Miracle-dz/UniFAR-FTUs huggingface dataset:
Pedestrian-Aware Panoramic Video Stitching Based on a Structured Camera Array

The panorama stitching system is an indispensable module in surveillance or space exploration. Such a system enables the viewer to understand the surroundings instantly by aligning the surrounding images on a plane and fusing them naturally. The bottleneck of existing systems mainly lies in alignment and naturalness of the transition of adjacent images. When facing dynamic foregrounds, they may produce outputs with misaligned semantic objects, which is evident and sensitive to human perception. We solve three key issues in the existing workflow that can affect its efficiency and the quality of the obtained panoramic video and present Pedestrian360, a panoramic video system based on a structured camera array (a spatial surround-view camera system). First, to get a geometrically aligned 360○ view in the horizontal direction, we build a unified multi-camera coordinate system via a novel refinement approach that jointly optimizes camera poses. Second, to eliminate the brightness and color difference of images taken by different cameras, we design a photometric alignment approach by introducing a bias to the baseline linear adjustment model and solving it with two-step least-squares. Third, considering that the human visual system is more sensitive to high-level semantic objects, such as pedestrians and vehicles, we integrate the results of instance segmentation into the framework of dynamic programming in the seam-cutting step. To our knowledge, we are the first to introduce instance segmentation to the seam-cutting problem, which can ensure the integrity of the salient objects in a panorama. Specifically, in our surveillance oriented system, we choose the most significant target, pedestrians, as the seam avoidance target, and this accounts for the name Pedestrian360. To validate the effectiveness and efficiency of Pedestrian360, a large-scale dataset composed of videos with pedestrians in five scenes is established. The test results on this dataset demonstrate the superiority of Pedestrian360 compared to its competitors. Experimental results show that Pedestrian360 can stitch videos at a speed of 12 to 26 fps, which depends on the number of objects in the shooting scene and their frequencies of movements. To make our reported results reproducible, the relevant code and collected data are publicly available at https://cslinzhang.github. io/Pedestrian360-Homepage/.
Advances in Deep Learning-Based Medical Image Analysis

Importance. With the booming growth of artificial intelligence (AI), especially the recent advancements of deep learning, utilizing advanced deep learning-based methods for medical image analysis has become an active research area both in medical industry and academia. This paper reviewed the recent progress of deep learning research in medical image analysis and clinical applications. It also discussed the existing problems in the field and provided possible solutions and future directions.Highlights. This paper reviewed the advancement of convolutional neural network-based techniques in clinical applications. More specifically, state-of-the-art clinical applications include four major human body systems: the nervous system, the cardiovascular system, the digestive system, and the skeletal system. Overall, according to the best available evidence, deep learning models performed well in medical image analysis, but what cannot be ignored are the algorithms derived from small-scale medical datasets impeding the clinical applicability. Future direction could include federated learning, benchmark dataset collection, and utilizing domain subject knowledge as priors.Conclusion. Recent advanced deep learning technologies have achieved great success in medical image analysis with high accuracy, efficiency, stability, and scalability. Technological advancements that can alleviate the high demands on high-quality large-scale datasets could be one of the future developments in this area.
Bailouts for Sale

This paper estimates the impact that campaign contributions from the financial sector had in influencing U.S. legislators to support the financial sector bailout bill (TARP) passed by the United States Congress in October 2008. After expanding on a classic theory of moral hazard and electoral accountability, I use a probit analysis to estimate the probability that a legislator supported the bailout bill. The primary explanatory variables of interest, which are motivated by the theoretical section, are campaign contributions to legislators from special interest groups and a measure of constituency characteristics. Controlling for heterogeneity of districts follows from the paper’s theoretical advancement, which is to allow for heterogeneous electoral constraints on the legislators’ ability to collect rents from and vote with the financial special interest. The heterogeneity is based on the importance of the financial sector for employment in districts. The probit estimation results are nothing new to Public Choice adherents. Influence over Senators can be bought and this was true of the financial bailout of 2008: all else equal, an additional $100,000 in campaign contributions from the commercial banking interest is estimated to increase the probability that a Senator supported the bailout by 15.4 percentage points. ∗Department of Economics, The American University of Paris, mdorsch@aup.fr. I am grateful to Marcus Casey for his comments, to Jason Moyer-Lee for his excellent research assistance, and to Stephen Dorsch for his hospitality in the North Cascade National Park, where the bulk of this work was completed.
Simplified Natural Language Processing and Discrimination in Antonyms-Synonyms Word Structure in a Neural Network Approach to Exploit Lexico-Semantic Pattern

— To get high efficiency or high yielding in NLP system the vital challenge is to distinguish between antonym and synonym. By using lexico-syntactic patterns in pattern-based models, which are string-matching patterns based on lexical and syntactic structure, we exploiting to represent the distinguish between antonyms and synonyms word pairs as vector representation in Arabic word structure. It is very difficult to make automatic distinguish between antonymy-synonymy in NLP system because of they have a tendency to occur in similar contexts. I intend a 2-step novel process that exploit lexico-semantic pattern to distinguish antonymy-synonymy from syntactic parse tress. The experiment result shows the improvement of the performance over prior pattern-based method.
BLOWN: A Blockchain Protocol for Single-Hop Wireless Networks Under Adversarial SINR

Known as a distributed ledger technology (DLT), blockchain has attracted much attention due to its properties such as decentralization, security, immutability and transparency, and its potential of servicing as an infrastructure for various applications. Blockchain can empower wireless networks with identity management, data integrity, access control, and high-level security. However, previous studies on blockchain-enabled wireless networks mostly focus on proposing architectures or building systems with popular blockchain protocols. Nevertheless, such existing protocols have obvious shortcomings when adopted in wireless networks where nodes may have limited physical resources, may fall short of well-established reliable channels, or may suffer from variable bandwidths impacted by environments or jamming attacks. In this paper, we propose a novel consensus protocol named Proof-of-Channel (PoC) leveraging the natural properties of wireless communications, and develop a permissioned BLOWN protocol (BLOckchain protocol for Wireless Networks) for single-hop wireless networks under an adversarial SINR model. We formalize BLOWN with the universal composition framework and prove its security properties, namely persistence and liveness, as well as its strengths in countering against adversarial jamming, double-spending, and Sybil attacks, which are also demonstrated by extensive simulation studies.
Polymorphism of PIT-1 Genes and its Relationship with Traits in the Limousine Cattle Breed

Jarosław Pytlewski1, Ireneusz R. Antkowiak1 and Ewa Czerniawska-Piątkowska2* 1Department of Animal Breeding and Product Quality Assessment, Faculty of Veterinary Medicine and Animal Science, Poznań University of Life Sciences, Poznań, Poland. Złotniki, ul. Słoneczna 1, 62-002 Suchy Las. 2Department of Ruminant Science, West Pomeranian University of Technology, ul. Klemensa Janickiego 29, 71-270 Szczecin, Poland Article Information Received 16 November 2019 Revised 02 January 2020 Accepted 09 January 2020 Available online 25 Februay 2021
Quasi-ordering of composition fluctuations and their interaction with lattice imperfections in an optical spectra of dilute nitride alloys

Using optical spectroscopy experiments, including conventional low-temperature photo-reflectance and photoluminescence (PL) together with time-resolved PL and near-field magneto PL, we distinguish between statistical composition fluctuations (CFs) and nitrogen clusters (Ncl) in i- and n-type GaAs1−xNx alloys (x = 0.01–0.03). We measured the size (l = 3–24 nm), activation energy (Ea = 15–30 meV) and density (nl ∼ 1011 cm−2) of CFs in i-type material. In n-type material we resolved the emission of nitrogen clusters (Ncl) having activation energy ∼70 meV and density ncl ∼ 1012 cm−2. Analysis of l and Ea values in i-GaAs1−xNx revealed a strong interaction between substitutional (Ns) and interstitial (Ni) nitrogen, which results in band gap splitting accompanied by activation of the heavy-hole exciton and Ncl emission. In the optical spectra of i-GaAs1−xNx, we suggest the identification of different Ni species and Ni-Ns arrangements, and in n-GaAs1−xNx we observed activation/suppression of Ncl/CF emission and interference between CFs and ionized donors. Using numerical analysis of amplitude, size and density of CFs we revealed unexpected quasi-ordering which results in modulation of CFs having period 2l. This is consistent with the high nl values observed. We show that this quasi-ordering, arising from statistical non-uniformity of the CFs, creates a set of latent states, which allows us to probe exciton properties, defect density and the structural homogeneity in GaAs1−xNx.
Intelligent Shape-Morphing Micromachines

Intelligent machines are capable of switching shape configurations to adapt to changes in dynamic environments and thus have offered the potentials in many applications such as precision medicine, lab on a chip, and bioengineering. Even though the developments of smart materials and advanced micro/nanomanufacturing are flouring, how to achieve intelligent shape-morphing machines at micro/nanoscales is still significantly challenging due to the lack of design methods and strategies especially for small-scale shape transformations. This review is aimed at summarizing the principles and methods for the construction of intelligent shape-morphing micromachines by introducing the dimensions, modes, realization methods, and applications of shape-morphing micromachines. Meanwhile, this review highlights the advantages and challenges in shape transformations by comparing micromachines with the macroscale counterparts and presents the future outlines for the next generation of intelligent shape-morphing micromachines.
End to End Speech Recognition System

Speech Recognition is the task of transcribing the speech signal into equivalent text. While automatic speech recognition has greatly beneﬁted from the introduction of neural networks, the networks are at present only a single component in a complex pipeline in state of the art systems. In existing systems, the ﬁrst stage of the pipeline is input feature extraction: standard techniques include melscale ﬁlterbanks. Neural networks are then trained to classify individual frames of acoustic data, and their output distributions are reformulated as emission probabilities for a hidden Markov model. The objective function used to train the networks is therefore substantially different from the true performance measure which is sequence-level transcription accuracy. This is precisely the sort of inconsistency that end-to-end learning seeks to avoid. In practice thus even is there is large gain in frame accuracy it translates to a negligible improvement, or even deterioration in transcription accuracy. An additional problem is that the frame-level training targets must be inferred from the alignments determined by the HMM. This leads to an awkward iterative procedure, where network retraining is alternated with HMM realignments to generate more accurate targets. In this report, we will describe various models for sequence labelling task and the problem of labelling unsegmented sequence data. We will explain a end to end speech recognition system that directly transcribes the audio data with text/phonemes. The system tries to replace the conventional speech recognition pipeline by a single recurrent neural network (RNN) architecture. We have chosen the spectrograms as a minimal preprocessing scheme. The system is based on the combination of a deep bidirectional LSTM recurrent neural network architecture and the Connectionist Temporal Classiﬁcation objective function.
Explainable Machine Learning-driven Strategy for Automated Trading Pattern Extraction A Case Study of CME Futures

Financial markets are a source of non-stationary multidimensional time series which has been drawing attention for decades. Each financial instrument has its specific changing over time properties, making their analysis a complex task. Improvement of understanding and development of methods for financial time series analysis is essential for successful operation on financial markets. In this study we propose a volume-based data pre-processing method for making financial time series more suitable for machine learning pipelines. We use a statistical approach for assessing the performance of the method. Namely, we propose a set of research questions, formally state the hypotheses, compute effect sizes with confidence intervals, and reject the null hypotheses. We additionally assess the trading performance of the proposed method on historical data and compare it to a previously published approach. Our analysis shows that the proposed volume-based method allows successful classification of the financial time series patterns, and also leads to better classification performance than a price action-based method, excelling specifically on more liquid financial instruments. Finally, we propose an approach for obtaining feature interactions directly from tree-based models and compare the approach to SHAP method.
Experimental study on treatment of beneficiation wastewater by Fenton reagent

The treatment of aniline aerofloat in artificial and actual beneficiation wastewater by Fenton reagent was investigated.The effects of initial pH value,concentration of Fe~(2+),and H_2O_2 dosage on the removal efficiency of COD were studied respectively.The results indicated that,when reactive time was 10? min,pH=4,ρ(Fe~(2+))=1.83? g/L,(ρ(H_2O_2)=5.55? g/L,) and the initial concentration of aniline aerofloat was 300? mg/L,the removal rate of COD in artificial wastewater could reach 83.6%. As to actual beneficiation wastewater,when ρ(Fe~(2+))=50? mg/L,pH=3.5,and ρ(H_2O_2)=1? 800? mg/L,ρ(COD) decreased from 1? 000? mg/L to 32? mg/L,and the removal rate of COD was about 96.8%,reaching the standard for wastewater discharge.The dosage cost was about 18? yuan for 1? m~3 wastewater.
Comparison and Analysis of Deep Audio Embeddings for Music Emotion Recognition

Emotion is a complicated notion present in music that is hard to capture even with fine-tuned feature engineering. In this paper, we investigate the utility of state-of-the-art pre-trained deep audio embedding methods to be used in the Music Emotion Recognition (MER) task. Deep audio embedding methods allow us to efficiently capture the high dimensional features into a compact representation. We implement several multi-class classifiers with deep audio embeddings to predict emotion semantics in music. We investigate the effectiveness of L3-Net and VGGish deep audio embedding methods for music emotion inference over four music datasets. The experiments with several classifiers on the task show that the deep audio embedding solutions can improve the performances of the previous baseline MER models. We conclude that deep audio embeddings represent musical emotion semantics for the MER task without expert human engineering.
Revisiting Light Field Rendering With Deep Anti-Aliasing Neural Network

The light field (LF) reconstruction is mainly confronted with two challenges, large disparity and the non-Lambertian effect. Typical approaches either address the large disparity challenge using depth estimation followed by view synthesis or eschew explicit depth information to enable non-Lambertian rendering, but rarely solve both challenges in a unified framework. In this paper, we revisit the classic LF rendering framework to address both challenges by incorporating it with advanced deep learning techniques. First, we analytically show that the essential issue behind the large disparity and non-Lambertian challenges is the aliasing problem. Classic LF rendering approaches typically mitigate the aliasing with a reconstruction filter in the Fourier domain, which is, however, intractable to implement within a deep learning pipeline. Instead, we introduce an alternative framework to perform anti-aliasing reconstruction in the image domain and analytically show comparable efficacy on the aliasing issue. To explore the full potential, we then embed the anti-aliasing framework into a deep neural network through the design of an integrated architecture and trainable parameters. The network is trained through end-to-end optimization using a peculiar training set, including regular LFs and unstructured LFs. The proposed deep learning pipeline shows a substantial superiority in solving both the large disparity and the non-Lambertian challenges compared with other state-of-the-art approaches. In addition to the view interpolation for an LF, we also show that the proposed pipeline also benefits light field view extrapolation.
Can a Transformer Pass the Wug Test? Tuning Copying Bias in Neural Morphological Inflection Models

Deep learning sequence models have been successful with morphological inflection generation. The SIGMORPHON shared task results in the past several years indicate that such models can perform well, but only if the training data covers a good amount of different lemmata, or if the lemmata to be inflected at test time have also been seen in training, as has indeed been largely the case in these tasks. Surprisingly, we find that standard models such as the Transformer almost completely fail at generalizing inflection patterns when trained on a limited number of lemmata and asked to inflect previously unseen lemmata—i.e. under “wug test”-like circumstances. This is true even though the actual number of training examples is very large. While established data augmentation techniques can be employed to alleviate this shortcoming by introducing a copying bias through hallucinating synthetic new word forms using the alphabet in the language at hand, our experiment results show that, to be more effective, the hallucination process needs to pay attention to substrings of syllable-like length rather than individual characters.
Ask what’s missing and what’s useful: Improving Clarification Question Generation using Global Knowledge

The ability to generate clarification questions i.e., questions that identify useful missing information in a given context, is important in reducing ambiguity. Humans use previous experience with similar contexts to form a global view and compare it to the given context to ascertain what is missing and what is useful in the context. Inspired by this, we propose a model for clarification question generation where we first identify what is missing by taking a difference between the global and the local view and then train a model to identify what is useful and generate a question about it. Our model outperforms several baselines as judged by both automatic metrics and humans.
Dependent censoring based on copulas

Consider a survival time T that is subject to random right censoring, and suppose that T is stochastically dependent on the censoring time C. We are interested in the marginal distribution of T. This situation is often encountered in practice. Consider for instance the case where T is the time to death of a patient suffering from a certain disease. Then, the censoring time C is for instance the time until the person leaves the study or the time until he/she dies from another disease. If the reason for leaving the study is related to the health condition of the patient or if he/she dies from a disease that has similar risk factors as the disease of interest, then T and C are likely dependent. In this paper we propose a new model that takes this dependence into account. The model is based on a parametric copula for the relationship between T and C, and on parametric marginal distributions for T and C. Unlike most other papers in the literature, we do not assume that the parameter defining the copula function is known. We give sufficient conditions on these parametric copula and marginals under which the bivariate distribution of (T;C) is identifed. These sufficient conditions are then checked for a wide range of common copulas and marginal distributions. We also study the estimation of the model, and carry out extensive simulations and the analysis of data on pancreas cancer to illustrate the proposed model and estimation procedure.
Mitigating Hand Blockage with Non-Directional Beamforming Codebooks

Hand blockage leads to significant performance impairments at millimeter wave carrier frequencies. A number of prior works have characterized the loss in signal strength with the hand using studies with horn antennas and form-factor user equipments (UEs). However, the impact of the hand on the effective phase response seen by the antenna elements has not been studied so far. Towards this goal, we consider a measurement framework that uses a hand phantom holding the UE in relaxed positions reflective of talk mode, watching videos, and playing games. We first study the impact of blockage on a directional beam steering codebook. The tight phase relationship across antenna elements needed to steer beams leads to a significant performance degradation as the hand surface can distort the observed amplitudes and phases across the antenna elements, which cannot be matched by this codebook. To overcome this loss, we propose a non-directional beamforming codebook made of amplitudes and/or quantized phases with both these quantities estimated as necessary. Theoretical as well as numerical studies show that the proposed codebook can de-randomize the phase distortions induced by the hand and coherently combine the energy across antenna elements and thus help in mitigating hand blockage losses.