OpenRaiser/Intern-Atlas huggingface dataset:
Dynamic Selection of Perception Models for Robotic Control
Venue: arXiv cs.RO 2022
Year: 2022

Robotic perception models, such as Deep Neural Networks (DNNs), are becoming more computationally intensive and there are several models being trained with accuracy and latency trade-offs. However, modern latency accuracy trade-offs largely report mean accuracy for single-step vision tasks, but there is little work showing which model to invoke for multi-step control tasks in robotics. The key challenge in a multi-step decision making is to make use of the right models at right times to accomplish the given task. That is, the accomplishment of the task with a minimum control cost and minimum perception time is a desideratum; this is known as the model selection problem. In this work, we precisely address this problem of invoking the correct sequence of perception models for multi-step control. In other words, we provide a provably optimal solution to the model selection problem by casting it as a multi-objective optimization problem balancing the control cost and perception time. The key insight obtained from our solution is how the variance of the perception models matters (not just the mean accuracy) for multi-step decision making, and to show how to use diverse perception models as a primitive for energy-efficient robotics. Further, we demonstrate our approach on a photo-realistic drone landing simulation using visual navigation in AirSim. Using our proposed policy, we achieved 38.04% lower control cost with 79.1% less perception time than other competing benchmarks.
On Market Clearing of Day Ahead Auctions for European Power Markets: Cost Minimisation versus Social Welfare Maximisation
Venue: arXiv cs.GT 2022
Year: 2022

For the case of inflexible demand and considering network constraints, we introduce a Cost Minimisation (CM) based market clearing mechanism, and a model representing the standard Social Welfare Maximisation mechanism used in European Day Ahead Electricity Markets. Since the CM model corresponds to a more challenging optimisation problem, we propose four numerical algorithms that leverage the problem structure, each with different trade-offs between computational cost and convergence guarantees. These algorithms are evaluated on synthetic data to provide some intuition of their performance. We also provide strong (but partial) analytical results to facilitate efficient solution of the CM problem, which call for the introduction of a new concept: optimal zonal stack curves, and these results are used to devise one of the four solution algorithms. An evaluation of the CM and SWM models and their comparison is performed, under the assumption of truthful bidding, on the real world data of Central Western European Day Ahead Power Market during the period of 2019-2020. We show that the SWM model we introduce gives a good representation of the historical time series of the real prices. Further, the CM reduces the market power of producers, as generally this results in decreased zonal prices and always decreases the total cost of electricity procurement when compared to the currently employed SWM.
RobustAnalog: Fast Variation-Aware Analog Circuit Design Via Multi-task RL
Venue: arXiv cs.ET 2022
Year: 2022

Analog/mixed-signal circuit design is one of the most complex and time-consuming stages in the whole chip design process. Due to various process, voltage, and temperature (PVT) variations from chip manufacturing, analog circuits inevitably suffer from performance degradation. Although there has been plenty of work on automating analog circuit design under the typical condition, limited research has been done on exploring robust designs under real and unpredictable silicon variations. Automatic analog design against variations requires prohibitive computation and time costs. To address the challenge, we present RobustAnalog, a robust circuit design framework that involves the variation information in the optimization process. Specifically, circuit optimizations under different variations are considered as a set of tasks. Similarities among tasks are leveraged and competitions are alleviated to realize a sample-efficient multi-task training. Moreover, RobustAnalog prunes the task space according to the current performance in each iteration, leading to a further simulation cost reduction. In this way, RobustAnalog can rapidly produce a set of circuit parameters that satisfies diverse constraints (e.g. gain, bandwidth, noise...) across variations. We compare RobustAnalog with Bayesian optimization, Evolutionary algorithm, and Deep Deterministic Policy Gradient (DDPG) and demonstrate that RobustAnalog can significantly reduce required optimization time by 14-30 times. Therefore, our study provides a feasible method to handle various real silicon conditions.
Wakeword Detection under Distribution Shifts
Venue: arXiv cs.SD 2022
Year: 2022

We propose a novel approach for semi-supervised learning (SSL) designed to overcome distribution shifts between training and real-world data arising in the keyword spotting (KWS) task. Shifts from training data distribution are a key challenge for real-world KWS tasks: when a new model is deployed on device, the gating of the accepted data undergoes a shift in distribution, making the problem of timely updates via subsequent deployments hard. Despite the shift, we assume that the marginal distributions on labels do not change. We utilize a modified teacher/student training framework, where labeled training data is augmented with unlabeled data. Note that the teacher does not have access to the new distribution as well. To train effectively with a mix of human and teacher labeled data, we develop a teacher labeling strategy based on confidence heuristics to reduce entropy on the label distribution from the teacher model; the data is then sampled to match the marginal distribution on the labels. Large scale experimental results show that a convolutional neural network (CNN) trained on far-field audio, and evaluated on far-field audio drawn from a different distribution, obtains a 14.3% relative improvement in false discovery rate (FDR) at equal false reject rate (FRR), while yielding a 5% improvement in FDR under no distribution shift. Under a more severe distribution shift from far-field to near-field audio with a smaller fully connected network (FCN) our approach achieves a 52% relative improvement in FDR at equal FRR, while yielding a 20% relative improvement in FDR on the original distribution.
Optimal control of dielectric elastomer actuated multibody dynamical systems
Venue: arXiv cs.CE 2022
Year: 2022

In this work, a simulation model for the optimal control of dielectric elastomer actuated flexible multibody dynamics systems is presented. The Dielectric Elastomer Actuator (DEA) behaves like a flexible artificial muscles in soft robotics. It is modeled as an electromechanically coupled geometrically exact beam, where the electric charges serve as control variables. The DEA-beam is integrated as an actuator into multibody systems consisting of rigid and flexible components. The model also represents contact interaction via unilateral constraints between the beam actuator and e.g. a rigid body during the grasping process of a soft robot. Specifically for the DEA, a work conjugated electric displacement and strain-like electric variables are derived for the Cosserat beam. With a mathematically concise and physically representative formulation, a reduced free energy function is developed for the beam-DEA. In the optimal control problem, an objective function is minimized while the dynamic balance equations for the multibody system have to be fulfilled together with the complementarity conditions for the contact and boundary conditions. The optimal control problem is solved via a direct transcription method, transforming it into a constrained nonlinear optimization problem. The beam is firstly semidiscretized with 1D finite elements and then the multibody dynamics is temporally discretized with a variational integrator leading to the discrete Euler-Lagrange equations, which are further reduced with the null space projection. The discrete Euler-Lagrange equations and the boundary conditions serve as equality constraints, whereas the contact constraints are treated as inequality constraints in the optimization of the discretized objective. The effectiveness of the developed model is demonstrated by three numerical examples, including a cantilever beam, a soft robotic worm and a soft grasper.
Analysis of Catastrophic Forgetting for Random Orthogonal Transformation Tasks in the Overparameterized Regime
Venue: arXiv cs.LG 2022
Year: 2022

Overparameterization is known to permit strong generalization performance in neural networks. In this work, we provide an initial theoretical analysis of its effect on catastrophic forgetting in a continual learning setup. We show experimentally that in permuted MNIST image classification tasks, the generalization performance of multilayer perceptrons trained by vanilla stochastic gradient descent can be improved by overparameterization, and the extent of the performance increase achieved by overparameterization is comparable to that of state-of-the-art continual learning algorithms. We provide a theoretical explanation of this effect by studying a qualitatively similar two-task linear regression problem, where each task is related by a random orthogonal transformation. We show that when a model is trained on the two tasks in sequence without any additional regularization, the risk gain on the first task is small if the model is sufficiently overparameterized.
A Robustly Optimized Long Text to Math Models for Numerical Reasoning On FinQA
Venue: arXiv cs.CL 2022
Year: 2022

Numerical reasoning is required when solving most problems in our life, but it has been neglected in previous artificial intelligence researches. FinQA challenge has been organized to strengthen the study on numerical reasoning where the participants are asked to predict the numerical reasoning program to solve financial question. The result of FinQA will be evaluated by both execution accuracy and program accuracy. In this paper, we present our approach to tackle the task objective by developing models with different specialized capabilities and fusing their strength. Overall, our approach achieves the 1st place in FinQA challenge, with 71.93% execution accuracy and 67.03% program accuracy.
Automated Cause Analysis of Latency Outliers Using System-Level Dependency Graphs
Venue: arXiv cs.PF 2022
Year: 2022

Detecting performance issues and identifying their root causes in the runtime is a challenging task. Typically, developers use methods such as logging and tracing to identify bottlenecks. These solutions are, however, not ideal as they are time-consuming and require manual effort. In this paper, we propose a method to automate the task of detecting latency outliers using system-level traces and then comparing them to identify the root cause(s). Our method makes use of dependency graphs to show internal interactions between threads and system resources. With these graphs, one can pinpoint where performance issues occur. However, a single trace can be composed of a large number of requests, each generating one graph. To automate the task of identifying outliers within the dataset, we use machine learning density-based models and statistical calculations such as -score. Our evaluation shows an accuracy greater than 97 % on outlier detection, making them appropriate for in-production servers and industry-level use cases.
Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
Venue: arXiv cs.LG 2022
Year: 2022

A broad class of stochastic volatility models are defined by systems of stochastic differential equations. While these models have seen widespread success in domains such as finance and statistical climatology, they typically lack an ability to condition on historical data to produce a true posterior distribution. To address this fundamental limitation, we show how to re-cast a class of stochastic volatility models as a hierarchical Gaussian process (GP) model with specialized covariance functions. This GP model retains the inductive biases of the stochastic volatility model while providing the posterior predictive distribution given by GP inference. Within this framework, we take inspiration from well studied domains to introduce a new class of models, Volt and Magpie, that significantly outperform baselines in stock and wind speed forecasting, and naturally extend to the multitask setting.
QML for Argoverse 2 Motion Forecasting Challenge
Venue: arXiv cs.CV 2022
Year: 2022

To safely navigate in various complex traffic scenarios, autonomous driving systems are generally equipped with a motion forecasting module to provide vital information for the downstream planning module. For the real-world onboard applications, both accuracy and latency of a motion forecasting model are essential. In this report, we present an effective and efficient solution, which ranks the 3rd place in the Argoverse 2 Motion Forecasting Challenge 2022.
Streamlining Visualization Authoring in D3 Through User-Driven Templates
Venue: arXiv cs.HC 2022
Year: 2022

D3 is arguably the most popular tool for implementing web based visualizations. Yet D3 has a steep learning curve that may hinder its adoption and continued use. To simplify the process of programming D3 visualizations, we must first understand the space of implementation practices that D3 users engage in. We present a qualitative analysis of 2500 D3 visualizations and their corresponding implementations. We find that 5 visualization types (Bar Charts, Geomaps, Line Charts, Scatterplots, and Force Directed Graphs) account for 80% of D3 visualizations found in our corpus. While implementation styles vary slightly across designs, the underlying code structure for all visualization types remains the same; presenting an opportunity for code reuse. Using our corpus of D3 examples, we synthesize reusable code templates for eight popular D3 visualization types and share them in our open source repository. Based on our results, we discuss design considerations for leveraging users' implementation patterns to reduce visualization design effort through design templates and auto-generated code recommendations.
Improved Parameterized Complexity of Happy Set Problems
Venue: arXiv cs.DS 2022
Year: 2022

We present fixed-parameter tractable (FPT) algorithms for two problems, Maximum Happy Set (MaxHS) and Maximum Edge Happy Set (MaxEHS)--also known as Densest k-Subgraph. Given a graph $G$ and an integer $k$, MaxHS asks for a set $S$ of $k$ vertices such that the number of $\textit{happy vertices}$ with respect to $S$ is maximized, where a vertex $v$ is happy if $v$ and all its neighbors are in $S$. We show that MaxHS can be solved in time $\mathcal{O}\left(2^\textsf{mw} \cdot \textsf{mw} \cdot k^2 \cdot |V(G)|\right)$ and $\mathcal{O}\left(8^\textsf{cw} \cdot k^2 \cdot |V(G)|\right)$, where $\textsf{mw}$ and $\textsf{cw}$ denote the $\textit{modular-width}$ and the $\textit{clique-width}$ of $G$, respectively. This resolves the open questions posed in literature. The MaxEHS problem is an edge-variant of MaxHS, where we maximize the number of $\textit{happy edges}$, the edges whose endpoints are in $S$. In this paper we show that MaxEHS can be solved in time $f(\textsf{nd})\cdot|V(G)|^{\mathcal{O}(1)}$ and $\mathcal{O}\left(2^{\textsf{cd}}\cdot k^2 \cdot |V(G)|\right)$, where $\textsf{nd}$ and $\textsf{cd}$ denote the $\textit{neighborhood diversity}$ and the $\textit{cluster deletion number}$ of $G$, respectively, and $f$ is some computable function. This result implies that MaxEHS is also fixed-parameter tractable by $\textit{twin cover number}$.
Influential Billboard Slot Selection using Pruned Submodularity Graph
Venue: arXiv cs.DB 2022
Year: 2022

Billboard Advertisement has emerged as an effective out-of-home advertisement technique and adopted by many commercial houses. In this case, the billboards are owned by some companies and they are provided to the commercial houses slot\mbox{-}wise on a payment basis. Now, given the database of billboards along with their slot information which $k$ slots should be chosen to maximize the influence. Formally, we call this problem as the \textsc{Influential Billboard Slot Selection} Problem. In this paper, we pose this problem as a combinatorial optimization problem. Under the `triggering model of influence', the influence function is non-negative, monotone, and submodular. However, as the incremental greedy approach for submodular function maximization does not scale well along with the size of the problem instances, there is a need to develop efficient solution methodologies for this problem.
Exploration of an End-to-End Automatic Number-plate Recognition neural network for Indian datasets
Venue: arXiv cs.CV 2022
Year: 2022

Indian vehicle number plates have wide variety in terms of size, font, script and shape. Development of Automatic Number Plate Recognition (ANPR) solutions is therefore challenging, necessitating a diverse dataset to serve as a collection of examples. However, a comprehensive dataset of Indian scenario is missing, thereby, hampering the progress towards publicly available and reproducible ANPR solutions. Many countries have invested efforts to develop comprehensive ANPR datasets like Chinese City Parking Dataset (CCPD) for China and Application-oriented License Plate (AOLP) dataset for US. In this work, we release an expanding dataset presently consisting of 1.5k images and a scalable and reproducible procedure of enhancing this dataset towards development of ANPR solution for Indian conditions. We have leveraged this dataset to explore an End-to-End (E2E) ANPR architecture for Indian scenario which was originally proposed for Chinese Vehicle number-plate recognition based on the CCPD dataset. As we customized the architecture for our dataset, we came across insights, which we have discussed in this paper. We report the hindrances in direct reusability of the model provided by the authors of CCPD because of the extreme diversity in Indian number plates and differences in distribution with respect to the CCPD dataset. An improvement of 42.86% was observed in LP detection after aligning the characteristics of Indian dataset with Chinese dataset. In this work, we have also compared the performance of the E2E number-plate detection model with YOLOv5 model, pre-trained on COCO dataset and fine-tuned on Indian vehicle images. Given that the number Indian vehicle images used for fine-tuning the detection module and yolov5 were same, we concluded that it is more sample efficient to develop an ANPR solution for Indian conditions based on COCO dataset rather than CCPD dataset.
Golden Reference-Free Hardware Trojan Localization using Graph Convolutional Network
Venue: arXiv cs.CY 2022
Year: 2022

The globalization of the Integrated Circuit (IC) supply chain has moved most of the design, fabrication, and testing process from a single trusted entity to various untrusted third-party entities worldwide. The risk of using untrusted third-Party Intellectual Property (3PIP) is the possibility for adversaries to insert malicious modifications known as Hardware Trojans (HTs). These HTs can compromise the integrity, deteriorate the performance, deny the service, and alter the functionality of the design. While numerous HT detection methods have been proposed in the literature, the crucial task of HT localization is overlooked. Moreover, a few existing HT localization methods have several weaknesses: reliance on a golden reference, inability to generalize for all types of HT, lack of scalability, low localization resolution, and manual feature engineering/property definition. To overcome their shortcomings, we propose a novel, golden reference-free HT localization method at the pre-silicon stage by leveraging Graph Convolutional Network (GCN). In this work, we convert the circuit design to its intrinsic data structure, graph and extract the node attributes. Afterward, the graph convolution performs automatic feature extraction for nodes to classify the nodes as Trojan or benign. Our automated approach does not burden the designer with manual code review. It locates the Trojan signals with 99.6% accuracy, 93.1% F1-score, and a false-positive rate below 0.009%.
Reinforced Path Reasoning for Counterfactual Explainable Recommendation
Venue: arXiv cs.IR 2022
Year: 2022

Counterfactual explanations interpret the recommendation mechanism via exploring how minimal alterations on items or users affect the recommendation decisions. Existing counterfactual explainable approaches face huge search space and their explanations are either action-based (e.g., user click) or aspect-based (i.e., item description). We believe item attribute-based explanations are more intuitive and persuadable for users since they explain by fine-grained item demographic features (e.g., brand). Moreover, counterfactual explanation could enhance recommendations by filtering out negative items. In this work, we propose a novel Counterfactual Explainable Recommendation (CERec) to generate item attribute-based counterfactual explanations meanwhile to boost recommendation performance. Our CERec optimizes an explanation policy upon uniformly searching candidate counterfactuals within a reinforcement learning environment. We reduce the huge search space with an adaptive path sampler by using rich context information of a given knowledge graph. We also deploy the explanation policy to a recommendation model to enhance the recommendation. Extensive explainability and recommendation evaluations demonstrate CERec's ability to provide explanations consistent with user preferences and maintain improved recommendations. We release our code at https://github.com/Chrystalii/CERec.
Multi: a Formal Playground for Multi-Smart Contract Interaction
Venue: arXiv cs.LO 2022
Year: 2022

Blockchains are maintained by a network of participants that run algorithms designed to maintain collectively a distributed machine tolerant to Byzantine attacks. From the point of view of users, blockchains provide the illusion of centralized computers that perform trustable verifiable computations, where all computations are deterministic and the results cannot be manipulated or undone. Smart-contracts are written in a special-purpose programming language with deterministic semantics. Each transaction begins with an invocation from an external user to a smart contract. Contracts have local storage and can call other contracts, and more importantly, they store, send and receive cryptocurrency. It is very important to guarantee that contracts are correct before deployment since their code cannot be modified afterward deployment. However, the resulting ecosystem makes it very difficult to reason about program correctness, since contracts can be executed by malicious users or malicious contracts can be designed to exploit other contracts that call them. Many attacks and bugs are caused by unexpected interactions between multiple contracts, the attacked contract and unknown code that performs the exploit. Moreover, there is a very aggressive competition between different blockchains to expand their user base. Ideas are implemented fast and blockchains compete to offer and adopt new features quickly. In this paper, we propose a formal extensible playground that allows reasoning about multi-contract interactions to ultimately prove properties before features are incorporated into the real blockchain. We implemented a model of computation that models the execution platform, abstracts the internal code of each individual contract and focuses on contract interactions. Moreover, we show how many features, existing or proposed, can be used to reason about multi-contract interactions.