Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-Rank Matrix Completion Theory via Plücker Coordinates

Despite the popularity of low-rank matrix completion, the majority of its theory has been developed under the assumption of random observation patterns, whereas very little is known about the practically relevant case of non-random patterns. Specifically, a fundamental yet largely open question is to describe patterns that allow for unique or finitely many completions. This paper provides three such families of patterns for any rank and any matrix size. A key to achieving this is a novel formulation of low-rank matrix completion in terms of Plücker coordinates, the latter a traditional tool in computer vision. This connection is of potential significance to a wide family of matrix and subspace learning problems with incomplete data.
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Representation

—Community detection, a fundamental task for network analysis, aims to partition a network into multiple sub-structures to help reveal their latent functions. Community detection has been extensively studied in and broadly applied to many real-world network problems. Classical approaches to community detection typically utilize probabilistic graphical models and adopt a variety of prior knowledge to infer community structures. As the problems that network methods try to solve and the network data to be analyzed become increasingly more sophisticated, new approaches have also been proposed and developed, particularly those that utilize deep learning and convert networked data into low dimensional representation. Despite all the recent advancement, there is still a lack of insightful understanding of the theoretical and methodological underpinning of community detection, which will be critically important for future development of the area of network analysis. In this paper, we develop and present a uniﬁed architecture of network community-ﬁnding methods to characterize the state-of-the-art of the ﬁeld of community detection. Speciﬁcally, we provide a comprehensive review of the existing community detection methods and introduce a new taxonomy that divides the existing methods into two categories, namely probabilistic graphical model and deep learning. We then discuss in detail the main idea behind each method in the two categories. Furthermore, to promote future development of community detection, we release several benchmark datasets from several problem domains and highlight their applications to various network analysis tasks. We conclude with discussions of the challenges of the ﬁeld and suggestions of possible directions for future research.
Dynamical evolution of the inner asteroid belt

A determination of the dynamical evolution of the asteroid belt is difficult because the asteroid belt has evolved since the time of asteroid formation through mechanisms that include: (1) catastrophic collisions, (2) rotational disruption, (3) chaotic orbital evolution, and (4) orbital evolution driven by Yarkovsky radiation forces. The time-scales of these loss mechanisms are uncertain and there is a need for more observational constraints. In the inner main belt (IMB), the mean size of the non-family asteroids increases with increasing inclination. Here, we use that observation to show that all IMB asteroids originate from either the known families or from ghost families, that is, old families with dispersed orbital elements. We estimate that the average age of the asteroids in the ghost families is a factor of 1/3 less than the Yarkovsky orbital evolution time-scale. However, this orbital evolution time-scale is a long-term average that must allow for the collisional evolution of the asteroids and for stochastic changes in their spin directions. By applying these constraints on the orbital evolution time-scales to the evolution of the size-frequency distribution of the Vesta asteroid family, we estimate that the age of this family is greater than 1.3 Gyr and could be comparable with the age of the Solar system. By estimating the number of ghost families, we calculate that the number of asteroids that are the root sources of the meteorites and the near-Earth asteroids that originate from the IMB is about 20.
Thing Translator: An Efficient Way to Classify Different Things

Translation is the communication of a source-language text into an equivalent target-language text. A translator always risks in advertently introducing source-language words, grammar, or syntax into the target-language rendering. Thing Translator is an open-source offline application that uses the modern machine learning techniques – namely, computer vision and natural language translation. Thing Translator is a web app that lets you point your phone at particular object to hear it in a different language. The task of the translator here is to maintain a delicate balance between the source language and the language of translation. Thing translator is using Google’s Translate API's and cloud vision as it was developed by Google as a part of AI experiment. The main reason behind building this application is to help each and every person to know different things in many languages and understand them better. When we want to travel outside our place or outside India then there may be difficulty that we may not know that language of that particular region, we find language barrier due to not understanding the language they speak there is also a language barrier if want to shop from another region. We have designed an application ―Thing Translator: An Efficient Way to Classify Different Things‖ for every person to classify or recognize different things present around them. Thing translator is an android application which uses image recognition and object character recognition. This is useful to understand and to learn names of different objects just a capturing the image and also to learn pronunciation of the objects in native or other languages. This application can be used for the classification of necessary household objects present around us such as bottle, clock, facemask, wrist watch, comb etc. and also some electronic objects present in house that include laptop, television, charger etc. Also, we have included some flowers and animals.
Improving Coffee Productivity in Ethiopia: The Impact of a Coffee Tree Rejuvenation Training Program on Stumping

Coffee is Ethiopia’s most important export commodity, cultivated by over 6 million smallholder farmers in the country, and accounting for about one-third of the country’s commodity exports. While coffee production has increased over the last decade, coffee yields are low and several constraints to improved productivity remain. With two-three decades old and low-yielding coffee trees in particular, the sector cannot attain its full potential. In this paper, we assess the short-term impact of a coffee tree rejuvenation training program in Sidama on adoption rate and intensity of stumping – currently the best practice to revitalize ageing coffee trees and substantially improve their productivity. Using baseline and follow-up data and a difference-in-difference approach, we find that the adoption rate and intensity of stumping has increased by about threefold during the first year of the rejuvenation training intervention.
DSLR: Dynamic to Static LiDAR Scan Reconstruction Using Adversarially Trained Autoencoder

Accurate reconstruction of static environments from LiDAR scans of scenes containing dynamic objects, which we refer to as Dynamic to Static Translation (DST), is an important area of research in Autonomous Navigation. This problem has been recently explored for visual SLAM, but to the best of our knowledge no work has been attempted to address DST for LiDAR scans. The problem is of critical importance due to wide-spread adoption of LiDAR in Autonomous Vehicles. We show that state-of the art methods developed for the visual domain when adapted for LiDAR scans perform poorly. We develop DSLR, a deep generative model which learns a mapping between dynamic scan to its static counterpart through an adversarially trained autoencoder. Our model yields the first solution for DST on LiDAR that generates static scans without using explicit segmentation labels. DSLR cannot always be applied to real world data due to lack of paired dynamic-static scans. Using Unsupervised Domain Adaptation, we propose DSLR-UDA for transfer to real world data and experimentally show that this performs well in real world settings. Additionally, if segmentation information is available, we extend DSLR to DSLR-Seg to further improve the reconstruction quality. DSLR gives the state of the art performance on simulated and real-world datasets and also shows at least 4× improvement. We show that DSLR, unlike the existing baselines, is a practically viable model with its reconstruction quality within the tolerable limits for tasks pertaining to autonomous navigation like SLAM in dynamic environments.
LVD-NMPC: A learning-based vision dynamics approach to nonlinear model predictive control for autonomous vehicles

In this article, we introduce a learning-based vision dynamics approach to nonlinear model predictive control (NMPC) for autonomous vehicles, coined learning-based vision dynamics (LVD) NMPC. LVD-NMPC uses an a-priori process model and a learned vision dynamics model used to calculate the dynamics of the driving scene, the controlled system’s desired state trajectory, and the weighting gains of the quadratic cost function optimized by a constrained predictive controller. The vision system is defined as a deep neural network designed to estimate the dynamics of the image scene. The input is based on historic sequences of sensory observations and vehicle states, integrated by an augmented memory component. Deep Q-learning is used to train the deep network, which once trained can also be used to calculate the desired trajectory of the vehicle. We evaluate LVD-NMPC against a baseline dynamic window approach (DWA) path planning executed using standard NMPC and against the PilotNet neural network. Performance is measured in our simulation environment GridSim, on a real-world 1:8 scaled model car as well as on a real size autonomous test vehicle and the nuScenes computer vision dataset.
Volume complexity for the nonsupersymmetric Janus 
AdS5
 geometry

We compute holographic complexity for the non-supersymmetric Janus deformation of AdS$_5$ according to the volume conjecture. The result is characterized by a power-law ultraviolet divergence. When a ball-shaped region located around the interface is considered, a sub-leading logarithmic divergent term and a finite part appear in the corresponding subregion volume complexity. Using two different prescriptions to regularize the divergences, we find that the coefficient of the logarithmic term is universal.
Beam Focusing for Near-Field Multi-User MIMO Communications

Large antenna arrays and high-frequency bands are two key features of future wireless communication systems. The combination of large-scale antennas with high transmission frequencies often results in the communicating devices operating in the near-field (Fresnel) region. In this paper, we study the potential of beam focusing, feasible in near-field operation, in facilitating high-rate multi-user downlink multiple-input multiple-output (MIMO) systems. As the ability to achieve beam focusing is dictated by the transmit antenna, we study near-field signalling considering different antenna structures, including fully-digital architectures, hybrid phase shifter-based precoders, and the emerging dynamic metasurface antenna (DMA) architecture for massive MIMO arrays. We first provide a mathematical model to characterize near-field wireless channels as well as the transmission pattern for the considered antenna architectures. Then, we formulate the beam focusing problem for the goal of maximizing the achievable sum-rate in multi-user networks. We propose efficient solutions based on the sum-rate maximization task for fully-digital, (phase shifters based-) hybrid and DMA architectures. Simulation results show the feasibility of the proposed beam focusing scheme for both single- and multi-user scenarios. In particular, the designed focused beams provide a new degree of freedom to mitigate interference in both angle and distance domains, which is not achievable using conventional far-field beam steering, allowing reliable communications for uses even residing at the same angular direction.
Learning to Optimize Industry-Scale Dynamic Pickup and Delivery Problems

The Dynamic Pickup and Delivery Problem (DPDP) is aimed at dynamically scheduling vehicles among multiple sites in order to minimize the cost when delivery orders are not known a priori. Although DPDP plays an important role in modern logistics and supply chain management, state-of-the-art DPDP algorithms are still limited on their solution quality and efficiency. In practice, they fail to provide a scalable solution as the numbers of vehicles and sites become large. In this paper, we propose a data-driven approach, Spatial-Temporal Aided Double Deep Graph Network (ST-DDGN), to solve industry-scale DPDP. In our method, the delivery demands are first forecast using spatial-temporal prediction method, which guides the neural network to perceive spatial-temporal distribution of delivery demand when dispatching vehicles. Besides, the relationships of individuals such as vehicles are modelled by establishing a graph-based value function. ST-DDGN incorporates attention-based graph embedding with Double DQN (DDQN). As such, it can make the inference across vehicles more efficiently compared with traditional methods. Our method is entirely data driven and thus adaptive, i.e., the relational representation of adjacent vehicles can be learned and corrected by ST-DDGN from data periodically. We have conducted extensive experiments over real-world data to evaluate our solution. The results show that ST-DDGN reduces 11.27% number of the used vehicles and decreases 13.12% total transportation cost on average over the strong baselines, including the heuristic algorithm deployed in our UAT (User Acceptance Test) environment and a variety of vanilla DRL methods. We are due to fully deploy our solution into our online logistics system and it is estimated that millions of USD logistics cost can be saved per year.
The de Rham–Fargues–Fontaine
cohomology

We show how to attach to any rigid analytic variety $V$ over a perfectoid space $P$ a rigid analytic motive over the Fargues-Fontaine curve $\mathcal{X}(P)$ functorially in $V$ and $P$. We combine this construction with the overconvergent relative de Rham cohomology to produce a complex of solid quasi-coherent sheaves over $\mathcal{X}(P)$, and we show that its cohomology groups are vector bundles if $V$ is smooth and proper over $P$ or if $V$ is quasi-compact and $P$ is a perfectoid field, thus proving and generalizing a conjecture of Scholze. The main ingredients of the proofs are explicit $\mathbb{B}^1$-homotopies, the motivic proper base change and the formalism of solid quasi-coherent sheaves.
Statistical Depth Meets Machine Learning: Kernel Mean Embeddings and Depth in Functional Data Analysis

Statistical depth is the act of gauging how representative a point is compared to a reference probability measure. The depth allows introducing rankings and orderings to data living in multivariate, or function spaces. Though widely applied and with much experimental success, little theoretical progress has been made in analysing functional depths. This article highlights how the common $h$-depth and related statistical depths for functional data can be viewed as a kernel mean embedding, a technique used widely in statistical machine learning. This connection facilitates answers to open questions regarding statistical properties of functional depths, as well as it provides a link between the depth and empirical characteristic function based procedures for functional data.
Joint Channel Estimation and Device Activity Detection in Heterogeneous Networks

Internet of Things (IoT) has triggered a rapid increase in the number of connected devices and new use cases of wireless communications. To meet the new demands, the fifth generation (5G) of wireless communication systems features native machine type communication (MTC) services in addition to traditional human type communication (HTC) services. Some of the main challenges are the heterogeneous requirements and the sporadic traffic of massive MTC (mMTC), which makes the orthogonal allocation of resources infeasible. To overcome this problem, grant free non-orthogonal multiple access schemes have been proposed alongside with sparse signal recovery algorithms. While most of the related works have considered only homogeneous networks, we focus on a scenario where an enhanced mobile broadband (eMBB) device and multiple MTC devices share the same radio resources. We exploit the approximate message passing (AMP) algorithm for joint device activity detection and channel estimation of MTC devices in the presence of interference from eMBB, and evaluate the system performance in terms of receiver operating characteristics (ROC) and channel estimation errors. Moreover, we also propose two new pilot sequence generation strategies which improve the detection capabilities of the MTC receiver without affecting the eMBB service.
Constraining nuclear effects in Argon using machine learning algorithms

Neutrino oscillation experiments aim to measure the neutrino oscillation parameters with accuracy and achieve a complete understanding of neutrino physics. For determining the neutrino oscillation parameters, knowledge of neutrino energy is a prerequisite. But neutrino energy needs to be reconstructed, based on the particles in the final state that emerge out of the nucleus following a neutrino-nucleus interaction. Current and upcoming neutrino oscillation experiments use heavy nuclear targets (viz. Argon(Ar), Calcium(Ca), etc.) but the neutrino scattering with such targets becomes complicated as compared to that with a clean target like Hydrogen(H). This work explores the viability of using machine learning algorithms (MLA) in reconstructing neutrino energy. We use final state kinematics generated from two neutrino event generators viz. GENIE and GiBUU to train the MLA. We calculate the Ar/H ratio in an attempt to quantify nuclear effects in the Ar target. We observe a significant improvement in our results when we train the MLA by combining the FSI kinematics of neutrino interactions from both the neutrino event generators.
Adaptive Nearest Neighbor Machine Translation

kNN-MT, recently proposed by Khandelwal et al. (2020a), successfully combines pre-trained neural machine translation (NMT) model with token-level k-nearest-neighbor (kNN) retrieval to improve the translation accuracy. However, the traditional kNN algorithm used in kNN-MT simply retrieves a same number of nearest neighbors for each target token, which may cause prediction errors when the retrieved neighbors include noises. In this paper, we propose Adaptive kNN-MT to dynamically determine the number of k for each target token. We achieve this by introducing a light-weight Meta-k Network, which can be efficiently trained with only a few training samples. On four benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively filter out the noises in retrieval results and significantly outperforms the vanilla kNN-MT model. Even more noteworthy is that the Meta-k Network learned on one domain could be directly applied to other domains and obtain consistent improvements, illustrating the generality of our method. Our implementation is open-sourced at https://github.com/zhengxxn/adaptive-knn-mt.
Scalar Casimir effect for a conducting cylinder in a Lorentz-violating background

Following a field-theoretical approach, we study the scalar Casimir effect upon a perfectly conducting cylindrical shell in the presence of spontaneous Lorentz symmetry breaking. The scalar field is modeled by a Lorentz-breaking extension of the theory for a real scalar quantum field in the bulk regions. The corresponding Green’s functions satisfying Dirichlet boundary conditions on the cylindrical shell are derived explicitly. We express the Casimir pressure (i.e. the vacuum expectation value of the normal–normal component of the stress–energy tensor) as a suitable second-order differential operator acting on the corresponding Green’s functions at coincident arguments. The divergences are regulated by making use of zeta function techniques, and our results are successfully compared with the Lorentz invariant case. Numerical calculations are carried out for the Casimir pressure as a function of the Lorentz-violating coefficient, and an approximate analytical expression for the force is presented as well. It turns out that the Casimir pressure strongly depends on the Lorentz-violating coefficient and it tends to diminish the force.
AndroidEnv: A Reinforcement Learning Platform for Android

We introduce AndroidEnv, an open-source platform for Reinforcement Learning (RL) research built on top of the Android ecosystem. AndroidEnv allows RL agents to interact with a wide variety of apps and services commonly used by humans through a universal touchscreen interface. Since agents train on a realistic simulation of an Android device, they have the potential to be deployed on real devices. In this report, we give an overview of the environment, highlighting the significant features it provides for research, and we present an empirical evaluation of some popular reinforcement learning agents on a set of tasks built on this platform.
Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance

Query focused summarization (QFS) models aim to generate summaries from source documents that can answer the given query. Most previous work on QFS only considers the query relevance criterion when producing the summary. However, studying the effect of answer relevance in the summary generating process is also important. In this paper, we propose QFS-BART, a model that incorporates the explicit answer relevance of the source documents given the query via a question answering model, to generate coherent and answer-related summaries. Furthermore, our model can take advantage of large pre-trained models which improve the summarization performance significantly. Empirical results on the Debatepedia dataset show that the proposed model achieves the new state-of-the-art performance.
Exploring the redshift-space peculiar velocity field and its power spectrum

Redshift-space distortions (RSD) generically affect any spatially-dependent observable that is mapped using redshift information. The effect on the observed clustering of galaxies is the primary example of this. This paper is devoted to another example: the effect of RSD on the apparent peculiar motions of tracers as inferred from their positions in redshift space (i.e. the observed distance). Our theoretical study is motivated by practical considerations, mainly, the direct estimation of the velocity power spectrum, which is preferably carried out using the tracer's redshift-space position (so as to avoid uncertainties in distance measurements). We formulate the redshift-space velocity field and show that RSD enters as a higher-order effect. Physically, this effect may be interpreted as a dissipative correction to the usual perfect-fluid description of dark matter. We show that the effect on the power spectrum is a damping on relatively large, quasilinear scales (k≳0.01 h-1), as was observed, though unexplained, in N-body simulations elsewhere. This paper presents two power spectrum models for the peculiar velocity field in redshift space, both of which can be considered velocity analogues of existing clustering models. In particular, we show that the “Finger-of-God” effect, while also present in the velocity field, cannot be entirely blamed for the observed damping in simulations. Our work provides some of the missing modelling ingredients required for a density-velocity multi-tracer analysis, which has been proposed for upcoming redshift surveys.