OpenRaiser/Intern-Atlas huggingface dataset:
Parameter-Efficient Person Re-identification in the 3D Space
Venue: arXiv cs.CV 2021
Year: 2021

People live in a 3D world. However, existing works on person re-identification (re-id) mostly consider the semantic representation learning in a 2D space, intrinsically limiting the understanding of people. In this work, we address this limitation by exploring the prior knowledge of the 3D body structure. Specifically, we project 2D images to a 3D space and introduce a novel parameter-efficient Omni-scale Graph Network (OG-Net) to learn the pedestrian representation directly from 3D point clouds. OG-Net effectively exploits the local information provided by sparse 3D points and takes advantage of the structure and appearance information in a coherent manner. With the help of 3D geometry information, we can learn a new type of deep re-id feature free from noisy variants, such as scale and viewpoint. To our knowledge, we are among the first attempts to conduct person re-identification in the 3D space. We demonstrate through extensive experiments that the proposed method (1) eases the matching difficulty in the traditional 2D space, (2) exploits the complementary information of 2D appearance and 3D structure, (3) achieves competitive results with limited parameters on four large-scale person re-id datasets, and (4) has good scalability to unseen datasets. Our code, models and generated 3D human data are publicly available at https://github.com/layumi/person-reid-3d .
Understanding the Diverging User Trajectories in Highly-related Online Communities during the COVID-19 Pandemic
Venue: arXiv cs.SI 2021
Year: 2021

As the COVID-19 pandemic is disrupting life worldwide, related online communities are popping up. In particular, two "new" communities, /r/China flu and /r/Coronavirus, emerged on Reddit and have been dedicated to COVID- related discussions from the very beginning of this pandemic. With /r/Coronavirus promoted as the official community on Reddit, it remains an open question how users choose between these two highly-related communities. In this paper, we characterize user trajectories in these two communities from the beginning of COVID-19 to the end of September 2020. We show that new users of /r/China flu and /r/Coronavirus were similar from January to March. After that, their differences steadily increase, evidenced by both language distance and membership prediction, as the pandemic continues to unfold. Furthermore, users who started at /r/China flu from January to March were more likely to leave, while those who started in later months tend to remain highly "loyal". To understand this difference, we develop a movement analysis framework to understand membership changes in these two communities and identify a significant proportion of /r/China flu members (around 50%) that moved to /r/Coronavirus in February. This movement turns out to be highly predictable based on other subreddits that users were previously active in. Our work demonstrates how two highly-related communities emerge and develop their own identity in a crisis, and highlights the important role of existing communities in understanding such an emergence.
On the Gap between Scalar and Vector Solutions of Generalized Combination Networks
Venue: arXiv cs.IT 2021
Year: 2021

We study scalar-linear and vector-linear solutions of the generalized combination network. We derive new upper and lower bounds on the maximum number of nodes in the middle layer, depending on the network parameters and the alphabet size. These bounds improve and extend the parameter range of known bounds. Using these new bounds we present a lower bound and an upper bound on the gap in the alphabet size between optimal scalar-linear and optimal vector-linear network coding solutions. For a fixed network structure, while varying the number of middle-layer nodes $r$, the asymptotic behavior of the upper and lower bounds shows that the gap is in $Θ(\log(r))$.
Hardware Implementation of Spiking Neural Networks Using Time-To-First-Spike Encoding
Venue: arXiv cs.NE 2021
Year: 2021

Hardware-based spiking neural networks (SNNs) are regarded as promising candidates for the cognitive computing system due to low power consumption and highly parallel operation. In this work, we train the SNN in which the firing time carries information using temporal backpropagation. The temporally encoded SNN with 512 hidden neurons showed an accuracy of 96.90% for the MNIST test set. Furthermore, the effect of the device variation on the accuracy in temporally encoded SNN is investigated and compared with that of the rate-encoded network. In a hardware configuration of our SNN, NOR-type analog memory having an asymmetric floating gate is used as a synaptic device. In addition, we propose a neuron circuit including a refractory period generator for temporally encoded SNN. The performance of the 2-layer neural network consisting of synapses and proposed neurons is evaluated through circuit simulation using SPICE. The network with 128 hidden neurons showed an accuracy of 94.9%, a 0.1% reduction compared to that of the system simulation of the MNIST dataset. Finally, the latency and power consumption of each block constituting the temporal network is analyzed and compared with those of the rate-encoded network depending on the total time step. Assuming that the total time step number of the network is 256, the temporal network consumes 15.12 times lower power than the rate-encoded network and can make decisions 5.68 times faster.
PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction
Venue: arXiv cs.LG 2021
Year: 2021

Improving the robustness of neural nets in regression tasks is key to their application in multiple domains. Deep learning-based approaches aim to achieve this goal either by improving their prediction of specific values (i.e., point prediction), or by producing prediction intervals (PIs) that quantify uncertainty. We present PIVEN, a deep neural network for producing both a PI and a value prediction. Our loss function expresses the value prediction as a function of the upper and lower bounds, thus ensuring that it falls within the interval without increasing model complexity. Moreover, our approach makes no assumptions regarding data distribution within the PI, making its value prediction more effective for various real-world problems. Experiments and ablation tests on known benchmarks show that our approach produces tighter uncertainty bounds than the current state-of-the-art approaches for producing PIs, while maintaining comparable performance to the state-of-the-art approach for value-prediction. Additionally, we go beyond previous work and include large image datasets in our evaluation, where PIVEN is combined with modern neural nets.
Matrix games with bandit feedback
Venue: arXiv cs.LG 2021
Year: 2021

We study a version of the classical zero-sum matrix game with unknown payoff matrix and bandit feedback, where the players only observe each others actions and a noisy payoff. This generalizes the usual matrix game, where the payoff matrix is known to the players. Despite numerous applications, this problem has received relatively little attention. Although adversarial bandit algorithms achieve low regret, they do not exploit the matrix structure and perform poorly relative to the new algorithms. The main contributions are regret analyses of variants of UCB and K-learning that hold for any opponent, e.g., even when the opponent adversarially plays the best-response to the learner's mixed strategy. Along the way, we show that Thompson fails catastrophically in this setting and provide empirical comparison to existing algorithms.
A program for the full axiom of choice
Venue: arXiv cs.LO 2021
Year: 2021

The theory of classical realizability is a framework for the Curry-Howard correspondence which enables to associate a program with each proof in Zermelo-Fraenkel set theory. But, almost all the applications of mathematics in physics, probability, statistics, etc. use Analysis i.e. the axiom of dependent choice (DC) or even the (full) axiom of choice (AC). It is therefore important to find explicit programs for these axioms. Various solutions have been found for DC, for instance the lambda-term called "bar recursion" or the instruction "quote" of LISP. We present here the first program for AC.
ToolNet: Using Commonsense Generalization for Predicting Tool Use for Robot Plan Synthesis
Venue: arXiv cs.RO 2021
Year: 2021

A robot working in a physical environment (like home or factory) needs to learn to use various available tools for accomplishing different tasks, for instance, a mop for cleaning and a tray for carrying objects. The number of possible tools is large and it may not be feasible to demonstrate usage of each individual tool during training. Can a robot learn commonsense knowledge and adapt to novel settings where some known tools are missing, but alternative unseen tools are present? We present a neural model that predicts the best tool from the available objects for achieving a given declarative goal. This model is trained by user demonstrations, which we crowd-source through humans instructing a robot in a physics simulator. This dataset maintains user plans involving multi-step object interactions along with symbolic state changes. Our neural model, ToolNet, combines a graph neural network to encode the current environment state, and goal-conditioned spatial attention to predict the appropriate tool. We find that providing metric and semantic properties of objects, and pre-trained object embeddings derived from a commonsense knowledge repository such as ConceptNet, significantly improves the model's ability to generalize to unseen tools. The model makes accurate and generalizable tool predictions. When compared to a graph neural network baseline, it achieves 14-27% accuracy improvement for predicting known tools from new world scenes, and 44-67% improvement in generalization for novel objects not encountered during training.
Probably Approximately Correct Constrained Learning
Venue: arXiv cs.LG 2021
Year: 2021

As learning solutions reach critical applications in social, industrial, and medical domains, the need to curtail their behavior has become paramount. There is now ample evidence that without explicit tailoring, learning can lead to biased, unsafe, and prejudiced solutions. To tackle these problems, we develop a generalization theory of constrained learning based on the probably approximately correct (PAC) learning framework. In particular, we show that imposing requirements does not make a learning problem harder in the sense that any PAC learnable class is also PAC constrained learnable using a constrained counterpart of the empirical risk minimization (ERM) rule. For typical parametrized models, however, this learner involves solving a constrained non-convex optimization program for which even obtaining a feasible solution is challenging. To overcome this issue, we prove that under mild conditions the empirical dual problem of constrained learning is also a PAC constrained learner that now leads to a practical constrained learning algorithm based solely on solving unconstrained problems. We analyze the generalization properties of this solution and use it to illustrate how constrained learning can address problems in fair and robust classification.
Predicative theories of continuous lattices
Venue: arXiv cs.LO 2021
Year: 2021

We introduce a notion of strong proximity join-semilattice, a predicative notion of continuous lattice which arises as the Karoubi envelop of the category of algebraic lattices. Strong proximity join-semilattices can be characterised by the coalgebras of the lower powerlocale on the wider category of proximity posets (also known as abstract bases or R-structures). Moreover, locally compact locales can be characterised in terms of strong proximity join-semilattices by the coalgebras of the double powerlocale on the category of proximity posets. We also provide more logical characterisation of a strong proximity join-semilattice, called a strong continuous finitary cover, which uses an entailment relation to present the underlying join-semilattice. We show that this structure naturally corresponds to the notion of continuous lattice in the predicative point-free topology. Our result makes the predicative and finitary aspect of the notion of continuous lattice in point-free topology more explicit.
3D Human Mesh Regression with Dense Correspondence
Venue: arXiv cs.CV 2021
Year: 2021

Estimating 3D mesh of the human body from a single 2D image is an important task with many applications such as augmented reality and Human-Robot interaction. However, prior works reconstructed 3D mesh from global image feature extracted by using convolutional neural network (CNN), where the dense correspondences between the mesh surface and the image pixels are missing, leading to suboptimal solution. This paper proposes a model-free 3D human mesh estimation framework, named DecoMR, which explicitly establishes the dense correspondence between the mesh and the local image features in the UV space (i.e. a 2D space used for texture mapping of 3D mesh). DecoMR first predicts pixel-to-surface dense correspondence map (i.e., IUV image), with which we transfer local features from the image space to the UV space. Then the transferred local image features are processed in the UV space to regress a location map, which is well aligned with transferred features. Finally we reconstruct 3D human mesh from the regressed location map with a predefined mapping function. We also observe that the existing discontinuous UV map are unfriendly to the learning of network. Therefore, we propose a novel UV map that maintains most of the neighboring relations on the original mesh surface. Experiments demonstrate that our proposed local feature alignment and continuous UV map outperforms existing 3D mesh based methods on multiple public benchmarks. Code will be made available at https://github.com/zengwang430521/DecoMR
S-semantics -- an example
Venue: arXiv cs.LO 2021
Year: 2021

The s-semantics makes it possible to explicitly deal with variables in program answers. So it seems suitable for programs using nonground data structures, like open lists. However it is difficult to find published examples of using the s-semantics to reason about particular programs. Here we apply s-semantics to prove correctness and completeness of Frühwirth's $n$ queens program. This is compared with a proof, published elsewhere, based on the standard semantics and Herbrand interpretations.
Fast Privacy-Preserving Punch Cards
Venue: arXiv cs.CR 2021
Year: 2021

Loyalty programs in the form of punch cards that can be redeemed for benefits have long been a ubiquitous element of the consumer landscape. However, their increasingly popular digital equivalents, while providing more convenience and better bookkeeping, pose a considerable privacy risk. This paper introduces a privacy-preserving punch card protocol that allows firms to digitize their loyalty programs without forcing customers to submit to corporate surveillance. We also present a number of extensions that allow our scheme to provide other privacy-preserving customer loyalty features. Compared to the best prior work, we achieve a $14\times$ reduction in the computation and a $11\times$ reduction in the communication required to perform a "hole punch," a $55\times$ reduction in the communication required to redeem a punch card, and a $128\times$ reduction in the computation time required to redeem a card. Much of our performance improvement can be attributed to removing the reliance on pairings or range proofs present in prior work, which has only addressed this problem in the context of more general loyalty systems. By tailoring our scheme to punch cards and related loyalty systems, we demonstrate that we can reduce communication and computation costs by orders of magnitude.
Geometric Solutions for General Actuator Routing on Inflated-Beam Soft Growing Robots
Venue: arXiv cs.RO 2021
Year: 2021

Continuum and soft robots can leverage complex actuator shapes to take on useful shapes while actuating only a few of their many degrees of freedom. Continuum robots that also grow increase the range of potential shapes that can be actuated and enable easier access to constrained environments. Existing models for describing the complex kinematics involved in general actuation of continuum robots rely on simulation or well-behaved stress-strain relationships, but the non-linear behavior of the thin-walled inflated-beams used in growing robots makes these techniques difficult to apply. Here we derive kinematic models of single, generally routed tendon paths on a soft pneumatic backbone of inextensible but flexible material from geometric relationships alone. This allows for forward modeling of the resulting shapes with only knowledge of the geometry of the system. We show that this model can accurately predict the shape of the whole robot body and how the model changes with actuation type. We also demonstrate the use of this kinematic model for inverse design, where actuator designs are found based on desired final robot shapes. We deploy these designed actuators on soft pneumatic growing robots to show the benefits of simultaneous growth and shape change.
Sovereign: User-Controlled Smart Homes
Venue: arXiv cs.NI 2021
Year: 2021

Recent years have witnessed the rapid deployment of smart homes; most of them are controlled by remote servers in the cloud. Such designs raise security and privacy concerns for end users. In this paper, we describe the design of Sovereign, a home IoT system framework that provides end users complete control of their home IoT systems. Sovereign lets home IoT devices and applications communicate via application-named data and secures data directly. This enables direct, secure, one-to-one and one-to-many device-to-device communication over wireless broadcast media. Sovereign utilizes semantic names to construct usable security solutions. We implement Sovereign as a publish-subscribe-based development platform together with a prototype home IoT controller. Our preliminary evaluation shows that Sovereign provides a systematic, easy-to-use solution to user-controlled, self-contained smart homes running on existing IoT hardware without imposing noticeable overhead.
Gender disparity in the authorship of biomedical research publications during the COVID-19 pandemic
Venue: arXiv cs.DL 2021
Year: 2021

Preliminary evidence suggests that women, including female researchers, are disproportionately affected by the COVID-19 pandemic in terms of unequal distribution of childcare, elderly care and other kinds of domestic and emotional labor. Sudden lockdowns and abrupt shifts in daily routines have disproportionate consequences on their productivity, which is reflected by a sudden drop in research output in biomedical research, consequently affecting the number of female authors of scientific publications. We investigate the proportion of male and female researchers who published scientific papers during the COVID-19 pandemic, using bibliometric data from biomedical preprint servers and selected Springer-Nature journals. Our findings document a decrease in the number of publications by female authors in biomedical field during the global pandemic. This effect is particularly pronounced for papers related to COVID-19, indicating that women are producing fewer publications related to COVID-19 research. This sudden increase in the gender gap is persistent across the ten countries with the highest number of researchers. These results should be used to inform the scientific community of the worrying trend in COVID-19 research and the disproportionate effect that the pandemic has on female academics.
Implicit Kernel Attention
Venue: arXiv cs.LG 2021
Year: 2021

\textit{Attention} computes the dependency between representations, and it encourages the model to focus on the important selective features. Attention-based models, such as Transformer and graph attention network (GAT), are widely utilized for sequential data and graph-structured data. This paper suggests a new interpretation and generalized structure of the attention in Transformer and GAT. For the attention in Transformer and GAT, we derive that the attention is a product of two parts: 1) the RBF kernel to measure the similarity of two instances and 2) the exponential of $L^{2}$ norm to compute the importance of individual instances. From this decomposition, we generalize the attention in three ways. First, we propose implicit kernel attention with an implicit kernel function instead of manual kernel selection. Second, we generalize $L^{2}$ norm as the $L^{p}$ norm. Third, we extend our attention to structured multi-head attention. Our generalized attention shows better performance on classification, translation, and regression tasks.
Evently: Modeling and Analyzing Reshare Cascades with Hawkes Processes
Venue: arXiv cs.SI 2021
Year: 2021

Modeling online discourse dynamics is a core activity in understanding the spread of information, both offline and online, and emergent online behavior. There is currently a disconnect between the practitioners of online social media analysis -- usually social, political and communication scientists -- and the accessibility to tools capable of examining online discussions of users. Here we present evently, a tool for modeling online reshare cascades, and particularly retweet cascades, using self-exciting processes. It provides a comprehensive set of functionalities for processing raw data from Twitter public APIs, modeling the temporal dynamics of processed retweet cascades and characterizing online users with a wide range of diffusion measures. This tool is designed for researchers with a wide range of computer expertise, and it includes tutorials and detailed documentation. We illustrate the usage of evently with an end-to-end analysis of online user behavior on a topical dataset relating to COVID-19. We show that, by characterizing users solely based on how their content spreads online, we can disentangle influential users and online bots.
Hypernetwork-Based Augmentation
Venue: arXiv cs.CV 2021
Year: 2021

Data augmentation is an effective technique to improve the generalization of deep neural networks. Recently, AutoAugment proposed a well-designed search space and a search algorithm that automatically finds augmentation policies in a data-driven manner. However, AutoAugment is computationally intensive. In this paper, we propose an efficient gradient-based search algorithm, called Hypernetwork-Based Augmentation (HBA), which simultaneously learns model parameters and augmentation hyperparameters in a single training. Our HBA uses a hypernetwork to approximate a population-based training algorithm, which enables us to tune augmentation hyperparameters by gradient descent. Besides, we introduce a weight sharing strategy that simplifies our hypernetwork architecture and speeds up our search algorithm. We conduct experiments on CIFAR-10, CIFAR-100, SVHN, and ImageNet. Our results show that HBA is competitive to the state-of-the-art methods in terms of both search speed and accuracy.
Extracting and categorising the reactions to COVID-19 by the South African public -- A social media study
Venue: arXiv cs.SI 2021
Year: 2021

Social Media can be used to extract discussion topics during a disaster. With the COVID-19 pandemic impact on South Africa, we need to understand how the law and regulation promulgated by the government in response to the pandemic contrasts with discussion topics social media users have been engaging in. In this work, we expand on traditional media analysis by using Social Media discussions driven by or directed to South African government officials. We find topics that are similar as well as different in some cases. The findings can inform further study into social media during disaster settings in South Africa and beyond. This paper sets a framework for future analysis in understanding the opinions of the public during a pandemic and how these opinions can be distilled [in a semi-automated approach] to inform government communication in the future.