OpenRaiser/Intern-Atlas huggingface dataset:
Introducing the diagrammatic semiotic mode
Venue: arXiv cs.CL 2022
Year: 2022

As the use and diversity of diagrams across many disciplines grows, there is an increasing interest in the diagrams research community concerning how such diversity might be documented and explained. In this article, we argue that one way of achieving increased reliability, coverage, and utility for a general classification of diagrams is to draw on recently developed semiotic principles developed within the field of multimodality. To this end, we sketch out the internal details of what may tentatively be termed the diagrammatic semiotic mode. This provides a natural account of how diagrammatic representations may integrate natural language, various forms of graphics, diagrammatic elements such as arrows, lines and other expressive resources into coherent organisations, while still respecting the crucial diagrammatic contributions of visual organisation. We illustrate the proposed approach using two recent diagram corpora and show how a multimodal approach supports the empirical analysis of diagrammatic representations, especially in identifying diagrammatic constituents and describing their interrelations in a manner that may be generalised across diagram types and be used to characterise distinct kinds of functionality.
Error-feedback stochastic modeling strategy for time series forecasting with convolutional neural networks
Venue: arXiv cs.LG 2022
Year: 2022

Despite the superiority of convolutional neural networks demonstrated in time series modeling and forecasting, it has not been fully explored on the design of the neural network architecture and the tuning of the hyper-parameters. Inspired by the incremental construction strategy for building a random multilayer perceptron, we propose a novel Error-feedback Stochastic Modeling (ESM) strategy to construct a random Convolutional Neural Network (ESM-CNN) for time series forecasting task, which builds the network architecture adaptively. The ESM strategy suggests that random filters and neurons of the error-feedback fully connected layer are incrementally added to steadily compensate the prediction error during the construction process, and then a filter selection strategy is introduced to enable ESM-CNN to extract the different size of temporal features, providing helpful information at each iterative process for the prediction. The performance of ESM-CNN is justified on its prediction accuracy of one-step-ahead and multi-step-ahead forecasting tasks respectively. Comprehensive experiments on both the synthetic and real-world datasets show that the proposed ESM-CNN not only outperforms the state-of-art random neural networks, but also exhibits stronger predictive power and less computing overhead in comparison to trained state-of-art deep neural network models.
Optimal Exact Matrix Completion Under new Parametrization
Venue: arXiv cs.LG 2022
Year: 2022

We study the problem of exact completion for $m \times n$ sized matrix of rank $r$ with the adaptive sampling method. We introduce a relation of the exact completion problem with the sparsest vector of column and row spaces (which we call \textit{sparsity-number} here). Using this relation, we propose matrix completion algorithms that exactly recovers the target matrix. These algorithms are superior to previous works in two important ways. First, our algorithms exactly recovers $μ_0$-coherent column space matrices by probability at least $1 - ε$ using much smaller observations complexity than $\mathcal{O}(μ_0 rn \mathrm{log}\frac{r}ε)$ the state of art. Specifically, many of the previous adaptive sampling methods require to observe the entire matrix when the column space is highly coherent. However, we show that our method is still able to recover this type of matrices by observing a small fraction of entries under many scenarios. Second, we propose an exact completion algorithm, which requires minimal pre-information as either row or column space is not being highly coherent. At the end of the paper, we provide experimental results that illustrate the strength of the algorithms proposed here.
Differentially Quantized Gradient Methods
Venue: arXiv cs.LG 2022
Year: 2022

Consider the following distributed optimization scenario. A worker has access to training data that it uses to compute the gradients while a server decides when to stop iterative computation based on its target accuracy or delay constraints. The server receives all its information about the problem instance from the worker via a rate-limited noiseless communication channel. We introduce the principle we call Differential Quantization (DQ) that prescribes compensating the past quantization errors to direct the descent trajectory of a quantized algorithm towards that of its unquantized counterpart. Assuming that the objective function is smooth and strongly convex, we prove that Differentially Quantized Gradient Descent (DQ-GD) attains a linear contraction factor of $\max\{σ_{\mathrm{GD}}, ρ_n 2^{-R}\}$, where $σ_{\mathrm{GD}}$ is the contraction factor of unquantized gradient descent (GD), $ρ_n \geq 1$ is the covering efficiency of the quantizer, and $R$ is the bitrate per problem dimension $n$. Thus at any $R\geq\log_2 ρ_n /σ_{\mathrm{GD}}$ bits, the contraction factor of DQ-GD is the same as that of unquantized GD, i.e., there is no loss due to quantization. We show that no algorithm within a certain class can converge faster than $\max\{σ_{\mathrm{GD}}, 2^{-R}\}$. Since quantizers exist with $ρ_n \to 1$ as $n \to \infty$ (Rogers, 1963), this means that DQ-GD is asymptotically optimal. The principle of differential quantization continues to apply to gradient methods with momentum such as Nesterov's accelerated gradient descent, and Polyak's heavy ball method. For these algorithms as well, if the rate is above a certain threshold, there is no loss in contraction factor obtained by the differentially quantized algorithm compared to its unquantized counterpart. Experimental results on least-squares problems validate our theoretical analysis.
Hierarchical Overlapping Belief Estimation by Structured Matrix Factorization
Venue: arXiv cs.SI 2022
Year: 2022

Much work on social media opinion polarization focuses on a flat categorization of stances (or orthogonal beliefs) of different communities from media traces. We extend in this work in two important respects. First, we detect not only points of disagreement between communities, but also points of agreement. In other words, we estimate community beliefs in the presence of overlap. Second, in lieu of flat categorization, we consider hierarchical belief estimation, where communities might be hierarchically divided. For example, two opposing parties might disagree on core issues, but within a party, despite agreement on fundamentals, disagreement might occur on further details. We call the resulting combined problem a hierarchical overlapping belief estimation problem. To solve it, this paper develops a new class of unsupervised Non-negative Matrix Factorization (NMF) algorithms, we call Belief Structured Matrix Factorization (BSMF). Our proposed unsupervised algorithm captures both the latent belief intersections and dissimilarities, as well as a hierarchical structure. We discuss the properties of the algorithm and evaluate it on both synthetic and real-world datasets. In the synthetic dataset, our model reduces error by 40%. In real Twitter traces, it improves accuracy by around 10%. The model also achieves 96.08% self-consistency in a sanity check.
Existence and Complexity of Approximate Equilibria in Weighted Congestion Games
Venue: arXiv cs.GT 2022
Year: 2022

We study the existence of approximate pure Nash equilibria ($α$-PNE) in weighted atomic congestion games with polynomial cost functions of maximum degree $d$. Previously it was known that $d$-approximate equilibria always exist, while nonexistence was established only for small constants, namely for $1.153$-PNE. We improve significantly upon this gap, proving that such games in general do not have $\tildeΘ(\sqrt{d})$-approximate PNE, which provides the first super-constant lower bound. Furthermore, we provide a black-box gap-introducing method of combining such nonexistence results with a specific circuit gadget, in order to derive NP-completeness of the decision version of the problem. In particular, deploying this technique we are able to show that deciding whether a weighted congestion game has an $\tilde{O}(\sqrt{d})$-PNE is NP-complete. Previous hardness results were known only for the special case of exact equilibria and arbitrary cost functions. The circuit gadget is of independent interest and it allows us to also prove hardness for a variety of problems related to the complexity of PNE in congestion games. For example, we demonstrate that the question of existence of $α$-PNE in which a certain set of players plays a specific strategy profile is NP-hard for any $α< 3^{d/2}$, even for unweighted congestion games. Finally, we study the existence of approximate equilibria in weighted congestion games with general (nondecreasing) costs, as a function of the number of players $n$. We show that $n$-PNE always exist, matched by an almost tight nonexistence bound of $\tildeΘ(n)$ which we can again transform into an NP-completeness proof for the decision problem.
Massively Parallel Algorithms for Small Subgraph Counting
Venue: arXiv cs.DS 2022
Year: 2022

Over the last two decades, frameworks for distributed-memory parallel computation, such as MapReduce, Hadoop, Spark and Dryad, have gained significant popularity with the growing prevalence of large network datasets. The Massively Parallel Computation (MPC) model is the de-facto standard for studying graph algorithms in these frameworks theoretically. Subgraph counting is one such fundamental problem in analyzing massive graphs, with the main algorithmic challenges centering on designing methods which are both scalable and accurate. Given a graph $G=(V, E)$ with $n$ vertices, $m$ edges and $T$ triangles, our first result is an algorithm that outputs a $(1+\varepsilon)$-approximation to $T$, with asymptotically \emph{optimal round and total space complexity} provided any $S \geq \max{(\sqrt m, n^2/m)}$ space per machine and assuming $T=Ω(\sqrt{m/n})$. Our result gives a quadratic improvement on the bound on $T$ over previous works. We also provide a simple extension of our result to counting \emph{any} subgraph of $k$ size for constant $k \geq 1$. Our second result is an $O_{\varepsilon}(\log \log n)$-round algorithm for exactly counting the number of triangles, whose total space usage is parametrized by the \emph{arboricity} $α$ of the input graph. We extend this result to exactly counting $k$-cliques for any constant $k$. Finally, we prove that a recent result of Bera, Pashanasangi and Seshadhri (ITCS 2020) for exactly counting all subgraphs of size at most $5$ can be implemented in the MPC model in total space.
Compression with wildcards: All spanning trees with prescribed vertex-degrees
Venue: arXiv cs.DS 2022
Year: 2022

By processing all minimal cutsets of a graph G, and by using novel wildcards, all spanning trees of G can be compactly encoded. Surprisingly, a 1986 algorithm of Winter seems to achieve (Conjecture 2) exactly the same compression, although in totally unrelated ways! Although Winter's algorithm is faster, our method adapts to relevant variations of the task, e.g. to the enumeration of only those spanning trees that obey prescribed degree conditions
Fast and Accurate Optical Fiber Channel Modeling Using Generative Adversarial Network
Venue: arXiv cs.IT 2022
Year: 2022

In this work, a new data-driven fiber channel modeling method, generative adversarial network (GAN) is investigated to learn the distribution of fiber channel transfer function. Our investigation focuses on joint channel effects of attenuation, chromic dispersion, self-phase modulation (SPM), and amplified spontaneous emission (ASE) noise. To achieve the success of GAN for channel modeling, we modify the loss function, design the condition vector of input and address the mode collapse for the long-haul transmission. The effective architecture, parameters, and training skills of GAN are also displayed in the paper. The results show that the proposed method can learn the accurate transfer function of the fiber channel. The transmission distance of modeling can be up to 1000 km and can be extended to arbitrary distance theoretically. Moreover, GAN shows robust generalization abilities under different optical launch powers, modulation formats, and input signal distributions. Comparing the complexity of GAN with the split-step Fourier method (SSFM), the total multiplication number is only 2% of SSFM and the running time is less than 0.1 seconds for 1000-km transmission, versus 400 seconds using the SSFM under the same hardware and software conditions, which highlights the remarkable reduction in complexity of the fiber channel modeling.
Simple Mechanisms for Agents with Non-linear Utilities
Venue: arXiv cs.GT 2022
Year: 2022

We show that economic conclusions derived from Bulow and Roberts (1989) for linear utility models approximately extend to non-linear utility models. Specifically, we quantify the extent to which agents with non-linear utilities resemble agents with linear utilities, and we show that the approximation of mechanisms for agents with linear utilities approximately extend for agents with non-linear utilities. We illustrate the framework for the objectives of revenue and welfare on non-linear models that include agents with budget constraints, agents with risk aversion, and agents with endogenous valuations. We derive bounds on how much these models resemble the linear utility model and combine these bounds with well-studied approximation results for linear utility models. We conclude that simple mechanisms are approximately optimal for these non-linear agent models.
Model Selection in Contextual Stochastic Bandit Problems
Venue: arXiv cs.LG 2022
Year: 2022

We study bandit model selection in stochastic environments. Our approach relies on a meta-algorithm that selects between candidate base algorithms. We develop a meta-algorithm-base algorithm abstraction that can work with general classes of base algorithms and different type of adversarial meta-algorithms. Our methods rely on a novel and generic smoothing transformation for bandit algorithms that permits us to obtain optimal $O(\sqrt{T})$ model selection guarantees for stochastic contextual bandit problems as long as the optimal base algorithm satisfies a high probability regret guarantee. We show through a lower bound that even when one of the base algorithms has $O(\log T)$ regret, in general it is impossible to get better than $Ω(\sqrt{T})$ regret in model selection, even asymptotically. Using our techniques, we address model selection in a variety of problems such as misspecified linear contextual bandits, linear bandit with unknown dimension and reinforcement learning with unknown feature maps. Our algorithm requires the knowledge of the optimal base regret to adjust the meta-algorithm learning rate. We show that without such prior knowledge any meta-algorithm can suffer a regret larger than the optimal base regret.
Directional Message Passing for Molecular Graphs
Venue: arXiv cs.LG 2022
Year: 2022

Graph neural networks have recently achieved great successes in predicting quantum mechanical properties of molecules. These models represent a molecule as a graph using only the distance between atoms (nodes). They do not, however, consider the spatial direction from one atom to another, despite directional information playing a central role in empirical potentials for molecules, e.g. in angular potentials. To alleviate this limitation we propose directional message passing, in which we embed the messages passed between atoms instead of the atoms themselves. Each message is associated with a direction in coordinate space. These directional message embeddings are rotationally equivariant since the associated directions rotate with the molecule. We propose a message passing scheme analogous to belief propagation, which uses the directional information by transforming messages based on the angle between them. Additionally, we use spherical Bessel functions and spherical harmonics to construct theoretically well-founded, orthogonal representations that achieve better performance than the currently prevalent Gaussian radial basis representations while using fewer than 1/4 of the parameters. We leverage these innovations to construct the directional message passing neural network (DimeNet). DimeNet outperforms previous GNNs on average by 76% on MD17 and by 31% on QM9. Our implementation is available online.
Revisit Systematic Generalization via Meaningful Learning
Venue: arXiv cs.CL 2022
Year: 2022

Humans can systematically generalize to novel compositions of existing concepts. Recent studies argue that neural networks appear inherently ineffective in such cognitive capacity, leading to a pessimistic view and a lack of attention to optimistic results. We revisit this controversial topic from the perspective of meaningful learning, an exceptional capability of humans to learn novel concepts by connecting them with known ones. We reassess the compositional skills of sequence-to-sequence models conditioned on the semantic links between new and old concepts. Our observations suggest that models can successfully one-shot generalize to novel concepts and compositions through semantic linking, either inductively or deductively. We demonstrate that prior knowledge plays a key role as well. In addition to synthetic tests, we further conduct proof-of-concept experiments in machine translation and semantic parsing, showing the benefits of meaningful learning in applications. We hope our positive findings will encourage excavating modern neural networks' potential in systematic generalization through more advanced learning schemes.
Apps Gone Rogue: Maintaining Personal Privacy in an Epidemic
Venue: arXiv cs.CR 2022
Year: 2022

Containment, the key strategy in quickly halting an epidemic, requires rapid identification and quarantine of the infected individuals, determination of whom they have had close contact with in the previous days and weeks, and decontamination of locations the infected individual has visited. Achieving containment demands accurate and timely collection of the infected individual's location and contact history. Traditionally, this process is labor intensive, susceptible to memory errors, and fraught with privacy concerns. With the recent almost ubiquitous availability of smart phones, many people carry a tool which can be utilized to quickly identify an infected individual's contacts during an epidemic, such as the current 2019 novel Coronavirus crisis. Unfortunately, the very same first-generation contact tracing tools have been used to expand mass surveillance, limit individual freedoms and expose the most private details about individuals. We seek to outline the different technological approaches to mobile-phone based contact-tracing to date and elaborate on the opportunities and the risks that these technologies pose to individuals and societies. We describe advanced security enhancing approaches that can mitigate these risks and describe trade-offs one must make when developing and deploying any mass contact-tracing technology. With this paper, our aim is to continue to grow the conversation regarding contact-tracing for epidemic and pandemic containment and discuss opportunities to advance this space. We invite feedback and discussion.
Optimal Algorithm Allocation for Single Robot Cloud Systems
Venue: arXiv cs.RO 2022
Year: 2022

In order for a robot to perform a task, several algorithms need to be executed, sometimes, simultaneously. Algorithms can be run either on the robot itself or, upon request, be performed on cloud infrastructure. The term cloud infrastructure is used to describe hardware, storage, abstracted resources, and network resources related to cloud computing. Depending on the decisions on where to execute the algorithms, the overall execution time and necessary memory space for the robot will change accordingly. The price of a robot depends, among other things, on its memory capacity and computational power. We answer the question of how to keep a given performance and use a cheaper robot (lower resources) by assigning computational tasks to the cloud infrastructure, depending on memory, computational power, and communication constraints. Also, for a fixed robot, our model provides a way to have optimal overall performance. We provide a general model for the optimal decision of algorithm allocation under certain constraints. We exemplify the model with simulation results. The main advantage of our model is that it provides an optimal task allocation simultaneously for memory and time.
Probabilistic Visual Navigation with Bidirectional Image Prediction
Venue: arXiv cs.RO 2022
Year: 2022

Humans can robustly follow a visual trajectory defined by a sequence of images (i.e. a video) regardless of substantial changes in the environment or the presence of obstacles. We aim at endowing similar visual navigation capabilities to mobile robots solely equipped with a RGB fisheye camera. We propose a novel probabilistic visual navigation system that learns to follow a sequence of images with bidirectional visual predictions conditioned on possible navigation velocities. By predicting bidirectionally (from start towards goal and vice versa) our method extends its predictive horizon enabling the robot to go around unseen large obstacles that are not visible in the video trajectory. Learning how to react to obstacles and potential risks in the visual field is achieved by imitating human teleoperators. Since the human teleoperation commands are diverse, we propose a probabilistic representation of trajectories that we can sample to find the safest path. Integrated into our navigation system, we present a novel localization approach that infers the current location of the robot based on the virtual predicted trajectories required to reach different images in the visual trajectory. We evaluate our navigation system quantitatively and qualitatively in multiple simulated and real environments and compare to state-of-the-art baselines.Our approach outperforms the most recent visual navigation methods with a large margin with regard to goal arrival rate, subgoal coverage rate, and success weighted by path length (SPL). Our method also generalizes to new robot embodiments never used during training.
Emotion Recognition From Gait Analyses: Current Research and Future Directions
Venue: arXiv cs.HC 2022
Year: 2022

Human gait refers to a daily motion that represents not only mobility, but it can also be used to identify the walker by either human observers or computers. Recent studies reveal that gait even conveys information about the walker's emotion. Individuals in different emotion states may show different gait patterns. The mapping between various emotions and gait patterns provides a new source for automated emotion recognition. Compared to traditional emotion detection biometrics, such as facial expression, speech and physiological parameters, gait is remotely observable, more difficult to imitate, and requires less cooperation from the subject. These advantages make gait a promising source for emotion detection. This article reviews current research on gait-based emotion detection, particularly on how gait parameters can be affected by different emotion states and how the emotion states can be recognized through distinct gait patterns. We focus on the detailed methods and techniques applied in the whole process of emotion recognition: data collection, preprocessing, and classification. At last, we discuss possible future developments of efficient and effective gait-based emotion recognition using the state of the art techniques on intelligent computation and big data.