OpenRaiser/Intern-Atlas huggingface dataset:
PnP-DETR: Towards Efficient Visual Analysis with Transformers
Venue: arXiv cs.CV 2022
Year: 2022

Recently, DETR pioneered the solution of vision tasks with transformers, it directly translates the image feature map into the object detection result. Though effective, translating the full feature map can be costly due to redundant computation on some area like the background. In this work, we encapsulate the idea of reducing spatial redundancy into a novel poll and pool (PnP) sampling module, with which we build an end-to-end PnP-DETR architecture that adaptively allocates its computation spatially to be more efficient. Concretely, the PnP module abstracts the image feature map into fine foreground object feature vectors and a small number of coarse background contextual feature vectors. The transformer models information interaction within the fine-coarse feature space and translates the features into the detection result. Moreover, the PnP-augmented model can instantly achieve various desired trade-offs between performance and computation with a single model by varying the sampled feature length, without requiring to train multiple models as existing methods. Thus it offers greater flexibility for deployment in diverse scenarios with varying computation constraint. We further validate the generalizability of the PnP module on panoptic segmentation and the recent transformer-based image recognition model ViT and show consistent efficiency gain. We believe our method makes a step for efficient visual analysis with transformers, wherein spatial redundancy is commonly observed. Code will be available at \url{https://github.com/twangnh/pnp-detr}.
Dataflow Accelerator Architecture for Autonomous Machine Computing
Venue: arXiv cs.AR 2022
Year: 2022

Commercial autonomous machines is a thriving sector, one that is likely the next ubiquitous computing platform, after Personal Computers (PC), cloud computing, and mobile computing. Nevertheless, a suitable computing substrate for autonomous machines is missing, and many companies are forced to develop ad hoc computing solutions that are neither principled nor extensible. By analyzing the demands of autonomous machine computing, this article proposes Dataflow Accelerator Architecture (DAA), a modern instantiation of the classic dataflow principle, that matches the characteristics of autonomous machine software.
Can Edge Probing Tasks Reveal Linguistic Knowledge in QA Models?
Venue: arXiv cs.CL 2022
Year: 2022

There have been many efforts to try to understand what grammatical knowledge (e.g., ability to understand the part of speech of a token) is encoded in large pre-trained language models (LM). This is done through `Edge Probing' (EP) tests: supervised classification tasks to predict the grammatical properties of a span (whether it has a particular part of speech) using only the token representations coming from the LM encoder. However, most NLP applications fine-tune these LM encoders for specific tasks. Here, we ask: if an LM is fine-tuned, does the encoding of linguistic information in it change, as measured by EP tests? Specifically, we focus on the task of Question Answering (QA) and conduct experiments on multiple datasets. We find that EP test results do not change significantly when the fine-tuned model performs well or in adversarial situations where the model is forced to learn wrong correlations. From a similar finding, some recent papers conclude that fine-tuning does not change linguistic knowledge in encoders but they do not provide an explanation. We find that EP models themselves are susceptible to exploiting spurious correlations in the EP datasets. When this dataset bias is corrected, we do see an improvement in the EP test results as expected.
Infusing model predictive control into meta-reinforcement learning for mobile robots in dynamic environments
Venue: arXiv cs.RO 2022
Year: 2022

The successful operation of mobile robots requires them to adapt rapidly to environmental changes. To develop an adaptive decision-making tool for mobile robots, we propose a novel algorithm that combines meta-reinforcement learning (meta-RL) with model predictive control (MPC). Our method employs an off-policy meta-RL algorithm as a baseline to train a policy using transition samples generated by MPC when the robot detects certain events that can be effectively handled by MPC, with its explicit use of robot dynamics. The key idea of our method is to switch between the meta-learned policy and the MPC controller in a randomized and event-triggered fashion to make up for suboptimal MPC actions caused by the limited prediction horizon. During meta-testing, the MPC module is deactivated to significantly reduce computation time in motion control. We further propose an online adaptation scheme that enables the robot to infer and adapt to a new task within a single trajectory. The performance of our method has been demonstrated through simulations using a nonlinear car-like vehicle model with (i) synthetic movements of obstacles, and (ii) real-world pedestrian motion data. The simulation results indicate that our method outperforms other algorithms in terms of learning efficiency and navigation quality.
Combining Context Awareness and Planning to Learn Behavior Trees from Demonstration
Venue: arXiv cs.RO 2022
Year: 2022

Fast changing tasks in unpredictable, collaborative environments are typical for medium-small companies, where robotised applications are increasing. Thus, robot programs should be generated in short time with small effort, and the robot able to react dynamically to the environment. To address this we propose a method that combines context awareness and planning to learn Behavior Trees (BTs), a reactive policy representation that is becoming more popular in robotics and has been used successfully in many collaborative scenarios. Context awareness allows to infer from the demonstration the frames in which actions are executed and to capture relevant aspects of the task, while a planner is used to automatically generate the BT from the sequence of actions from the demonstration. The learned BT is shown to solve non-trivial manipulation tasks where learning the context is fundamental to achieve the goal. Moreover, we collected non-expert demonstrations to study the performances of the algorithm in industrial scenarios.
Limitations of the Invertible-Map Equivalences
Venue: arXiv cs.LO 2022
Year: 2022

This note draws conclusions that arise by combining two recent papers, by Anuj Dawar, Erich Grädel, and Wied Pakusa, published at ICALP 2019 and by Moritz Lichter, published at LICS 2021. In both papers, the main technical results rely on the combinatorial and algebraic analysis of the invertible-map equivalences $\equiv^\text{IM}_{k,Q}$ on certain variants of Cai-Fürer-Immerman (CFI) structures. These $\equiv^\text{IM}_{k,Q}$-equivalences, for a number $k$ and a set of primes~$Q$, refine the well-known Weisfeiler-Leman equivalences used in algorithms for graph isomorphism. The intuition is that two graphs $G\equiv^\text{IM}_{k,Q}H$ cannot be distinguished by iterative refinements of equivalences on $k$-tuples defined via linear operators on vector spaces over fields of characteristic $p \in Q$. In the first paper it has been shown, using considerable algebraic machinery, that for a prime $q \notin Q$, the $\equiv^\text{IM}_{k,Q}$ equivalences do not distinguish between non-isomorphic CFI-structures over the field $\mathbb{F}_q$. In the second paper, a similar but not identical construction for CFI-structures over the rings $\mathbb{Z}_{2^i}$ has, again by rather involved combinatorial and algebraic arguments, been shown to be indistinguishable with respect to $\equiv^\text{IM}_{k,\{2\}}$. Together with earlier work, this second result separates rank logic from polynomial time. We unify the two approaches and prove that CFI-structures over the rings $\mathbb{Z}_{2^i}$ are indistinguishable with respect to $\equiv^\text{IM}_{k,\mathbb{P}}$, for the set $\mathbb{P}$ of all primes. This implies the following two results. 1. There is no fixed $k$ such that the invertible-map equivalence $\equiv^\text{IM}_{k,\mathbb{P}}$ coincides with isomorphism on all finite graphs. 2. No extension of fixed-point logic by linear-algebraic operators over fields can capture polynomial time.
A Wide-area, Low-latency, and Power-efficient 6-DoF Pose Tracking System for Rigid Objects
Venue: arXiv cs.RO 2022
Year: 2022

Position sensitive detectors (PSDs) offer possibility to track single active marker's two (or three) degrees of freedom (DoF) position with a high accuracy, while having a fast response time with high update frequency and low latency, all using a very simple signal processing circuit. However they are not particularly suitable for 6-DoF object pose tracking system due to lack of orientation measurement, limited tracking range, and sensitivity to environmental variation. We propose a novel 6-DoF pose tracking system for a rigid object tracking requiring a single active marker. The proposed system uses a stereo-based PSD pair and multiple Inertial Measurement Units (IMUs). This is done based on a practical approach to identify and control the power of Infrared-Light Emitting Diode (IR-LED) active markers, with an aim to increase the tracking work space and reduce the power consumption. Our proposed tracking system is validated with three different work space sizes and for static and dynamic positional accuracy using robotic arm manipulator with three different dynamic motion patterns. The results show that the static position root-mean-square (RMS) error is 0.6mm. The dynamic position RMS error is 0.7-0.9mm. The orientation RMS error is between 0.04 and 0.9 degree at varied dynamic motion. Overall, our proposed tracking system is capable of tracking a rigid object pose with sub-millimeter accuracy at the mid range of the work space and sub-degree accuracy for all work space under a lab setting.
Graph skeletonization of high-dimensional point cloud data via topological method
Venue: arXiv cs.CG 2022
Year: 2022

Geometric graphs form an important family of hidden structures behind data. In this paper, we develop an efficient and robust algorithm to infer a graph skeleton of a high-dimensional point cloud dataset (PCD). Previously, there has been much work to recover a hidden graph from a low-dimensional density field, or from a relatively clean high-dimensional PCD. Our proposed approach builds upon the recent line of work on using a persistence-guided discrete Morse (DM) theory based approach to reconstruct a geometric graph from a density field defined over a low-dimensional triangulation. In particular, we first give a very simple generalization of this DM-based algorithm from a density-function perspective to a general filtration perspective. On the theoretical front, we show that the output of the generalized algorithm contains a so-called lexicographic-optimal persistent cycle basis w.r.t the input filtration, justifying that the output is indeed meaningful. On the algorithmic front, the generalization allows us to combine sparsified weighted Rips filtration to develop a new graph reconstruction algorithm for noisy point cloud data. The new algorithm is robust to background noise and non-uniform distribution of input points, and we provide various experimental results to show its effectiveness.
Adversarially Regularized Policy Learning Guided by Trajectory Optimization
Venue: arXiv cs.RO 2022
Year: 2022

Recent advancement in combining trajectory optimization with function approximation (especially neural networks) shows promise in learning complex control policies for diverse tasks in robot systems. Despite their great flexibility, the large neural networks for parameterizing control policies impose significant challenges. The learned neural control policies are often overcomplex and non-smooth, which can easily cause unexpected or diverging robot motions. Therefore, they often yield poor generalization performance in practice. To address this issue, we propose adVErsarially Regularized pOlicy learNIng guided by trajeCtory optimizAtion (VERONICA) for learning smooth control policies. Specifically, our proposed approach controls the smoothness (local Lipschitz continuity) of the neural control policies by stabilizing the output control with respect to the worst-case perturbation to the input state. Our experiments on robot manipulation show that our proposed approach not only improves the sample efficiency of neural policy learning but also enhances the robustness of the policy against various types of disturbances, including sensor noise, environmental uncertainty, and model mismatch.
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition
Venue: arXiv cs.AI 2022
Year: 2022

Reinforcement Learning (RL) is emerging as tool for tackling complex control and decision-making problems. However, in high-risk environments such as healthcare, manufacturing, automotive or aerospace, it is often challenging to bridge the gap between an apparently optimal policy learnt by an agent and its real-world deployment, due to the uncertainties and risk associated with it. Broadly speaking RL agents face two kinds of uncertainty, 1. aleatoric uncertainty, which reflects randomness or noise in the dynamics of the world, and 2. epistemic uncertainty, which reflects the bounded knowledge of the agent due to model limitations and finite amount of information/data the agent has acquired about the world. These two types of uncertainty carry fundamentally different implications for the evaluation of performance and the level of risk or trust. Yet these aleatoric and epistemic uncertainties are generally confounded as standard and even distributional RL is agnostic to this difference. Here we propose how a distributional approach (UA-DQN) can be recast to render uncertainties by decomposing the net effects of each uncertainty. We demonstrate the operation of this method in grid world examples to build intuition and then show a proof of concept application for an RL agent operating as a clinical decision support system in critical care
Reframing Instructional Prompts to GPTk's Language
Venue: arXiv cs.CL 2022
Year: 2022

What kinds of instructional prompts are easier to follow for Language Models (LMs)? We study this question by conducting extensive empirical analysis that shed light on important features of successful instructional prompts. Specifically, we study several classes of reframing techniques for manual reformulation of prompts into more effective ones. Some examples include decomposing a complex task instruction into multiple simpler tasks or itemizing instructions into sequential steps. Our experiments compare the zero-shot and few-shot performance of LMs prompted with reframed instructions on 12 NLP tasks across 6 categories. Compared with original instructions, our reframed instructions lead to significant improvements across LMs with different sizes. For example, the same reframed prompts boost few-shot performance of GPT3-series and GPT2-series by 12.5% and 6.7% respectively averaged over all tasks. Furthermore, reframed instructions reduce the number of examples required to prompt LMs in the few-shot setting. We hope these empirically-driven techniques will pave the way towards more effective future prompting algorithms.
Achieving Model Fairness in Vertical Federated Learning
Venue: arXiv cs.LG 2022
Year: 2022

Vertical federated learning (VFL) has attracted greater and greater interest since it enables multiple parties possessing non-overlapping features to strengthen their machine learning models without disclosing their private data and model parameters. Similar to other machine learning algorithms, VFL faces demands and challenges of fairness, i.e., the learned model may be unfairly discriminatory over some groups with sensitive attributes. To tackle this problem, we propose a fair VFL framework in this work. First, we systematically formulate the problem of training fair models in VFL, where the learning task is modelled as a constrained optimization problem. To solve it in a federated and privacy-preserving manner, we consider the equivalent dual form of the problem and develop an asynchronous gradient coordinate-descent ascent algorithm, where some active data parties perform multiple parallelized local updates per communication round to effectively reduce the number of communication rounds. The messages that the server sends to passive parties are deliberately designed such that the information necessary for local updates is released without intruding on the privacy of data and sensitive attributes. We rigorously study the convergence of the algorithm when applied to general nonconvex-concave min-max problems. We prove that the algorithm finds a $δ$-stationary point of the dual objective in $\mathcal{O}(δ^{-4})$ communication rounds under mild conditions. Finally, the extensive experiments on three benchmark datasets demonstrate the superior performance of our method in training fair models.
Focus on Impact: Indoor Exploration with Intrinsic Motivation
Venue: arXiv cs.RO 2022
Year: 2022

Exploration of indoor environments has recently experienced a significant interest, also thanks to the introduction of deep neural agents built in a hierarchical fashion and trained with Deep Reinforcement Learning (DRL) on simulated environments. Current state-of-the-art methods employ a dense extrinsic reward that requires the complete a priori knowledge of the layout of the training environment to learn an effective exploration policy. However, such information is expensive to gather in terms of time and resources. In this work, we propose to train the model with a purely intrinsic reward signal to guide exploration, which is based on the impact of the robot's actions on its internal representation of the environment. So far, impact-based rewards have been employed for simple tasks and in procedurally generated synthetic environments with countable states. Since the number of states observable by the agent in realistic indoor environments is non-countable, we include a neural-based density model and replace the traditional count-based regularization with an estimated pseudo-count of previously visited states. The proposed exploration approach outperforms DRL-based competitors relying on intrinsic rewards and surpasses the agents trained with a dense extrinsic reward computed with the environment layouts. We also show that a robot equipped with the proposed approach seamlessly adapts to point-goal navigation and real-world deployment.
A Direct Construction of GCP and Binary CCC of Length Non Power of Two
Venue: arXiv cs.IT 2022
Year: 2022

Golay complementary pairs (GCPs) and complete complementary codes (CCCs) have found a wide range of practical applications in coding, signal processing and wireless communication due to their ideal correlation properties. In fact, binary CCCs have special advantages in spread spectrum communication due to their simple modulo-2 arithmetic operation, modulation and correlation simplicity, but they are limited in length. In this paper, we present a direct construction of GCPs, mutually orthogonal complementary sets (MOCSs) and binary CCCs of non-power of two lengths to widen their application in the recent field. First, a generalised Boolean function (GBF) based truncation technique has been used to construct GCPs of non-power of two lengths. Then Complementary sets (CSs) and MOCSs of lengths of the form $2^{m-1}+2^{m-3}$ ($m \geq 5$) and $2^{m-1}+2^{m-2}+2^{m-4}$ ($m \geq 6$) are generated by GBFs. Finally, binary CCCs with desired lengths are constructed using the union of MOCSs. The row and column sequence peak to mean envelope power ratio (PMEPR) has been investigated and compared with existing work. The column sequence PMEPR of resultant CCCs can be effectively upper bounded by $2$.
TVStoryGen: A Dataset for Generating Stories with Character Descriptions
Venue: arXiv cs.CL 2022
Year: 2022

We introduce TVStoryGen, a story generation dataset that requires generating detailed TV show episode recaps from a brief summary and a set of documents describing the characters involved. Unlike other story generation datasets, TVStoryGen contains stories that are authored by professional screen-writers and that feature complex interactions among multiple characters. Generating stories in TVStoryGen requires drawing relevant information from the lengthy provided documents about characters based on the brief summary. In addition, we propose to train reverse models on our dataset for evaluating the faithfulness of generated stories. We create TVStoryGen from fan-contributed websites, which allows us to collect 26k episode recaps with 1868.7 tokens on average. Empirically, we take a hierarchical story generation approach and find that the neural model that uses oracle content selectors for character descriptions demonstrates the best performance on automatic metrics, showing the potential of our dataset to inspire future research on story generation with constraints. Qualitative analysis shows that the best-performing model sometimes generates content that is unfaithful to the short summaries, suggesting promising directions for future work.
The Undecidability of Network Coding with some Fixed-Size Messages and Edges
Venue: arXiv cs.IT 2022
Year: 2022

We consider a network coding setting where some of the messages and edges have fixed alphabet sizes, that do not change when we increase the common alphabet size of the rest of the messages and edges. We prove that the problem of deciding whether such network admits a coding scheme is undecidable. This can be considered as a partial solution to the conjecture that network coding (without fixed-size messages/edges) is undecidable. The proof, which makes heavy use of analogies with digital circuits, is essentially constructing a digital circuit of logic gates and flip-flops within a network coding model that is capable of simulating an arbitrary Turing machine.
Machine Learning-Based COVID-19 Patients Triage Algorithm using Patient-Generated Health Data from Nationwide Multicenter Database
Venue: arXiv cs.LG 2022
Year: 2022

A prompt severity assessment model of patients confirmed for having infectious diseases could enable efficient diagnosis while alleviating burden on the medical system. This study aims to develop a SARS-CoV-2 severity assessment model and establish a medical system that allows patients to check the severity of their cases and informs them to visit the appropriate clinic center based on past treatment data of other patients with similar severity levels. This paper provides the development processes of a severity assessment model using machine learning techniques and its application on SARS-CoV-2 patients. The proposed model is trained on a nationwide dataset provided by a Korean government agency and only requires patients' basic personal data, allowing them to judge the severity of their own cases. After modeling, the boosting-based decision tree model was selected as the classifier while mortality rate was interpreted as the probability score. The dataset was collected from all Korean citizens who were confirmed with COVID-19 between February, 2020 and July, 2021. The experiments achieved high model performance with an approximate precision of $0{\cdot}923$ and AUROC score of $0{\cdot}950$ [$95$% Tolerance Interval $0{\cdot}940$-$0{\cdot}958$, $95$% Confidence Interval $0{\cdot}949$-$0{\cdot}950$]. Moreover, our experiments identified the most important variables affecting the severity in the model via sensitivity analysis. The prompt severity assessment model for managing infectious people has been attained through using a nationwide dataset. It has demonstrated its superior performance by surpassing that of conventional risk assessments. With the model's high performance and easily accessible features, the triage algorithm is expected to be particularly useful when patients monitor their health status by themselves through smartphone applications.