Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics-aware Adversarial Attack of 3D Sparse Convolution Network

In this paper, we investigate the dynamics-aware adversarial attack problem in deep neural networks. Most existing adversarial attack algorithms are designed under a basic assumption -- the network architecture is fixed throughout the attack process. However, this assumption does not hold for many recently proposed networks, e.g. 3D sparse convolution network, which contains input-dependent execution to improve computational efficiency. It results in a serious issue of lagged gradient, making the learned attack at the current step ineffective due to the architecture changes afterward. To address this issue, we propose a Leaded Gradient Method (LGM) and show the significant effects of the lagged gradient. More specifically, we re-formulate the gradients to be aware of the potential dynamic changes of network architectures, so that the learned attack better"leads"the next step than the dynamics-unaware methods when network architecture changes dynamically. Extensive experiments on various datasets show that our LGM achieves impressive performance on semantic segmentation and classification. Compared with the dynamic-unaware methods, LGM achieves about 20% lower mIoU averagely on the ScanNet and S3DIS datasets. LGM also outperforms the recent point cloud attacks.
Pixel Distillation: A New Knowledge Distillation Scheme for Low-Resolution Image Recognition

The great success of deep learning is mainly due to the large-scale network architecture and the high-quality training data. However, it is still challenging to deploy recent deep models on portable devices with limited memory and imaging ability. Some existing works have engaged to compress the model via knowledge distillation. Unfortunately, these methods cannot deal with images with reduced image quality, such as the low-resolution (LR) images. To this end, we make a pioneering effort to distill helpful knowledge from a heavy network model learned from high-resolution (HR) images to a compact network model that will handle LR images, thus advancing the current knowledge distillation technique with the novel pixel distillation. To achieve this goal, we propose a Teacher-Assistant-Student (TAS) framework, which disentangles knowledge distillation into the model compression stage and the high resolution representation transfer stage. By equipping a novel Feature Super Resolution (FSR) module, our approach can learn lightweight network model that can achieve similar accuracy as the heavy teacher model but with much fewer parameters, faster inference speed, and lower-resolution inputs. Comprehensive experiments on three widely-used benchmarks, \ie, CUB-200-2011, PASCAL VOC 2007, and ImageNetSub, demonstrate the effectiveness of our approach.
Communication-Oriented Model Fine-Tuning for Packet-Loss Resilient Distributed Inference Under Highly Lossy IoT Networks

The distributed inference (DI) framework has gained traction as a technique for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of things (IoT) devices. In DI, computational tasks are offloaded from the IoT device to the edge server via lossy IoT networks. However, generally, there is a communication system-level trade-off between communication latency and reliability; thus, to provide accurate DI results, a reliable and high-latency communication system is required to be adapted, which results in non-negligible end-to-end latency of the DI. This motivated us to improve the trade-off between the communication latency and accuracy by efforts on ML techniques. Specifically, we have proposed a communication-oriented model tuning (COMtune), which aims to achieve highly accurate DI with low-latency but unreliable communication links. In COMtune, the key idea is to fine-tune the ML model by emulating the effect of unreliable communication links through the application of the dropout technique. This enables the DI system to obtain robustness against unreliable communication links. Our ML experiments revealed that COMtune enables accurate predictions with low latency and under lossy networks.
DNA: Dynamic Network Augmentation

In many classification problems, we want a classifier that is robust to a range of non-semantic transformations. For example, a human can identify a dog in a picture regardless of the orientation and pose in which it appears. There is substantial evidence that this kind of invariance can significantly improve the accuracy and generalization of machine learning models. A common technique to teach a model geometric invariances is to augment training data with transformed inputs. However, which invariances are desired for a given classification task is not always known. Determining an effective data augmentation policy can require domain expertise or extensive data pre-processing. Recent efforts like AutoAugment optimize over a parameterized search space of data augmentation policies to automate the augmentation process. While AutoAugment and similar methods achieve state-of-the-art classification accuracy on several common datasets, they are limited to learning one data augmentation policy. Often times different classes or features call for different geometric invariances. We introduce Dynamic Network Augmentation (DNA), which learns input-conditional augmentation policies. Augmentation parameters in our model are outputs of a neural network and are implicitly learned as the network weights are updated. Our model allows for dynamic augmentation policies and performs well on data with geometric transformations conditional on input features.
An Integrated Risk Assessment Process of Safety-Related Digital I&C Systems in Nuclear Power Plants

Abstract Upgrading the existing analog instrumentation and control (I&C) systems to state-of-the-art digital I&C (DI&C) systems will greatly benefit existing light water reactors. However, the issue of software common cause failure (CCF) remains an obstacle in terms of qualification for digital technologies. Existing analyses of CCFs in I&C systems mainly focus on hardware failures. With the application and upgrading of new DI&C systems, design flaws could cause software CCFs to become a potential threat to plant safety, considering that most redundancy designs use similar digital platforms or software in their operating and application systems. With complex multilayer redundancy designs to meet the single failure criterion, these I&C safety systems are of particular concern in U.S. Nuclear Regulatory Commission licensing procedures. In Fiscal Year 2019, the Risk-Informed Systems Analysis (RISA) Pathway of the U.S. Department of Energy’s Light Water Reactor Sustainability Program initiated a project to develop a risk assessment strategy for delivering a strong technical basis to support effective, licensable, and secure DI&C technologies for digital upgrades and designs. An integrated risk assessment for the DI&C process was proposed for this strategy to identify potential key digital-induced failures, implement reliability analyses of related digital safety I&C systems, and evaluate the unanalyzed sequences introduced by these failures (particularly software CCFs) at the plant level. This paper summarizes these RISA efforts in the risk analysis of safety-related DI&C systems at Idaho National Laboratory.
Climate change projections for variables affecting road networks in Europe

Global climate change will affect road networks during this century. The effects will be different in various parts of the world due to differences in local climate change and in the structure and properties of roads. In this paper, climate change projections are presented for climate variables that are most likely to affect the long-term performance of road networks in Europe. We apply four regional climate simulations up to the year 2100 using two plausible future emission scenarios. The results show that the changing climate will require significant adaptation measures in the near future in order to maintain the operability of the European road network.
Topic-Aware Encoding for Extractive Summarization

Document summarization provides an instrument for faster understanding the collection of text documents and has several real-life applications. With the growth of online text data, numerous summarization models have been proposed recently. The Sequence-to-Sequence (Seq2Seq) based neural summarization model is the most widely used in the summarization field due to its high performance. This is because semantic information and structure information in the text is adequately considered when encoding. However, the existing extractive summarization models pay little attention to and use the central topic information to assist the generation of summaries, which leads to models not ensuring the generated summary under the primary topic. A lengthy document can span several topics, and a single summary cannot do justice to all the topics. Therefore, the key to generating a high-quality summary is determining the central topic and building a summary based on it, especially for a long document. We propose a topic-aware encoding for document summarization to deal with this issue. This model effectively combines syntactic-level and topic-level information to build a comprehensive sentence representation. Specifically, a neural topic model is added in the neural-based sentence-level representation learning to adequately consider the central topic information for capturing the critical content in the original document. The experimental results on three public datasets show that our model outperforms the state-of-the-art models.
Expedition: A System for the Unsupervised Learning of a Hierarchy of Concepts

We present a system for bottom-up cumulative learning of myriad concepts corresponding to meaningful character strings, and their part-related and prediction edges. The learning is self-supervised in that the concepts discovered are used as predictors as well as targets of prediction. We devise an objective for segmenting with the learned concepts, derived from comparing to a baseline prediction system, that promotes making and using larger concepts, which in turn allows for predicting larger spans of text, and we describe a simple technique to promote exploration, i.e. trying out newly generated concepts in the segmentation process. We motivate and explain a layering of the concepts, to help separate the (conditional) distributions learnt among concepts. The layering of the concepts roughly corresponds to a part-whole concept hierarchy. With rudimentary segmentation and learning algorithms, the system is promising in that it acquires many concepts (tens of thousands in our small-scale experiments), and it learns to segment text well: when fed with English text with spaces removed, starting at the character level, much of what is learned respects word or phrase boundaries, and over time the average number of"bad"splits within segmentations, i.e. splits inside words, decreases as larger concepts are discovered and the system learns when to use them during segmentation. We report on promising experiments when the input text is converted to binary and the system begins with only two concepts,"0"and"1". The system is transparent, in the sense that it is easy to tell what the concepts learned correspond to, and which ones are active in a segmentation, or how the system"sees"its input. We expect this framework to be extensible and we discuss the current limitations and a number of directions for enhancing the learning and inference capabilities.
Effects of direction reversals on patterns of active filaments

Active matter systems provide fascinating examples of pattern formation and collective motility without counterparts in equilibrium systems. Here, we employ Brownian dynamics simulations to study the collective motion and self-organization in systems of self-propelled semiflexible filaments, inspired by the gliding motility of \textit{filamentous Cyanobacteria}. Specifically, we investigate the influence of stochastic direction reversals on the patterns. We explore pattern formation and dynamics by modulating three relevant physical parameters, the bending stiffness, the activity, and the reversal rate. In the absence of reversals, our results show rich dynamical behavior including spiral formation and collective motion of aligned clusters of various sizes, depending on the bending stiffness and self-propulsion force. The presence of reversals diminishes spiral formation and reduces the sizes of clusters or suppresses clustering entirely. This homogenizing effect of direction reversals can be understood as reversals providing an additional mechanism to either unwind spirals or to resolve clusters.
Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift

A biosignal is a signal that can be continuously measured from human bodies, such as respiratory sounds, heart activity (ECG), brain waves (EEG), etc, based on which, machine learning models have been developed with very promising performance for automatic disease detection and health status monitoring. However, dataset shift, i.e., data distribution of inference varies from the distribution of the training, is not uncommon for real biosignal-based applications. To improve the robustness, probabilistic models with uncertainty quantification are adapted to capture how reliable a prediction is. Yet, assessing the quality of the estimated uncertainty remains a challenge. In this work, we propose a framework to evaluate the capability of the estimated uncertainty in capturing different types of biosignal dataset shifts with various degrees. In particular, we use three classification tasks based on respiratory sounds and electrocardiography signals to benchmark five representative uncertainty quantification methods. Extensive experiments show that, although Ensemble and Bayesian models could provide relatively better uncertainty estimations under dataset shifts, all tested models fail to meet the promise in trustworthy prediction and model calibration. Our work paves the way for a comprehensive evaluation for any newly developed biosignal classifiers.
Automatically Identifying Semantic Bias in Crowdsourced Natural Language Inference Datasets

Natural language inference (NLI) is an important task for producing useful models of human language. Unfortunately large-scale NLI dataset production relies on crowdwork-ers who are prone to introduce biases in the sentences they write. In particular, without quality control they produce hypotheses from which the relational label can be predicted, without the premise, better than chance. We introduce a model-driven, unsupervised technique to ﬁnd “bias clusters” in a learned embedding space of the hypotheses in NLI datasets, from which interventions and additional rounds of labeling can be performed to ameliorate the semantic bias of the hypothesis distribution of a dataset.
Performance characterization of high-bit-rate optical chaotic communication systems in a back-to-back configuration

A comparative study of three data-encoding techniques in optical chaotic communication systems is reported. The chaotic carrier is generated by a semiconductor laser subjected to optical feedback and the data are encoded on it by chaotic modulation (CM), chaotic masking (CMS), or chaotic shift keying (CSK) methods. In all cases, the receiver-which is directly connected to the transmitter-consists of a semiconductor laser similar to that of the transmitter subjected to the same optical feedback. The performance of this back to back configuration is numerically tested by calculating the Q-factor of the eye diagram of the received data for different bit rates from 1 to 20 Gb/s. The CM scheme appears to have the best performance relative to the CMS and CSK scheme, before and after filtering the residual high-frequency oscillations remaining due to nonperfect synchronization between the transmitter and receiver. Moreover, in all encoding methods, a decrease in the Q-factor is observed when the repetition bit-rate of the encoding message increases. In order to achieve as high Q-factor values as possible, a well-synchronized chaotic master-slave system is required.
DTVNet+: A High-Resolution Scenic Dataset for Dynamic Time-lapse Video Generation

This paper presents a novel end-to-end dynamic time-lapse video generation framework, named DTVNet, to generate diversified time-lapse videos from a single landscape image conditioned on normalized motion vectors. The proposed DTVNet consists of two submodules: \emph{Optical Flow Encoder} (OFE) and \emph{Dynamic Video Generator} (DVG). The OFE maps a sequence of optical flow maps to a \emph{normalized motion vector} that encodes the motion information of the generated video. The DVG contains motion and content streams to learn from the motion vector and the single landscape image. Besides, it contains an encoder to learn shared content features and a decoder to construct video frames with corresponding motion. Specifically, the \emph{motion stream} introduces multiple \emph{adaptive instance normalization} (AdaIN) layers to integrate multi-level motion information for controlling the object motion. In the testing stage, videos with the same content but various motion information can be generated by different \emph{normalized motion vectors} based on only one input image. Also, we propose a high-resolution scenic time-lapse video dataset, named Quick-Sky-Time, to evaluate different approaches, which can be viewed as a new benchmark for high-quality scenic image and video generation tasks. We further conduct experiments on Sky Time-lapse, Beach, and Quick-Sky-Time datasets. The results demonstrate the superiority of our approach over state-of-the-art methods for generating high-quality and various dynamic videos.
On the Rise and Fall of Online Social Networks

The rise and fall of online social networks recently generated an enormous amount of interest among people, both inside and outside of academia. Gillette [Businessweek magazine, 2011] did a detailed analysis of MySpace, which started losing its popularity since 2008. Cannarella and Spechler [ArXiv, 2014] used a model of disease spread to explain the rise and fall of MySpace. In this paper, we present a graph theoretical model that may be able to provide an alternative explanation for the rise and fall of online social networks. Our model is motivated by the well-known Barabasi-Albert model of generating random scale-free networks using preferential attachment or `rich-gets-richer' phenomenon. As shown by our empirical analysis, we conjecture that such an online social network growth model is inherently flawed as it fails to maintain the stability of such networks while ensuring their growth. In the process, we also conjecture that our model of preferential attachment also exhibits scale-free phenomenon.
Balancing Exploration for Online Receding Horizon Learning Control with Provable Regret Guarantees

We address the problem of simultaneously learning and control in an online receding horizon control setting. We consider the control of an unknown linear dynamical system with general cost functions and affine constraints on the control input. Our goal is to develop an online learning algorithm that minimizes the dynamic regret, which is defined as the difference between the cumulative cost incurred by the algorithm and that of the best policy with full knowledge of the system, cost functions and state and that satisfies the control input constraints. We propose a novel approach to explore in an online receding horizon setting. The key challenge is to ensure that the control generated by the receding horizon controller is persistently exciting. Our approach is to apply a perturbation to the control input generated by the receding horizon controller that balances the exploration-exploitation trade-off. By exploring the conditions under which sub-linear regret is guaranteed, We show that the proposed controller's performance is upper bounded by $\tilde{\mathcal{O}}(T^{3/4})$ for both regret and cumulative constraint violation when the controller has preview of the cost functions for the interval that doubles in size from one interval to the next.
Discrete symmetries in the ABJ and related theories

The zero-form and one-form global symmetries of the Aharony-BergmanJafferis (ABJ) and related theories, with at least N = 6 supersymmetry in three dimensions, are examined in detail. Starting from well-known dualities between theories with orthogonal and symplectic gauge groups and those with unitary gauge groups, we gauge their one-form symmetries or their subgroups and obtain new dualities. One side of the latter involves theories with special orthogonal and symplectic gauge groups, and the other side involves theories with unitary gauge groups; there is a discrete quotient on one or both sides of the duality. We study the refined superconformal indices of such theories and map the symmetries across the dualities, with particular attention to their discrete part. As a generalisation, we also find a new duality between a circular quiver with a discrete quotient of alternating special orthogonal and symplectic gauge groups and a three-dimensional N = 4 circular (Kronheimer-Nakajima) quiver with unitary gauge groups, whose Higgs or Coulomb branch describes an instanton on a singular orbifold. ar X iv :2 11 2. 09 53 1v 1 [ he pth ] 1 7 D ec 2 02 1
On the thermoeconomic approach to the diagnosis of energy system malfunctions – Part 3 Approaches to the diagnosis problem.

This paper introduces a series of papers representing the continuation of a project begun with two papers (parts 1 and 2) presented at ECOS 2002 [1,2], aiming at show ing the capabilities of the thermoeconomic approach to solve the diagnosis problem. In the previous papers, the diagnosis problem was defined and the general thermoeconomic approach discussed using a combined cycle power plant as a test case. The aim of these five papers is to present the state of the art of thermoeconomic diagnosis. Both theoretical aspects and the application to real plants are discussed. The core of a real diagnosis system is the procedure for the analysis of the operating data. Different diagnosis procedure are discussed in detail in the accompanying papers [3-5]. In this first paper, two operating conditions of the test case plant are provided for this goal: an operating condition obtained by simulating the presence of several anomalies and a reference condition without anomalies. In the accompanying papers, the different procedures are applied to the problem introduced here.