Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Activity Recognition using Attribute-Based Neural Networks and Context Information

We consider human activity recognition (HAR) from wearable sensor data in manual-work processes, like warehouse order-picking. Such structured domains can often be partitioned into distinct process steps, e.g., packaging or transporting. Each process step can have a different prior distribution over activity classes, e.g., standing or walking, and different system dynamics. Here, we show how such context information can be integrated systematically into a deep neural network-based HAR system. Specifically, we propose a hybrid architecture that combines a deep neural network-that estimates high-level movement descriptors, attributes, from the raw-sensor data-and a shallow classifier, which predicts activity classes from the estimated attributes and (optional) context information, like the currently executed process step. We empirically show that our proposed architecture increases HAR performance, compared to state-of-the-art methods. Additionally, we show that HAR performance can be further increased when information about process steps is incorporated, even when that information is only partially correct.
Inferring untrained complex dynamics of delay systems using an adapted echo state network

Caused by ﬁnite signal propagation velocities, many complex systems feature time delays that may induce high-dimensional chaotic behavior and make forecasting intricate. Here, we propose an echo state network adaptable to the physics of systems with arbitrary delays. After training the network to forecast a system with a unique and suﬃciently long delay, it already learned to predict the system dynamics for all other delays. A simple adaptation of the network’s topology allows us to infer untrained features such as high-dimensional chaotic attractors, bifurcations, and even multistabilities, that emerge with shorter and longer delays. Thus, the fusion of physical knowledge of the delay system and data-driven machine learning yields a model with high generalization capabilities and unprecedented prediction accuracy.
Direct Multi-view Multi-person 3D Pose Estimation

We present Multi-view Pose transformer (MvP) for estimating multi-person 3D poses from multi-view images. Instead of estimating 3D joint locations from costly volumetric representation or reconstructing the per-person 3D pose from multiple detected 2D poses as in previous methods, MvP directly regresses the multi-person 3D poses in a clean and efficient way, without relying on intermediate tasks. Specifically, MvP represents skeleton joints as learnable query embeddings and let them progressively attend to and reason over the multi-view information from the input images to directly regress the actual 3D joint locations. To improve the accuracy of such a simple pipeline, MvP presents a hierarchical scheme to concisely represent query embeddings of multi-person skeleton joints and introduces an input-dependent query adaptation approach. Further, MvP designs a novel geometrically guided attention mechanism, called projective attention, to more precisely fuse the cross-view information for each joint. MvP also introduces a RayConv operation to integrate the view-dependent camera geometry into the feature representations for augmenting the projective attention. We show experimentally that our MvP model outperforms the state-of-the-art methods on several benchmarks while being much more efficient. Notably, it achieves 92.3% AP25 on the challenging Panoptic dataset, improving upon the previous best approach [36] by 9.8%. MvP is general and also extendable to recovering human mesh represented by the SMPL model, thus useful for modeling multi-person body shapes. Code and models are available at https://github.com/sail-sg/mvp.
Exponential GARCH-Ito Volatility Models

This paper introduces a novel Itô diffusion process to model high-frequency financial data, which can accommodate low-frequency volatility dynamics by embedding the discrete-time non-linear exponential GARCH structure with log-integrated volatility in a continuous instantaneous volatility process. The key feature of the proposed model is that, unlike existing GARCH-Itô models, the instantaneous volatility process has a non-linear structure, which ensures that the log-integrated volatilities have the realized GARCH structure. We call this the exponential realized GARCH-Itô (ERGI) model. Given the auto-regressive structure of the log-integrated volatility, we propose a quasilikelihood estimation procedure for parameter estimation and establish its asymptotic properties. We conduct a simulation study to check the finite sample performance of the proposed model and an empirical study with 50 assets among the S&P 500 compositions. The numerical studies show the advantages of the new proposed model.
Identifying the Leading Factors of Significant Weight Gains Using a New Rule Discovery Method

Overweight and obesity remain a major global public health concern and identifying the individualized patterns that increase the risk of future weight gains has a crucial role in preventing obesity and numerous sub-sequent diseases associated with obesity. In this work, we use a rule discovery method to study this problem, by presenting an approach that offers genuine interpretability and concurrently optimizes the accuracy(being correct often) and support (applying to many samples) of the identified patterns. Specifically, we extend an established subgroup-discovery method to generate the desired rules of type X ->Y and show how top features can be extracted from the X side, functioning as the best predictors of Y. In our obesity problem, X refers to the extracted features from very large and multi-site EHR data, and Y indicates significant weight gains. Using our method, we also extensively compare the differences and inequities in patterns across 22 strata determined by the individual's gender, age, race, insurance type, neighborhood type, and income level. Through extensive series of experiments, we show new and complementary findings regarding the predictors of future dangerous weight gains.
Theoretical prediction of a non-Hermitian skin effect in ultracold-atom systems

Non-Hermitian skin effect, which refers to the phenomenon that an extensive number of eigenstates are localized at the boundary, has been widely studied in lattice models and experimentally observed in several classical systems. In this work, we predict that the existence of the non-Hermitian skin effect in the dissipative ultracold fermions with spin-orbit coupling, a continuous model that has been implemented by the Hong-Kong group in a recent experiment. This skin effect is robust against the variation of external parameters and trapping potentials. We further reveal a dynamic sticky effect in our system, which has a common physical origin with the non-Hermitian skin effect. Our work paves the way for studying novel physical responses of non-Hermitian skin effect in quantum systems.
3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds

3D object tracking in point clouds is still a challenging problem due to the sparsity of LiDAR points in dynamic environments. In this work, we propose a Siamese voxel-to-BEV tracker, which can significantly improve the tracking performance in sparse 3D point clouds. Specifically, it consists of a Siamese shape-aware feature learning network and a voxel-to-BEV target localization network. The Siamese shape-aware feature learning network can capture 3D shape information of the object to learn the discriminative features of the object so that the potential target from the background in sparse point clouds can be identified. To this end, we first perform template feature embedding to embed the template's feature into the potential target and then generate a dense 3D shape to characterize the shape information of the potential target. For localizing the tracked target, the voxel-to-BEV target localization network regresses the target's 2D center and the $z$-axis center from the dense bird's eye view (BEV) feature map in an anchor-free manner. Concretely, we compress the voxelized point cloud along $z$-axis through max pooling to obtain a dense BEV feature map, where the regression of the 2D center and the $z$-axis center can be performed more effectively. Extensive evaluation on the KITTI and nuScenes datasets shows that our method significantly outperforms the current state-of-the-art methods by a large margin.
Nonnegative Tensor Completion via Integer Optimization

Unlike matrix completion, tensor completion does not have an algorithm that is known to achieve the information-theoretic sample complexity rate. This paper develops a new algorithm for the special case of completion for nonnegative tensors. We prove that our algorithm converges in a linear (in numerical tolerance) number of oracle steps, while achieving the information-theoretic rate. Our approach is to define a new norm for nonnegative tensors using the gauge of a particular 0-1 polytope; integer linear programming can, in turn, be used to solve linear separation problems over this polytope. We combine this insight with a variant of the Frank-Wolfe algorithm to construct our numerical algorithm, and we demonstrate its effectiveness and scalability through computational experiments using a laptop on tensors with up to one-hundred million entries.
The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle

Thompson sampling (TS) has attracted a lot of interest in the bandit area. It was introduced in the 1930s but has not been theoretically proven until recent years. All of its analysis in the combinatorial multi-armed bandit (CMAB) setting requires an exact oracle to provide optimal solutions with any input. However, such an oracle is usually not feasible since many combinatorial optimization problems are NP-hard and only approximation oracles are available. An example (Wang and Chen, 2018) has shown the failure of TS to learn with an approximation oracle. However, this oracle is uncommon and is designed only for a specific problem instance. It is still an open question whether the convergence analysis of TS can be extended beyond the exact oracle in CMAB. In this paper, we study this question under the greedy oracle, which is a common (approximation) oracle with theoretical guarantees to solve many (offline) combinatorial optimization problems. We provide a problem-dependent regret lower bound of order $\Omega(\log T/\Delta^2)$ to quantify the hardness of TS to solve CMAB problems with greedy oracle, where $T$ is the time horizon and $\Delta$ is some reward gap. We also provide an almost matching regret upper bound. These are the first theoretical results for TS to solve CMAB with a common approximation oracle and break the misconception that TS cannot work with approximation oracles.
Water Entry and the Cavity-Running Behavior of Missiles

Abstract : This report contains a comprehensive compilation of test data and analytical techniques for predicting the behavior of vehicles during water entry and during the cavity-running phase. It contains data to predict the water impact forces on nose shapes such as disks, cones, ogives, spheres, cusps, disk ogives, etc. during vertical and oblique water entry. Cavity development phenomena are discussed and approximate ways of predicting cavity size and shape are presented. Stability characteristics during the early running phase are also discussed. In general, the report is intended to provide the necessary data for the design of water-entry ordnance.
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes

Variable selection in Gaussian processes (GPs) is typically undertaken by thresholding the inverse lengthscales of automatic relevance determination kernels, but in high-dimensional datasets this approach can be unreliable. A more probabilistically principled alternative is to use spike and slab priors and infer a posterior probability of variable inclusion. However, existing implementations in GPs are very costly to run in both high-dimensional and large-$n$ datasets, or are only suitable for unsupervised settings with specific kernels. As such, we develop a fast and scalable variational inference algorithm for the spike and slab GP that is tractable with arbitrary differentiable kernels. We improve our algorithm's ability to adapt to the sparsity of relevant variables by Bayesian model averaging over hyperparameters, and achieve substantial speed ups using zero temperature posterior restrictions, dropout pruning and nearest neighbour minibatching. In experiments our method consistently outperforms vanilla and sparse variational GPs whilst retaining similar runtimes (even when $n=10^6$) and performs competitively with a spike and slab GP using MCMC but runs up to $1000$ times faster.
SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving

Aiming at facilitating a real-world, ever-evolving and scalable autonomous driving system, we present a large-scale dataset for standardizing the evaluation of different self-supervised and semi-supervised approaches by learning from raw data, which is the first and largest dataset to date. Existing autonomous driving systems heavily rely on `perfect' visual perception models (i.e., detection) trained using extensive annotated data to ensure safety. However, it is unrealistic to elaborately label instances of all scenarios and circumstances (i.e., night, extreme weather, cities) when deploying a robust autonomous driving system. Motivated by recent advances of self-supervised and semi-supervised learning, a promising direction is to learn a robust detection model by collaboratively exploiting large-scale unlabeled data and few labeled data. Existing datasets either provide only a small amount of data or covers limited domains with full annotation, hindering the exploration of large-scale pre-trained models. Here, we release a Large-Scale 2D Self/semi-supervised Object Detection dataset for Autonomous driving, named as SODA10M, containing 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected within 27833 driving hours under different weather conditions, periods and location scenes of 32 different cities. We provide extensive experiments and deep analyses of existing popular self/semi-supervised approaches, and give some interesting findings in autonomous driving scope. Experiments show that SODA10M can serve as a promising pre-training dataset for different self-supervised learning methods, which gives superior performance when fine-tuning with different downstream tasks (i.e., detection, semantic/instance segmentation) in autonomous driving domain. More information can refer to https://soda-2d.github. io.
Set-Sequential Labelings of Odd Trees

Abstract A tree T on 2n vertices is called set-sequential if the elements in V (T) ∪ E(T) can be labeled with distinct nonzero (n + 1)-dimensional 01-vectors such that the vector labeling each edge is the component-wise sum modulo 2 of the labels of the endpoints. It has been conjectured that all trees on 2n vertices with only odd degree are set-sequential (the “Odd Tree Conjecture”), and in this paper, we present progress toward that conjecture. We show that certain kinds of caterpillars (with restrictions on the degrees of the vertices, but no restrictions on the diameter) are set-sequential. Additionally, we introduce some constructions of new set-sequential graphs from smaller set-sequential bipartite graphs (not necessarily odd trees). We also make a conjecture about pairings of the elements of 𝔽2n \mathbb{F}_2^n in a particular way; in the process, we provide a substantial clarification of a proof of a theorem that partitions 𝔽2n \mathbb{F}_2^n from a paper [Coloring vertices and edges of a graph by nonempty subsets of a set, European J. Combin. 32 (2011) 533–537] by Balister et al. Finally, we put forward a result on bipartite graphs that is a modification of a theorem in the aforementioned paper.
Near-Optimal Randomized Exploration for Tabular MDP

We study exploration using randomized value functions in Thompson Sampling (TS)-like algorithms in reinforcement learning. This type of algorithms enjoys appealing empirical performance. We show that when we use 1) a single random seed in each episode, and 2) a Bernstein-type magnitude of noise, we obtain a worst-case e O (cid:16) H √ SAT (cid:17) regret bound for episodic time-inhomogeneous Markov Decision Process where S is the size of state space, A is the size of action space, H is the planning horizon and T is the number of interactions. This bound polynomially improves all existing bounds for TS-like algorithms based on randomized value functions, and for the ﬁrst time, matches the Ω (cid:16) H √ SAT (cid:17) lower bound up to logarithmic factors. Our result highlights that randomized exploration can be near-optimal, which was previously only achieved by optimistic algorithms. To achieve the desired result, we develop 1) a new clipping operation to ensure both the probability being optimistic and the probability being pessimistic are lower bounded by a constant, and 2) a new recursion formula for the absolute value of estimations errors to analyze the regret.
Digital Rubbish: A Natural History of Electronics

This is a study of the material life of information and its devices; of electronic waste in its physical and electronic incarnations; a cultural and material mapping of the spaces where electronics in the form of both hardware and information accumulate, break down, or are stowed away. Where other studies have addressed "digital" technology through a focus on its immateriality or virtual qualities, Gabrys traces the material, spatial, cultural and political infrastructures that enable the emergence and dissolution of these technologies. In the course of her book, she explores five interrelated "spaces" where electronics fall apart: from Silicon Valley to Nasdaq, from containers bound for China to museums and archives that preserve obsolete electronics as cultural artifacts, to the landfill as material repository. Digital Rubbish describes the materiality of electronics from a unique perspective, examining the multiple forms of waste that electronics create as evidence of the resources, labor, and imaginaries that are bundled into these machines. Ranging across studies of media and technology, as well as environments, geography, and design, Jennifer Gabrys draws together the far-reaching material and cultural processes that enable the making and breaking of these technologies.
Analisa Customer Value terhadap Customer Loyalty dengan Customer Satisfaction sebagai Variabel Intervening pada Restoran Bandar Djakarta Surabaya

Analisa Customer Value Terhadap Customer Loyalty Dengan Customer Satisfaction Sebagai Variabel Intervening Pada Restoran Bandar Djakarta Surabaya Seiring dengan peningkatan jumlah restoran dan cafe di Surabaya, maka dapat dilihat bahwa sektor dunia usaha di bidang restoran dan cafe telah menjadi suatu arena persaingan dan tidak ada henti-hentinya. Oleh karena itu, diperlukan suatu usaha agar sebuah restoran atau cafe dapat bertahan di tengah maraknya persaingan. Salah satu usaha yang dapat dilakukan adalah menciptakan strategi pemasaran yang berorientasi pada konsumen yang berusaha memberikan kepuasan atas kebutuhan dan keinginan pelanggan. Penelitian ini berisi tentang customer value yang memiliki pengaruh terhadap loyalitas konsumen dengan mediasi kepuasan konsumen. Penelitian ini merupakan penelitian kausal yang dilakukan secara kuantitatif. Seluruh data yang ada dalam penelitian ini didapatkan melalui metode wawancara dengan alat bantu kuisioner. Sampel penelitian adalah 200 pengunjung yang pernah melakukan proses pembelian di restoran Bandar Djakarta Surabaya. Hasil observasi akan di analisis dengan menggunakan metode Structural Equation Model (SEM). Hasil analisis menggunakan Structural Equation Model (SEM) menyimpulkan bahwa customer value yang dimiliki oleh restoran Bandar Djakarta Surabaya memiliki pengaruh yang signifikan terhadap kepuasan konsumen. Hasil lain ditunjukkan bahwa kepuasan konsumen restoran Bandar Djakarta Surabaya memiliki pengaruh yang signifikan terhadap loyalitas yang bisa diberikan.
The experience economy : work is theatre & every business a stage

Future economic growth lies in the value of experiences and transformations--good and services are no longer enough. We are on the threshold, say authors Pine and Gilmore, of the Experience Economy, a new economic era in which all businesses must orchestrate memorable events for their customers. The Experience Economy offers a creative, highly original, and yet eminently practical strategy for companies to script and stage the experiences that will transform the value of what they produce. From America Online to Walt Disney, the authors draw from a rich and varied mix of examples that showcase businesses in the midst of creating personal experiences for both consumers and businesses. The authors urge managers to look beyond traditional pricing factors like time and cost, and consider charging for the value of the transformation that an experience offers. Goods and services, say Pine and Gilmore, are no longer enough. Experiences and transformations are the basis for future economic growth, and The Experience Economy is the script from which managers can begin to direct their own transformations.