OpenRaiser/Intern-Atlas huggingface dataset:
Aggressive Racecar Drifting Control Using Onboard Cameras and Inertial Measurement Unit
Venue: arXiv cs.RO 2022
Year: 2022

Complex autonomous driving, such as drifting, requires high-precision and high-frequency pose information to ensure accuracy and safety, which is notably difficult when using only onboard sensors. In this paper, we propose a drift controller with two feedback control loops: sideslip controller that stabilizes the sideslip angle by tuning the front wheel steering angle, and circle controller that maintains a stable trajectory radius and circle center by controlling the wheel rotational speed. We use an extended Kalman filter to estimate the state. A robustified KASA algorithm is further proposed to accurately estimate the parameters of the circle (i.e., the center and radius) that best fits into the current trajectory. On the premise of the uniform circular motion of the vehicle in the process of stable drift, we use angle information instead of acceleration to describe the dynamic of the vehicle. We implement our method on a 1/10 scale race car. The car drifts stably with a given center and radius, which illustrates the effectiveness of our method.
Machine Learning Empowered Intelligent Data Center Networking: A Survey
Venue: arXiv cs.NI 2022
Year: 2022

To support the needs of ever-growing cloud-based services, the number of servers and network devices in data centers is increasing exponentially, which in turn results in high complexities and difficulties in network optimization. To address these challenges, both academia and industry turn to artificial intelligence technology to realize network intelligence. To this end, a considerable number of novel and creative machine learning-based (ML-based) research works have been put forward in recent few years. Nevertheless, there are still enormous challenges faced by the intelligent optimization of data center networks (DCNs), especially in the scenario of online real-time dynamic processing of massive heterogeneous services and traffic data. To best of our knowledge, there is a lack of systematic and original comprehensively investigations with in-depth analysis on intelligent DCN. To this end, in this paper, we comprehensively investigate the application of machine learning to data center networking, and provide a general overview and in-depth analysis of the recent works, covering flow prediction, flow classification, load balancing, resource management, routing optimization, and congestion control. In order to provide a multi-dimensional and multi-perspective comparison of various solutions, we design a quality assessment criteria called REBEL-3S to impartially measure the strengths and weaknesses of these research works. Moreover, we also present unique insights into the technology evolution of the fusion of data center network and machine learning, together with some challenges and potential future research opportunities.
Towards Machine Learning for Placement and Routing in Chip Design: a Methodological Overview
Venue: arXiv cs.LG 2022
Year: 2022

Placement and routing are two indispensable and challenging (NP-hard) tasks in modern chip design flows. Compared with traditional solvers using heuristics or expert-well-designed algorithms, machine learning has shown promising prospects by its data-driven nature, which can be of less reliance on knowledge and priors, and potentially more scalable by its advanced computational paradigms (e.g. deep networks with GPU acceleration). This survey starts with the introduction of basics of placement and routing, with a brief description on classic learning-free solvers. Then we present detailed review on recent advance in machine learning for placement and routing. Finally we discuss the challenges and opportunities for future research.
On the Application of Quasi-Degradation to Network NOMA in Downlink CoMP Systems
Venue: arXiv cs.IT 2022
Year: 2022

The application of network non-orthogonal multiple access (N-NOMA) technique to coordinated multi-point (CoMP) systems has attracted significant attention due to its superior capability to improve connectivity and maintain reliable transmission for CoMP users simultaneously. Based on the concept of quasi-degraded channel for N-NOMA, this paper studies the precoding design for downlink N-NOMA scenarios with two base stations (BSs) equipped with multiple antennas. In specific, under quasi-degraded channels, simple linear precoding based N-NOMA can achieve the same minimal total transmission power as theoretically optimal but complicated dirty paper coding (DPC) scheme, when the users' target rates and minimal transmission power of each BS are given. In this paper, the channel quasi-degradation (QD) condition is first rigorously derived for the scenario with single CoMP user and two NOMA users. The closed-form optimal precoders for N-NOMA under quasi-degraded channels are also provided. Then, based on QD condition, a novel hybrid N-NOMA (H-N-NOMA) scheme is proposed, which is a mixture of N-NOMA and conventional zero-forcing beamforming (ZFBF) scheme. Further, for the scenarios with more users, a low-complexity QD based user pairing (QDUP) algorithm is proposed. Numerical results are presented to reveal the impact factors of QD channels, and also demonstrate the superior performance of the proposed H-N-NOMA/QDUP scheme. It is shown that the proposed H-N-NOMA/QDUP scheme can effectively exploit the benefit of multi user diversity.
Optimizing Information Freshness in RIS-assisted NOMA-based IoT Networks
Venue: arXiv cs.IT 2022
Year: 2022

This paper investigates the benefits of integrating reconfigurable intelligent surface (RIS) on minimizing the average sum age of information (AoI) in uplink non-orthogonal multiple access-based Internet-of-Things (IoT) networks. In this setup, an optimization problem is formulated to optimize the RIS configuration, the transmit power per IoT device and the clustering policy of IoT devices. The formulated problem is a mixed-integer non-convex one, and in order to solve it we obtain first the RIS configuration by adopting a semi-definite relaxation (SDR) approach. Afterwards, the joint power allocation and user-clustering problem is solved using the concept of bi-level optimization and is decomposed into an outer user clustering problem and an inner power allocation problem. Optimal closed-form expressions are derived for the inner problem and the Hungarian method is employed to solve the outer one. Numerical results demonstrate the performance superiority of our approach.
KL Divergence Estimation with Multi-group Attribution
Venue: arXiv cs.LG 2022
Year: 2022

Estimating the Kullback-Leibler (KL) divergence between two distributions given samples from them is well-studied in machine learning and information theory. Motivated by considerations of multi-group fairness, we seek KL divergence estimates that accurately reflect the contributions of sub-populations to the overall divergence. We model the sub-populations coming from a rich (possibly infinite) family $\mathcal{C}$ of overlapping subsets of the domain. We propose the notion of multi-group attribution for $\mathcal{C}$, which requires that the estimated divergence conditioned on every sub-population in $\mathcal{C}$ satisfies some natural accuracy and fairness desiderata, such as ensuring that sub-populations where the model predicts significant divergence do diverge significantly in the two distributions. Our main technical contribution is to show that multi-group attribution can be derived from the recently introduced notion of multi-calibration for importance weights [HKRR18, GRSW21]. We provide experimental evidence to support our theoretical results, and show that multi-group attribution provides better KL divergence estimates when conditioned on sub-populations than other popular algorithms.
Cutting a tree with Subgraph Complementation is hard, except for some small trees
Venue: arXiv cs.DS 2022
Year: 2022

For a graph property $Π$, Subgraph Complementation to $Π$ is the problem to find whether there is a subset $S$ of vertices of the input graph $G$ such that modifying $G$ by complementing the subgraph induced by $S$ results in a graph satisfying the property $Π$. We prove that the problem of Subgraph Complementation to $T$-free graphs is NP-Complete, for $T$ being a tree, except for 41 trees of at most 13 vertices (a graph is $T$-free if it does not contain any induced copies of $T$). This result, along with the 4 known polynomial-time solvable cases (when $T$ is a path on at most 4 vertices), leaves behind 37 open cases. Further, we prove that these hard problems do not admit any subexponential-time algorithms, assuming the Exponential Time Hypothesis. As an additional result, we obtain that Subgraph Complementation to paw-free graphs can be solved in polynomial-time.
Weakly Supervised Disentangled Representation for Goal-conditioned Reinforcement Learning
Venue: arXiv cs.RO 2022
Year: 2022

Goal-conditioned reinforcement learning is a crucial yet challenging algorithm which enables agents to achieve multiple user-specified goals when learning a set of skills in a dynamic environment. However, it typically requires millions of the environmental interactions explored by agents, which is sample-inefficient. In the paper, we propose a skill learning framework DR-GRL that aims to improve the sample efficiency and policy generalization by combining the Disentangled Representation learning and Goal-conditioned visual Reinforcement Learning. In a weakly supervised manner, we propose a Spatial Transform AutoEncoder (STAE) to learn an interpretable and controllable representation in which different parts correspond to different object attributes (shape, color, position). Due to the high controllability of the representations, STAE can simply recombine and recode the representations to generate unseen goals for agents to practice themselves. The manifold structure of the learned representation maintains consistency with the physical position, which is beneficial for reward calculation. We empirically demonstrate that DR-GRL significantly outperforms the previous methods in sample efficiency and policy generalization. In addition, DR-GRL is also easy to expand to the real robot.
Enhance transferability of adversarial examples with model architecture
Venue: arXiv cs.LG 2022
Year: 2022

Transferability of adversarial examples is of critical importance to launch black-box adversarial attacks, where attackers are only allowed to access the output of the target model. However, under such a challenging but practical setting, the crafted adversarial examples are always prone to overfitting to the proxy model employed, presenting poor transferability. In this paper, we suggest alleviating the overfitting issue from a novel perspective, i.e., designing a fitted model architecture. Specifically, delving the bottom of the cause of poor transferability, we arguably decompose and reconstruct the existing model architecture into an effective model architecture, namely multi-track model architecture (MMA). The adversarial examples crafted on the MMA can maximumly relieve the effect of model-specified features to it and toward the vulnerable directions adopted by diverse architectures. Extensive experimental evaluation demonstrates that the transferability of adversarial examples based on the MMA significantly surpass other state-of-the-art model architectures by up to 40% with comparable overhead.
Quality Monitoring and Assessment of Deployed Deep Learning Models for Network AIOps
Venue: arXiv cs.AI 2022
Year: 2022

Artificial Intelligence (AI) has recently attracted a lot of attention, transitioning from research labs to a wide range of successful deployments in many fields, which is particularly true for Deep Learning (DL) techniques. Ultimately, DL models being software artifacts, they need to be regularly maintained and updated: AIOps is the logical extension of the DevOps software development practices to AI-software applied to network operation and management. In the lifecycle of a DL model deployment, it is important to assess the quality of deployed models, to detect "stale" models and prioritize their update. In this article, we cover the issue in the context of network management, proposing simple yet effective techniques for (i) quality assessment of individual inference, and for (ii) overall model quality tracking over multiple inferences, that we apply to two use cases, representative of the network management and image recognition fields.
Cross-Lingual Text Classification with Multilingual Distillation and Zero-Shot-Aware Training
Venue: arXiv cs.CL 2022
Year: 2022

Multilingual pre-trained language models (MPLMs) not only can handle tasks in different languages but also exhibit surprising zero-shot cross-lingual transferability. However, MPLMs usually are not able to achieve comparable supervised performance on rich-resource languages compared to the state-of-the-art monolingual pre-trained models. In this paper, we aim to improve the multilingual model's supervised and zero-shot performance simultaneously only with the resources from supervised languages. Our approach is based on transferring knowledge from high-performance monolingual models with a teacher-student framework. We let the multilingual model learn from multiple monolingual models simultaneously. To exploit the model's cross-lingual transferability, we propose MBLM (multi-branch multilingual language model), a model built on the MPLMs with multiple language branches. Each branch is a stack of transformers. MBLM is trained with the zero-shot-aware training strategy that encourages the model to learn from the mixture of zero-shot representations from all the branches. The results on two cross-lingual classification tasks show that, with only the task's supervised data used, our method improves both the supervised and zero-shot performance of MPLMs.
The Complexity of Evaluating nfer
Venue: arXiv cs.LO 2022
Year: 2022

Nfer is a rule-based language for abstracting event streams into a hierarchy of intervals with data. Nfer has multiple implementations and has been applied in the analysis of spacecraft telemetry and autonomous vehicle logs. This work provides the first complexity analysis of nfer evaluation, i.e., the problem of deciding whether a given interval is generated by applying rules. We show that the full nfer language is undecidable and that this depends on both recursion in the rules and an infinite data domain. By restricting either or both of those capabilities, we obtain tight decidability results. We also examine the impact on complexity of exclusive rules and minimality. For the most practical case, which is minimality with finite data, we provide a polynomial-time algorithm.
Rate-Distortion Problems of the Poisson Process based on a Group-Theoretic Approach
Venue: arXiv cs.IT 2022
Year: 2022

We study rate-distortion problems of a Poisson process using a group theoretic approach. By describing a realization of a Poisson point process with either point timings or inter-event (inter-point) intervals and by choosing appropriate distortion measures, we establish rate-distortion problems of a homogeneous Poisson process as ball- or sphere-covering problems for realizations of the hyperoctahedral group in $\mathbb{R}^n$. Specifically, the realizations we investigate are a hypercube and a hyperoctahedron. Thereby we unify three known rate-distortion problems of a Poisson process (with different distortion measures, but resulting in the same rate-distortion function) with the Laplacian-$\ell_1$ rate-distortion problem.
Points-of-Interest Relationship Inference with Spatial-enriched Graph Neural Networks
Venue: arXiv cs.AI 2022
Year: 2022

As a fundamental component in location-based services, inferring the relationship between points-of-interests (POIs) is very critical for service providers to offer good user experience to business owners and customers. Most of the existing methods for relationship inference are not targeted at POI, thus failing to capture unique spatial characteristics that have huge effects on POI relationships. In this work we propose PRIM to tackle POI relationship inference for multiple relation types. PRIM features four novel components, including a weighted relational graph neural network, category taxonomy integration, a self-attentive spatial context extractor, and a distance-specific scoring function. Extensive experiments on two real-world datasets show that PRIM achieves the best results compared to state-of-the-art baselines and it is robust against data sparsity and is applicable to unseen cases in practice.
SFIP: Coarse-Grained Syscall-Flow-Integrity Protection in Modern Systems
Venue: arXiv cs.CR 2022
Year: 2022

Growing code bases of modern applications have led to a steady increase in the number of vulnerabilities. Control-Flow Integrity (CFI) is one promising mitigation that is more and more widely deployed and prevents numerous exploits. CFI focuses purely on one security domain. That is, transitions between user space and kernel space are not protected by CFI. Furthermore, if user space CFI is bypassed, the system and kernel interfaces remain unprotected, and an attacker can run arbitrary transitions. In this paper, we introduce the concept of syscall-flow-integrity protection (SFIP) that complements the concept of CFI with integrity for user-kernel transitions. Our proof-of-concept implementation relies on static analysis during compilation to automatically extract possible syscall transitions. An application can opt-in to SFIP by providing the extracted information to the kernel for runtime enforcement. The concept is built on three fully-automated pillars: First, a syscall state machine, representing possible transitions according to a syscall digraph model. Second, a syscall-origin mapping, which maps syscalls to the locations at which they can occur. Third, an efficient enforcement of syscall-flow integrity in a modified Linux kernel. In our evaluation, we show that SFIP can be applied to large scale applications with minimal slowdowns. In a micro- and a macrobenchmark, it only introduces an overhead of 13.1% and 1.8%, respectively. In terms of security, we discuss and demonstrate its effectiveness in preventing control-flow-hijacking attacks in real-world applications. Finally, to highlight the reduction in attack surface, we perform an analysis of the state machines and syscall-origin mappings of several real-world applications. On average, SFIP decreases the number of possible transitions by 38.6% compared to seccomp and 90.9% when no protection is applied.
Guarding Polygons with Holes
Venue: arXiv cs.DC 2022
Year: 2022

We study the Cooperative Guarding problem for polygons with holes in a mobile multi-agents setting. Given a set of agents, initially deployed at a point in a polygon with $n$ vertices and $h$ holes, we require the agents to collaboratively explore and position themselves in such a way that every point in the polygon is visible to at least one agent and that the set of agents are visibly connected. We study the problem under two models of computation, one in which the agents can compute exact distances and angles between two points in its visibility, and one in which agents can only compare distances and angles. In the stronger model, we provide a deterministic $O(n)$ round algorithm to compute such a cooperative guard set while not requiring more than $\frac{n + h}{2}$ agents and $O(\log n)$ bits of persistent memory per agent. In the weaker model, we provide an $O(n^4)$ round algorithm, that does not require more than $\frac{n+2h}{2}$ agents.
A Market Mechanism for Trading Flexibility Between Interconnected Electricity Markets
Venue: arXiv cs.GT 2022
Year: 2022

Electricity markets differ in their ability to meet power imbalances in short notice in a controlled fashion. Relatively flexible markets have the ability to ramp up (or down) power flows across interties without compromising their ability to reliably meet internal demand. In this paper, a market mechanism to enable flexibility trading amongst market operators is introduced. In the proposed market mechanism, market operators exchange information regarding optimal terms of trade (nodal prices and flows) along interconnection lines at every trading round. Equipped with this information, each market operator then independently solves its own internal chance-constrained economic dispatch problem and broadcasts the updated optimal terms of trade for flows across markets. We show the proposed decentralized market mechanism for flexibility trading converges to a Nash equilibrium of the intraday market coupling game, i.e. a combination of internal market clearing solutions (one for each participating market) and flows and prices along interconnection lines so that no individual market operator has an incentive to modify its own internal solution and/or the terms of trade along interties. For a specific class of chance constraints, we show that the limiting equilibrium outcome is efficient, i.e. it corresponds to the solution of the single market clearing problem for all participating markets. The proposed market mechanism is illustrated with an application to the three-area IEEE Reliability Test System.
A review on geometric constraint solving
Venue: arXiv cs.CG 2022
Year: 2022

This paper presents a comprehensive review of geometric constraint solving in parametric computer-aided design (CAD), with the major focus on its advances in the last 15 years. Geometric constraint solving can date back to the very first CAD prototype, Sketchpad, in the 1960s, but serious research studies were carried out only after parametric CAD was introduced in the late 1980s. In the following 30-year history of GCS research, two development stages may be identified: (1) the first 15 years (late 1980s - mid 2000s) were primarily devoted to geometric constraint decomposition for well-constrained systems or those with only structural constraint dependencies; and (2) the second 15 years (late 2000s - now) have seen research efforts shifted towards classification criteria and decomposition algorithms for general constraint systems (with and without non-structural constraint dependencies). Most existing reviews focused on the first 15 years. The problem researched in the second 15 years is, however, equally important, considering that a manually specified constraint system usually contains under- and over-constrained parts, and that such parts must be correctly detected and resolved before numerical solving can work. In this regard, this review paper covers both stages and will discusses what has already been made possible for handling general constraint systems, what developments can be expected in the near future, and which areas remain problematic.
Robust Textual Embedding against Word-level Adversarial Attacks
Venue: arXiv cs.CL 2022
Year: 2022

We attribute the vulnerability of natural language processing models to the fact that similar inputs are converted to dissimilar representations in the embedding space, leading to inconsistent outputs, and we propose a novel robust training method, termed Fast Triplet Metric Learning (FTML). Specifically, we argue that the original sample should have similar representation with its adversarial counterparts and distinguish its representation from other samples for better robustness. To this end, we adopt the triplet metric learning into the standard training to pull words closer to their positive samples (i.e., synonyms) and push away their negative samples (i.e., non-synonyms) in the embedding space. Extensive experiments demonstrate that FTML can significantly promote the model robustness against various advanced adversarial attacks while keeping competitive classification accuracy on original samples. Besides, our method is efficient as it only needs to adjust the embedding and introduces very little overhead on the standard training. Our work shows great potential of improving the textual robustness through robust word embedding.