Reviewerly/exHarmony huggingface dataset:
Predicting approximate seismic responses in multistory buildings from real-time earthquake source information, for earthquake early warning applications

Abstract Regional earthquake early warning (EEW) alerts and related risk-mitigation actions are often triggered when the expected value of a ground-motion intensity measure (IM), computed from real-time magnitude and source location estimates, exceeds a predefined critical IM threshold. However, the shaking experienced in mid- to high-rise buildings may be significantly different from that on the ground, which could lead to sub-optimal decision-making (i.e., increased occurrences of false and missed EEW alarms) with the aforementioned strategy. This study facilitates an important advancement in EEW decision-support, by developing empirical models that directly relate earthquake source parameters to resulting approximate responses in multistory buildings. The proposed models can leverage real-time earthquake information provided by a regional EEW system, to provide rapid predictions of structure-specific engineering demand parameters that can be used to more accurately determine whether or not an alert is triggered. We use a simplified continuum building model consisting of a flexural/shear beam combination and vary its parameters to capture a wide range of deformation modes in different building types. We analyse the approximate responses for the building model variations, using Italian accelerometric data and corresponding source parameter information from 54 earthquakes. The resulting empirical prediction equations are incorporated in a real-time Bayesian framework that can be used for building-specific EEW applications, such as (1) early warning of floor-shaking sensed by occupants; and (2) elevator control. Finally, we demonstrate the improvement in EEW alert accuracy that can be achieved using the proposed models.
Deep Learning meets Knowledge Graphs for Scholarly Data Classification

The amount of scientific literature continuously grows, which poses an increasing challenge for researchers to manage, find and explore research results. Therefore, the classification of scientific work is widely applied to enable the retrieval, support the search of suitable reviewers during the reviewing process, and in general to organize the existing literature according to a given schema. The automation of this classification process not only simplifies the submission process for authors, but also ensures the coherent assignment of classes. However, especially fine-grained classes and new research fields do not provide sufficient training data to automatize the process. Additionally, given the large number of not mutual exclusive classes, it is often difficult and computationally expensive to train models able to deal with multi-class multi-label settings. To overcome these issues, this work presents a preliminary Deep Learning framework as a solution for multi-label text classification for scholarly papers about Computer Science. The proposed model addresses the issue of insufficient data by utilizing the semantics of classes, which is explicitly provided by latent representations of class labels. This study uses Knowledge Graphs as a source of these required external class definitions by identifying corresponding entities in DBpedia to improve the overall classification.
Japanese–English Conversation Parallel Corpus for Promoting Context-aware Machine Translation Research

Most machine translation (MT) research has focused on sentences as translation units (sentence-level MT), and has achieved acceptable translation quality for sentences where cross-sentential context is not required in mainly high-resourced languages. Recently, many researchers have worked on MT models that can consider a cross-sentential context. These models are often called context-aware MT or document-level MT models. Document-level MT is difficult to 1) train with a small amount of document-level data; and 2) evaluate, as the main methods and datasets focus on sentence-level evaluation. To address the first issue, we present a Japanese–English conversation corpus in which the cross-sentential context is available. As for the second issue, we manually identify the main areas where sentence-level MT fails to produce adequate translations in the lack of context. We then create an evaluation set in which these phenomena are annotated to alleviate the automatic evaluation of document-level systems. We train MT models using our corpus to demonstrate how the use of context leads to improvements.
Maximizing Extractable Value from Automated Market Makers

Automated Market Makers (AMMs) are decentralized applications that allow users to exchange crypto-tokens without the need for a matching exchange order. AMMs are one of the most successful DeFi use cases: indeed, major AMM platforms process a daily volume of transactions worth USD billions. Despite their popularity, AMMs are well-known to suffer from transaction-ordering issues: adversaries can influence the ordering of user transactions, and possibly front-run them with their own, to extract value from AMMs, to the detriment of users. We devise an effective procedure to construct a strategy through which an adversary can maximize the value extracted from user transactions.
Deep neural networks-based classification optimization by reducing the feature dimensionality with the variants of gravitational search algorithm

The optimization of the problems significantly improves the solution of the complex problems. The reduction in the feature dimensionality is enormously salient to reduce the redundant features and improve the system accuracy. In this paper, an amalgamation of different concepts is proposed to optimize the features and improve the system classification. The experiment is performed on the facial expression detection application by proposing the amalgamation of deep neural network models with the variants of the gravitational search algorithm. Facial expressions are the movement of the facial components such as lips, nose, eyes that are considered as the features to classify human emotions into different classes. The initial feature extraction is performed with the local binary pattern. The extracted feature set is optimized with the variants of gravitational search algorithm (GSA) as standard gravitational search algorithm (SGSA), binary gravitational search algorithm (BGSA) and fast discrete gravitational search algorithm (FDGSA). The deep neural network models of deep convolutional neural network (DCNN) and extended deep convolutional neural network (EDCNN) are employed for the classification of emotions from imagery datasets of JAFFE and KDEF. The fixed pose images of both the datasets are acquired and comparison based on average recognition accuracy is performed. The comparative analysis of the mentioned techniques and state-of-the-art techniques illustrates the superior recognition accuracy of the FDGSA with the EDCNN technique.
Sliding Selector Network with Dynamic Memory for Extractive Summarization of Long Documents

Neural-based summarization models suffer from the length limitation of text encoder.Long documents have to been truncated before they are sent to the model, which results in huge loss of summary-relevant contents.To address this issue, we propose the sliding selector network with dynamic memory for extractive summarization of long-form documents, which employs a sliding window to extract summary sentences segment by segment.Moreover, we adopt memory mechanism to preserve and update the history information dynamically, allowing the semantic flow across different windows.Experimental results on two large-scale datasets that consist of scientific papers demonstrate that our model substantially outperforms previous state-of-the-art models.Besides, we perform qualitative and quantitative investigations on how our model works and where the performance gain comes from. 1
Challenges and solutions for big data in personalized healthcare

“Big data” is a term that has been used often in the past decade to describe datasets that are extremely large and complex so that traditional software is unable to store and analyze them in an accurate way. It can refer to “long data,” “wide data,” and both. Big data is of increasing importance in healthcare as well: new methods dedicated to improving data collection, storage, cleaning, processing, and interpretation for medical research continue to be developed. Exploiting new tools and methods to extract meaning from large volume information has the potential to drive real change in clinical practice, and combining this novel data-driven research with the classical hypothesis-driven research will have a large impact on personalized healthcare. However, significant challenges remain. Here we discuss the challenges (and possible solutions) posed to biomedical research by our increasing ability to collect, store, and analyze large datasets. Important challenges include: (1) the need for standardization of data content, format, and clinical definitions, adhering to the FAIR guiding principles; (2) the need for collaborative networks with sharing of both data and expertise, for example through a federated approach; (3) stricter privacy and ethics regulations, in particular the GDPR in the European Union; and (4) a need to reconsider how and when analytic methodology (data science) is taught to medical researchers. Overcoming these challenges will help to make a success of the use of big data in medical and translational research.
Multi-person Implicit Reconstruction from a Single Image

We present a new end-to-end learning framework to obtain detailed and spatially coherent reconstructions of multiple people from a single image. Existing multi-person methods suffer from two main drawbacks: they are often model-based and therefore cannot capture accurate 3D models of people with loose clothing and hair; or they require manual intervention to resolve occlusions or interactions. Our method addresses both limitations by introducing the first end-to-end learning approach to perform model-free implicit reconstruction for realistic 3D capture of multiple clothed people in arbitrary poses (with occlusions) from a single image. Our network simultaneously estimates the 3D geometry of each person and their 6DOF spatial locations, to obtain a coherent multi-human reconstruction. In addition, we introduce a new synthetic dataset that depicts images with a varying number of inter-occluded humans and a variety of clothing and hair styles. We demonstrate robust, high-resolution reconstructions on images of multiple humans with complex occlusions, loose clothing and a large variety of poses and scenes. Our quantitative evaluation on both synthetic and real world datasets demonstrates state-of-the-art performance with significant improvements in the accuracy and completeness of the reconstructions over competing approaches.
Research on the impact of 5G technology on teaching behavior

Abstract Affected by the COVID-19, the opening of higher education institutions in China has been postponed, and the policy of “suspending classes without stopping” has become a litmus test for emergency response to the epidemic and for education informationization. Under this opportunity, the online teaching model has risen rapidly and has become a hot spot for teaching reform. This article explains the development process from traditional teaching to smart teaching, and the shortcomings of online teaching at this stage. On this basis, with the new generation of communication technology 5G as the background of the times, the future teaching is prospected from the aspects of AI teaching, holographic interactive classroom, and virtual technology teaching.
An Intelligent Baby Monitor with Automatic Sleeping Posture Detection and Notification

Artificial intelligence (AI) has brought lots of excitement to our day-to-day lives. Some examples are spam email detection, language translation, etc. Baby monitoring devices are being used to send video data of the baby to the caregiver’s smartphone. However, the automatic understanding of the data was not implemented in most of these devices. In this research, AI and image processing techniques were developed to automatically recognize unwanted situations that the baby was in. The monitoring device automatically detected: (a) whether the baby’s face was covered due to sleeping on the stomach; (b) whether the baby threw off the blanket from the body; (c) whether the baby was moving frequently; (d) whether the baby’s eyes were opened due to awakening. The device sent notifications and generated alerts to the caregiver’s smartphone whenever one or more of these situations occurred. Thus, the caregivers were not required to monitor the baby at regular intervals. They were notified when their attention was required. The device was developed using NVIDIA’s Jetson Nano microcontroller. A night vision camera and Wi-Fi connectivity were interfaced. Deep learning models for pose detection, face and landmark detection were implemented in the microcontroller. A prototype of the monitoring device and the smartphone app were developed and tested successfully for different scenarios. Compared with general baby monitors, the proposed device gives more peace of mind to the caregivers by automatically detecting un-wanted situations.
A stochastic quantum program synthesis framework based on Bayesian optimization

Abstract Quantum computers and algorithms can offer exponential performance improvement over some NP-complete programs which cannot be run efficiently through a Von Neumann computing approach. In this paper, we present BayeSyn, which utilizes an enhanced stochastic program synthesis and Bayesian optimization to automatically generate quantum programs from high-level languages subject to certain constraints. We find that stochastic synthesis can comparatively and efficiently generate a program with a lower cost from the high dimensional program space. We also realize that hyperparameters used in stochastic synthesis play a significant role in determining the optimal program. Therefore, BayeSyn utilizes Bayesian optimization to fine-tune such parameters to generate a suitable quantum program.
Forensic Analysis of Video Files Using Metadata

The unprecedented ease and ability to manipulate video content has led to a rapid spread of manipulated media. The availability of video editing tools greatly increased in recent years, allowing one to easily generate photo-realistic alterations. Such manipulations can leave traces in the metadata embedded in video files. This metadata information can be used to determine video manipulations, brand of video recording device, the type of video editing tool, and other important evidence. In this paper, we focus on the metadata contained in the popular MP4 video wrapper/container. We describe our method for metadata extractor that uses the MP4’s tree structure. Our approach for analyzing the video metadata produces a more compact representation. We will describe how we construct features from the metadata and then use dimensionality reduction and nearest neighbor classification for forensic analysis of a video file. Our approach allows one to visually inspect the distribution of metadata features and make decisions. The experimental results confirm that the performance of our approach surpasses other methods.
Gradient Flow Evolution for 3D Fusion From a Single Depth Sensor

We present a novel real-time framework for non-rigid 3D reconstruction that is robust to noise, camera poses, and large deformation from a single depth camera. KinectFusion has achieved high-quality 3D object reconstructions in real-time by implicitly representing an object’s surface with a signed distance field (SDF) representation from a single depth camera. Many studies for incremental reconstruction have been presented since then, with the surface estimation improving over time. Previous works primarily focused on improving conventional SDF matching and deformation schemes. In contrast to these works, the proposed framework tackles the problem of temporal inconsistency caused by SDF approximation and fusion to manipulate SDFs and reconstruct a target more accurately over time. In our reconstruction pipeline, we introduce a refinement evolution method, where an erroneous SDF from a depth sensor is recovered more accurately in a few iterations by propagating erroneous SDF values from the surface. Reliable gradients of refined SDFs enable more accurate non-rigid tracking of a target object. Furthermore, we propose a level-set evolution for SDF fusion, enabling SDFs to be manipulated stably in the reconstruction pipeline over time. The proposed methods are fully parallelizable and can be executed in real-time. Qualitative and quantitative evaluations show that incorporating the refinement and fusion methods into the reconstruction pipeline improves 3D reconstruction accuracy and temporal reliability by avoiding cumulative errors over time. Evaluation results show that our pipeline results in more accurate reconstruction that is robust to noise and large motions, as well as outperforms previous state-of-the-art reconstruction methods.
Using Social and Linguistic Information to Adapt Pretrained Representations for Political Perspective Identification

Understanding the political perspective shaping the way events are discussed in the media is increasingly important due to the dramatic change in news distribution.With the advance in text classification models, the performance of political perspective detection is also improving rapidly.However, current deep learning based text models often require a large amount of supervised data for training, which can be very expensive to obtain for this task.Meanwhile, models pre-trained on the general source and task (e.g.BERT) lack the ability to focus on bias-related text span.In this paper, we propose a novel framework that pretrains the text model using signals from the rich social and linguistic context that is readily available, including entity mentions, news sharing, and frame indicators.The pre-trained models benefit from tasks related to bias detection and therefore are easier to train with the bias labels.We demonstrate the effectiveness of our proposed framework by experiments on two news bias datasets.The models with pre-training achieve significant improvement in performance and are capable of identifying the text span for bias better.
No-go theorem behind the limit of the heat-bath algorithmic cooling

Algorithmic cooling techniques provide tools to increase the purity of quantum states. It is known that the cooling with these techniques is limited. However, the physical root of the limit is still unclear. Here we show that the unitarity of the compression operation imposes the cooling limit of heat-bath algorithmic cooling. Specifically, we prove that the unitarity of the compression operation does not allow increasing the purity beyond the maximum of the individual purities. We formalize the limitations imposed by the unitarity of the compression operation in two theorems. We then introduce an optimal cooling technique and show that without the limitations of the unitary operations the cooling technique would exceed the limit of heat-bath algorithmic cooling. We also show that the limitations of unitarity lead to the exact cooling limit of algorithmic cooling. This work opens up new avenues to understanding the limits of dynamic cooling.
HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields

Neural Radiance Fields (NeRF) are able to reconstruct scenes with unprecedented fidelity, and various recent works have extended NeRF to handle dynamic scenes. A common approach to reconstruct such non-rigid scenes is through the use of a learned deformation field mapping from coordinates in each input image into a canonical template coordinate space. However, these deformation-based approaches struggle to model changes in topology, as topological changes require a discontinuity in the deformation field, but these deformation fields are necessarily continuous. We address this limitation by lifting NeRFs into a higher dimensional space, and by representing the 5D radiance field corresponding to each individual input image as a slice through this "hyper-space". Our method is inspired by level set methods, which model the evolution of surfaces as slices through a higher dimensional surface. We evaluate our method on two tasks: (i) interpolating smoothly between "moments", i.e., configurations of the scene, seen in the input images while maintaining visual plausibility, and (ii) novel-view synthesis at fixed moments. We show that our method, which we dub HyperNeRF, outperforms existing methods on both tasks. Compared to Nerfies, HyperNeRF reduces average error rates by 4.1% for interpolation and 8.6% for novel-view synthesis, as measured by LPIPS. Additional videos, results, and visualizations are available at https://hypernerf.github.io.
De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation

Logical table-to-text generation aims to automatically generate fluent and logically faithful text from tables.The task remains challenging where deep learning models often generated linguistically fluent but logically inconsistent text.The underlying reason may be that deep learning models often capture surface-level spurious correlations rather than the causal relationships between the table x and the sentence y.Specifically, in the training stage, a model can get a low empirical loss without understanding x and use spurious statistical cues instead.In this paper, we propose a de-confounded variational encoder-decoder (DCVED) based on causal intervention, learning the objective p(y|do(x)).Firstly, we propose to use variational inference to estimate the confounders in the latent space and cooperate with the causal intervention based on Pearl's do-calculus to alleviate the spurious correlations.Secondly, to make the latent confounder meaningful, we propose a backprediction process to predict the not-used entities but linguistically similar to the exactly selected ones.Finally, since our variational model can generate multiple candidates, we train a table-text selector to find out the best candidate sentence for the given table.An extensive set of experiments show that our model outperforms the baselines and achieves new state-of-the-art performance on two logical table-to-text datasets in terms of logical fidelity.
Searching for Machine Learning Pipelines Using a Context-Free Grammar

AutoML automatically selects, composes and parameterizes machine learning algorithms into a workflow or pipeline of operations that aims at maximizing performance on a given dataset. Although current methods for AutoML achieved impressive results they mostly concentrate on optimizing fixed linear workflows. In this paper, we take a different approach and focus on generating and optimizing pipelines of complex directed acyclic graph shapes. These complex pipeline structure may lead to discovering hidden features and thus boost performance considerably. We explore the power of heuristic search and context-free grammars to search and optimize these kinds of pipelines. Experiments on various benchmark datasets show that our approach is highly competitive and often outperforms existing AutoML systems.