Miracle-dz/UniFAR-FTUs huggingface dataset:
How do extracellular membrane-associated proteins stand erect and signal?

Even after decades of research, a comprehensive mechanism that elucidates the underpinnings of signalling through the cell membrane is still elusive. Here, we address a simple question “how do the ectodomain of a membrane-associated protein consisting of multiple domains and connected by flexible linkers stand ‘upright’ on the membrane? ”. Our analysis based on large amount of available functional and structural data, looking for a pattern of association of these molecules in the crystal structures and with the concept that ‘random things seldom repeat’ lead to a surprisingly interesting and consistent observation that the weak cis interaction mediated ordered array of constitutively expressed signalling molecules not only support their ‘upright’ orientation but also bury their ligand-binding surface to avoid spurious signaling in the non-signalling resting state. With CD4, pMHCII, CD2 and TNFR1 as examples, we show that the observed association of molecules also correlate well with their functional role. Further, our model reconciles the long-standing controversies related to these molecules and appear to be generic enough to be applied to other signalling molecules
Identifying complex sequence patterns with a variable-convolutional layer effectively and efficiently

Motif identification is among the most canonical and essential computational tasks for bioinformatics and genomics. Here we proposed a simple and scalable novel convolution-based layer, Variable Convolutional neural layer (vConv), for effective motif identification in high-throughput omics data by learning kernel length on-the-fly. Empirical evaluations on DNA-protein binding and DNase footprinting cases well demonstrated that vConv-based networks have superior performance to their convolutional counterparts regardless of model complexity. All source codes are freely available on GitHub (https://github.com/gao-lab/vConv) for academic usage.
Formation of Ultrathin Nitrided SiO2 Oxides by Direct Nitrogen Implantation into Silicon

A nitridation technique is proposed for ultrathin, SiO{sub 2} oxides in deep submicron CMOS technology, which involves direct implantation of molecular nitrogen (N{sub 2}) into the silicon substrate. N{sub 2} ions were implanted into silicon at different doses and energies, through a 150 {angstrom} thick screen oxide. In this study the effect of implanted N atoms on silicon oxidation, and SiO{sub 2} oxide nitridation process have been studied. Two groups of the N{sub 2}-implanted wafers were used: wafers from one group were annealed prior to the screen oxide removal, whereas wafers of the other group did not receive this anneal. It is shown that nitridation can be achieved both ways, allowing this technique to be easily integrated into a semiconductor IC fabrication process.
A probabilistic approach of Liouville field theory

In this article, we present the Liouville field theory, which was introduced in the eighties in physics by Polyakov as a model for fluctuating metrics in 2D quantum gravity, and outline recent mathematical progress in its study. In particular, we explain the probabilistic construction of this theory carried out by David–Kupiainen–Rhodes–Vargas in [] and how this construction connects to the modern and general approach of Conformal Field Theories in physics, called conformal bootstrap and based on representation theory.
BeFair: Addressing Fairness in the Banking Sector

Algorithmic bias mitigation has been one of the most difficult conundrums for the data science community and Machine Learning (ML) experts. Over several years, there have appeared enormous efforts in the field of fairness in ML. Despite the progress toward identifying biases and designing fair algorithms, translating them into the industry remains a major challenge. In this paper, we present the initial results of an industrial open innovation project in the banking sector: we propose a general roadmap for fairness in ML and the implementation of a toolkit called BeFair that helps to identify and mitigate bias. Results show that training a model without explicit constraints may lead to bias exacerbation in the predictions.
DiSCoL: Toward Engaging Dialogue Systems through Conversational Line Guided Response Generation

Having engaging and informative conversations with users is the utmost goal for open-domain conversational systems. Recent advances in transformer-based language models and their applications to dialogue systems have succeeded to generate fluent and human-like responses. However, they still lack control over the generation process towards producing contentful responses and achieving engaging conversations. To achieve this goal, we present DiSCoL (Dialogue Systems through Coversational Line guided response generation). DiSCoL is an open-domain dialogue system that leverages conversational lines (briefly convlines) as controllable and informative content-planning elements to guide the generation model produce engaging and informative responses. Two primary modules in DiSCoL’s pipeline are conditional generators trained for 1) predicting relevant and informative convlines for dialogue contexts and 2) generating high-quality responses conditioned on the predicted convlines. Users can also change the returned convlines to control the direction of the conversations towards topics that are more interesting for them. Through automatic and human evaluations, we demonstrate the efficiency of the convlines in producing engaging conversations.
THE VIX INDEX UNDER SCRUTINY OF MACHINE LEARNING TECHNIQUES AND NEURAL NETWORKS

The CBOE Volatility Index, known by its ticker symbol VIX, is a popular measure of the market's expected volatility on the SP 500 Index, calculated and published by the Chicago Board Options Exchange (CBOE). It is also often referred to as the fear index or the fear gauge. The current VIX index value quotes the expected annualized change in the SP 500 index over the following 30 days, based on options-based theory and current options-market data. Despite its theoretical foundation in option price theory, CBOE's Volatility Index is prone to inadvertent and deliberate errors because it is weighted average of out-of-the-money calls and puts which could be illiquid. Many claims of market manipulation have been brought up against VIX in recent years. This paper discusses several approaches to replicate the VIX index as well as VIX futures by using a subset of relevant options as well as neural networks that are trained to automatically learn the underlying formula. Using subset selection approaches on top of the original CBOE methodology, as well as building machine learning and neural network models including Random Forests, Support Vector Machines, feed-forward neural networks, and long short-term memory (LSTM) models, we will show that a small number of options is sufficient to replicate the VIX index. Once we are able to actually replicate the VIX using a small number of SP options we will be able to exploit potential arbitrage opportunities between the VIX index and its underlying derivatives. The results are supposed to help investors to better understand the options market, and more importantly, to give guidance to the US regulators and CBOE that have been investigating those manipulation claims for several years.
Recent progress of high efficiency Si thin‐film solar cells in large area

Si thin‐film solar cells are suitable to the sunbelt region due to a low temperature coefficient and to building integrated photovoltaics owing to flexible size, easily controllable transmittance, and an aesthetic design. Nevertheless, the application is limited until now due to their low conversion efficiency. We have developed a triple junction cell (a‐Si:H/a‐SiGe:H/µc‐Si:H) providing efficient light utilization. For the high efficiency, we have focused on the smoothing of high haze TCO, a low absorption window layer, a low refractive index interlayer, uniformity control of the thickness and crystalline volume fraction in the microcrystalline silicon layer, and a low absorption back reflector. Through these activities, we have achieved a world record of 13.4% stabilized efficiency in the small size cell (1 cm2) and 10.5% stabilized efficiency in the large area module (1.1 × 1.3 m2), certificated by the National Renewable Energy Laboratory and Advanced Industrial Science and Technology, respectively. This result was presented in solar cell efficiency tables (Version 41). At this moment, we have increased a stabilized efficiency of 11.2% (Output power 160 W) in the large area module. We will report on the advanced materials in detail for high efficiency. Copyright © 2014 John Wiley & Sons, Ltd.
A Survey on Understanding, Visualizations, and Explanation of Deep Neural Networks

Recent advancements in machine learning and signal processing domains have resulted in an extensive surge of interest in Deep Neural Networks (DNNs) due to their unprecedented performance and high accuracy for different and challenging problems of significant engineering importance. However, when such deep learning architectures are utilized for making critical decisions such as the ones that involve human lives (e.g., in control systems and medical applications), it is of paramount importance to understand, trust, and in one word “explain” the argument behind deep models’ decisions. In many applications, artificial neural networks (including DNNs) are considered as black-box systems, which do not provide sufficient clue on their internal processing actions. Although some recent efforts have been initiated to explain the behaviors and decisions of deep networks, explainable artificial intelligence (XAI) domain, which aims at reasoning about the behavior and decisions of DNNs, is still in its infancy. The aim of this paper is to provide a comprehensive overview on Understanding, Visualization, and Explanation of the internal and overall behavior of DNNs.
CountSketches, Feature Hashing and the Median of Three

In this paper, we revisit the classic CountSketch method, which is a sparse, random projection that transforms a (high-dimensional) Euclidean vector $v$ to a vector of dimension $(2t-1) s$, where $t, s>0$ are integer parameters. It is known that even for $t=1$, a CountSketch allows estimating coordinates of $v$ with variance bounded by $\|v\|_2^2/s$. For $t>1$, the estimator takes the median of $2t-1$ independent estimates, and the probability that the estimate is off by more than $2 \|v\|_2/\sqrt{s}$ is exponentially small in $t$. This suggests choosing $t$ to be logarithmic in a desired inverse failure probability. However, implementations of CountSketch often use a small, constant $t$. Previous work only predicts a constant factor improvement in this setting. Our main contribution is a new analysis of Count-Sketch, showing an improvement in variance to $O(\min\{\|v\|_1^2/s^2,\|v\|_2^2/s\})$ when $t>1$. That is, the variance decreases proportionally to $s^{-2}$, asymptotically for large enough $s$. We also study the variance in the setting where an inner product is to be estimated from two CountSketches. This finding suggests that the Feature Hashing method, which is essentially identical to CountSketch but does not make use of the median estimator, can be made more reliable at a small cost in settings where using a median estimator is possible. We confirm our theoretical findings in experiments and thereby help justify why a small constant number of estimates often suffice in practice. Our improved variance bounds are based on new general theorems about the variance and higher moments of the median of i.i. d. random variables that may be of independent interest.
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization

Wasserstein barycenters provide a geometric notion of the weighted average of probability measures based on optimal transport. In this paper, we present a scalable algorithm to compute Wasserstein-2 barycenters given sample access to the input measures, which are not restricted to being discrete. While past approaches rely on entropic or quadratic regularization, we employ input convex neural networks and cycle-consistency regularization to avoid introducing bias. As a result, our approach does not resort to minimax optimization. We provide theoretical analysis on error bounds as well as empirical evidence of the effectiveness of the proposed approach in low-dimensional qualitative scenarios and high-dimensional quantitative experiments.
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function

We consider local planning in fixed-horizon MDPs with a generative model under the assumption that the optimal value function lies close to the span of a feature map. The generative model provides a local access to the MDP: The planner can ask for random transitions from previously returned states and arbitrary actions, and features are only accessible for states that are encountered in this process. As opposed to previous work (e.g. Lattimore et al. (2020)) where linear realizability of all policies was assumed, we consider the significantly relaxed assumption of a single linearly realizable (deterministic) policy. A recent lower bound by Weisz et al. (2020) established that the related problem when the action-value function of the optimal policy is linearly realizable requires an exponential number of queries, either in $H$ (the horizon of the MDP) or $d$ (the dimension of the feature mapping). Their construction crucially relies on having an exponentially large action set. In contrast, in this work, we establish that poly$(H,d)$ planning is possible with state value function realizability whenever the action set has a constant size. In particular, we present the TensorPlan algorithm which uses poly$((dH/\delta)^A)$ simulator queries to find a $\delta$-optimal policy relative to any deterministic policy for which the value function is linearly realizable with some bounded parameter. This is the first algorithm to give a polynomial query complexity guarantee using only linear-realizability of a single competing value function. Whether the computation cost is similarly bounded remains an open question. We extend the upper bound to the near-realizable case and to the infinite-horizon discounted setup. We also present a lower bound in the infinite-horizon episodic setting: Planners that achieve constant suboptimality need exponentially many queries, either in $d$ or the number of actions.
Magnetically modified spherical accretion in GRMHD: reconnection-driven convection and jet propagation

We present 3D general relativistic magnetohydrodynamic(GRMHD) simulations of zero angular momentum accretion around a rapidly rotating black hole, modified by the presence of initially uniform magnetic fields. We consider serveral angles between the magnetic field direction and the black hole spin. In the resulting flows, the midplane dynamics are governed by magnetic reconnection-driven turbulence in a magnetically arrested (or a nearly arrested) state. Electromagnetic jets with outflow efficiencies ∼ 10–200% occupy the polar regions, reaching several hundred gravitational radii before they dissipate due to the kink instability. The jet directions fluctuate in time and can be tilted by as much as ∼ 30◦ with respect to black hole spin, but this tilt does not depend strongly on the tilt of the initial magnetic field. A jet forms even when there is no initial net vertical magnetic flux since turbulent, horizon-scale fluctuations can generate a net vertical field locally. Peak jet power is obtained for an initial magnetic field tilted by 40–80◦ with respect to the black hole spin because this maximizes the amount of magnetic flux that can reach the black hole. These simulations may be a reasonable model for low luminosity black hole accretion flows such as Sgr A* or M87.
A Neurorobotic Embodiment for Exploring the Dynamical Interactions of a Spiking Cerebellar Model and a Robot Arm During Vision-based Manipulation Tasks

While the original goal for developing robots is replacing humans in dangerous and tedious tasks, the final target shall be completely mimicking the human cognitive and motor behavior. Hence, building detailed computational models for the human brain is one of the reasonable ways to attain this. The cerebellum is one of the key players in our neural system to guarantee dexterous manipulation and coordinated movements as concluded from lesions in that region. Studies suggest that it acts as a forward model providing anticipatory corrections for the sensory signals based on observed discrepancies from the reference values. While most studies consider providing the teaching signal as error in joint-space, few studies consider the error in task-space and even fewer consider the spiking nature of the cerebellum on the cellular-level. In this study, a detailed cellular-level forward cerebellar model is developed, including modeling of Golgi and Basket cells which are usually neglected in previous studies. To preserve the biological features of the cerebellum in the developed model, a hyperparameter optimization method tunes the network accordingly. The efficiency and biological plausibility of the proposed cerebellar-based controller is then demonstrated under different robotic manipulation tasks reproducing motor behavior observed in human reaching experiments.
High‐Throughput Discovery of Novel Cubic Crystal Materials Using Deep Generative Neural Networks

High‐throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of sufficient and diverse materials in current materials repositories such as the open quantum materials database (OQMD). Recent progress in deep learning have enabled generative strategies that learn implicit chemical rules for creating hypothetical materials with new compositions and structures. However, current materials generative models have difficulty in generating structurally diverse, chemically valid, and stable materials. Here we propose CubicGAN, a generative adversarial network (GAN) based deep neural network model for large scale generative design of novel cubic materials. When trained on 375 749 ternary materials from the OQMD database, the authors show that the model is able to not only rediscover most of the currently known cubic materials but also generate hypothetical materials of new structure prototypes. A total of 506 such materials have been verified by phonon dispersion calculation. Considering the importance of cubic materials in wide applications such as solar panels, the GAN model provides a promising approach to significantly expand existing materials repositories, enabling the discovery of new functional materials via screening. The new crystal structures discovered are freely accessible at www.carolinamatdb. org.
Llama: A Heterogeneous & Serverless Framework for Auto-Tuning Video Analytics Pipelines

The proliferation of camera-enabled devices and large video repositories has led to a diverse set of video analytics applications. These applications rely on video pipelines, represented as DAGs of operations, to transform videos, process extracted metadata, and answer questions like, "Is this intersection congested? " The latency and resource efficiency of pipelines can be optimized using configurable knobs for each operation (e.g., sampling rate, batch size, or type of hardware used). However, determining efficient configurations is challenging because (a) the configuration search space is exponentially large, and (b) the optimal configuration depends on users' desired latency and cost targets, (c) input video contents may exercise different paths in the DAG and produce a variable amount intermediate results. Existing video analytics and processing systems leave it to the users to manually configure operations and select hardware resources. We present Llama: a heterogeneous and serverless framework for auto-tuning video pipelines. Given an end-to-end latency target, Llama optimizes for cost efficiency by (a) calculating a latency target for each operation invocation, and (b) dynamically running a cost-based optimizer to assign configurations across heterogeneous hardware that best meet the calculated per-invocation latency target. This makes the problem of auto-tuning large video pipelines tractable and allows us to handle input-dependent behavior, conditional branches in the DAG, and execution variability. We describe the algorithms in Llama and evaluate it on a cloud platform using serverless CPU and GPU resources. We show that compared to state-of-the-art cluster and serverless video analytics and processing systems, Llama achieves 7.8x lower latency and 16x cost reduction on average.
Níveis adequados e tóxicos de zinco na produção de arroz, feijão, milho, soja e trigo em solo de cerrado

Five greenhouse experiments were conducted to determine adequate and toxic levels of zinc in upland rice, common bean, corn, soybean, and wheat. The Zn treatments were 0, 5, 10, 20, 40, 80, and 120 mg Zn kg-1 of soil. Relative dry matter yield of 90% was used as a parameter to define the adequate level of Zn applied or the Zn content in the soil and Zn in the plant tissues. Similarly, a 10% reduction in relative dry matter yield was used as a criterion for defining toxic levels in the soil as well as in the plants. An adequate level of applied Zn was 10 mg kg-1 for rice, 1 mg kg-1 for common bean, 3 mg kg-1 for corn, 2 mg kg-1 for soybean and 1 mg kg-1 for wheat. The toxic levels of soil applied Zn were 70, 57, 110, 59, and 40 mg kg-1 for the same crops, respectively. An adequate content of Zn in soil analysis by Mehlich 1 extractant for the rice was 5 mg Zn kg-1, 0.7 mg Zn kg-1 for the common bean, 2 mg Zn kg-1 for corn, 0.8 mg Zn kg-1 for soybean and 0.5 mg Zn kg-1 for wheat, where as by extractant DTPA the contents were 4 mg kg-1 for rice, 1 mg kg-1 for corn and 0.3 mg-1 for bean, soybean and wheat. The toxic levels of Zn in soil, depending on crop species varied from 25 to 94 mg kg-1 by Mehlich 1 and 25 to 60 mg kg-1 with DTPA extractant. Plant tissue analysis showed variation in adequate level from 18 to 67 mg Zn kg-1 and toxic levels varied from 100 to 673 mg kg-1 depending on crop species.
Frank-Wolfe with a Nearest Extreme Point Oracle

We consider variants of the classical Frank-Wolfe algorithm for constrained smooth convex minimization, that instead of access to the standard oracle for minimizing a linear function over the feasible set, have access to an oracle that can find an extreme point of the feasible set that is closest in Euclidean distance to a given vector. We first show that for many feasible sets of interest, such an oracle can be implemented with the same complexity as the standard linear optimization oracle. We then show that with such an oracle we can design new Frank-Wolfe variants which enjoy significantly improved complexity bounds in case the set of optimal solutions lies in the convex hull of a subset of extreme points with small diameter (e.g., a low-dimensional face of a polytope). In particular, for many $0\text{--}1$ polytopes, under quadratic growth and strict complementarity conditions, we obtain the first linearly convergent variant with rate that depends only on the dimension of the optimal face and not on the ambient dimension.
Deep CNNs for large scale species classification

Large Scale image classification is a challenging problem within the field of computer vision. As the real world contains billions of different objects, understanding the performance of popular techniques and models is vital in order to apply them to real world tasks. In this paper, we evaluate techniques and popular CNN based deep learning architectures to perform large scale species classification on the dataset from iNaturalist 2019 Challenge. Methods utilizing dataset pruning and transfer learning are shown to outperform models trained without either of the two techniques. The ResNext based classifier outperforms other model architectures over 10 epochs and achieves a top-one validation error of 0.68 when classifying amongst the 1,010 species.