Miracle-dz/UniFAR-FTUs huggingface dataset:
Descending Price Auctions with Bounded Number of Price Levels and Batched Prophet Inequality

We consider descending price auctions for selling m units of a good to unit demand i.i. d. buyers where there is an exogenous bound of k on the number of price levels the auction clock can take. The auctioneer's problem is to choose price levels p1 > p2 > ․․․ > pk for the auction clock such that auction expected revenue is maximized. The price levels are announced prior to the auction. We reduce this problem to a new variant of prophet inequality, which we call batched prophet inequality, where a decision-maker chooses k (decreasing) thresholds and then sequentially collects rewards (up to m) that are above the thresholds with ties broken uniformly at random. For the special case of m=1 (i.e., selling a single item), we show that the resulting descending auction with k price levels achieves 1- 1/ek of the unrestricted (without the bound of k) optimal revenue. That means a descending auction with just 4 price levels can achieve more than 98% of the optimal revenue. We then extend our results for m>1 and provide a closed-form bound on the competitive ratio of our auction as a function of the number of units m and the number of price levels k. The full paper is available at: https://arxiv.org/abs/2203.01384
Syntax-Aware Network for Handwritten Mathematical Expression Recognition

Handwritten mathematical expression recognition (HMER) is a challenging task that has many potential applications. Recent methods for HMER have achieved outstanding performance with an encoder-decoder architecture. However, these methods adhere to the paradigm that the prediction is made “from one character to another”, which inevitably yields prediction errors due to the complicated structures of mathematical expressions or crabbed handwritings. In this paper, we propose a simple and efficient method for HMER, which is the first to incorporate syntax information into an encoder-decoder network. Specifically, we present a set of grammar rules for converting the LaTeX markup sequence of each expression into a parsing tree; then, we model the markup sequence prediction as a tree traverse process with a deep neural network. In this way, the proposed method can effectively describe the syntax context of expressions, alleviating the structure prediction errors of HMER. Experiments on three benchmark datasets demonstrate that our method achieves better recognition performance than prior arts. To further validate the effectiveness of our method, we create a large-scale dataset consisting of 100k handwritten mathematical expression images acquired from ten thousand writers. The source code, new dataset††https://ai.100tal. com/dataset, and pre-trained models of this work will be publicly available.
Mathematical aspects of Multiwell Deconvolution and its relation to Capacitance Resistance Model

The paper provides introduction into the mathematical aspects of Multiwell Deconvolution (MDCV) and Capacitance Resistance Model (CRM) and connection between them. Both methods are trying to train a model over the long-term history of surface flowrates and bottomhole pressure readings and then predict bottomhole and formation pressure in response to a given production/injection flowrate scenario (called"rate control simulation") or alternatively may predict flowrate and formation pressure in response to a given bottomhole pressure scenario (called"pressure control simulation"). It has been shown that CRM can be viewed as a partial case of MDCV with a specific type of a drawdown and cross-well pressure transient responses which is not always met in practice. The paper also explains limitations which are common for both methods and specify additional limitations of CRM which MDCV can handle.
Censoring and censoring mechanisms in oncology in light of the estimands framework

In oncology clinical trials with time-to-event endpoints, censoring rules have traditionally been defined and applied following standard approaches based on longstanding regulatory guidelines. The estimand framework (addendum to the ICH E9 guideline) calls for precisely defining the treatment effect of interest to align with the clinical question of interest, and requires predefining the handling of intercurrent events that occur after treatment initiation and either preclude the observation of an event of interest or impact the interpretation of the treatment effect. In the context of time to event endpoints, this requires a careful discussion on how censoring rules are applied. This paper explains the importance of distinguishing censoring concepts that have traditionally been merged. Specifically, noumenal censoring as an estimation method to address an intercurrent event which influences the clinical question itself (i.e. defines the pre-specified estimand), distinguishing it from phenomenal censoring that addresses administratively missing information (i.e. defines missing data handling in the estimate). Strategies for dealing with the most relevant intercurrent events, the need for close alignment of the clinical question of interest and study design, impact on data collection and other practical implications will be discussed. The authors recommend defining trial-specific rules as well as considering when ‘noumenal’ and ‘phenomenal censoring’ is used. These considerations also apply to defining relevant and interpretable sensitivity and supplementary analyses.
Query Processing on Tensor Computation Runtimes

The huge demand for computation in artificial intelligence (AI) is driving unparalleled investments in hardware and software systems for AI. This leads to an explosion in the number of specialized hardware devices, which are now offered by major cloud vendors. By hiding the low-level complexity through a tensor-based interface, tensor computation runtimes (TCRs) such as PyTorch allow data scientists to efficiently exploit the exciting capabilities offered by the new hardware. In this paper, we explore how database management systems can ride the wave of innovation happening in the AI space. We design, build, and evaluate Tensor Query Processor (TQP): TQP transforms SQL queries into tensor programs and executes them on TCRs. TQP is able to run the full TPC-H benchmark by implementing novel algorithms for relational operators on the tensor routines. At the same time, TQP can support various hardware while only requiring a fraction of the usual development effort. Experiments show that TQP can improve query execution time by up to 10X over specialized CPU- and GPU-only systems. Finally, TQP can accelerate queries mixing ML predictions and SQL end-to-end, and deliver up to 9X speedup over CPU baselines.
Object Pose Estimation using Mid-level Visual Representations

This work proposes a novel pose estimation model for object categories that can be effectively transferred to pre-viously unseen environments. The deep convolutional network models (CNN) for pose estimation are typically trained and evaluated on datasets specifically curated for object detection, pose estimation, or 3D reconstruction, which requires large amounts of training data. In this work, we propose a model for pose estimation that can be trained with small amount of data and is built on the top of generic mid-level represen-tations [33] (e.g. surface normal estimation and re-shading). These representations are trained on a large dataset without requiring pose and object annotations. Later on, the predictions are refined with a small CNN neural network that exploits object masks and silhouette retrieval. The presented approach achieves superior performance on the Pix3D dataset [26] and shows nearly 35 % improvement over the existing models when only 25 % of the training data is available. We show that the approach is favorable when it comes to generalization and transfer to novel environments. Towards this end, we introduce a new pose estimation benchmark for commonly encountered furniture categories on challenging Active Vision Dataset [1] and evaluated the models trained on the Pix3D dataset.
Risk-aware Stochastic Shortest Path

We treat the problem of risk-aware control for stochastic shortest path (SSP) on Markov decision processes (MDP). Typically, expectation is considered for SSP, which however is oblivious to the incurred risk. We present an alternative view, instead optimizing conditional value-at-risk (CVaR), an established risk measure. We treat both Markov chains as well as MDP and introduce, through novel insights, two algorithms, based on linear programming and value iteration, respectively. Both algorithms offer precise and provably correct solutions. Evaluation of our prototype implementation shows that risk-aware control is feasible on several moderately sized models.
Updated Gaia-2MASS 3D maps of Galactic interstellar dust

Three-dimensional (3D) maps of Galactic interstellar dust are a tool for a wide range of uses. We aim to construct 3D maps of dust extinction in the Local Arm and surrounding regions. Gaia EDR3 photometric data were combined with 2MASS measurements to derive extinction towards stars with accurate photometry and relative uncertainties on parallaxes of less than 20%. We applied our hierarchical inversion algorithm adapted to inhomogeneous spatial distributions of target stars to this catalogue of extinctions. We present the updated 3D dust extinction distribution and provide an estimate of the error on integrated extinctions from the Sun to each area in the 3D map. The computational area is similar to the one of the previous DR2 map, a 6 kpc x 6 kpc x 0.8 kpcAstrophysics volume around the Sun. Due to the addition of fainter target stars, the volume in which the clouds can be reconstructed has increased. Due to the improved accuracy of the parallaxes and photometric data in EDR3, extinctions among neighbouring targets are more consistent, allowing one to reach an increased contrast in the dense areas, while cavity contours are more regular. We show several comparisons with recent results on dust and star distributions. The wavy pattern around the Plane of the dust concentrations is better seen and exists over large regions. Its mean vertical peak-to-peak amplitude is of the order of 300 pc; interestingly, it is similar to the vertical period of the spectacular snail-shaped stellar kinematical pattern discovered in Gaia data. The Gaia EDR3 catalogue allows for a significant improvement of the extinction maps to be made and the hierarchical technique confirms its efficiency for massive datasets. Future comparisons between 3D maps of interstellar matter and stellar distributions may help to understand which mergers or internal perturbations have shaped the Galaxy within the first 3 kpc.
Deformable Radar Polygon: A Lightweight and Predictable Occupancy Representation for Short-range Collision Avoidance

Inferring the drivable area in a scene is a key capability for ensuring the vehicle avoids obstacles and enabling safe autonomous driving. However, a traditional occupancy grid map suffers from high memory consumption when forming a fine-resolution grid for a large map. In this paper, we propose a lightweight, accurate, and predictable occupancy representation for automotive radars working for short-range applications that take interest in instantaneous free space surrounding the sensor. This new occupancy format is a polygon composed of a bunch of vertices selected from noisy radar measurements, which covers free space inside and gives a Doppler moving velocity for each vertex. It not only takes a very small memory and computing resources for storage and updating at every timeslot but also has the predictable shape-change property based on vertex's Doppler velocity. We name this kind of occupancy representation 'deformable radar polygon'. Two formation algorithms for radar polygon are introduced for both single timeslot and continuous inverse sensor model update. To fit this new polygon representation, a matrix-form collision detection method has been modeled as well. The radar polygon algorithms and collision detection model have been validated via extensive experiments with real collected data and simulations, showing that the deformable radar polygon is very competitive in terms of its completeness, smoothness, accuracy, lightweight as well as the shape-predictable property. Our codes will be made available at https://github.com/Xiangyu-Gao/deformable_radar_polygon_occupancy_representation.
Magnetism on the stretched H〈−1〉 harmonic honeycomb in lanthanide orthotantalates

Nicola D. Kelly, ∗ Lei Yuan, Rosalyn L. Pearson, † Emmanuelle Suard, Inés Puente Orench, 3 and Siân E. Dutton ‡ Cavendish Laboratory, University of Cambridge, J J Thomson Avenue, Cambridge, CB3 0HE, UK Institut Laue-Langevin, 71 Avenue des Martyrs, 38000 Grenoble, France Instituto de Nanociencia y Materiales de Aragón (INMA), CSIC-Universidad de Zaragoza, Zaragoza, 50009, Spain (Dated: March 4, 2022)
3D Human Motion Prediction: A Survey

3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to the increasing development and understanding of Deep Neural Networks (DNNs) and the availability of large-scale human motion datasets, the human motion prediction has been remarkably advanced with a surge of interest among academia and industrial community. In this context, a comprehensive survey on 3D human motion prediction is conducted for the purpose of retrospecting and analyzing relevant works from existing released literature. In addition, a pertinent taxonomy is constructed to categorize these existing approaches for 3D human motion prediction. In this survey, relevant methods are categorized into three categories: human pose representation, network structure design, and \textit{prediction target}. We systematically review all relevant journal and conference papers in the field of human motion prediction since 2015, which are presented in detail based on proposed categorizations in this survey. Furthermore, the outline for the public benchmark datasets, evaluation criteria, and performance comparisons are respectively presented in this paper. The limitations of the state-of-the-art methods are discussed as well, hoping for paving the way for future explorations.
Vision-Language Intelligence: Tasks, Representation Learning, and Large Models

This paper presents a comprehensive survey of vision-language (VL) intelligence from the perspective of time. This survey is inspired by the remarkable progress in both computer vision and natural language processing, and recent trends shifting from single modality processing to multiple modality comprehension. We summarize the development in this field into three time periods, namely task-specific methods, vision-language pre-training (VLP) methods, and larger models empowered by large-scale weakly-labeled data. We first take some common VL tasks as examples to introduce the development of task-specific methods. Then we focus on VLP methods and comprehensively review key components of the model structures and training methods. After that, we show how recent work utilizes large-scale raw image-text data to learn language-aligned visual representations that generalize better on zero or few shot learning tasks. Finally, we discuss some potential future trends towards modality cooperation, unified representation, and knowledge incorporation. We believe that this review will be of help for researchers and practitioners of AI and ML, especially those interested in computer vision and natural language processing.
Why Do Machine Learning Practitioners Still Use Manual Tuning? A Qualitative Study

Current advanced hyperparameter optimization (HPO) methods, such as Bayesian optimization, have high sampling efﬁciency and facilitate replicability. Nonetheless, machine learning (ML) practitioners (e.g., engineers, scientists) mostly apply less advanced HPO methods, which can increase resource consumption during HPO or lead to underoptimized ML models. Therefore, we suspect that practitioners choose their HPO method to achieve different goals, such as decrease practitioner effort and target audience compliance. To develop HPO methods that align with such goals, the reasons why practitioners decide for speciﬁc HPO methods must be unveiled and thoroughly understood. Because qualitative research is most suitable to uncover such reasons and ﬁnd potential explanations for them, we conducted semi-structured interviews to explain why practitioners choose different HPO methods. The interviews revealed six principal practitioner goals (e.g., increasing model comprehension), and eleven key factors that impact decisions for HPO methods (e.g., available computing resources). We deepen the understanding about why practitioners decide for different HPO methods and outline recommendations for improvements of HPO methods by aligning them with practitioner goals.
Code properties of the holographic Sierpinski triangle

We study the holographic quantum error correcting code properties of a Sierpinski triangle-shaped boundary subregion in AdS 4 /CFT 3 . Due to existing no-go theorems in topological quantum error correction regarding fractal noise, this gives holographic codes a speciﬁc advantage over topological codes. We then further argue that a boundary subregion in the shape of the Sierpinski gasket in AdS 5 /CFT 4 does not possess these holographic quantum error correction properties
EXTINCTION OF POPULATIONS AND A TEAM OF LYAPUNOV FUNCTIONS

We investigate the d -dimensional model, z j where i = 1 ,. ,d. where each z j ( t ) is a time ﬂuctuating resource that can depend on x 1 ,. ,x d and even on the weather or stock market. This “nonautonomous” model is a generalization of an autonomous Lotka-Volterra d -dimensional model. It is nonautonomous and it is not speciﬁed how the z j ( t ) are determined. Write S T for the transpose of S = ( s ij ). When the kernel of S T has dimension k and k > 0, we show that for any bounded solution X ( t ) = ( x 1 ,.,x d )( t ), at least k coordinates (or species) will die out and will do so exponentially fast. For the proof, we invent a family of “die-out” Lyapunov functions, a “team” of Lyapunov functions that work together to show that k species must die. Each die-out Lyapunov function implies one species must die out and provides constraints as to which species must die out. Together they provide a picture of which are likely to die out. We present a “trophic” condition for Lotka-Volterra systems that guarantees that there is a trapping region
Quasi time crystal

We discuss the possibility of making a quasi time crystal. A simple two-state model is studied to clarify our definition. In a superposition of the ground state and the excited state and the probability of observation varies periodically in time during the lifetime of the excited state. The quasi time crystal is also discussed around the first order quantum phase transition, which is characterized by the degeneracy and crossing of the two lowest-energy states in the infinite-volume limit. Our results have broad validity. As an example, the one-dimensional transverse field Ising model with surface fields is shown to have similar behavior. The oscillating magnetization profile is solved exactly.
Improved convergence of the Arrow-Hurwicz iteration for the Navier-Stokes equation via grad-div stabilization and Anderson acceleration

We consider two modifications of the Arrow-Hurwicz (AH) iteration for solving the incompressible steady Navier-Stokes equations for the purpose of accelerating the algorithm: grad-div stabilization, and Anderson acceleration. AH is a classical iteration for general saddle point linear systems and it was later extended to Navier-Stokes iterations in the 1970's which has recently come under study again. We apply recently developed ideas for grad-div stabilization and divergence-free finite element methods along with Anderson acceleration of fixed point iterations to AH in order to improve its convergence. Analytical and numerical results show that each of these methods improves AH convergence, but the combination of them yields an efficient and effective method that is competitive with more commonly used solvers.
Adaptive Path Planning for UAVs for Multi-Resolution Semantic Segmentation

Efficient data collection methods play a major role in helping us better understand the Earth and its ecosystems. In many applications, the usage of unmanned aerial vehicles (UAVs) for monitoring and remote sensing is rapidly gaining momentum due to their high mobility, low cost, and flexible deployment. A key challenge is planning missions to maximize the value of acquired data in large environments given flight time limitations. This is, for example, relevant for monitoring agricultural fields. This paper addresses the problem of adaptive path planning for accurate semantic segmentation of using UAVs. We propose an online planning algorithm which adapts the UAV paths to obtain high-resolution semantic segmentations necessary in areas with fine details as they are detected in incoming images. This enables us to perform close inspections at low altitudes only where required, without wasting energy on exhaustive mapping at maximum image resolution. A key feature of our approach is a new accuracy model for deep learning-based architectures that captures the relationship between UAV altitude and semantic segmentation accuracy. We evaluate our approach on different domains using real-world data, proving the efficacy and generability of our solution.
Personal Data Visualisation on Mobile Devices: A Systematic Literature Review

Personal data cover multiple aspects of our daily life and activities, including health, finance, social, Internet, Etc. Personal data visualisations aim to improve the user experience when exploring these large amounts of personal data and potentially provide insights to assist individuals in their decision making and achieving goals. People with different backgrounds, gender and ages usually need to access their data on their mobile devices. Although there are many personal tracking apps, the user experience when using these apps and visualisations is not evaluated yet. There are publications on personal data visualisation in the literature. Still, no systematic literature review investigated the gaps in this area to assist in developing new personal data visualisation techniques focusing on user experience. In this systematic literature review, we considered studies published between 2010 and 2020 in three online databases. We screened 195 studies and identified 29 papers that met our inclusion criteria. Our key findings are various types of personal data, and users have been addressed well in the found papers, including health, sport, diet, Driving habits, lifelogging, productivity, Etc. The user types range from naive users to expert and developers users based on the experiment's target. However, mobile device capabilities and limitations regarding data visualisation tasks have not been well addressed. There are no studies on the best practices of personal data visualisation on mobile devices, assessment frameworks for data visualisation, or design frameworks for personal data visualisations