identification properties, makes them very appealing in practical applications. The reported numerical results clearly show the effectiveness of the proposed approach.
Strain dependence of the optical properties and band gap of transparent conducting BaSnO3 and SrSnO3

Recent work has shown that perovskite BaSnO3, when doped n-type by rare earth or Sb substitution, has potential as a transparent conducting oxide (TCO), replacement for In2O3:Sn (ITO). Here we discuss the properties of this material and the related SrSnO3, as well as strain dependence and epitaxy using first principles calculations. The compounds show remarkably strong strain tunability, but in a manner very different from well-studied perovskites such as SrTiO3. The differences are explained in terms of the s-electron nature of the conduction bands in these stannates.
Leveraging a Joint of Phenotypic and Genetic Features on Cancer Patient Subgrouping

Background: Cancer is responsible for millions of deaths worldwide every year. Although significant progress has been achieved in cancer medicine, many issues remain to be addressed for improving cancer therapy. Appropriate cancer patient stratification is the prerequisite for selecting appropriate treatment plan, as cancer patients are of known heterogeneous genetic make-ups and phenotypic differences. In this study, built upon deep phenotypic characterizations extractable from Mayo Clinic electronic health records (EHRs) and genetic test reports for a collection of cancer patients, we developed a system leveraging a joint of phenotypic and genetic features for cancer patient subgrouping.
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model

We provide a lowerbound on the sample complexity of distribution-free parity learning in the realizable case in the shuffle model of differential privacy. Namely, we show that the sample complexity of learning d-bit parity functions is Ω(2d/2). Our result extends a recent similar lowerbound on the sample complexity of private agnostic learning of parity functions in the shuffle model by Cheu and Ullman . We also sketch a simple shuffle model protocol demon- strating that our results are tight up to poly(d) factors.
Deep Gaussian Processes for Few-Shot Segmentation

Few-shot segmentation is a challenging task, requiring the extraction of a generalizable representation from only a few annotated samples, in order to segment novel query images. A common approach is to model each class with a single prototype. While conceptually simple, these methods suffer when the target appearance distribution is multi-modal or not linearly separable in feature space. To tackle this issue, we propose a few-shot learner formulation based on Gaussian process (GP) regression. Through the expressivity of the GP, our approach is capable of modeling complex appearance distributions in the deep feature space. The GP provides a principled way of capturing uncertainty, which serves as another powerful cue for the final segmentation, obtained by a CNN decoder. We further exploit the end-to-end learning capabilities of our approach to learn the output space of the GP learner, ensuring a richer encoding of the segmentation mask. We perform comprehensive experimental analysis of our few-shot learner formulation. Our approach sets a new state-of-the-art for 5-shot segmentation, with mIoU scores of 68.1 and 49.8 on PASCAL-5i and COCO-20i, respectively
Learning Deep Neural Policies with Stability Guarantees

Reinforcement learning (RL) has been successfully used to solve various robotic control tasks. However, most of the existing works do not address the issue of control stability. This is in sharp contrast to the control theory community where the well-established norm is to prove stability whenever a control law is synthesized. What makes guaranteeing stability during RL difficult is threefold: non interpretable neural network policies, unknown system dynamics and random exploration. We contribute towards solving the stable RL problem in the context of robotic manipulation that may involve physical contact with the environment. Our solution is derived from physics-based prior that originates from Lagrangian mechanics and does not involve learning any dynamics model. We show how to parameterize the resulting $\textit{energy shaping}$ policy as a deep neural network that consists of a convex potential function and a velocity dependent damping component. Our experiments, that include a real-world peg insertion task by a 7-DOF robot, validate the proposed policy structure and demonstrate the benefits of stability in RL.
6-DoF Contrastive Grasp Proposal Network

Proposing grasp poses for novel objects is an essential component for any robot manipulation task. Planning six degrees of freedom (DoF) grasps with a single camera, however, is challenging due to the complex object shape, incomplete object information, and sensor noise. In this paper, we present a 6-DoF contrastive grasp proposal network (CGPN) to infer 6-DoF grasps from a single-view depth image. First, an image encoder is used to extract the feature map from the input depth image, after which 3-DoF grasp regions are proposed from the feature map with a rotated region proposal network. Feature vectors that within the proposed grasp regions are then extracted and refined to 6-DoF grasps. The proposed model is trained offline with synthetic grasp data. To improve the robustness in reality and bridge the simulation-to-real gap, we further introduce a contrastive learning module and variant image processing techniques during the training. CGPN can locate collision-free grasps of an object using a single-view depth image within 0.5 second. Experiments on a physical robot further demonstrate the effectiveness of the algorithm. The experimental videos are available at [1].
Theory-Guided Machine Learning for Process Simulation of Advanced Composites

Science-based simulation tools such as Finite Element (FE) models are routinely used in scientific and engineering applications. While their success is strongly dependent on our understanding of underlying governing physical laws, they suffer inherent limitations including trade-off between fidelity/accuracy and speed. The recent rise of Machine Learning (ML) proposes a theory-agnostic paradigm. In complex multi-physics problems, however, creating large enough datasets for successful training of ML models has proven to be challenging. One promising strategy to bridge the divide between these approaches and take advantage of their respective strengths is Theory-Guided Machine Learning (TGML) which aims to integrate physical laws into ML algorithms. In this paper, three case studies on thermal management during processing of advanced composites are presented and studied using FE, ML and TGML. A structured approach to incrementally adding increasingly complex physics to training of TGML model is presented. The benefits of TGML over ML models are seen in more accurate predictions, particularly outside the training region, and ability to train with small datasets. One benefit of TGML over FE is significant speed improvement to potentially develop real-time feedback systems. A recent successful implementation of a TGML model to assess producibility of aerospace composite parts is presented.
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air

For distributed learning among collaborative users, this paper develops and analyzes a communication-efficient scheme for federated learning (FL) over the air, which incorporates 1-bit compressive sensing (CS) into analog-aggregation transmissions. To facilitate design parameter optimization, we analyze the efficacy of the proposed scheme by deriving a closed-form expression for the expected convergence rate. Our theoretical results unveil the tradeoff between convergence performance and communication efficiency as a result of the aggregation errors caused by sparsification, dimension reduction, quantization, signal reconstruction and noise. Then, we formulate a joint optimization problem to mitigate the impact of these aggregation errors through joint optimal design of worker scheduling and power scaling policy. An enumeration-based method is proposed to solve this non-convex problem, which is optimal but becomes computationally infeasible as the number of devices increases. For scalable computing, we resort to the alternating direction method of multipliers (ADMM) technique to develop an efficient implementation that is suitable for large-scale networks. Simulation results show that our proposed 1-bit CS based FL over the air achieves comparable performance to the ideal case where conventional FL without compression and quantification is applied over error-free aggregation, at much reduced communication overhead and transmission latency.
Quantum phase transition of many interacting spins coupled to a bosonic bath: Static and dynamical properties

By using worldline and diagrammatic quantum Monte Carlo techniques, matrix product state and a variational approach \`a la Feynman, we investigate the equilibrium properties and relaxation features of a quantum system of $N$ spins antiferromagnetically interacting with each other, with strength $J$, and coupled to a common bath of bosonic oscillators, with strength $\alpha$. We show that, in the Ohmic regime, a Beretzinski-Thouless-Kosterlitz quantum phase transition occurs. While for $J=0$ the critical value of $\alpha$ decreases asymptotically with $1/N$ by increasing $N$, for nonvanishing $J$ it turns out to be practically independent on $N$, allowing to identify a finite range of values of $\alpha$ where spin phase coherence is preserved also for large $N$. Then, by using matrix product state simulations, and the Mori formalism and the variational approach \`a la Feynman jointly, we unveil the features of the relaxation, that, in particular, exhibits a non monotonic dependence on the temperature reminiscent of the Kondo effect. For the observed quantum phase transition we also establish a criterion analogous to that of the metal-insulator transition in solids.
Saddle point optimization with approximate minimization oracle

A major approach to saddle point optimization minx maxy f(x, y) is a gradient based approach as is popularized by generative adversarial networks (GANs). In contrast, we analyze an alternative approach relying only on an oracle that solves a minimization problem approximately. Our approach locates approximate solutions x′ and y′ to minx′ f(x′, y) and maxy′ f(x, y′) at a given point (x, y) and updates (x, y) toward these approximate solutions (x′, y′) with a learning rate η. On locally strong convex-concave smooth functions, we derive conditions on η to exhibit linear convergence to a local saddle point, which reveals a possible shortcoming of recently developed robust adversarial reinforcement learning algorithms. We develop a heuristic approach to adapt η derivative-free and implement zero-order and first-order minimization algorithms. Numerical experiments are conducted to show the tightness of the theoretical results as well as the usefulness of the η adaptation mechanism.
Resolving the Complex Evolution of a Supermassive Black Hole Triplet in a Cosmological Simulation

We present here a self-consistent cosmological zoom-in simulation of a triple supermassive black hole (SMBH) system forming in a complex multiple galaxy merger. The simulation is run with an updated version of our code KETJU, which is able to follow the motion of SMBHs down to separations of tens of Schwarzschild radii while simultaneously modeling the large-scale astrophysical processes in the surrounding galaxies, such as gas cooling, star formation, and stellar and AGN feedback. Our simulation produces initially an SMBH binary system for which the hardening process is interrupted by the late arrival of a third SMBH. The KETJU code is able to accurately model the complex behavior occurring in such a triple SMBH system, including the ejection of one SMBH to a kiloparsec-scale orbit in the galaxy due to strong three-body interactions as well as Lidov–Kozai oscillations suppressed by relativistic precession when the SMBHs are in a hierarchical configuration. One pair of SMBHs merges ∼3 Gyr after the initial galaxy merger, while the remaining binary is at a parsec-scale separation when the simulation ends at redshift z = 0. We also show that KETJU can capture the effects of the SMBH binaries and triplets on the surrounding stellar population, which can affect the binary merger timescales as the stellar density in the system evolves. Our results demonstrate the importance of dynamically resolving the complex behavior of multiple SMBHs in galactic mergers, as such systems cannot be readily modeled using simple orbit-averaged semianalytic models.
Data-Driven Synthesis of Optimization-Based Controllers for Regulation of Unknown Linear Systems

This paper proposes a data-driven framework to solve time-varying optimization problems associated with unknown linear dynamical systems. Making online control decisions to regulate a dynamical system to the solution of an optimization problem is a central goal in many modern engineering applications. Yet, the available methods critically rely on a precise knowledge of the system dynamics, thus mandating a preliminary system identification phase before a controller can be designed. In this work, we leverage results from behavioral theory to show that the steady-state transfer function of a linear system can be computed from data samples without any knowledge or estimation of the system model. We then use this data-driven representation to design a controller, inspired by a gradient-descent optimization method, that regulates the system to the solution of a convex optimization problem, without requiring any knowledge of the time-varying disturbances affecting the model equation. Results are tailored to cost functions satisfy the Polyak-Łojasiewicz inequality.