Miracle-dz/UniFAR-FTUs huggingface dataset:
Explanatory Learning: Beyond Empiricism in Neural Networks

We introduce Explanatory Learning (EL), a framework to let machines use existing knowledge buried in symbolic sequences -- e.g. explanations written in hieroglyphic -- by autonomously learning to interpret them. In EL, the burden of interpreting symbols is not left to humans or rigid human-coded compilers, as done in Program Synthesis. Rather, EL calls for a learned interpreter, built upon a limited collection of symbolic sequences paired with observations of several phenomena. This interpreter can be used to make predictions on a novel phenomenon given its explanation, and even to find that explanation using only a handful of observations, like human scientists do. We formulate the EL problem as a simple binary classification task, so that common end-to-end approaches aligned with the dominant empiricist view of machine learning could, in principle, solve it. To these models, we oppose Critical Rationalist Networks (CRNs), which instead embrace a rationalist view on the acquisition of knowledge. CRNs express several desired properties by construction, they are truly explainable, can adjust their processing at test-time for harder inferences, and can offer strong confidence guarantees on their predictions. As a final contribution, we introduce Odeen, a basic EL environment that simulates a small flatland-style universe full of phenomena to explain. Using Odeen as a testbed, we show how CRNs outperform empiricist end-to-end approaches of similar size and architecture (Transformers) in discovering explanations for novel phenomena.
Hard-state Optical Wind during the Discovery Outburst of the Black Hole X-Ray Dipper MAXI J1803–298

We present 12 epochs of optical spectroscopy taken across the discovery outburst of the black hole (BH) candidate MAXI J1803−298 with the Gran Telescopio Canarias and Very Large Telescope. The source followed a standard outburst evolution with hard and soft states. The system displays a triangular shape in the hardness intensity diagram, consistent with that seen in high-inclination BH transients and the previously reported detection of X-ray dips. The two epochs observed during the initial hard state exhibited asymmetric emission-line profiles, including a P-Cygni profile simultaneously detected in Hα and He i 6678, which indicates the presence of an optical wind in the system. The remaining spectra, obtained during the transition to the soft state and the subsequent decay, are instead characterized by narrower, double-peaked emission lines embedded into broad absorption components. One epoch (intermediate state) also includes near-infrared (NIR) coverage, revealing complex line profiles in the Paschen and Bracket series, which suggests that the outflow is still present during the outburst decay through the soft state. The growing list of low-mass X-ray binaries with optical and NIR outflow signatures indicates that these are common features. Furthermore, the lowest luminosity spectrum exhibits an Hα FWHM of 1570 ± 100 km s−1. This, together with previous constraints on the binary parameters, allows us to favor a compact object mass of ∼3–10 M ⊙, further supporting its BH nature.
Unsupervised Image Fusion Method based on Feature Mutual Mapping

Deep learning-based image fusion approaches have obtained wide attention in recent years, achieving promising performance in terms of visual perception. However, the fusion module in the current deep learning-based methods suffers from two limitations, \textit{i.e.}, manually designed fusion function, and input-independent network learning. In this paper, we propose an unsupervised adaptive image fusion method to address the above issues. We propose a feature mutual mapping fusion module and dual-branch multi-scale autoencoder. More specifically, we construct a global map to measure the connections of pixels between the input source images. % The found mapping relationship guides the image fusion. Besides, we design a dual-branch multi-scale network through sampling transformation to extract discriminative image features. We further enrich feature representations of different scales through feature aggregation in the decoding process. Finally, we propose a modified loss function to train the network with efficient convergence property. Through sufficient training on infrared and visible image data sets, our method also shows excellent generalized performance in multi-focus and medical image fusion. Our method achieves superior performance in both visual perception and objective evaluation. Experiments prove that the performance of our proposed method on a variety of image fusion tasks surpasses other state-of-the-art methods, proving the effectiveness and versatility of our approach.
A Wearable ECG Monitor for Deep Learning Based Real-Time Cardiovascular Disease Detection

Cardiovascular disease has become one of the most significant threats endangering human life and health. Recently, Electrocardiogram (ECG) monitoring has been transformed into remote cardiac monitoring by Holter surveillance. However, the widely used Holter can bring a great deal of discomfort and inconvenience to the individuals who carry them. We developed a new wireless ECG patch in this work and applied a deep learning framework based on the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) models. However, we find that the models using the existing techniques are not able to differentiate two main heartbeat types (Supraventricular premature beat and Atrial fibrillation) in our newly obtained dataset, resulting in low accuracy of 58.0 %. We proposed a semi-supervised method to process the badly labelled data samples with using the confidence-level-based training. The experiment results conclude that the proposed method can approach an average accuracy of 90.2 %, i.e., 5.4 % higher than the accuracy of conventional ECG classification methods.
AutoMC: Automated Model Compression based on Domain Knowledge and Progressive search strategy

Model compression methods can reduce model complexity on the premise of maintaining acceptable performance, and thus promote the application of deep neural networks under resource constrained environments. Despite their great success, the selection of suitable compression methods and design of details of the compression scheme are difficult, requiring lots of domain knowledge as support, which is not friendly to non-expert users. To make more users easily access to the model compression scheme that best meet their needs, in this paper, we propose AutoMC, an effective automatic tool for model compression. AutoMC builds the domain knowledge on model compression to deeply understand the characteristics and advantages of each compression method under different settings. In addition, it presents a progressive search strategy to efficiently explore pareto optimal compression scheme according to the learned prior knowledge combined with the historical evaluation information. Extensive experimental results show that AutoMC can provide satisfying compression schemes within short time, demonstrating the effectiveness of AutoMC.
Unitary-Precoded Single-Carrier Waveforms for High Mobility: Detection and Channel Estimation

This paper presents unitary-precoded single-carrier (USC) modulation as a family of waveforms based on multiplexing the information symbols on time domain unitary basis functions. The common property of these basis functions is that they span the entire time and frequency plane. The recently proposed orthogonal time frequency space (OTFS) and orthogonal time sequency multiplexing (OTSM) based on discrete Fourier transform (DFT) and Walsh Hadamard transform (WHT), respectively, fall in the general framework of USC waveforms. In this work, we present channel estimation and detection methods that work for any USC waveform and numerically show that any choice of unitary precoding results in the same error performance. Lastly, we implement some USC systems and compare their performance with OFDM in a real-time indoor setting using an SDR platform.
Pre-Trained Language Transformers are Universal Image Classifiers

Facial images disclose many hidden personal traits such as age, gender, race, health, emotion, and psychology. Understanding these traits will help to classify the people in different attributes. In this paper, we have presented a novel method for classifying images using a pretrained transformer model. We apply the pretrained transformer for the binary classification of facial images in criminal and non-criminal classes. The pretrained transformer of GPT-2 is trained to generate text and then fine-tuned to classify facial images. During the finetuning process with images, most of the layers of GT-2 are frozen during backpropagation and the model is frozen pretrained transformer (FPT). The FPT acts as a universal image classifier, and this paper shows the application of FPT on facial images. We also use our FPT on encrypted images for classification. Our FPT shows high accuracy on both raw facial images and encrypted images. We hypothesize the meta-learning capacity FPT gained because of its large size and trained on a large size with theory and experiments. The GPT-2 trained to generate a single word token at a time, through the autoregressive process, forced to heavy-tail distribution. Then the FPT uses the heavy-tail property as its meta-learning capacity for classifying images. Our work shows one way to avoid bias during the machine classification of images.The FPT encodes worldly knowledge because of the pretraining of one text, which it uses during the classification. The statistical error of classification is reduced because of the added context gained from the text.Our paper shows the ethical dimension of using encrypted data for classification.Criminal images are sensitive to share across the boundary but encrypted largely evades ethical concern.FPT showing good classification accuracy on encrypted images shows promise for further research on privacy-preserving machine learning.
Real-Time Deployment of a Large-Scale Multi-Quadcopter System (MQS)

This paper presents a continuum mechanics-based approach for real-time deployment (RTD) of a multi-quadcopter system between moving initial and final configurations arbitrarily distributed in a 3-D motion space. The proposed RTD problem is decomposed into spatial planning, temporal planning and acquisition sub-problems. For the spatial planning, the RTD desired coordination is defined by integrating (i) rigid-body rotation, (ii) one-dimensional homogeneous deformation, and (ii) one-dimensional heterogeneous coordination such that necessary conditions for inter-agent collision avoidance between every two quadcopter UAVs are satisfied. By the RTD temporal planning, this paper suffices the inter-agent collision avoidance between every two individual quadcopters, and assures the boundedness of the rotor angular speeds for every individual quadcopter. For the RTD acquisition, each quadcopter modeled by a nonlinear dynamics applies a nonlinear control to stably and safely track the desired RTD trajectory such that the angular speeds of each quadcopter remain bounded and do not exceed a certain upper limit.
Classification Of Fake News Headline Based On Neural Networks

Over the last few years, Text classification is one of the fundamental tasks in natural language processing (NLP) in which the objective is to categorize text documents into one of the predefined classes. The news is full of our life. Therefore, news headlines classification is a crucial task to connect users with the right news. The news headline classification is a kind of text classification, which can be generally divided into three mainly parts: feature extraction, classifier selection, and evaluations. In this article, we use the dataset, containing news over a period of eighteen years provided by Kaggle platform to classify news headlines. We choose TF-IDF to extract features and neural network as the classifier, while the evaluation metrics is accuracy. From the experiment result, it is obvious that our NN model has the best performance among these models in the metrics of accuracy. The higher the accuracy is, the better performance the model will gain. Our NN model owns the accuracy 0.8622, which is highest accuracy among these four models. And it is 0.0134, 0.033, 0.080 higher than its of other modes.
COVID-19 Detection Using CT Image Based On YOLOv5 Network

The COVID-19 pandemic has broken down the global medical order tremendously, we urgently need an efficient treatment. Computer aided diagnosis (CAD) increases diagnosis efficiency, helping doctors providing a quick and confident diagnosis, it has played an important role in the treatment of COVID-19. In our task, we solve the problem about abnormality detection and classification. The dataset provided by Kaggle platform and we choose YOLOv5 as our model.We introduce some methods on objective detection in the related work section, the objection detection can be divided into two streams: one-stage and two stage. The representational model are Faster RCNN and YOLO series. Then in the section III we describe YOLOv5 model in the detail. Compared Experiment and results are shown in section IV. We choose mean average precision (mAP) as our experiments’ metrics, and the higher (mean )mAP is, the better result the model will gain. mAP@0.5 of our YOLOv5s is 0.623 which is 0.157 and 0.101 higher than Faster RCNN and EfficientDet respectively.
Dynamics-Aware Comparison of Learned Reward Functions

The ability to learn reward functions plays an important role in enabling the deployment of intelligent agents in the real world. However, comparing reward functions, for example as a means of evaluating reward learning methods, presents a challenge. Reward functions are typically compared by considering the behavior of optimized policies, but this approach conflates deficiencies in the reward function with those of the policy search algorithm used to optimize it. To address this challenge, Gleave et al. (2020) propose the Equivalent-Policy Invariant Comparison (EPIC) distance. EPIC avoids policy optimization, but in doing so requires computing reward values at transitions that may be impossible under the system dynamics. This is problematic for learned reward functions because it entails evaluating them outside of their training distribution, resulting in inaccurate reward values that we show can render EPIC ineffective at comparing rewards. To address this problem, we propose the Dynamics-Aware Reward Distance (DARD), a new reward pseudometric. DARD uses an approximate transition model of the environment to transform reward functions into a form that allows for comparisons that are invariant to reward shaping while only evaluating reward functions on transitions close to their training distribution. Experiments in simulated physical domains demonstrate that DARD enables reliable reward comparisons without policy optimization and is significantly more predictive than baseline methods of downstream policy performance when dealing with learned reward functions.
Load-Altering Attacks under COVID-19 Low-Inertia Conditions

The COVID-19 pandemic has impacted our society by forcing shutdowns and shifting the way people interacted worldwide. In relation to the impacts on the electric grid, it created a significant decrease in energy demands across the globe. Recent studies have shown that the low load demand conditions caused by COVID-19 lockdowns combined with large renewable generation have resulted in extremely low-inertia grid conditions. In this work, we examine how an attacker could exploit these conditions to cause unsafe grid operating conditions by executing load-altering attacks (LAAs) targeted at compromising hundreds of thousands of IoT-connected high-wattage loads in low-inertia power systems. Our study focuses on analyzing the impact of the COVID-19 mitigation measures on U.S. regional transmission operators (RTOs), formulating a plausible and realistic least-effort LAA targeted at transmission systems with low-inertia conditions, and evaluating the probability of these large-scale LAAs. Theoretical and simulation results are presented based on the WSCC 9-bus test system. Results demonstrate how adversaries could provoke major frequency disturbances by targeting vulnerable load buses in low-inertia systems.
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes

Less than 35% of recyclable waste is being actually recycled in the US [2], which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem are the inefficiencies of the waste sorting process (separating paper, plastic, metal, glass, etc.) due to the extremely complex and cluttered nature of the waste stream. Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets. This challenging computer vision task currently lacks suitable datasets or methods in the available literature. In this paper, we take a step towards computer-aided waste detection and present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. We believe that ZeroWaste will catalyze research in object detection and semantic segmentation in extreme clutter as well as applications in the recycling domain. Our project page can be found at http://ai.bu. edu/zerowaste/
Temporal Stream Logic modulo Theories (Full Version)

Temporal stream logic (TSL) extends LTL with updates and predicates over arbitrary function terms. This allows for specifying dataintensive systems for which LTL is not expressive enough. In the semantics of TSL, functions and predicates are left uninterpreted. In this paper, we extend TSL with first-order theories, enabling us to specify systems using interpreted functions and predicates such as incrementation or equality. We investigate the satisfiability problem of TSL modulo the standard underlying theory of uninterpreted functions as well as with respect to Presburger arithmetic and the theory of equality: For all three theories, TSL satisfiability is highly undecidable. Nevertheless, we identify three fragments of TSL for which the satisfiability problem is (semi-)decidable in the theory of uninterpreted functions. Despite the high undecidability, we present an algorithm – which is not guaranteed to terminate – for checking the satisfiability of a TSL formula in the theory of uninterpreted functions and evaluate it: It scales well and is able to validate assumptions in a real-world system design.
Global and local thermodynamics of the (
2+1
)-dimensional rotating Gauss-Bonnet black hole

The aim of this paper is to study the local and the global thermodynamic properties of the 3-dimensional rotating Gauss-Bonnet black hole. To this end we consider the conditions for local and global thermodynamic stability of the solution in a given ensemble of state quantities. Concerning the local analysis we found the regions of stability for every physical specific heat together with the existing Davies curves. Another central result is the generalization of the notion of global thermodynamic stability, known from the standard thermodynamics, to describe the global equilibrium of black holes. The new approach consists of applying specific Legendre transformation of the energy or the entropy to find the natural thermodynamic potential for the given ensemble of macro parameters. The global stability analysis, restricted to the week positivity conjecture is based on the properties of the new thermodynamic potential. The advantage of this method is that it allows one to chose different potentials, corresponding to different constraints to which the system may be subjected. Finally, we find it natural to impose global thermodynamic stability only where local one exists for the given black hole solution. ar X iv :2 11 0. 05 16 6v 2 [ gr -q c] 2 4 Ja n 20 22
Deep Learning Using Multiple Degrees of Maximum-Intensity Projection for PET/CT Image Classification in Breast Cancer

Deep learning (DL) has become a remarkably powerful tool for image processing recently. However, the usefulness of DL in positron emission tomography (PET)/computed tomography (CT) for breast cancer (BC) has been insufficiently studied. This study investigated whether a DL model using images with multiple degrees of PET maximum-intensity projection (MIP) images contributes to increase diagnostic accuracy for PET/CT image classification in BC. We retrospectively gathered 400 images of 200 BC and 200 non-BC patients for training data. For each image, we obtained PET MIP images with four different degrees (0°, 30°, 60°, 90°) and made two DL models using Xception. One DL model diagnosed BC with only 0-degree MIP and the other used four different degrees. After training phases, our DL models analyzed test data including 50 BC and 50 non-BC patients. Five radiologists interpreted these test data. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Our 4-degree model, 0-degree model, and radiologists had a sensitivity of 96%, 82%, and 80–98% and a specificity of 80%, 88%, and 76–92%, respectively. Our 4-degree model had equal or better diagnostic performance compared with that of the radiologists (AUC = 0.936 and 0.872–0.967, p = 0.036–0.405). A DL model similar to our 4-degree model may lead to help radiologists in their diagnostic work in the future.
Translating the 10 golden rules of reforestation for coral reef restoration

Efforts are accelerating to protect and restore ecosystems globally. With trillions of dollars in ecosystem services at stake, no clear framework exists for developing or prioritizing approaches to restore coral reefs even as efforts and investment opportunities to do so grow worldwide. Restoration may buy time for climate change mitigation, but it lacks rigorous guidance to meet objectives of scalability and effectiveness. Lessons from restoration of terrestrial ecosystems can and should be rapidly adopted for coral reef restoration. We propose how the 10 golden rules of effective forest restoration can be translated to accelerate efforts to restore coral reefs based on established principles of resilience, management, and local stewardship. We summarize steps to undertake reef restoration as a management strategy in the context of the diverse ecosystem service values that coral reefs provide. Outlining a clear blueprint is timely as more stakeholders seek to undertake restoration as the UN Decade on Ecosystem Restoration begins.