OpenRaiser/Intern-Atlas huggingface dataset:
Derivative-Free Placement Optimization for Multi-UAV Wireless Networks with Channel Knowledge Map
Venue: arXiv cs.IT 2022
Year: 2022

This paper studies a multi-UAV wireless network, in which multiple UAV users share the same spectrum to send individual messages to their respectively associated ground base stations (GBSs). The UAV users aim to optimize their locations to maximize the weighted sum rate. While most existing work considers simplified line-of-sight (LoS) or statistic air-to-ground (A2G) channel models, we exploit the location-specific channel knowledge map (CKM) to enhance the placement performance in practice. However, as the CKMs normally contain discrete site- and location-specific channel data without analytic model functions, the corresponding weighted sum rate function becomes non-differentiable in general. In this case, conventional optimization techniques relying on function derivatives are inapplicable to solve the resultant placement optimization problem. To address this issue, we propose a novel iterative algorithm based on the derivative-free optimization. In each iteration, we first construct a quadratic function to approximate the non-differentiable weighted sum rate under a set of interpolation conditions, and then update the UAVs' placement locations by maximizing the approximate quadratic function subject to a trust region constraint. Numerical results show the convergence of the proposed algorithm. It is also shown that the proposed algorithm achieves a weighted sum rate close to the optimal design based on exhaustive search with much lower implementation complexity, and it significantly outperforms the conventional optimization method based on simplified LoS channel models and the heuristic design with each UAV hovering above its associated GBS.
Cascaded Gaps: Towards Gap-Dependent Regret for Risk-Sensitive Reinforcement Learning
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we study gap-dependent regret guarantees for risk-sensitive reinforcement learning based on the entropic risk measure. We propose a novel definition of sub-optimality gaps, which we call cascaded gaps, and we discuss their key components that adapt to the underlying structures of the problem. Based on the cascaded gaps, we derive non-asymptotic and logarithmic regret bounds for two model-free algorithms under episodic Markov decision processes. We show that, in appropriate settings, these bounds feature exponential improvement over existing ones that are independent of gaps. We also prove gap-dependent lower bounds, which certify the near optimality of the upper bounds.
Dynamic Template Selection Through Change Detection for Adaptive Siamese Tracking
Venue: arXiv cs.CV 2022
Year: 2022

Deep Siamese trackers have recently gained much attention in recent years since they can track visual objects at high speeds. Additionally, adaptive tracking methods, where target samples collected by the tracker are employed for online learning, have achieved state-of-the-art accuracy. However, single object tracking (SOT) remains a challenging task in real-world application due to changes and deformations in a target object's appearance. Learning on all the collected samples may lead to catastrophic forgetting, and thereby corrupt the tracking model. In this paper, SOT is formulated as an online incremental learning problem. A new method is proposed for dynamic sample selection and memory replay, preventing template corruption. In particular, we propose a change detection mechanism to detect gradual changes in object appearance and select the corresponding samples for online adaption. In addition, an entropy-based sample selection strategy is introduced to maintain a diversified auxiliary buffer for memory replay. Our proposed method can be integrated into any object tracking algorithm that leverages online learning for model adaptation. Extensive experiments conducted on the OTB-100, LaSOT, UAV123, and TrackingNet datasets highlight the cost-effectiveness of our method, along with the contribution of its key components. Results indicate that integrating our proposed method into state-of-art adaptive Siamese trackers can increase the potential benefits of a template update strategy, and significantly improve performance.
Knowledge Amalgamation for Object Detection with Transformers
Venue: arXiv cs.CV 2022
Year: 2022

Knowledge amalgamation (KA) is a novel deep model reusing task aiming to transfer knowledge from several well-trained teachers to a multi-talented and compact student. Currently, most of these approaches are tailored for convolutional neural networks (CNNs). However, there is a tendency that transformers, with a completely different architecture, are starting to challenge the domination of CNNs in many computer vision tasks. Nevertheless, directly applying the previous KA methods to transformers leads to severe performance degradation. In this work, we explore a more effective KA scheme for transformer-based object detection models. Specifically, considering the architecture characteristics of transformers, we propose to dissolve the KA into two aspects: sequence-level amalgamation (SA) and task-level amalgamation (TA). In particular, a hint is generated within the sequence-level amalgamation by concatenating teacher sequences instead of redundantly aggregating them to a fixed-size one as previous KA works. Besides, the student learns heterogeneous detection tasks through soft targets with efficiency in the task-level amalgamation. Extensive experiments on PASCAL VOC and COCO have unfolded that the sequence-level amalgamation significantly boosts the performance of students, while the previous methods impair the students. Moreover, the transformer-based students excel in learning amalgamated knowledge, as they have mastered heterogeneous detection tasks rapidly and achieved superior or at least comparable performance to those of the teachers in their specializations.
Maximizing Conditional Independence for Unsupervised Domain Adaptation
Venue: arXiv cs.LG 2022
Year: 2022

Unsupervised domain adaptation studies how to transfer a learner from a labeled source domain to an unlabeled target domain with different distributions. Existing methods mainly focus on matching the marginal distributions of the source and target domains, which probably lead a misalignment of samples from the same class but different domains. In this paper, we deal with this misalignment by achieving the class-conditioned transferring from a new perspective. We aim to maximize the conditional independence of feature and domain given class in the reproducing kernel Hilbert space. The optimization of the conditional independence measure can be viewed as minimizing a surrogate of a certain mutual information between feature and domain. An interpretable empirical estimation of the conditional dependence is deduced and connected with the unconditional case. Besides, we provide an upper bound on the target error by taking the class-conditional distribution into account, which provides a new theoretical insight for most class-conditioned transferring methods. In addition to unsupervised domain adaptation, we extend our method to the multi-source scenario in a natural and elegant way. Extensive experiments on four benchmarks validate the effectiveness of the proposed models in both unsupervised domain adaptation and multiple source domain adaptation.
Precognition in Task-oriented Dialogue Understanding: Posterior Regularization by Future Context
Venue: arXiv cs.CL 2022
Year: 2022

Task-oriented dialogue systems have become overwhelmingly popular in recent researches. Dialogue understanding is widely used to comprehend users' intent, emotion and dialogue state in task-oriented dialogue systems. Most previous works on such discriminative tasks only models current query or historical conversations. Even if in some work the entire dialogue flow was modeled, it is not suitable for the real-world task-oriented conversations as the future contexts are not visible in such cases. In this paper, we propose to jointly model historical and future information through the posterior regularization method. More specifically, by modeling the current utterance and past contexts as prior, and the entire dialogue flow as posterior, we optimize the KL distance between these distributions to regularize our model during training. And only historical information is used for inference. Extensive experiments on two dialogue datasets validate the effectiveness of our proposed method, achieving superior results compared with all baseline models.
Evaluating State of the Art, Forecasting Ensembles- and Meta-learning Strategies for Model Fusion
Venue: arXiv cs.LG 2022
Year: 2022

Techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the utility of the Exponential-Smoothing-Recurrent Neural Network (ES-RNN) in the pool of base models for different ensembles. We compare against some state of the art ensembling techniques and arithmetic model averaging as a benchmark. We experiment with the M4 forecasting data set of 100,000 time-series, and the results show that the Feature-based Forecast Model Averaging (FFORMA), on average, is the best technique for late data fusion with the ES-RNN. However, considering the M4's Daily subset of data, stacking was the only successful ensemble at dealing with the case where all base model performances are similar. Our experimental results indicate that we attain state of the art forecasting results compared to N-BEATS as a benchmark. We conclude that model averaging is a more robust ensemble than model selection and stacking strategies. Further, the results show that gradient boosting is superior for implementing ensemble learning strategies.
Handling Higher-Order Effects
Venue: arXiv cs.PL 2022
Year: 2022

Algebraic effect handlers is a programming paradigm where programmers can declare their own syntactic operations, and modularly define the semantics of these using effect handlers. However, we cannot directly define algebraic effect handlers for many higher-order operations (or higher-order effects) -- i.e., operations that have computations as parameters. Examples of such higher-order effects include common programming features, such as try-catch exception handlers, function abstraction, and more. In this paper we present a new kind of effect handler that addresses this shortcoming. Our effect handler approach is closely related to previous work on scoped effect handlers, which also supports higher-order effects. A key difference is that our effect handlers make it easy to understand separate (higher-order) effects as separate concerns, since effects do not interact. In contrast, effect interaction is the default with scoped effect handlers. While separate concerns is the default with our handlers, it is also possible to define handlers where effects interact.
$D$-semifaithful codes that are universal over both memoryless sources and distortion measures
Venue: arXiv cs.IT 2022
Year: 2022

We prove the existence of codebooks for d-semifaithful lossy compression that are simultaneously universal with respect to both the class of finite-alphabet memoryless sources and the class of all bounded additive distortion measures. By applying independent random selection of the codewords according to a mixture of all memoryless sources, we achieve redundancy rates that are within O(log n/n) close to the empirical rate-distortion function of every given source vector with respect to every bounded distortion measure. As outlined in the last section, the principal ideas can also be extended significantly beyond the class of memoryless sources, namely, to the setting of individual sequences encoded by finite-state machines.
Efficient Two-Dimensional Self-Stabilizing Byzantine Clock Synchronization in WALDEN
Venue: arXiv cs.DC 2022
Year: 2022

For tolerating Byzantine faults of both the terminal and communication components in self-stabilizing clock synchronization, the two-dimensional self-stabilizing Byzantine-fault-tolerant clock synchronization problem is investigated and solved. By utilizing the time-triggered (TT) stage provided in the underlying networks as TT communication windows, the approximate agreement, hopping procedure, and randomized grandmasters are integrated into the overall solution. It is shown that with partitioning the communication components into $3$ arbitrarily connected subnetworks, efficient synchronization can be achieved with one such subnetwork and less than $1/3$ terminal components being Byzantine. Meanwhile, the desired stabilization can be reached for the specific networks in one or several seconds with high probabilities. This helps in developing various distributed hard-real-time systems with stringent time, resources, and safety requirements.
Neural Enhancement of Factor Graph-based Symbol Detection
Venue: arXiv cs.IT 2022
Year: 2022

We study the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Cyclic factor graphs have the potential to yield low-complexity symbol detectors, but are suboptimal if the ubiquitous sum-product algorithm is applied. In this paper, we present and evaluate strategies to improve the performance of cyclic factor graph-based symbol detection algorithms by means of neural enhancement. In particular, we apply neural belief propagation as an effective way to counteract the effect of cycles within the factor graph. We further propose the application and optimization of a linear preprocessor of the channel output. By modifying the observation model, the preprocessing can effectively change the underlying factor graph, thereby significantly improving the detection performance as well as reducing the complexity.
Turbocharging Heuristics for Weak Coloring Numbers
Venue: arXiv cs.DS 2022
Year: 2022

Bounded expansion and nowhere-dense classes of graphs capture the theoretical tractability for several important algorithmic problems. These classes of graphs can be characterized by the so-called weak coloring numbers of graphs, which generalize the well-known graph invariant degeneracy (also called k-core number). Being NP-hard, weak-coloring numbers were previously computed on real-world graphs mainly via incremental heuristics. We study whether it is feasible to augment such heuristics with exponential-time subprocedures that kick in when a desired upper bound on the weak coloring number is breached. We provide hardness and tractability results on the corresponding computational subproblems. We implemented several of the resulting algorithms and show them to be competitive with previous approaches on a previously studied set of benchmark instances containing 86 graphs with up to 183831 edges. We obtain improved weak coloring numbers for over half of the instances.
Spatio-temporal Gait Feature with Global Distance Alignment
Venue: arXiv cs.CV 2022
Year: 2022

Gait recognition is an important recognition technology, because gait is not easy to camouflage and does not need cooperation to recognize subjects. However, many existing methods are inadequate in preserving both temporal information and fine-grained information, thus reducing its discrimination. This problem is more serious when the subjects with similar walking postures are identified. In this paper, we try to enhance the discrimination of spatio-temporal gait features from two aspects: effective extraction of spatio-temporal gait features and reasonable refinement of extracted features. Thus our method is proposed, it consists of Spatio-temporal Feature Extraction (SFE) and Global Distance Alignment (GDA). SFE uses Temporal Feature Fusion (TFF) and Fine-grained Feature Extraction (FFE) to effectively extract the spatio-temporal features from raw silhouettes. GDA uses a large number of unlabeled gait data in real life as a benchmark to refine the extracted spatio-temporal features. GDA can make the extracted features have low inter-class similarity and high intra-class similarity, thus enhancing their discrimination. Extensive experiments on mini-OUMVLP and CASIA-B have proved that we have a better result than some state-of-the-art methods.
Screentone-Preserved Manga Retargeting
Venue: arXiv cs.CV 2022
Year: 2022

As a popular comic style, manga offers a unique impression by utilizing a rich set of bitonal patterns, or screentones, for illustration. However, screentones can easily be contaminated with visual-unpleasant aliasing and/or blurriness after resampling, which harms its visualization on displays of diverse resolutions. To address this problem, we propose the first manga retargeting method that synthesizes a rescaled manga image while retaining the screentone in each screened region. This is a non-trivial task as accurate region-wise segmentation remains challenging. Fortunately, the rescaled manga shares the same region-wise screentone correspondences with the original manga, which enables us to simplify the screentone synthesis problem as an anchor-based proposals selection and rearrangement problem. Specifically, we design a novel manga sampling strategy to generate aliasing-free screentone proposals, based on hierarchical grid-based anchors that connect the correspondences between the original and the target rescaled manga. Furthermore, a Recurrent Proposal Selection Module (RPSM) is proposed to adaptively integrate these proposals for target screentone synthesis. Besides, to deal with the translation insensitivity nature of screentones, we propose a translation-invariant screentone loss to facilitate the training convergence. Extensive qualitative and quantitative experiments are conducted to verify the effectiveness of our method, and notably compelling results are achieved compared to existing alternative techniques.
Building and curating conversational corpora for diversity-aware language science and technology
Venue: arXiv cs.CL 2022
Year: 2022

We present an analysis pipeline and best practice guidelines for building and curating corpora of everyday conversation in diverse languages. Surveying language documentation corpora and other resources that cover 67 languages and varieties from 28 phyla, we describe the compilation and curation process, specify minimal properties of a unified format for interactional data, and develop methods for quality control that take into account turn-taking and timing. Two case studies show the broad utility of conversational data for (i) charting human interactional infrastructure and (ii) tracing challenges and opportunities for current ASR solutions. Linguistically diverse conversational corpora can provide new insights for the language sciences and stronger empirical foundations for language technology.
SOCIOFILLMORE: A Tool for Discovering Perspectives
Venue: arXiv cs.CL 2022
Year: 2022

SOCIOFILLMORE is a multilingual tool which helps to bring to the fore the focus or the perspective that a text expresses in depicting an event. Our tool, whose rationale we also support through a large collection of human judgements, is theoretically grounded on frame semantics and cognitive linguistics, and implemented using the LOME frame semantic parser. We describe SOCIOFILLMORE's development and functionalities, show how non-NLP researchers can easily interact with the tool, and present some example case studies which are already incorporated in the system, together with the kind of analysis that can be visualised.
PhD thesis: SQL Comprehension and Synthesis
Venue: arXiv cs.DB 2022
Year: 2022

Structured Query Language (SQL) remains the standard language used in Relational Database Management Systems (RDBMSs) and has found applications in healthcare (patient registries), businesses (inventories, trend analysis), military, education, etc. Although SQL statements are English-like, the process of writing SQL queries is often problematic for nontechnical end-users in the industry. Similarly, formulating and comprehending written queries can be confusing, especially for undergraduate students. One of the pivotal reasons given for these difficulties lies with the simple syntax of SQL, which is often misleading and hard to understand. An ideal solution is to present these two audiences: undergraduate students and nontechnical end-users with learning and practice tools. These tools are mostly electronic and can be used to aid their understanding, as well as enable them to write correct SQL queries. This work proposes a new approach aimed at understanding and writing correct SQL queries using principles from Formal Language and Automata Theory. We present algorithms based on: regular expressions for the recognition of simple query constructs, context-free grammars for the recognition of nested queries, and a jumping finite automaton for the synthesis of SQL queries from natural language descriptions. As proof of concept, these algorithms were further implemented into interactive software tools aimed at improving SQL comprehension. Evaluation of these tools showed that the majority of participants agreed that the tools were intuitive and aided their understanding of SQL queries. These tools should, therefore, find applications in aiding SQL comprehension at higher learning institutions and assist in the writing of correct queries in data-centered industries.
Weakly Supervised Learning of Keypoints for 6D Object Pose Estimation
Venue: arXiv cs.CV 2022
Year: 2022

State-of-the-art approaches for 6D object pose estimation require large amounts of labeled data to train the deep networks. However, the acquisition of 6D object pose annotations is tedious and labor-intensive in large quantity. To alleviate this problem, we propose a weakly supervised 6D object pose estimation approach based on 2D keypoint detection. Our method trains only on image pairs with known relative transformations between their viewpoints. Specifically, we assign a set of arbitrarily chosen 3D keypoints to represent each unknown target 3D object and learn a network to detect their 2D projections that comply with the relative camera viewpoints. During inference, our network first infers the 2D keypoints from the query image and a given labeled reference image. We then use these 2D keypoints and the arbitrarily chosen 3D keypoints retained from training to infer the 6D object pose. Extensive experiments demonstrate that our approach achieves comparable performance with state-of-the-art fully supervised approaches.
To Migrate or not to Migrate: An Analysis of Operator Migration in Distributed Stream Processing
Venue: arXiv cs.DC 2022
Year: 2022

One of the most important issues in data stream processing systems is to use operator migration to handle highly variable workloads in a cost-efficient manner and adapt to the needs at any given time on demand. Operator migration is a complex process that involves changes in the state and stream management of a running query, typically without any loss of data, and with as little disruption to the execution as possible. This survey provides an overview of solutions for operator migration from a historical perspective as well as the perspective of the goal of migration. It introduces a conceptual model of operator migration to establish a unified terminology and classify existing solutions. Existing work in the area is analyzed to separate the mechanism of migration from the decision to migrate the data. In case of the latter, a cost-benefit analysis is emphasized that is important for operator migration but is often only implicitly addressed, or is neglected altogether. A description of the available solutions provides the reader with a good understanding of the design alternatives from an algorithmic viewpoint. We complement this with an empirical study to provide quantitative insights on the impact of different design alternatives on the mechanisms of migration.