OpenRaiser/Intern-Atlas huggingface dataset:
A Safety Assurable Human-Inspired Perception Architecture
Venue: arXiv cs.LG 2022
Year: 2022

Although artificial intelligence-based perception (AIP) using deep neural networks (DNN) has achieved near human level performance, its well-known limitations are obstacles to the safety assurance needed in autonomous applications. These include vulnerability to adversarial inputs, inability to handle novel inputs and non-interpretability. While research in addressing these limitations is active, in this paper, we argue that a fundamentally different approach is needed to address them. Inspired by dual process models of human cognition, where Type 1 thinking is fast and non-conscious while Type 2 thinking is slow and based on conscious reasoning, we propose a dual process architecture for safe AIP. We review research on how humans address the simplest non-trivial perception problem, image classification, and sketch a corresponding AIP architecture for this task. We argue that this architecture can provide a systematic way of addressing the limitations of AIP using DNNs and an approach to assurance of human-level performance and beyond. We conclude by discussing what components of the architecture may already be addressed by existing work and what remains future work.
Quality versus speed in energy demand prediction for district heating systems
Venue: arXiv cs.LG 2022
Year: 2022

In this paper, we consider energy demand prediction in district heating systems. Effective energy demand prediction is essential in combined heat power systems when offering electrical energy in competitive electricity markets. To address this problem, we propose two sets of algorithms: (1) a novel extension to the algorithm proposed by E. Dotzauer and (2) an autoregressive predictor based on hour-of-week adjusted linear regression on moving averages of energy consumption. These two methods are compared against state-of-the-art artificial neural networks. Energy demand predictor algorithms have various computational costs and prediction quality. While prediction quality is a widely used measure of predictor superiority, computational costs are less frequently analyzed and their impact is not so extensively studied. When predictor algorithms are constantly updated using new data, some computationally expensive forecasting methods may become inapplicable. The computational costs can be split into training and execution parts. The execution part is the cost paid when the already trained algorithm is applied to predict something. In this paper, we evaluate the above methods with respect to the quality and computational costs, both in the training and in the execution. The comparison is conducted on a real-world dataset from a district heating system in the northwest part of Poland.
Minimal Neural Network Models for Permutation Invariant Agents
Venue: arXiv cs.LG 2022
Year: 2022

Organisms in nature have evolved to exhibit flexibility in face of changes to the environment and/or to themselves. Artificial neural networks (ANNs) have proven useful for controlling of artificial agents acting in environments. However, most ANN models used for reinforcement learning-type tasks have a rigid structure that does not allow for varying input sizes. Further, they fail catastrophically if inputs are presented in an ordering unseen during optimization. We find that these two ANN inflexibilities can be mitigated and their solutions are simple and highly related. For permutation invariance, no optimized parameters can be tied to a specific index of the input elements. For size invariance, inputs must be projected onto a common space that does not grow with the number of projections. Based on these restrictions, we construct a conceptually simple model that exhibit flexibility most ANNs lack. We demonstrate the model's properties on multiple control problems, and show that it can cope with even very rapid permutations of input indices, as well as changes in input size. Ablation studies show that is possible to achieve these properties with simple feedforward structures, but that it is much easier to optimize recurrent structures.
Many Field Packet Classification with Decomposition and Reinforcement Learning
Venue: arXiv cs.NI 2022
Year: 2022

Scalable packet classification is a key requirement to support scalable network applications like firewalls, intrusion detection, and differentiated services. With ever increasing in the line-rate in core networks, it becomes a great challenge to design a scalable packet classification solution using hand-tuned heuristics approaches. In this paper, we present a scalable learning-based packet classification engine by building an efficient data structure for different ruleset with many fields. Our method consists of the decomposition of fields into subsets and building separate decision trees on those subsets using a deep reinforcement learning procedure. To decompose given fields of a ruleset, we consider different grouping metrics like standard deviation of individual fields and introduce a novel metric called diversity index (DI). We examine different decomposition schemes and construct decision trees for each scheme using deep reinforcement learning and compare the results. The results show that the SD decomposition metrics results in 11.5% faster than DI metrics, 25% faster than random 2 and 40% faster than random 1. Furthermore, our learning-based selection method can be applied to varying rulesets due to its ruleset independence.
Accelerating X-Ray Tracing for Exascale Systems using Kokkos
Venue: arXiv cs.DC 2022
Year: 2022

The upcoming exascale computing systems Frontier and Aurora will draw much of their computing power from GPU accelerators. The hardware for these systems will be provided by AMD and Intel, respectively, each supporting their own GPU programming model. The challenge for applications that harness one of these exascale systems will be to avoid lock-in and to preserve performance portability. We report here on our results of using Kokkos to accelerate a real-world application on NERSC's Perlmutter Phase 1 (using NVIDIA A100 accelerators) and the testbed system for OLCF's Frontier (using AMD MI250X). By porting to Kokkos, we were able to successfully run the same X-ray tracing code on both systems and achieved speed-ups between 13% and 66% compared to the original CUDA code. These results are a highly encouraging demonstration of using Kokkos to accelerate production science code.
Budge: a programming language and a theorem prover
Venue: arXiv cs.PL 2022
Year: 2022

We present a simple programming language based on Gödel numbering and prime factorization, enhanced with explicit, scoped loops, allowing for easy program composition. Further, we will present a theorem prover that allows expressing and working with formal systems. The theorem prover is simple as it relies merely on a substitution rule and set equality to derive theorems. Finally, we will represent the programming language in the theorem prover. We will show the syntax and semantics of both, and then provide a few example programs and their evaluation.
Test-Time Adaptation with Shape Moments for Image Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

Supervised learning is well-known to fail at generalization under distribution shifts. In typical clinical settings, the source data is inaccessible and the target distribution is represented with a handful of samples: adaptation can only happen at test time on a few or even a single subject(s). We investigate test-time single-subject adaptation for segmentation, and propose a Shape-guided Entropy Minimization objective for tackling this task. During inference for a single testing subject, our loss is minimized with respect to the batch normalization's scale and bias parameters. We show the potential of integrating various shape priors to guide adaptation to plausible solutions, and validate our method in two challenging scenarios: MRI-to-CT adaptation of cardiac segmentation and cross-site adaptation of prostate segmentation. Our approach exhibits substantially better performances than the existing test-time adaptation methods. Even more surprisingly, it fares better than state-of-the-art domain adaptation methods, although it forgoes training on additional target data during adaptation. Our results question the usefulness of training on target data in segmentation adaptation, and points to the substantial effect of shape priors on test-time inference. Our framework can be readily used for integrating various priors and for adapting any segmentation network, and our code is available.
$\mathscr{H}$-Consistency Estimation Error of Surrogate Loss Minimizers
Venue: arXiv cs.LG 2022
Year: 2022

We present a detailed study of estimation errors in terms of surrogate loss estimation errors. We refer to such guarantees as $\mathscr{H}$-consistency estimation error bounds, since they account for the hypothesis set $\mathscr{H}$ adopted. These guarantees are significantly stronger than $\mathscr{H}$-calibration or $\mathscr{H}$-consistency. They are also more informative than similar excess error bounds derived in the literature, when $\mathscr{H}$ is the family of all measurable functions. We prove general theorems providing such guarantees, for both the distribution-dependent and distribution-independent settings. We show that our bounds are tight, modulo a convexity assumption. We also show that previous excess error bounds can be recovered as special cases of our general results. We then present a series of explicit bounds in the case of the zero-one loss, with multiple choices of the surrogate loss and for both the family of linear functions and neural networks with one hidden-layer. We further prove more favorable distribution-dependent guarantees in that case. We also present a series of explicit bounds in the case of the adversarial loss, with surrogate losses based on the supremum of the $ρ$-margin, hinge or sigmoid loss and for the same two general hypothesis sets. Here too, we prove several enhancements of these guarantees under natural distributional assumptions. Finally, we report the results of simulations illustrating our bounds and their tightness.
M6-Rec: Generative Pretrained Language Models are Open-Ended Recommender Systems
Venue: arXiv cs.IR 2022
Year: 2022

Industrial recommender systems have been growing increasingly complex, may involve \emph{diverse domains} such as e-commerce products and user-generated contents, and can comprise \emph{a myriad of tasks} such as retrieval, ranking, explanation generation, and even AI-assisted content production. The mainstream approach so far is to develop individual algorithms for each domain and each task. In this paper, we explore the possibility of developing a unified foundation model to support \emph{open-ended domains and tasks} in an industrial recommender system, which may reduce the demand on downstream settings' data and can minimize the carbon footprint by avoiding training a separate model from scratch for every task. Deriving a unified foundation is challenging due to (i) the potentially unlimited set of downstream domains and tasks, and (ii) the real-world systems' emphasis on computational efficiency. We thus build our foundation upon M6, an existing large-scale industrial pretrained language model similar to GPT-3 and T5, and leverage M6's pretrained ability for sample-efficient downstream adaptation, by representing user behavior data as plain texts and converting the tasks to either language understanding or generation. To deal with a tight hardware budget, we propose an improved version of prompt tuning that outperforms fine-tuning with negligible 1\% task-specific parameters, and employ techniques such as late interaction, early exiting, parameter sharing, and pruning to further reduce the inference time and the model size. We demonstrate the foundation model's versatility on a wide range of tasks such as retrieval, ranking, zero-shot recommendation, explanation generation, personalized content creation, and conversational recommendation, and manage to deploy it on both cloud servers and mobile devices.
EvoRobogami: Co-designing with Humans in Evolutionary Robotics Experiments
Venue: arXiv cs.RO 2022
Year: 2022

We study the effects of injecting human-generated designs into the initial population of an evolutionary robotics experiment, where subsequent population of robots are optimised via a Genetic Algorithm and MAP-Elites. First, human participants interact via a graphical front-end to explore a directly-parameterised legged robot design space and attempt to produce robots via a combination of intuition and trial-and-error that perform well in a range of environments. Environments are generated whose corresponding high-performance robot designs range from intuitive to complex and hard to grasp. Once the human designs have been collected, their impact on the evolutionary process is assessed by replacing a varying number of designs in the initial population with human designs and subsequently running the evolutionary algorithm. Our results suggest that a balance of random and hand-designed initial solutions provides the best performance for the problems considered, and that human designs are most valuable when the problem is intuitive. The influence of human design in an evolutionary algorithm is a highly understudied area, and the insights in this paper may be valuable to the area of AI-based design more generally.
Efficient Stereo Depth Estimation for Pseudo LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder
Venue: arXiv cs.CV 2022
Year: 2022

Perception and localization are essential for autonomous delivery vehicles, mostly estimated from 3D LiDAR sensors due to their precise distance measurement capability. This paper presents a strategy to obtain the real-time pseudo point cloud instead of the laser sensor from the image sensor. We propose an approach to use different depth estimators to obtain pseudo point clouds like LiDAR to obtain better performance. Moreover, the training and validating strategy of the depth estimator has adopted stereo imagery data to estimate more accurate depth estimation as well as point cloud results. Our approach to generating depth maps outperforms on KITTI benchmark while yielding point clouds significantly faster than other approaches.
Scheduling in Wireless Networks using Whittle Index Theory
Venue: arXiv cs.NI 2022
Year: 2022

We consider the problem of scheduling packet transmissions in a wireless network of users while minimizing the energy consumed and the transmission delay. A challenge is that transmissions of users that are close to each other mutually interfere, while users that are far apart can transmit simultaneously without much interference. Each user has a queue of packets that are transmitted on a single channel and mutually non interfering users reuse the spectrum. Using the theory of Whittle index for cost minimizing restless bandits, we design four index-based policies and compare their performance with that of the well-known policies: Slotted ALOHA, maximum weight scheduling, quadratic Lyapunov drift, Cella and Cesa Bianchi algorithm, and two Whittle index based policies from a recently published paper. We make the code used to perform our simulations publicly available, so that it can be used for future work by the research community at large.
Detection Masking for Improved OCR on Noisy Documents
Venue: arXiv cs.CV 2022
Year: 2022

Optical Character Recognition (OCR), the task of extracting textual information from scanned documents is a vital and broadly used technology for digitizing and indexing physical documents. Existing technologies perform well for clean documents, but when the document is visually degraded, or when there are non-textual elements, OCR quality can be greatly impacted, specifically due to erroneous detections. In this paper we present an improved detection network with a masking system to improve the quality of OCR performed on documents. By filtering non-textual elements from the image we can utilize document-level OCR to incorporate contextual information to improve OCR results. We perform a unified evaluation on a publicly available dataset demonstrating the usefulness and broad applicability of our method. Additionally, we present and make publicly available our synthetic dataset with a unique hard-negative component specifically tuned to improve detection results, and evaluate the benefits that can be gained from its usage
A two-steps approach to improve the performance of Android malware detectors
Venue: arXiv cs.CR 2022
Year: 2022

The popularity of Android OS has made it an appealing target to malware developers. To evade detection, including by ML-based techniques, attackers invest in creating malware that closely resemble legitimate apps. In this paper, we propose GUIDED RETRAINING, a supervised representation learning-based method that boosts the performance of a malware detector. First, the dataset is split into "easy" and "difficult" samples, where difficulty is associated to the prediction probabilities yielded by a malware detector: for difficult samples, the probabilities are such that the classifier is not confident on the predictions, which have high error rates. Then, we apply our GUIDED RETRAINING method on the difficult samples to improve their classification. For the subset of "easy" samples, the base malware detector is used to make the final predictions since the error rate on that subset is low by construction. For the subset of "difficult" samples, we rely on GUIDED RETRAINING, which leverages the correct predictions and the errors made by the base malware detector to guide the retraining process. GUIDED RETRAINING focuses on the difficult samples: it learns new embeddings of these samples using Supervised Contrastive Learning and trains an auxiliary classifier for the final predictions. We validate our method on four state-of-the-art Android malware detection approaches using over 265k malware and benign apps, and we demonstrate that GUIDED RETRAINING can reduce up to 40.41% prediction errors made by the malware detectors. Our method is generic and designed to enhance the classification performance on a binary classification task. Consequently, it can be applied to other classification problems beyond Android malware detection.
KGNN: Distributed Framework for Graph Neural Knowledge Representation
Venue: arXiv cs.LG 2022
Year: 2022

Knowledge representation learning has been commonly adopted to incorporate knowledge graph (KG) into various online services. Although existing knowledge representation learning methods have achieved considerable performance improvement, they ignore high-order structure and abundant attribute information, resulting unsatisfactory performance on semantics-rich KGs. Moreover, they fail to make prediction in an inductive manner and cannot scale to large industrial graphs. To address these issues, we develop a novel framework called KGNN to take full advantage of knowledge data for representation learning in the distributed learning system. KGNN is equipped with GNN based encoder and knowledge aware decoder, which aim to jointly explore high-order structure and attribute information together in a fine-grained fashion and preserve the relation patterns in KGs, respectively. Extensive experiments on three datasets for link prediction and triplet classification task demonstrate the effectiveness and scalability of KGNN framework.
SCL(EQ): SCL for First-Order Logic with Equality
Venue: arXiv cs.LO 2022
Year: 2022

We propose a new calculus SCL(EQ) for first-order logic with equality that only learns non-redundant clauses. Following the idea of CDCL (Conflict Driven Clause Learning) and SCL (Clause Learning from Simple Models) a ground literal model assumption is used to guide inferences that are then guaranteed to be non-redundant. Redundancy is defined with respect to a dynamically changing ordering derived from the ground literal model assumption. We prove SCL(EQ) sound and complete and provide examples where our calculus improves on superposition.
Sampling-Based Verification of CTMCs with Uncertain Rates
Venue: arXiv cs.LO 2022
Year: 2022

We employ uncertain parametric CTMCs with parametric transition rates and a prior on the parameter values. The prior encodes uncertainty about the actual transition rates, while the parameters allow dependencies between transition rates. Sampling the parameter values from the prior distribution then yields a standard CTMC, for which we may compute relevant reachability probabilities. We provide a principled solution, based on a technique called scenario-optimization, to the following problem: From a finite set of parameter samples and a user-specified confidence level, compute prediction regions on the reachability probabilities. The prediction regions should (with high probability) contain the reachability probabilities of a CTMC induced by any additional sample. To boost the scalability of the approach, we employ standard abstraction techniques and adapt our methodology to support approximate reachability probabilities. Experiments with various well-known benchmarks show the applicability of the approach.
Finite Element Method-enhanced Neural Network for Forward and Inverse Problems
Venue: arXiv cs.CE 2022
Year: 2022

We introduce a novel hybrid methodology combining classical finite element methods (FEM) with neural networks to create a well-performing and generalizable surrogate model for forward and inverse problems. The residual from finite element methods and custom loss functions from neural networks are merged to form the algorithm. The Finite Element Method-enhanced Neural Network hybrid model (FEM-NN hybrid) is data-efficient and physics conforming. The proposed methodology can be used for surrogate models in real-time simulation, uncertainty quantification, and optimization in the case of forward problems. It can be used for updating the models in the case of inverse problems. The method is demonstrated with examples, and the accuracy of the results and performance is compared against the conventional way of network training and the classical finite element method. An application of the forward-solving algorithm is demonstrated for the uncertainty quantification of wind effects on a high-rise buildings. The inverse algorithm is demonstrated in the speed-dependent bearing coefficient identification of fluid bearings. The hybrid methodology of this kind will serve as a paradigm shift in the simulation methods currently used.