Reviewerly/exHarmony huggingface dataset:
Anomaly Detection for Spacecraft using Hierarchical Agglomerative Clustering based on Maximal Information Coefficient

The spacecraft's telemetry data is the only basis for the ground transportation management system to monitor its on-orbit operating status. Anomaly detection of spacecraft has become an important means to enhance the reliability of spacecraft on-orbit operation. There are many ways to detect anomalies in spacecraft. With the increasing amount of telemetry data and the improvement of modern computing capabilities, anomaly detection methods have gradually transitioned to data-driven methods. Because the data-driven approach does not require a large amount of expert experience, it also tolerates that operators do not have sufficient theoretical knowledge. However, telemetry data has the characteristics of large scale, high dimensions, complex relationships, and strong professionalism. These bring severe challenges to achieve high detection rates, low false detection rates, and strong interpretive goals for anomaly detection methods. Current spacecraft monitoring systems generally only perform anomaly detection for a single parameter, and few studies have provided clear and effective methods for multivariate anomaly detection. This paper proposes an anomaly detection method for multivariate telemetry data. The idea is to propose a hierarchical clustering method based on the maximum information coefficient, mining the correlation between telemetry parameters, grouping the telemetry parameters to form a subspace; using the LSTM method to perform single-parameter anomaly detection on the subspace; using weighting The averaging method integrates the anomaly detection results in the subspace to achieve multivariate anomaly detection. The experiments were performed on a real satellite historical data set of the Beijing Aerospace Flight Control Center. The expert evaluation of the agency proves that the method proposed in this paper is feasible and can preliminary excavate the correlation between telemetry parameters. Although the accuracy needs to be improved, there is still room for optimization.
Wavelet Flow: Fast Training of High Resolution Normalizing Flows

Normalizing flows are a class of probabilistic generative models which allow for both fast density computation and efficient sampling and are effective at modelling complex distributions like images. A drawback among current methods is their significant training cost, sometimes requiring months of GPU training time to achieve state-of-the-art results. This paper introduces Wavelet Flow, a multi-scale, normalizing flow architecture based on wavelets. A Wavelet Flow has an explicit representation of signal scale that inherently includes models of lower resolution signals and conditional generation of higher resolution signals, i.e., super resolution. A major advantage of Wavelet Flow is the ability to construct generative models for high resolution data (e.g., 1024 x 1024 images) that are impractical with previous models. Furthermore, Wavelet Flow is competitive with previous normalizing flows in terms of bits per dimension on standard (low resolution) benchmarks while being up to 15x faster to train.
Apatite (U-Th-Sm)/He date dispersion: First insights from machine learning algorithms

Numerous parameters impact apatite (U-Th-Sm)/He (AHe) thermochronological dates, such as radiation damage, chemical content, crystal size and geometry, and their knowledge is essential for better geological interpretations. The present study investigates a new method based on advanced data mining techniques, to unravel the parameters that could play a role in He retention and thus on AHe date. The purpose is to decipher which factors influence the AHe date dispersion, and to exclude the impact of other parameters on helium retention. As an example, we use a dataset previously collected on apatite from basements rocks, sampled in French Brittany, where all samples underwent the same thermal history, and for which were reported a set of physical and chemical parameters. The dataset includes dimension and geometry, He, U, Th, Sm and major and trace element content for ∼35 crystals. The algorithm ranks the parameters according to their influence on helium retention, using predictive trees, which are commonly used in computing sciences. After looking at 100 simultaneous predictions, we compared the predicted and measured He content for each analyzed apatite crystal. For this particular case, the predictions confirmed the prominent role of the parent nuclides in the He production, as AHe dates can be predicted accurately with these parameters (especially U and Th). Additionally, the predictions without knowledge of the apatite chemical composition and dimension provided better results than using all available parameters (median error of 14% instead of 18%). Therefore, for this specific study, the apatite chemistry and crystal dimensions do not influence significantly He retention nor AHe date dispersion. Nevertheless, detailed inspection of analysis results suggests which parameters have the most discrimination ability, which in this study include crystal length, height, and Mn content. The latter may reveal an eventual influence on alpha damage annealing kinetics. Finally, this approach shows that some grains could never achieve good predictions, indicating that for these crystals the input parameters are not enough to predict the He content. We propose that such crystals are statistically different from the remaining dataset, and this suggests that machine learning has a strong potential to correct errors, or to detect anomalies.
Bound states of two-photon Rabi model at the collapse point

This paper presents a proof of the existence of novel bound states of the two-photon quantum Rabi model at the collapse point. The two-photon Rabi model is interesting not only for its important role on non-linear light–matter interaction, but also for the exhibition of the many-energy-levels degenerating process called the 'spectral collapse'. The squeezing property of the two-photon annihilation and creation operators is the origin for this phenomenon which is well studied without the energy-slitting term ω0. However, many numerical studies have pointed out that with the presence of ω0, some low-level isolated states exist while other high energy states collapse to , which is known as incomplete spectral collapse. From the eigenvalue equation in real space, a pair of second order differential equations, which are similarly to the Schrödinger equation, are derived at the collapse point. These differential equations provide an explanation to the existence of isolated bound states below with the presence of the spin slitting ω0 and a better numerical method to generate those bound states.
On the naturalness of hardware descriptions

Mining software repositories (MSR) has been shown effective for extracting data used to improve various software engineering tasks, including code completion, code repair, code search, and code summarization. Despite a large body of work on MSR, researchers have focused almost exclusively on repositories that contain code written in imperative programming languages, such as Java and C/C++. Unlike prior work, in this paper, we focus on mining publicly available hardware descriptions (HDs) written in hardware description languages (HDLs), such as VHDL. HDLs have unique syntax and semantics compared to popular imperative languages, and learning-based tools available to hardware designers are well behind those used in other application domains. We assembled large HD corpora consisting of source code written in several HDLs and report on their characteristics. Our language model evaluation reveals that HDs possess a high level of naturalness similar to software written in imperative languages. Further, by utilizing our corpora, we built several deep learning models for automated code completion in VHDL; our models take into account unique characteristics of HDLs, including similarities of nearby concurrent signal assignment statements, in-built concurrency, and the frequently used signal types. These characteristics led to more effective neural models, achieving a BLEU score of 37.3, an 8-14-point improvement over rule-based and neural baselines.
Robust Quantization: One Model to Rule Them All

Neural network quantization methods often involve simulating the quantization process during training, making the trained model highly dependent on the target bit-width and precise way quantization is performed. Robust quantization offers an alternative approach with improved tolerance to different classes of data-types and quantization policies. It opens up new exciting applications where the quantization process is not static and can vary to meet different circumstances and implementations. To address this issue, we propose a method that provides intrinsic robustness to the model against a broad range of quantization processes. Our method is motivated by theoretical arguments and enables us to store a single generic model capable of operating at various bit-widths and quantization policies. We validate our method's effectiveness on different ImageNet models.
Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting

We study reinforcement learning in non-episodic factored Markov decision processes (FMDPs). We propose two near-optimal and oracle-efficient algorithms for FMDPs. Assuming oracle access to an FMDP planner, they enjoy a Bayesian and a frequentist regret bound respectively, both of which reduce to the near-optimal bound $\widetilde{O}(DS\sqrt{AT})$ for standard non-factored MDPs. We propose a tighter connectivity measure, factored span, for FMDPs and prove a lower bound that depends on the factored span rather than the diameter $D$. In order to decrease the gap between lower and upper bounds, we propose an adaptation of the REGAL.C algorithm whose regret bound depends on the factored span. Our oracle-efficient algorithms outperform previously proposed near-optimal algorithms on computer network administration simulations.
Forecast Analysis of Research Chance on AES Algorithm to Encrypt during Data Transmission on Cloud Computing

Cloud computing can be described as a network technology among users to share data, resources and even services. Because million users have the same rights to use network for data transmission, data is vulnerable to be hacked by irresponsible person. The concentration of security on current system can only be found on data storage on cloud, meanwhile less concentration is found on data transmission. As we determine security can be potential issue, the proposed system (encryption) will secure data transfer. The proposed system, Advanced Encryption Standard (AES) algorithm, will anticipate every irresponsible person to hack cloud system. The proposed algorithm will encrypt and decrypt information on data transmission and storage. To improve published research on security of data transmission by using AES algorithm is the paper purpose. In order to do that, the research will use several mathematical methods. There are Markov Chain and Forecasting methods. Research results show that there are possibilities to research on AES algorithm for security of data transmission in the near future by 29% in 2023.
HOI Analysis: Integrating and Decomposing Human-Object Interaction

Human-Object Interaction (HOI) consists of human, object and implicit interaction/verb. Different from previous methods that directly map pixels to HOI semantics, we propose a novel perspective for HOI learning in an analytical manner. In analogy to Harmonic Analysis, whose goal is to study how to represent the signals with the superposition of basic waves, we propose the HOI Analysis. We argue that coherent HOI can be decomposed into isolated human and object. Meanwhile, isolated human and object can also be integrated into coherent HOI again. Moreover, transformations between human-object pairs with the same HOI can also be easier approached with integration and decomposition. As a result, the implicit verb will be represented in the transformation function space. In light of this, we propose an Integration-Decomposition Network (IDN) to implement the above transformations and achieve state-of-the-art performance on widely-used HOI detection benchmarks. Code is available at this https URL.
Jaint: A Framework for User-Defined Dynamic Taint-Analyses Based on Dynamic Symbolic Execution of Java Programs

We present Jaint, a generic security analysis for Java Web-applications that combines concolic execution and dynamic taint analysis in a modular way. Jaint executes user-defined taint analyses that are formally specified in a domain-specific language for expressing taint-flow analyses. We demonstrate how dynamic taint analysis can be integrated into JDart, a dynamic symbolic execution engine for the Java virtual machine in Java PathFinder. The integration of the two methods is modular in the sense that it traces taint independently of symbolic annotations. Therefore, Jaint is capable of sanitizing taint information (if specified by a taint analysis) and using multi-colored taint for running multiple taint analyses in parallel. We design a domain-specific language that enables users to define specific taint-based security analyses for Java Web-applications. Specifications in this domain-specific language serve as a basis for the automated generation of corresponding taint injectors, sanitization points and taint-flow monitors that implement taint analyses in Jaint. We demonstrate the generality and effectiveness of the approach by analyzing the OWASP benchmark set, using generated taint analyses for all 11 classes of CVEs in the benchmark set.
Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow

Flow models have recently made great progress at modeling ordinal discrete data such as images and audio. Due to the continuous nature of flow models, dequantization is typically applied when using them for such discrete data, resulting in lower bound estimates of the likelihood. In this paper, we introduce subset flows, a class of flows that can tractably transform finite volumes and thus allow exact computation of likelihoods for discrete data. Based on subset flows, we identify ordinal discrete autoregressive models, including WaveNets, PixelCNNs and Transformers, as single-layer flows. We use the flow formulation to compare models trained and evaluated with either the exact likelihood or its dequantization lower bound. Finally, we study multilayer flows composed of PixelCNNs and non-autoregressive coupling layers and demonstrate state-of-the-art results on CIFAR-10 for flow models trained with dequantization.
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples

We present a transductive learning algorithm that takes as input training examples from a distribution P and arbitrary (unlabeled) test examples, possibly chosen by an adversary. This is unlike prior work that assumes that test examples are small perturbations of P. Our algorithm outputs a selective classifier, which abstains from predicting on some examples. By considering selective transductive learning, we give the first nontrivial guarantees for learning classes of bounded VC dimension with arbitrary train and test distributions—no prior guarantees were known even for simple classes of functions such as intervals on the line. In particular, for any function in a class C of bounded VC dimension, we guarantee a low test error rate and a low rejection rate with respect to P. Our algorithm is efficient given an Empirical Risk Minimizer (ERM) for C. Our guarantees hold even for test examples chosen by an unbounded white-box adversary. We also give guarantees for generalization, agnostic, and unsupervised settings.
Strongly Incremental Constituency Parsing with Graph Neural Networks

Parsing sentences into syntax trees can benefit downstream applications in NLP. Transition-based parsers build trees by executing actions in a state transition system. They are computationally efficient, and can leverage machine learning to predict actions based on partial trees. However, existing transition-based parsers are predominantly based on the shift-reduce transition system, which does not align with how humans are known to parse sentences. Psycholinguistic research suggests that human parsing is strongly incremental: humans grow a single parse tree by adding exactly one token at each step. In this paper, we propose a novel transition system called attach-juxtapose. It is strongly incremental; it represents a partial sentence using a single tree; each action adds exactly one token into the partial tree. Based on our transition system, we develop a strongly incremental parser. At each step, it encodes the partial tree using a graph neural network and predicts an action. We evaluate our parser on Penn Treebank (PTB) and Chinese Treebank (CTB). On PTB, it outperforms existing parsers trained with only constituency trees; and it performs on par with state-of-the-art parsers that use dependency trees as additional training data. On CTB, our parser establishes a new state of the art. Code is available at this https URL.
Neuron Shapley: Discovering the Responsible Neurons

We develop Neuron Shapley as a new framework to quantify the contribution of individual neurons to the prediction and performance of a deep network. By accounting for interactions across neurons, Neuron Shapley is more effective in identifying important filters compared to common approaches based on activation patterns. Interestingly, removing just 30 filters with the highest Shapley scores effectively destroys the prediction accuracy of Inception-v3 on ImageNet. Visualization of these few critical filters provides insights into how the network functions. Neuron Shapley is a flexible framework and can be applied to identify responsible neurons in many tasks. We illustrate additional applications of identifying filters that are responsible for biased prediction in facial recognition and filters that are vulnerable to adversarial attacks. Removing these filters is a quick way to repair models. Enabling all these applications is a new multi-arm bandit algorithm that we developed to efficiently estimate Neuron Shapley values.
Additive and Subtractive Manufacturing Augmented Reality Interface (ASMARI)

Industry 4.0 represents the next evolution of industrial technology. Some of the key characteristics of Industry 4.0 include augmented reality and additive manufacturing. In support of this evolution, researchers at (blinded) created a prototype augmented reality (AR) interface, ASMARI. This prototype acts as a common interface for both industrial additive and subtractive machines. Additionally, in the changing industrial workspace of Industry 4.0, the role of humans will also change. This interface focuses on increasing productivity and situational awareness, while providing new interaction modalities for interacting with hardware. The prototype was also evaluated by multiple users who provided positive feedback on the system. Ultimately, the prototype represents a first step towards significant integration between augmented reality and additive manufacturing.
Learning Dynamic Belief Graphs to Generalize on Text-Based Games

Playing text-based games requires skill in processing natural language and in planning. Although a key goal for agents solving this task is to generalize across multiple games, most previous work has either focused on solving a single game or has tackled generalization with rule-based heuristics. In this work, we investigate how structured information in the form of a knowledge graph (KG) can facilitate effective planning and generalization. We introduce a novel transformer-based sequence-to-sequence model that constructs a “belief” KG from raw text observations of the environment, dynamically updating this belief graph at every game step as it receives new observations. To train this model to build useful graph representations, we introduce and analyze a set of graph-related pre-training tasks. We demonstrate empirically that KG-based representations from our model help agents to converge faster to better policies for multiple text-based games, and further, enable stronger zero-shot performance on unseen games. Experiments on unseen games show that our best agent outperforms text-based baselines by 21.6%.
A Limitation of the PAC-Bayes Framework

PAC-Bayes is a useful framework for deriving generalization bounds which was introduced by McAllester ('98). This framework has the flexibility of deriving distribution- and algorithm-dependent bounds, which are often tighter than VC-related uniform convergence bounds. In this manuscript we present a limitation for the PAC-Bayes framework. We demonstrate an easy learning task that is not amenable to a PAC-Bayes analysis. Specifically, we consider the task of linear classification in 1D; it is well-known that this task is learnable using just $O(\log(1/\delta)/\epsilon)$ examples. On the other hand, we show that this fact can not be proved using a PAC-Bayes analysis: for any algorithm that learns 1-dimensional linear classifiers there exists a (realizable) distribution for which the PAC-Bayes bound is arbitrarily large.
Can Q−Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?

We present Graph-$Q$-SAT, a branching heuristic for a Boolean SAT solver trained with value-based reinforcement learning (RL) using Graph Neural Networks for function approximation. Solvers using Graph-$Q$-SAT are complete SAT solvers that either provide a satisfying assignment or proof of unsatisfiability, which is required for many SAT applications. The branching heuristics commonly used in SAT solvers make poor decisions during their warm-up period, whereas Graph-$Q$-SAT is trained to examine the structure of the particular problem instance to make better decisions early in the search. Training Graph-$Q$-SAT is data efficient and does not require elaborate dataset preparation or feature engineering. We train Graph-$Q$-SAT using RL interfacing with MiniSat solver and show that Graph-$Q$-SAT can reduce the number of iterations required to solve SAT problems by 2-3X. Furthermore, it generalizes to unsatisfiable SAT instances, as well as to problems with 5X more variables than it was trained on. We show that for larger problems, reductions in the number of iterations lead to wall clock time reductions, the ultimate goal when designing heuristics. We also show positive zero-shot transfer behavior when testing Graph-$Q$-SAT on a task family different from that used for training. While more work is needed to apply Graph-$Q$-SAT to reduce wall clock time in modern SAT solving settings, it is a compelling proof-of-concept showing that RL equipped with Graph Neural Networks can learn a generalizable branching heuristic for SAT search.
Traffic Sign Recognition Based on Improved SSD Model

In view of the problems of missed detection and low detection accuracy of the SSD model in the detection of small targets, an improved SSD model for traffic road sign recognition was proposed. The model uses DenseNet to replace SSD's basic network VGG16 to reduce the amount of network parameters; learn from the feature fusion method of FCN to improve the detection ability of small targets; increase the convolution of holes to expand the perception domain and reduce the loss of small target information; use depth Separable convolution replaces the Maxpooling layer in DenseNet to avoid information loss during feature extraction. Experiments were carried out on the CTSD data set. The experimental results show that the improved SSD model improves the recognition accuracy and recall rate.