OpenRaiser/Intern-Atlas huggingface dataset:
Advanced Feature Learning on Point Clouds using Multi-resolution Features and Learnable Pooling
Venue: arXiv cs.CV 2022
Year: 2022

Existing point cloud feature learning networks often incorporate sequences of sampling, neighborhood grouping, neighborhood-wise feature learning, and feature aggregation to learn high-semantic point features that represent the global context of a point cloud. Unfortunately, the compounded loss of information concerning granularity and non-maximum point features due to sampling and max pooling could adversely affect the high-semantic point features from existing networks such that they are insufficient to represent the local context of a point cloud, which in turn may hinder the network in distinguishing fine shapes. To cope with this problem, we propose a novel point cloud feature learning network, PointStack, using multi-resolution feature learning and learnable pooling (LP). The multi-resolution feature learning is realized by aggregating point features of various resolutions in the multiple layers, so that the final point features contain both high-semantic and high-resolution information. On the other hand, the LP is used as a generalized pooling function that calculates the weighted sum of multi-resolution point features through the attention mechanism with learnable queries, in order to extract all possible information from all available point features. Consequently, PointStack is capable of extracting high-semantic point features with minimal loss of information concerning granularity and non-maximum point features. Therefore, the final aggregated point features can effectively represent both global and local contexts of a point cloud. In addition, both the global structure and the local shape details of a point cloud can be well comprehended by the network head, which enables PointStack to advance the state-of-the-art of feature learning on point clouds. The codes are available at https://github.com/kaist-avelab/PointStack.
A Fully Controllable Agent in the Path Planning using Goal-Conditioned Reinforcement Learning
Venue: arXiv cs.AI 2022
Year: 2022

The aim of path planning is to reach the goal from starting point by searching for the route of an agent. In the path planning, the routes may vary depending on the number of variables such that it is important for the agent to reach various goals. Numerous studies, however, have dealt with a single goal that is predefined by the user. In the present study, I propose a novel reinforcement learning framework for a fully controllable agent in the path planning. To do this, I propose a bi-directional memory editing to obtain various bi-directional trajectories of the agent, in which the behavior of the agent and sub-goals are trained on the goal-conditioned RL. As for moving the agent in various directions, I utilize the sub-goals dedicated network, separated from a policy network. Lastly, I present the reward shaping to shorten the number of steps for the agent to reach the goal. In the experimental result, the agent was able to reach the various goals that have never been visited by the agent in the training. We confirmed that the agent could perform difficult missions such as a round trip and the agent used the shorter route with the reward shaping.
An In-Pipe Inspection Robot With Sensorless Underactuated Magnets and Omnidirectional Tracks: Design and Implementation
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents the plan of an in-pipe climbing robot that works utilizing an astute transmission part to investigate complex relationship of lines. Standard wheeled/proceeded in-pipe climbing robots are inclined to slip and take while investigating in pipe turns. The instrument helps in accomplishing the main inevitable result of getting out slip and drag in the robot tracks during advancement. The proposed transmission appreciates the practical furthest reaches of the standard two-yield transmission, which is developed the basic time for a transmission with three results. The instrument conclusively changes the track speeds of the robot considering the powers applied on each track inside the line relationship, by getting out the essential for any remarkable control. The amusement of the robot crossing in the line network in various orientation and in pipe-turns without slip shows the proposed game plan's adequacy.
SALTED: A Framework for SAlient Long-Tail Translation Error Detection
Venue: arXiv cs.CL 2022
Year: 2022

Traditional machine translation (MT) metrics provide an average measure of translation quality that is insensitive to the long tail of behavioral problems in MT. Examples include translation of numbers, physical units, dropped content and hallucinations. These errors, which occur rarely and unpredictably in Neural Machine Translation (NMT), greatly undermine the reliability of state-of-the-art MT systems. Consequently, it is important to have visibility into these problems during model development. Towards this direction, we introduce SALTED, a specifications-based framework for behavioral testing of MT models that provides fine-grained views of salient long-tail errors, permitting trustworthy visibility into previously invisible problems. At the core of our approach is the development of high-precision detectors that flag errors (or alternatively, verify output correctness) between a source sentence and a system output. We demonstrate that such detectors could be used not just to identify salient long-tail errors in MT systems, but also for higher-recall filtering of the training data, fixing targeted errors with model fine-tuning in NMT and generating novel data for metamorphic testing to elicit further bugs in models.
ALPINE: Analog In-Memory Acceleration with Tight Processor Integration for Deep Learning
Venue: arXiv cs.AR 2022
Year: 2022

Analog in-memory computing (AIMC) cores offers significant performance and energy benefits for neural network inference with respect to digital logic (e.g., CPUs). AIMCs accelerate matrix-vector multiplications, which dominate these applications' run-time. However, AIMC-centric platforms lack the flexibility of general-purpose systems, as they often have hard-coded data flows and can only support a limited set of processing functions. With the goal of bridging this gap in flexibility, we present a novel system architecture that tightly integrates analog in-memory computing accelerators into multi-core CPUs in general-purpose systems. We developed a powerful gem5-based full system-level simulation framework into the gem5-X simulator, ALPINE, which enables an in-depth characterization of the proposed architecture. ALPINE allows the simulation of the entire computer architecture stack from major hardware components to their interactions with the Linux OS. Within ALPINE, we have defined a custom ISA extension and a software library to facilitate the deployment of inference models. We showcase and analyze a variety of mappings of different neural network types, and demonstrate up to 20.5x/20.8x performance/energy gains with respect to a SIMD-enabled ARM CPU implementation for convolutional neural networks, multi-layer perceptrons, and recurrent neural networks.
Error Probability Bounds for Coded-Index DNA Storage
Venue: arXiv cs.IT 2022
Year: 2022

The DNA storage channel is considered, in which a codeword is comprised of $M$ unordered DNA molecules. At reading time, $N$ molecules are sampled with replacement, and then each molecule is sequenced. A coded-index concatenated-coding scheme is considered, in which the $m$th molecule of the codeword is restricted to a subset of all possible molecules (an inner code), which is unique for each $m$. The decoder has low-complexity, and is based on first decoding each molecule separately (the inner code), and then decoding the sequence of molecules (an outer code). Only mild assumptions are made on the sequencing channel, in the form of the existence of an inner code and decoder with vanishing error. The error probability of a random code as well as an expurgated code is analyzed and shown to decay exponentially with $N$. This establishes the importance of increasing the coverage depth $N/M$ in order to obtain low error probability.
Unintended memorisation of unique features in neural networks
Venue: arXiv cs.LG 2022
Year: 2022

Neural networks pose a privacy risk due to their propensity to memorise and leak training data. We show that unique features occurring only once in training data are memorised by discriminative multi-layer perceptrons and convolutional neural networks trained on benchmark imaging datasets. We design our method for settings where sensitive training data is not available, for example medical imaging. Our setting knows the unique feature, but not the training data, model weights or the unique feature's label. We develop a score estimating a model's sensitivity to a unique feature by comparing the KL divergences of the model's output distributions given modified out-of-distribution images. We find that typical strategies to prevent overfitting do not prevent unique feature memorisation. And that images containing a unique feature are highly influential, regardless of the influence the images's other features. We also find a significant variation in memorisation with training seed. These results imply that neural networks pose a privacy risk to rarely occurring private information. This risk is more pronounced in healthcare applications since sensitive patient information can be memorised when it remains in training data due to an imperfect data sanitisation process.
Emergence of Double-slit Interference by Representing Visual Space in Artificial Neural Networks
Venue: arXiv cs.CV 2022
Year: 2022

Artificial neural networks have realized incredible successes at image recognition, but the underlying mechanism of visual space representation remains a huge mystery. Grid cells (2014 Nobel Prize) in the entorhinal cortex support a periodic representation as a metric for coding space. Here, we develop a self-supervised convolutional neural network to perform visual space location, leading to the emergence of single-slit diffraction and double-slit interference patterns of waves. Our discoveries reveal the nature of CNN encoding visual space to a certain extent. CNN is no longer a black box in terms of visual spatial encoding, it is interpretable. Our findings indicate that the periodicity property of waves provides a space metric, suggesting a general role of spatial coordinate frame in artificial neural networks.
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels
Venue: arXiv cs.LG 2022
Year: 2022

Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices. Hence, the labels in practice are usually annotated by clients of varying expertise or criteria and thus contain different amounts of noises. Local training on noisy labels can easily result in overfitting to noisy labels, which is devastating to the global model through aggregation. Although recent robust FL methods take malicious clients into account, they have not addressed local noisy labels on each device and the impact to the global model. In this paper, we develop a simple two-level sampling method "FedNoiL" that (1) selects clients for more robust global aggregation on the server; and (2) selects clean labels and correct pseudo-labels at the client end for more robust local training. The sampling probabilities are built upon clean label detection by the global model. Moreover, we investigate different schedules changing the local epochs between aggregations over the course of FL, which notably improves the communication and computation efficiency in noisy label setting. In experiments with homogeneous/heterogeneous data distributions and noise ratios, we observed that direct combinations of SOTA FL methods with SOTA noisy-label learning methods can easily fail but our method consistently achieves better and robust performance.
Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling
Venue: arXiv cs.NE 2022
Year: 2022

As two popular schools of machine learning, online learning and evolutionary computations have become two important driving forces behind real-world decision making engines for applications in biomedicine, economics, and engineering fields. Although there are prior work that utilizes bandits to improve evolutionary algorithms' optimization process, it remains a field of blank on how evolutionary approach can help improve the sequential decision making tasks of online learning agents such as the multi-armed bandits. In this work, we propose the Genetic Thompson Sampling, a bandit algorithm that keeps a population of agents and update them with genetic principles such as elite selection, crossover and mutations. Empirical results in multi-armed bandit simulation environments and a practical epidemic control problem suggest that by incorporating the genetic algorithm into the bandit algorithm, our method significantly outperforms the baselines in nonstationary settings. Lastly, we introduce EvoBandit, a web-based interactive visualization to guide the readers through the entire learning process and perform lightweight evaluations on the fly. We hope to engage researchers into this growing field of research with this investigation.
Testing predictive automated driving systems: lessons learned and future recommendations
Venue: arXiv cs.AI 2022
Year: 2022

Conventional vehicles are certified through classical approaches, where different physical certification tests are set up on test tracks to assess required safety levels. These approaches are well suited for vehicles with limited complexity and limited interactions with other entities as last-second resources. However, these approaches do not allow to evaluate safety with real behaviors for critical and edge cases, nor to evaluate the ability to anticipate them in the mid or long term. This is particularly relevant for automated and autonomous driving functions that make use of advanced predictive systems to anticipate future actions and motions to be considered in the path planning layer. In this paper, we present and analyze the results of physical tests on proving grounds of several predictive systems in automated driving functions developed within the framework of the BRAVE project. Based on our experience in testing predictive automated driving functions, we identify the main limitations of current physical testing approaches when dealing with predictive systems, analyze the main challenges ahead, and provide a set of practical actions and recommendations to consider in future physical testing procedures for automated and autonomous driving functions.
Evolving SimGANs to Improve Abnormal Electrocardiogram Classification
Venue: arXiv cs.NE 2022
Year: 2022

Machine Learning models are used in a wide variety of domains. However, machine learning methods often require a large amount of data in order to be successful. This is especially troublesome in domains where collecting real-world data is difficult and/or expensive. Data simulators do exist for many of these domains, but they do not sufficiently reflect the real world data due to factors such as a lack of real-world noise. Recently generative adversarial networks (GANs) have been modified to refine simulated image data into data that better fits the real world distribution, using the SimGAN method. While evolutionary computing has been used for GAN evolution, there are currently no frameworks that can evolve a SimGAN. In this paper we (1) extend the SimGAN method to refine one-dimensional data, (2) modify Easy Cartesian Genetic Programming (ezCGP), an evolutionary computing framework, to create SimGANs that more accurately refine simulated data, and (3) create new feature-based quantitative metrics to evaluate refined data. We also use our framework to augment an electrocardiogram (ECG) dataset, a domain that suffers from the issues previously mentioned. In particular, while healthy ECGs can be simulated there are no current simulators of abnormal ECGs. We show that by using an evolved SimGAN to refine simulated healthy ECG data to mimic real-world abnormal ECGs, we can improve the accuracy of abnormal ECG classifiers.
On Evaluating Power Loss with HATSGA Algorithm for Power Network Reconfiguration in the Smart Grid
Venue: arXiv cs.AI 2022
Year: 2022

This paper presents the power network reconfiguration algorithm HATSGA with a "R" modeling approach and evaluates its behavior in computing new reconfiguration topologies for the power network in the Smart Grid context. The modeling of the power distribution network with the language "R" is used to represent the network and support the computation of distinct algorithm configurations towards the evaluation of new reconfiguration topologies. The HATSGA algorithm adopts a hybrid Tabu Search and Genetic Algorithm strategy and can be configured in different ways to compute network reconfiguration solutions. The evaluation of power loss with HATSGA uses the IEEE 14-Bus topology as the power test scenario. The evaluation of reconfiguration topologies with minimum power loss with HATSGA indicates that an efficient solution can be reached with a feasible computational time. This suggests that HATSGA can be potentially used for computing reconfiguration network topologies and, beyond that, it can be used for autonomic self-healing management approaches where a feasible computational time is required.
Towards efficient feature sharing in MIMO architectures
Venue: arXiv cs.LG 2022
Year: 2022

Multi-input multi-output architectures propose to train multiple subnetworks within one base network and then average the subnetwork predictions to benefit from ensembling for free. Despite some relative success, these architectures are wasteful in their use of parameters. Indeed, we highlight in this paper that the learned subnetwork fail to share even generic features which limits their applicability on smaller mobile and AR/VR devices. We posit this behavior stems from an ill-posed part of the multi-input multi-output framework. To solve this issue, we propose a novel unmixing step in MIMO architectures that allows subnetworks to properly share features. Preliminary experiments on CIFAR-100 show our adjustments allow feature sharing and improve model performance for small architectures.
Investigating the impact of BTI, HCI and time-zero variability on neuromorphic spike event generation circuits
Venue: arXiv cs.ET 2022
Year: 2022

Neuromorphic computing refers to brain-inspired computers, that differentiate it from von Neumann architecture. Analog VLSI based neuromorphic circuits is a current research interest. Two simpler spiking integrate and fire neuron model namely axon-Hillock (AH) and voltage integrate, and fire (VIF) circuits are commonly used for generating spike events. This paper discusses the impact of reliability issues like Bias Temperature instability (BTI) and Hot Carrier Injection (HCI), and timezero variability on these CMOS based neuromorphic circuits. AH and VIF circuits are implemented using HKMG based 45nm technology. For reliability analysis, industry standard Cadence RelXpert tool is used. For time-zero variability analysis, 1000 Monte-Carlo simulations are performed.
Mapping Complex Technologies via Science-Technology Linkages; The Case of Neuroscience -- A transformer based keyword extraction approach
Venue: arXiv cs.DL 2022
Year: 2022

In this paper, we present an efficient deep learning based approach to extract technology-related topics and keywords within scientific literature, and identify corresponding technologies within patent applications. Specifically, we utilize transformer based language models, tailored for use with scientific text, to detect coherent topics over time and describe these by relevant keywords that are automatically extracted from a large text corpus. We identify these keywords using Named Entity Recognition, distinguishing between those describing methods, applications and other scientific terminology. We create a large amount of search queries based on combinations of method- and application-keywords, which we use to conduct semantic search and identify related patents. By doing so, we aim at contributing to the growing body of research on text-based technology mapping and forecasting that leverages latest advances in natural language processing and deep learning. We are able to map technologies identified in scientific literature to patent applications, thereby providing an empirical foundation for the study of science-technology linkages. We illustrate the workflow as well as results obtained by mapping publications within the field of neuroscience to related patent applications.
The role of the Big Geographic Sort in the circulation of misinformation among U.S. Reddit users
Venue: arXiv cs.SI 2022
Year: 2022

Past research has attributed the online circulation of misinformation to two main factors - individual characteristics (e.g., a person's information literacy) and social media effects (e.g., algorithm-mediated information diffusion) - and has overlooked a third one: the critical mass created by the offline self-segregation of Americans into like-minded geographical regions such as states (a phenomenon called "The Big Sort"). We hypothesized that this latter factor matters for the online spreading of misinformation not least because online interactions, despite having the potential of being global, end up being localized: interaction probability is known to rapidly decay with distance. Upon analysis of more than 8M Reddit comments containing news links spanning four years, from January 2016 to December 2019, we found that Reddit did not work as an "hype machine" for misinformation (as opposed to what previous work reported for other platforms, circulation was not mainly caused by platform-facilitated network effects) but worked as a supply-and-demand system: misinformation news items scaled linearly with the number of users in each state (with a scaling exponent beta=1, and a goodness of fit R2 = 0.95). Furthermore, deviations from such a universal pattern were best explained by state-level personality and cultural factors (R2 = {0.12, 0.39}), rather than socioeconomic conditions (R2 = {0.15, 0.29}) or, as one would expect, political characteristics (R2 ={0.06, 0.21}). Higher-than-expected circulation of any type of news (including reputable news) was found in states characterised by residents who tend to be less diligent in terms of their personality (low in conscientiousness) and by loose cultures understating the importance of adherence to norms (low in cultural tightness).
How to Guide Adaptive Depth Sampling?
Venue: arXiv cs.CV 2022
Year: 2022

Recent advances in depth sensing technologies allow fast electronic maneuvering of the laser beam, as opposed to fixed mechanical rotations. This will enable future sensors, in principle, to vary in real-time the sampling pattern. We examine here the abstract problem of whether adapting the sampling pattern for a given frame can reduce the reconstruction error or allow a sparser pattern. We propose a constructive generic method to guide adaptive depth sampling algorithms. Given a sampling budget B, a depth predictor P and a desired quality measure M, we propose an Importance Map that highlights important sampling locations. This map is defined for a given frame as the per-pixel expected value of M produced by the predictor P, given a pattern of B random samples. This map can be well estimated in a training phase. We show that a neural network can learn to produce a highly faithful Importance Map, given an RGB image. We then suggest an algorithm to produce a sampling pattern for the scene, which is denser in regions that are harder to reconstruct. The sampling strategy of our modular framework can be adjusted according to hardware limitations, type of depth predictor, and any custom reconstruction error measure that should be minimized. We validate through simulations that our approach outperforms grid and random sampling patterns as well as recent state-of-the-art adaptive algorithms.