Miracle-dz/UniFAR-FTUs huggingface dataset:
Countering Racial Bias in Computer Graphics Research

Current computer graphics research practices contain racial biases that have resulted in investigations into"skin"and"hair"that focus on the hegemonic visual features of Europeans and East Asians. To broaden our research horizons to encompass all of humanity, we propose a variety of improvements to quantitative measures and qualitative practices, and pose novel, open research problems.
Few-Shot Learning for Video Object Detection in a Transfer-Learning Scheme

Different from static images, videos contain additional temporal and spatial information for better object detection. However, it is costly to obtain a large number of videos with bounding box annotations that are required for supervised deep learning. Although humans can easily learn to recognize new objects by watching only a few video clips, deep learning usually suffers from overﬁtting. This leads to an important question: how to effectively learn a video object detector from only a few labeled video clips? In this paper, we study the new problem of few-shot learning for video object detection. We ﬁrst deﬁne the few-shot setting and create a new benchmark dataset for few-shot video object detection derived from the widely used ImageNet VID dataset. We employ a transfer-learning framework to effectively train the video object detector on a large number of base-class objects and a few video clips of novel-class objects. By analyzing the results of two methods under this framework (Joint and Freeze) on our designed weak and strong base datasets, we reveal insufﬁciency and overﬁtting problems. A simple but effective method, called Thaw, is naturally developed to trade off the two problems and validate our analysis. Extensive experiments on our proposed benchmark datasets with different scenarios demonstrate the effectiveness of our novel analysis in this new few-shot video object detection problem.
Rational design of Co-based redox mediators for dye-sensitized solar cells by density functional theory

Density functional theory (DFT) calculations were carried out to explore the effects of chemically modifying the polypyridine ligands and design efficient Co-based redox mediators for dye-sensitized solar cells (DSSCs). Our results showed that the redox properties of cobalt complexes can be well tuned by altering the number and position of nitrogen atoms on the ligand ring. Adding oxygen atoms on the ligand ring will evidently increase the redox potential, which might be unfavorable for the dye regeneration. The designed good redox mediators possess similar redox potential and reorganization energy to the current high-efficiency redox couples, thus are promising to be used in prospective DSSCs.
Deep reinforcement learning of event-triggered communication and control for multi-agent cooperative transport

In this paper, we explore a multi-agent reinforcement learning approach to address the design problem of communication and control strategies for multi-agent cooperative transport. Typical end-to-end deep neural network policies may be insufficient for covering communication and control; these methods cannot decide the timing of communication and can only work with fixed-rate communications. Therefore, our framework exploits event-triggered architecture, namely, a feedback controller that computes the communication input and a triggering mechanism that determines when the input has to be updated again. Such event-triggered control policies are efficiently optimized using a multi-agent deep deterministic policy gradient. We confirmed that our approach could balance the transport performance and communication savings through numerical simulations.
Cloud2Curve: Generation and Vectorization of Parametric Sketches

Analysis of human sketches in deep learning has advanced immensely through the use of waypoint-sequences rather than raster-graphic representations. We further aim to model sketches as a sequence of low-dimensional parametric curves. To this end, we propose an inverse graphics framework capable of approximating a raster or waypoint based stroke encoded as a point-cloud with a variable-degree Bézier curve. Building on this module, we present Cloud2Curve, a generative model for scalable high-resolution vector sketches that can be trained end-to-end using point-cloud data alone. As a consequence, our model is also capable of deterministic vectorization which can map novel raster or waypoint based sketches to their corresponding high-resolution scalable Bézier equivalent. We evaluate the generation and vectorization capabilities of our model on Quick, Draw! and K-MNIST datasets.
Rank Minimization-Based Toeplitz Reconstruction for DoA Estimation Using Coprime Array

In this letter, we address the problem of direction finding using coprime array, which is one of the most preferred sparse array configurations. Motivated by the fact that non-uniform element spacing hinders full utilization of the underlying information in the receive signals, we propose a direction-of-arrival (DoA) estimation algorithm based on low-rank reconstruction of the Toeplitz covariance matrix. The atomic-norm representation of the measurements from the interpolated virtual array is considered, and the equivalent dual-variable rank minimization problem is formulated and solved using a cyclic optimization approach. The recovered covariance matrix enables the application of conventional subspace-based spectral estimation algorithms, such as MUSIC, to achieve enhanced DoA estimation performance. The estimation performance of the proposed approach, in terms of the degrees-of-freedom and spatial resolution, is examined. We also show the superiority of the proposed method over the competitive approaches in the root-mean-square error sense.
Analysing the Effect of Recommendation Algorithms on the Amplification of Misinformation

Recommendation algorithms have been pointed out as one of the major culprits of misinformation spreading in the digital sphere. However, it is still unclear how these algorithms really propagate misinformation, e.g., it has not been shown which particular recommendation approaches are more prone to suggest misinforming items, or which internal parameters of the algorithms could be influencing more on their misinformation propagation capacity. Motivated by this fact, in this paper we present an analysis of the effect of some of the most popular recommendation algorithms on the spread of misinformation in Twitter. A set of guidelines on how to adapt these algorithms is provided based on such analysis and a comprehensive review of the research literature. A dataset is also generated and released to the scientific community to stimulate discussions on the future design and development of recommendation algorithms to counter misinformation. The dataset includes editorially labelled news items and claims regarding their misinformation nature.
MergeComp: A Compression Scheduler for Scalable Communication-Efficient Distributed Training

Large-scale distributed training is increasingly becoming communication bound. Many gradient compression algorithms have been proposed to reduce the communication overhead and improve scalability. However, it has been observed that in some cases gradient compression may even harm the performance of distributed training. In this paper, we propose MergeComp, a compression scheduler to optimize the scalability of communication-efficient distributed training. It automatically schedules the compression operations to optimize the performance of compression algorithms without the knowledge of model architectures or system parameters. We have applied MergeComp to nine popular compression algorithms. Our evaluations show that MergeComp can improve the performance of compression algorithms by up to 3.83x without losing accuracy. It can even achieve a scaling factor of distributed training up to 99% over high-speed networks.
Competing Adaptive Networks

Adaptive networks have the capability to pursue solutions of global stochastic optimization problems by relying only local interactions within neighborhoods. The diffusion of information through repeated interactions allows for globally optimal behavior, without the need for central coordination. Most existing strategies are developed for cooperative learning settings, where the objective of the network is common to all agents. We consider in this work a team setting, where a subset of the agents form a team with a common goal, while competing with the remainder of the network. We develop an algorithm for decentralized competition among teams of adaptive agents, analyze its dynamics and present an application in the decentralized training of generative adversarial neural networks.
Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding

Unobserved confounding is one of the greatest challenges for causal discovery. The case in which unobserved variables have a widespread effect on many of the observed ones is particularly difficult because most pairs of variables are conditionally dependent given any other subset, rendering the causal effect unidentifiable. In this paper we show that beyond conditional independencies, under the principle of independent mechanisms, unobserved confounding in this setting leaves a statistical footprint in the observed data distribution that allows for disentangling spurious and causal effects. Using this insight, we demonstrate that a sparse linear Gaussian directed acyclic graph among observed variables may be recovered approximately and propose an adjusted score-based causal discovery algorithm that may be implemented with general purpose solvers and scales to high-dimensional problems. We find, in addition, that despite the conditions we pose to guarantee causal recovery, performance in practice is robust to large deviations in model assumptions.
Unified Shape and SVBRDF Recovery using Differentiable Monte Carlo Rendering

Reconstructing the shape and appearance of real‐world objects using measured 2D images has been a long‐standing inverse rendering problem. In this paper, we introduce a new analysis‐by‐synthesis technique capable of producing high‐quality reconstructions through robust coarse‐to‐fine optimization and physics‐based differentiable rendering.
Synthesis of Compositional Animations from Textual Descriptions

"How can we animate 3D-characters from a movie script or move robots by simply telling them what we would like them to do? " "How unstructured and complex can we make a sentence and still generate plausible movements from it? " These are questions that need to be answered in the long-run, as the field is still in its infancy. Inspired by these problems, we present a new technique for generating compositional actions, which handles complex input sentences. Our output is a 3D pose sequence depicting the actions in the input sentence. We propose a hierarchical two-stream sequential model to explore a finer joint-level mapping between natural language sentences and 3D pose sequences corresponding to the given motion. We learn two manifold representations of the motion, one each for the upper body and the lower body movements. Our model can generate plausible pose sequences for short sentences describing single actions as well as long complex sentences describing multiple sequential and compositional actions. We evaluate our proposed model on the publicly available KIT Motion-Language Dataset containing 3D pose data with human-annotated sentences. Experimental results show that our model advances the state-of-the-art on text-based motion synthesis in objective evaluations by a margin of 50%. Qualitative evaluations based on a user study indicate that our synthesized motions are perceived to be the closest to the ground-truth motion captures for both short and compositional sentences.
Accurate and Reliable Forecasting using Stochastic Differential Equations

It is critical yet challenging for deep learning models to properly characterize uncertainty that is pervasive in real-world environments. Although a lot of efforts have been made, such as heteroscedastic neural networks (HNNs), little work has demonstrated satisfactory practicability due to the different levels of compromise on learning efficiency, quality of uncertainty estimates, and predictive performance. Moreover, existing HNNs typically fail to construct an explicit interaction between the prediction and its associated uncertainty. This paper aims to remedy these issues by developing SDE-HNN, a new heteroscedastic neural network equipped with stochastic differential equations (SDE) to characterize the interaction between the predictive mean and variance of HNNs for accurate and reliable regression. Theoretically, we show the existence and uniqueness of the solution to the devised neural SDE. Moreover, based on the bias-variance trade-off for the optimization in SDE-HNN, we design an enhanced numerical SDE solver to improve the learning stability. Finally, to more systematically evaluate the predictive uncertainty, we present two new diagnostic uncertainty metrics. Experiments on the challenging datasets show that our method significantly outperforms the state-of-the-art baselines in terms of both predictive performance and uncertainty quantification, delivering well-calibrated and sharp prediction intervals.
PENELOPIE: Enabling Open Information Extraction for the Greek Language through Machine Translation

In this work, we present a methodology that aims at bridging the gap between high and low-resource languages in the context of Open Information Extraction, showcasing it on the Greek language. The goals of this paper are twofold: First, we build Neural Machine Translation (NMT) models for English-to-Greek and Greek-to-English based on the Transformer architecture. Second, we leverage these NMT models to produce English translations of Greek text as input for our NLP pipeline, to which we apply a series of pre-processing and triple extraction tasks. Finally, we back-translate the extracted triples to Greek. We conduct an evaluation of both our NMT and OIE methods on benchmark datasets and demonstrate that our approach outperforms the current state-of-the-art for the Greek natural language.
Private Non-smooth Empirical Risk Minimization and Stochastic Convex Optimization in Subquadratic Steps

We study the differentially private Empirical Risk Minimization (ERM) and Stochastic Convex Optimization (SCO) problems for non-smooth convex functions. We get a (nearly) optimal bound on the excess empirical risk and excess population loss with subquadratic gradient complexity. More precisely, our differentially private algorithm requires $O(\frac{N^{3/2}}{d^{1/8}}+ \frac{N^2}{d})$ gradient queries for optimal excess empirical risk, which is achieved with the help of subsampling and smoothing the function via convolution. This is the first subquadratic algorithm for the non-smooth case when $d$ is super constant. As a direct application, using the iterative localization approach of Feldman et al. \cite{fkt20}, we achieve the optimal excess population loss for stochastic convex optimization problem, with $O(\min\{N^{5/4}d^{1/8},\frac{ N^{3/2}}{d^{1/8}}\})$ gradient queries. Our work makes progress towards resolving a question raised by Bassily et al. \cite{bfgt20}, giving first algorithms for private ERM and SCO with subquadratic steps. We note that independently Asi et al. \cite{afkt21} gave other algorithms for private ERM and SCO with subquadratic steps.
GRB160203A: an exploration of lumpy space

GRB160203A is a high redshift long gamma-ray burst presenting a collection of unusual features in the afterglow light curve. We study its optical and X-ray data. We find this event to occur within a constant density medium during the first part of the afterglow. However, after 13 ks we spot some flaring activities in the optical and X-ray light curves. We explain these flares by fluctuation of densities of the surrounding medium. Other scenarios, such as energy injection from a magnetar or variation of microphysical parameters are not supported by the data. We tentatively link these fluctuations to an unusual host galaxy, with gas density similar to the Milky Way and a dense cocoon of matter around a stellar progenitor similar to a Wolf-Rayet star. A termination shock scenario is found to be less likely.
Protonierte chirale Katalysatoren: vielseitige Hilfsmittel für die asymmetrische Synthese

Die asymmetrische Katalyse organischer Reaktionen ist eines der wichtigsten Forschungsgebiete in der Chemie. Obgleich in der asymmetrischen Katalyse viele Jahre lang der Einsatz chiraler bergangsmetallkomplexe vorherrschte, haben k rzlich Prozesse, die durch metallfreie Spezies katalysiert werden, maßgeblich an Interesse gewonnen. In vielen F llen beruht die Wirksamkeit dieser Katalysatoren auf der Bildung streng gerichteter Wasserstoffbr cken. Die Substrate werden dabei durch nichtkovalente Wechselwirkungen aktiviert, wodurch synthetisch wichtige enantioselektive Umsetzungen mit definierten, niedermolekularen, metallfreien Katalysatoren erreicht werden. Ein besonders sch nes Beispiel f r dieses Konzept ist die Taddol-katalysierte asymmetrische Hetero-Diels-Alder-Reaktion von Rawal et al. Dort komplexiert ein einfacher chiraler Alkohol eine Carbonylgruppe und beschleunigt den enantioselektiven C-C-Verkn pfungsschritt, sodass das cyclische Produkt mit > 92 % ee gebildet wird (Schema 1). In der Cycloaddition von Rawal und Mitarbeitern aktiviert ein kleines, ungeladenes (rein) organisches Molek l die Carbonylverbindung und f hrt zu ausgezeichneten Enantioselektivit ten. Ein grunds tzlich anderes Konzept besteht im Gebrauch einer chiralen BrønstedBase, die selbst katalytisch inaktiv ist und aus der erst durch Zugabe einer S ure ein aktiver Katalysator entsteht. Weder die Brønsted-Base noch die S ure wirken unabh ngig voneinander, doch ihre Kombination f hrt zu einer kationischen Spezies, einem hoch wirksamen Katalysator mit bemerkenswerter Aktivit t und (Stereo)Selektivit t. J ngst wurde ber einige aufregende Entwicklungen auf diesem Gebiet berichtet, und ausgew hlte Beispiele sollen in diesem Highlight hervorgehoben werden. Im Jahre 2001 beschrieben Yamamoto et al. Kombinationen aus Brønsted-S uren und Diaminen als Katalysatoren f r die direkte asymmetrische Aldolreaktion zwischen Aceton und einigen aromatischen und aliphatischen Aldehyden. Angeregt durch fr here Beobachtungen untersuchten sie Diamine – berwiegend mit Pyrrolidin-R ckgrat – in Kombination mit verschiedenen Sulfons uren (Schema 2). Unter den getesteten Diaminen, die als Brønsted-Basen fungierten, erwiesen sich solche mit einem sekund ren und einem terti ren Stickstoffatom, wie 9a– d, als hervorragende Katalysatoren f r Schema 1. Wasserstoffbr cken-Katalyse nach Rawal und Mitarbeitern. TBS = tert-Butyldimethylsilyl.
Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning

Professional race-car drivers can execute extreme overtaking maneuvers. However, existing algorithms for autonomous overtaking either rely on simplified assumptions about the vehicle dynamics or try to solve expensive trajectory-optimization problems online. When the vehicle approaches its physical limits, existing model-based controllers struggle to handle highly nonlinear dynamics, and cannot leverage the large volume of data generated by simulation or real-world driving. To circumvent these limitations, we propose a new learning-based method to tackle the autonomous overtaking problem. We evaluate our approach in the popular car racing game Gran Turismo Sport, which is known for its detailed modeling of various cars and tracks. By leveraging curriculum learning, our approach leads to faster convergence as well as increased performance compared to vanilla reinforcement learning. As a result, the trained controller outperforms the built-in model-based game AI and achieves comparable over-taking performance with an experienced human driver.
PlaneSegNet: Fast and Robust Plane Estimation Using a Single-stage Instance Segmentation CNN

Instance segmentation of planar regions in indoor scenes benefits visual SLAM and other applications such as augmented reality (AR) where scene understanding is required. Existing methods built upon two-stage frameworks show satisfactory accuracy but are limited by low frame rates. In this work, we propose a real-time deep neural architecture that estimates piece-wise planar regions from a single RGB image. Our model employs a variant of a fast single-stage CNN architecture to segment plane instances. Considering the particularity of the target detected, we propose Fast Feature Non-maximum Suppression (FF-NMS) to reduce the suppression errors resulted from overlapping bounding boxes of planes. We also utilize a Residual Feature Augmentation module in the Feature Pyramid Network (FPN) . Our method achieves significantly higher frame-rates and comparable segmentation accuracy against two-stage methods. We automatically label over 70,000 images as ground truth from the Stanford 2D-3D-Semantics dataset. Moreover, we incorporate our method with a state-of-the-art planar SLAM and validate its benefits.
Stress evolution as a function of substrate bias in rf magnetron sputtered yttria-stabilized zirconia films

An increase in compressive stress was observed in rf magnetron sputtered yttria-stabilized zirconia thin films upon exposure to ambient conditions (25°C and 75% relative humidity). This increase was attributed to absorption of water molecules into intergranular pores. It was shown that increasing substrate bias power disrupted columnar grain growth and reduced the percent change in compressive stress when exposed to ambient environments. Transmission electron microscopy confirmed a reduction in intergranular porosity for substrate bias depositions but an increase in lateral defects. These defects are hypothesized to be stress-induced microcracks caused by a tetragonal to monoclinic phase transformation.
A Comparative Evaluation of Predominant Deep Learning Quantified Stock Trading Strategies

Quantified Stock Trading refers to the technique of delegating the buying and selling of stock shares to machines running a programmed algorithm. The objective of this study is to, through comparative analysis, find a deep learning powered quantified trading model that can most effectively help an investment portfolio avert continued loss in adverse market climates. This study first reconstructs three deep learning powered trading models and their associated strategies that are representative of distinct approaches to the problem. It then seeks to compare the performance of these strategies from the perspectives of fully informed vs. projection models, returns, risk vs. reward as well as similarity to the benchmark’s return sequence’s patterns through trading simulations ran on three scenarios when the benchmarks are kept at historical low points for extended periods of time. The results show that in extremely adverse market climates, investment portfolios managed by deep learning powered algorithms are able to avert accumulated losses by generating return sequences that shift the constantly negative CSI 300 benchmark return upward. Among the three, the LSTM model’s strategy yields the best performance.