OpenRaiser/Intern-Atlas huggingface dataset:
DeepADMR: A Deep Learning based Anomaly Detection for MANET Routing
Venue: arXiv cs.NI 2023
Year: 2023

We developed DeepADMR, a novel neural anomaly detector for the deep reinforcement learning (DRL)-based DeepCQ+ MANET routing policy. The performance of DRL-based algorithms such as DeepCQ+ is only verified within the trained and tested environments, hence their deployment in the tactical domain induces high risks. DeepADMR monitors unexpected behavior of the DeepCQ+ policy based on the temporal difference errors (TD-errors) in real-time and detects anomaly scenarios with empirical and non-parametric cumulative-sum statistics. The DeepCQ+ design via multi-agent weight-sharing proximal policy optimization (PPO) is slightly modified to enable the real-time estimation of the TD-errors. We report the DeepADMR performance in the presence of channel disruptions, high mobility levels, and network sizes beyond the training environments, which shows its effectiveness.
Fully Immersive Virtual Reality for Skull-base Surgery: Surgical Training and Beyond
Venue: arXiv cs.RO 2023
Year: 2023

Purpose: A virtual reality (VR) system, where surgeons can practice procedures on virtual anatomies, is a scalable and cost-effective alternative to cadaveric training. The fully digitized virtual surgeries can also be used to assess the surgeon's skills using measurements that are otherwise hard to collect in reality. Thus, we present the Fully Immersive Virtual Reality System (FIVRS) for skull-base surgery, which combines surgical simulation software with a high-fidelity hardware setup. Methods: FIVRS allows surgeons to follow normal clinical workflows inside the VR environment. FIVRS uses advanced rendering designs and drilling algorithms for realistic bone ablation. A head-mounted display with ergonomics similar to that of surgical microscopes is used to improve immersiveness. Extensive multi-modal data is recorded for post-analysis, including eye gaze, motion, force, and video of the surgery. A user-friendly interface is also designed to ease the learning curve of using FIVRS. Results: We present results from a user study involving surgeons with various levels of expertise. The preliminary data recorded by FIVRS differentiates between participants with different levels of expertise, promising future research on automatic skill assessment. Furthermore, informal feedback from the study participants about the system's intuitiveness and immersiveness was positive. Conclusion: We present FIVRS, a fully immersive VR system for skull-base surgery. FIVRS features a realistic software simulation coupled with modern hardware for improved realism. The system is completely open-source and provides feature-rich data in an industry-standard format.
Stress Testing Control Loops in Cyber-Physical Systems
Venue: arXiv cs.SE 2023
Year: 2023

Cyber-Physical Systems (CPSs) are often safety-critical and deployed in uncertain environments. Identifying scenarios where CPSs do not comply with requirements is fundamental but difficult due to the multidisciplinary nature of CPSs. We investigate the testing of control-based CPSs, where control and software engineers develop the software collaboratively. Control engineers make design assumptions during system development to leverage control theory and obtain guarantees on CPS behaviour. In the implemented system, however, such assumptions are not always satisfied, and their falsification can lead to loss of guarantees. We define stress testing of control-based CPSs as generating tests to falsify such design assumptions. We highlight different types of assumptions, focusing on the use of linearised physics models. To generate stress tests falsifying such assumptions, we leverage control theory to qualitatively characterise the input space of a control-based CPS. We propose a novel test parametrisation for control-based CPSs and use it with the input space characterisation to develop a stress testing approach. We evaluate our approach on three case study systems, including a drone, a continuous-current motor (in five configurations), and an aircraft.Our results show the effectiveness of the proposed testing approach in falsifying the design assumptions and highlighting the causes of assumption violations.
TwERC: High Performance Ensembled Candidate Generation for Ads Recommendation at Twitter
Venue: arXiv cs.IR 2023
Year: 2023

Recommendation systems are a core feature of social media companies with their uses including recommending organic and promoted contents. Many modern recommendation systems are split into multiple stages - candidate generation and heavy ranking - to balance computational cost against recommendation quality. We focus on the candidate generation phase of a large-scale ads recommendation problem in this paper, and present a machine learning first heterogeneous re-architecture of this stage which we term TwERC. We show that a system that combines a real-time light ranker with sourcing strategies capable of capturing additional information provides validated gains. We present two strategies. The first strategy uses a notion of similarity in the interaction graph, while the second strategy caches previous scores from the ranking stage. The graph based strategy achieves a 4.08% revenue gain and the rankscore based strategy achieves a 1.38% gain. These two strategies have biases that complement both the light ranker and one another. Finally, we describe a set of metrics that we believe are valuable as a means of understanding the complex product trade offs inherent in industrial candidate generation systems.
Real-time Reconstruction of Markov Sources and Remote Actuation over Wireless Channels
Venue: arXiv cs.IT 2023
Year: 2023

In this work, we study the real-time tracking and reconstruction of an information source with the purpose of actuation. A device monitors the state of the information source and transmits status updates to a receiver over a wireless erasure channel. We consider two models for the source, namely an $N$-state Markov chain and an $N$-state Birth-Death Markov process. We investigate several joint sampling and transmission policies, including a semantics-aware one, and we study their performance with respect to a set of metrics. Specifically, we investigate the real-time reconstruction error and its variance, the cost of actuation error, the consecutive error, and the cost of memory error. These metrics capture different characteristics of the system performance, such as the impact of erroneous actions and the timing of errors. In addition, we propose a randomized stationary sampling and transmission policy and we derive closed-form expressions for the aforementioned metrics. We then formulate two optimization problems. The first optimization problem aims to minimize the time-averaged reconstruction error subject to time-averaged sampling cost constraint. Then, we compare the optimal randomized stationary policy with uniform, change-aware, and semantics-aware sampling policies. Our results show that in the scenario of constrained sampling generation, the optimal randomized stationary policy outperforms all other sampling policies when the source is rapidly evolving. Otherwise, the semantics-aware policy performs the best. The objective of the second optimization problem is to obtain an optimal sampling policy that minimizes the average consecutive error with a constraint on the time-averaged sampling cost. Based on this, we propose a \emph{wait-then-generate} sampling policy which is simple to implement.
LAformer: Trajectory Prediction for Autonomous Driving with Lane-Aware Scene Constraints
Venue: arXiv cs.CV 2023
Year: 2023

Trajectory prediction for autonomous driving must continuously reason the motion stochasticity of road agents and comply with scene constraints. Existing methods typically rely on one-stage trajectory prediction models, which condition future trajectories on observed trajectories combined with fused scene information. However, they often struggle with complex scene constraints, such as those encountered at intersections. To this end, we present a novel method, called LAformer. It uses a temporally dense lane-aware estimation module to select only the top highly potential lane segments in an HD map, which effectively and continuously aligns motion dynamics with scene information, reducing the representation requirements for the subsequent attention-based decoder by filtering out irrelevant lane segments. Additionally, unlike one-stage prediction models, LAformer utilizes predictions from the first stage as anchor trajectories and adds a second-stage motion refinement module to further explore temporal consistency across the complete time horizon. Extensive experiments on Argoverse 1 and nuScenes demonstrate that LAformer achieves excellent performance for multimodal trajectory prediction.
A Reinforcement Learning Approach for Scheduling Problems With Improved Generalization Through Order Swapping
Venue: arXiv cs.AI 2023
Year: 2023

The scheduling of production resources (such as associating jobs to machines) plays a vital role for the manufacturing industry not only for saving energy but also for increasing the overall efficiency. Among the different job scheduling problems, the JSSP is addressed in this work. JSSP falls into the category of NP-hard COP, in which solving the problem through exhaustive search becomes unfeasible. Simple heuristics such as FIFO, LPT and metaheuristics such as Taboo search are often adopted to solve the problem by truncating the search space. The viability of the methods becomes inefficient for large problem sizes as it is either far from the optimum or time consuming. In recent years, the research towards using DRL to solve COP has gained interest and has shown promising results in terms of solution quality and computational efficiency. In this work, we provide an novel approach to solve the JSSP examining the objectives generalization and solution effectiveness using DRL. In particular, we employ the PPO algorithm that adopts the policy-gradient paradigm that is found to perform well in the constrained dispatching of jobs. We incorporated an OSM in the environment to achieve better generalized learning of the problem. The performance of the presented approach is analyzed in depth by using a set of available benchmark instances and comparing our results with the work of other groups.
A Grammar for the Representation of Unmanned Aerial Vehicles with 3D Topologies
Venue: arXiv cs.RO 2023
Year: 2023

We propose a context-sensitive grammar for the systematic exploration of the design space of the topology of 3D robots, particularly unmanned aerial vehicles. It defines production rules for adding components to an incomplete design topology modeled over a 3D grid. The rules are local. The grammar is simple, yet capable of modeling most existing UAVs as well as novel ones. It can be easily generalized to other robotic platforms. It can be thought of as a building block for any design exploration and optimization algorithm.
The Freshness Game: Timely Communications in the Presence of an Adversary
Venue: arXiv cs.IT 2023
Year: 2023

We consider a communication system where a base station (BS) transmits update packets to $N$ users, one user at a time, over a wireless channel. We investigate the age of this status updating system with an adversary that jams the update packets in the downlink. We consider two system models: with diversity and without diversity. In the model without diversity, we show that if the BS schedules the users with a stationary randomized policy, then the optimal choice for the adversary is to block the user which has the lowest probability of getting scheduled by the BS, at the middle of the time horizon, consecutively for $αT$ time slots. In the model with diversity, we show that for large $T$, the uniform user scheduling algorithm together with the uniform sub-carrier choosing algorithm is $\frac{2 N_{sub}}{N_{sub}-1}$ optimal. Next, we investigate the game theoretic equilibrium points of this status updating system. For the model without diversity, we show that a Nash equilibrium does not exist, however, a Stackelberg equilibrium exists when the scheduling algorithm of the BS acts as the leader and the adversary acts as the follower. For the model with diversity, we show that a Nash equilibrium exists and identify the Nash equilibrium. Finally, we extend the model without diversity to the case where the BS can serve multiple users and the adversary can jam multiple users, at a time.
Revisiting Variable Ordering for Real Quantifier Elimination using Machine Learning
Venue: arXiv cs.FL 2023
Year: 2023

Cylindrical Algebraic Decomposition (CAD) is a key proof technique for formal verification of cyber-physical systems. CAD is computationally expensive, with worst-case doubly-exponential complexity. Selecting an optimal variable ordering is paramount to efficient use of CAD. Prior work has demonstrated that machine learning can be useful in determining efficient variable orderings. Much of this work has been driven by CAD problems extracted from applications of the MetiTarski theorem prover. In this paper, we revisit this prior work and consider issues of bias in existing training and test data. We observe that the classical MetiTarski benchmarks are heavily biased towards particular variable orderings. To address this, we apply symmetries to create a new dataset containing more than 41K MetiTarski challenges designed to remove bias. Furthermore, we evaluate issues of information leakage, and test the generalizability of our models on the new dataset.
Semi-supervised Clustering with Two Types of Background Knowledge: Fusing Pairwise Constraints and Monotonicity Constraints
Venue: arXiv cs.LG 2023
Year: 2023

This study addresses the problem of performing clustering in the presence of two types of background knowledge: pairwise constraints and monotonicity constraints. To achieve this, the formal framework to perform clustering under monotonicity constraints is, firstly, defined, resulting in a specific distance measure. Pairwise constraints are integrated afterwards by designing an objective function which combines the proposed distance measure and a pairwise constraint-based penalty term, in order to fuse both types of information. This objective function can be optimized with an EM optimization scheme. The proposed method serves as the first approach to the problem it addresses, as it is the first method designed to work with the two types of background knowledge mentioned above. Our proposal is tested in a variety of benchmark datasets and in a real-world case of study.
How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?
Venue: arXiv cs.LG 2023
Year: 2023

Automatic recommendation systems based on deep neural networks have become extremely popular during the last decade. Some of these systems can however be used for applications which are ranked as High Risk by the European Commission in the A.I. act, as for instance for online job candidate recommendation. When used in the European Union, commercial AI systems for this purpose will then be required to have to proper statistical properties with regard to potential discrimination they could engender. This motivated our contribution, where we present a novel optimal transport strategy to mitigate undesirable algorithmic biases in multi-class neural-network classification. Our stratey is model agnostic and can be used on any multi-class classification neural-network model. To anticipate the certification of recommendation systems using textual data, we then used it on the Bios dataset, for which the learning task consists in predicting the occupation of female and male individuals, based on their LinkedIn biography. Results show that it can reduce undesired algorithmic biases in this context to lower levels than a standard strategy.
Implementing a Model-based Engineering Tool as Web Application
Venue: arXiv cs.SE 2023
Year: 2023

This paper reports on a study of transferring a desktop-based model-based engineering tool to a web application. The study has been conducted in the WEBMODEL project where the well-established technology stack around the Eclipse platform and the Eclipse Modeling Framework was lifted into a cloud-based environment. As results, a modeling language independent tooling kernel for web-based modeling tools and a minimal prototypical web-based implementation of the AutoFOCUS 3 model-based engineering tool are presented. Furthermore, the report documents experiences and implementation advises gained during the implementation.
Distributional Method for Risk Averse Reinforcement Learning
Venue: arXiv cs.LG 2023
Year: 2023

We introduce a distributional method for learning the optimal policy in risk averse Markov decision process with finite state action spaces, latent costs, and stationary dynamics. We assume sequential observations of states, actions, and costs and assess the performance of a policy using dynamic risk measures constructed from nested Kusuoka-type conditional risk mappings. For such performance criteria, randomized policies may outperform deterministic policies, therefore, the candidate policies lie in the d-dimensional simplex where d is the cardinality of the action space. Existing risk averse reinforcement learning methods seldom concern randomized policies, naïve extensions to current setting suffer from the curse of dimensionality. By exploiting certain structures embedded in the corresponding dynamic programming principle, we propose a distributional learning method for seeking the optimal policy. The conditional distribution of the value function is casted into a specific type of function, which is chosen with in mind the ease of risk averse optimization. We use a deep neural network to approximate said function, illustrate that the proposed method avoids the curse of dimensionality in the exploration phase, and explore the method's performance with a wide range of model parameters that are picked randomly.
Leveraging Angular Distributions for Improved Knowledge Distillation
Venue: arXiv cs.CV 2023
Year: 2023

Knowledge distillation as a broad class of methods has led to the development of lightweight and memory efficient models, using a pre-trained model with a large capacity (teacher network) to train a smaller model (student network). Recently, additional variations for knowledge distillation, utilizing activation maps of intermediate layers as the source of knowledge, have been studied. Generally, in computer vision applications, it is seen that the feature activation learned by a higher capacity model contains richer knowledge, highlighting complete objects while focusing less on the background. Based on this observation, we leverage the dual ability of the teacher to accurately distinguish between positive (relevant to the target object) and negative (irrelevant) areas. We propose a new loss function for distillation, called angular margin-based distillation (AMD) loss. AMD loss uses the angular distance between positive and negative features by projecting them onto a hypersphere, motivated by the near angular distributions seen in many feature extractors. Then, we create a more attentive feature that is angularly distributed on the hypersphere by introducing an angular margin to the positive feature. Transferring such knowledge from the teacher network enables the student model to harness the higher discrimination of positive and negative features for the teacher, thus distilling superior student models. The proposed method is evaluated for various student-teacher network pairs on four public datasets. Furthermore, we show that the proposed method has advantages in compatibility with other learning techniques, such as using fine-grained features, augmentation, and other distillation methods.
GLOW: Global Layout Aware Attacks on Object Detection
Venue: arXiv cs.CV 2023
Year: 2023

Adversarial attacks aim to perturb images such that a predictor outputs incorrect results. Due to the limited research in structured attacks, imposing consistency checks on natural multi-object scenes is a promising yet practical defense against conventional adversarial attacks. More desired attacks, to this end, should be able to fool defenses with such consistency checks. Therefore, we present the first approach GLOW that copes with various attack requests by generating global layout-aware adversarial attacks, in which both categorical and geometric layout constraints are explicitly established. Specifically, we focus on object detection task and given a victim image, GLOW first localizes victim objects according to target labels. And then it generates multiple attack plans, together with their context-consistency scores. Our proposed GLOW, on the one hand, is capable of handling various types of requests, including single or multiple victim objects, with or without specified victim objects. On the other hand, it produces a consistency score for each attack plan, reflecting the overall contextual consistency that both semantic category and global scene layout are considered. In experiment, we design multiple types of attack requests and validate our ideas on MS COCO and Pascal. Extensive experimental results demonstrate that we can achieve about 30$\%$ average relative improvement compared to state-of-the-art methods in conventional single object attack request; Moreover, our method outperforms SOTAs significantly on more generic attack requests by about 20$\%$ in average; Finally, our method produces superior performance under challenging zero-query black-box setting, or 20$\%$ better than SOTAs. Our code, model and attack requests would be made available.
Signal Propagation in Double Edged Relays
Venue: arXiv cs.DS 2023
Year: 2023

A discrete signal propagation model blending characteristics of linear wave propagation and finite state automata is developed. We show this model obeys a limited form of superposition and is capable of displaying a wide variety of interesting behaviors. We show how the model's superposition properties permit information to be encoded and retained by signals that pass through discrete networks. We outline a SPIDER model replacement for Dijkstra's algorithm.
PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Clouds
Venue: arXiv cs.CV 2023
Year: 2023

An efficient 3D scene flow estimation method called PointFlowHop is proposed in this work. PointFlowHop takes two consecutive point clouds and determines the 3D flow vectors for every point in the first point cloud. PointFlowHop decomposes the scene flow estimation task into a set of subtasks, including ego-motion compensation, object association and object-wise motion estimation. It follows the green learning (GL) pipeline and adopts the feedforward data processing path. As a result, its underlying mechanism is more transparent than deep-learning (DL) solutions based on end-to-end optimization of network parameters. We conduct experiments on the stereoKITTI and the Argoverse LiDAR point cloud datasets and demonstrate that PointFlowHop outperforms deep-learning methods with a small model size and less training time. Furthermore, we compare the Floating Point Operations (FLOPs) required by PointFlowHop and other learning-based methods in inference, and show its big savings in computational complexity.