Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of EEG frequency bands for Envisioned Speech Recognition

The use of Automatic speech recognition (ASR) interfaces have become increasingly popular in daily life for use in interaction and control of electronic devices. The interfaces currently being used are not feasible for a variety of users such as those suffering from a speech disorder, locked-in syndrome, paralysis or people with utmost privacy requirements. In such cases, an interface that can identify envisioned speech using electroencephalogram (EEG) signals can be of great benefit. Various works targeting this problem have been done in the past. However, there has been limited work in identifying the frequency bands ($\delta, \theta, \alpha, \beta, \gamma$) of the EEG signal that contribute towards envisioned speech recognition. Therefore, in this work, we aim to analyze the significance of different EEG frequency bands and signals obtained from different lobes of the brain and their contribution towards recognizing envisioned speech. Signals obtained from different lobes and bandpass filtered for different frequency bands are fed to a spatio-temporal deep learning architecture with Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The performance is evaluated on a publicly available dataset comprising of three classification tasks - digit, character and images. We obtain a classification accuracy of $85.93\%$, $87.27\%$ and $87.51\%$ for the three tasks respectively. The code for the implementation has been made available at https://github.com/ayushayt/ImaginedSpeechRecognition.
Theory-guided investigation on magnetic evolution of MnPt$_{5-x}$Pd$_x$P and discovery of anti-CeCoIn$_5$-type ferromagnetic MnPd$_5$P

We report the magnetic changes from canted antiferromagnetic to ferromagnetic orderings in anti-115-type MnPt 5- x Pd x P ( x = 1, 2, 2.5, 3, 4, and 5) and the discovery of a new rare-earth-free ferromagnet, MnPd 5 P by both theoretical prediction and experimental investigation. The family compounds were synthesized using high temperature solid state method and characterized to crystalize in the anti-CeCoIn 5 type with the space group P 4/ mmm exhibiting a two-dimensional layered structural feature. The magnetic property measurements indicate that the compounds ordered from canted A-type antiferromagnet in MnPt 5 P to ferromagnet above the room temperature with varying degrees of coercivity and magnetic moments in MnPd 5 P by reducing the spin orbital coupling. The results of the MnPt 5- x Pd x P have been analyzed in comparison to the other candidates of the 151 family of Mn(Pt/Pd) 5 (P/As) to understand the complex structure-magnetism relationships.
Dressing in the Wild by Watching Dance Videos

While significant progress has been made in garment transfer, one of the most applicable directions of human-centric image generation, existing works overlook the in-the-wild imagery, presenting severe garment-person mis-alignment as well as noticeable degradation in fine texture details. This paper, therefore, attends to virtual try-on in real-world scenes and brings essential improvements in authenticity and naturalness especially for loose garment (e.g., skirts, formal dresses), challenging poses (e.g., cross arms, bent legs), and cluttered backgrounds. Specifically, we find that the pixel flow excels at handling loose gar-ments whereas the vertex flow is preferred for hard poses, and by combining their advantages we propose a novel generative network called wFlow that can effectively push up garment transfer to in-the-wild context. Moreover, former approaches require paired images for training. Instead, we cut down the laboriousness by working on a newly constructed large-scale video dataset named Dance50k with self-supervised cross-frame training and an online cycle op-timization. The proposed Dance50k can boost real-world virtual dressing by covering a wide variety of garments under dancing poses. Extensive experiments demonstrate the superiority of our w Flow in generating realistic garment transfer results for in-the-wild images without resorting to expensive paired datasets. 11Xiaodan Liang is the corresponding author. The project page of wFlow is https://awesome-wflow.github. io.
A Deep Learning Technique using a Sequence of Follow Up X-Rays for Disease classification

The ability to predict lung and heart based diseases using deep learning techniques is central to many researchers, particularly in the medical ﬁeld around the world. In this paper, we present a unique outlook of a very familiar problem of disease classiﬁcation using X-rays. We present a hypothesis that X-rays of patients included with their followup history of their most recent three chest X-ray images would perform better in disease classiﬁcation in comparison to one chest X-ray image input using an internal CNN to perform feature extraction. We have discovered that our generic deep learning architecture which we propose for solving this problem performs well with 3 input X-ray images provided per sample for each patient. In this paper, we have also established that without additional layers before the output classiﬁcation, the CNN models will improve the performance of predicting the disease labels for each patient. We have provided our results in ROC curves and AUROC scores. We deﬁne a fresh approach of collecting three X-ray images for training deep learning models, which we have concluded that it has clearly improved the performance of the models. We have shown that ResNet, in general has a better result than any other CNN model used in the feature extraction phase. With our orig-inal approach on data pre-processing, image training, and pre-trained models, we believe that the current research will assist many medical institutions around the world, and this will improve the prediction of patients’ symptoms and diagnose them with more accurate cure.
AUC Maximization in the Era of Big Data and AI: A Survey

Area under the ROC curve, a.k. a. AUC, is a measure of choice for assessing the performance of a classifier for imbalanced data. AUC maximization refers to a learning paradigm that learns a predictive model by directly maximizing its AUC score. It has been studied for more than two decades dating back to late 90s, and a huge amount of work has been devoted to AUC maximization since then. Recently, stochastic AUC maximization for big data and deep AUC maximization (DAM) for deep learning have received increasing attention and yielded dramatic impact for solving real-world problems. However, to the best our knowledge, there is no comprehensive survey of related works for AUC maximization. This article aims to address the gap by reviewing the literature in the past two decades. We not only give a holistic view of the literature but also present detailed explanations and comparisons of different papers from formulations to algorithms and theoretical guarantees. We also identify and discuss remaining and emerging issues for DAM and provide suggestions on topics for future work.
Multielectron effect in the strong-field ionization of aligned nonpolar molecules

We revisit strong-field ionization of aligned O$_2$, CO$_2$, and CS$_2$ molecules in light of recent advances in the field of strong-field physics, in particular the inclusion of multielectron polarization in the numerical solution of the time-dependent Schr{\"o}dinger equation (TDSE) within the singe-active-electron approximation. Upon inclusion of multielectron polarization and the associated induced dipole potential in the TDSE model, we obtain angular distributions of total ionization yields which are in better agreement with the available experimental results for CO$_2$ and CS$_2$. For the probed molecules, the main effect of the multielectron polarization on the photoelectron momentum distributions (PMDs) is captured by the introduction of a field at short distances that counteracts the applied external field and leads to a vanishing time-dependent interaction within a certain cut-off radius. Hence, for the PMDs we find no imprints of the long-range part of the laser-induced dipole potential.
Contextual Information Based Anomaly Detection for a Multi-Scene UAV Aerial Videos

UAV based surveillance is gaining much interest worldwide due to its extensive applications in monitoring wildlife, urban planning, disaster management, campus security, etc. These videos are analyzed for strange/odd/anomalous patterns which are essential aspects of surveillance. But manual analysis of these videos is tedious and laborious. Hence, the development of computer-aided systems for the analysis of UAV based surveillance videos is crucial. Despite this interest, in literature, several computer aided systems are developed focusing only on CCTV based surveillance videos. These methods are designed for single scene scenarios and lack contextual knowledge which is required for multi-scene scenarios. Furthermore, the lack of standard UAV based anomaly detection datasets limits the development of these systems. In this regard, the present work aims at the development of a Computer Aided Decision support system to analyse UAV based surveillance videos. A new UAV based multi-scene anomaly detection dataset is developed with frame-level annotations for the development of computer aided systems. It holistically uses contextual, temporal and appearance features for accurate detection of anomalies. Furthermore, a new inference strategy is proposed that utilizes few anomalous samples along with normal samples to identify better decision boundaries. The proposed method is extensively evaluated on the UAV based anomaly detection dataset and performed competitively with respect to state-of-the-art methods.
Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation

Speech segmentation, which splits long speech into short segments, is essential for speech translation (ST). Popular VAD tools like WebRTC VAD have generally relied on pause-based segmentation. Unfortunately, pauses in speech do not necessarily match sentence boundaries, and sentences can be connected by a very short pause that is difficult to detect by VAD. In this study, we propose a speech segmentation method using a binary classification model trained using a segmented bilingual speech corpus. We also propose a hybrid method that combines VAD and the above speech segmentation method. Experimental results revealed that the proposed method is more suitable for cascade and end-to-end ST systems than conventional segmentation methods. The hybrid approach further improved the translation performance.
Revisiting LiDAR Registration and Reconstruction: A Range Image Perspective

Spinning LiDAR data are prevalent for 3D vision tasks. Since LiDAR data is presented in the form of point clouds, expensive 3D operations are usually required. This paper revisits spinning LiDAR scan formation and presents a cylindrical range image representation with a ray-wise projection/unprojection model. It is built upon raw scans and supports lossless conversion from 2D to 3D, allowing fast 2D operations, including 2D index-based neighbor search and downsampling We then propose, to the best of our knowledge, the first multi-scale registration and dense signed distance function (SDF) reconstruction system for LiDAR range images. We further collect a dataset of indoor and outdoor LiDAR scenes in the posed range image format. A comprehensive evaluation of registration and reconstruction is conducted on the proposed dataset and the KITTI dataset. Experiments demonstrate that our approach outperforms surface reconstruction baselines and achieves similar performance to state-of-the-art LiDAR registration methods, including a modern learning-based registration approach. Thanks to the simplicity, our registration runs at 100Hz and SDF reconstruction in real time. The dataset and a modularized C++/Python toolbox will be released.
Analysis of $B_s\to\phi\nu\bar{\nu}$ at CEPC

The rare $b\to s\nu\bar{\nu}$ decays are sensitive to contributions of new physics (NP) and helpful to resolve the puzzle of multiple $B$ flavor anomalies. In this work, we propose to study the $b\to s\nu\bar{\nu}$ transition at a future lepton collider operating at the $Z$ pole through the $B_s \to \phi\nu\bar{\nu}$ decay. Using the $B_s\to\phi$ decay form factors from lattice simulations, we first update the SM prediction of BR($B_s \to \phi\nu\bar{\nu})_{\mathrm{SM}}=(9.93\pm 0.72)\times 10^{-6}$ and the corresponding $\phi$ longitudinal polarization fraction $F_{L,{\mathrm{SM}}}=0.53\pm 0.04$. Our analysis uses the full CEPC simulation samples with a net statistic of $\mathcal{O}(10^9)$ $Z$ decays. Precise $\phi$ and $B_s$ reconstructions are used to suppress backgrounds. The results show that BR($B_s \to \phi\nu\bar{\nu})$ can be measured with a statistical uncertainty of $\mathcal{O}(\%)$ and an $S/B$ ratio of $\mathcal{O}(1)$ at the CEPC. The quality measures for the event reconstruction are also derived. By combining the measurement of BR($B_s \to \phi\nu\bar{\nu})$ and $F_L$, the constraints on the effective theory couplings at low energy are given.
Laying Firm Foundations: Getting Reading Right

The central focus of this research project was the investigation of causes of weak South African student performance in literacy and numeracy in the Foundation Phase (Grades 1–3). Many South African children complete these grades without being able to read properly in their home-language, with little understanding of the language in which they will be taught from Grade 4 (English), and with an inability to move from basic counting to true calculation using the four operations. Less than half of all students learn to read for meaning in this critical period. These weak foundations provided in Grades 1–3 constitute one of the major factors leading to poor learning outcomes in later grades. This research project has taken a system view to analyse how these problems interact to create the low level equilibrium South African education finds itself in. This research has been undertaken through the lens of the ‘instructional core’, evaluating what happens in Foundation Phase classrooms and how this influences learning. The departure point is the view that any attempt to raise the quality of education in South Africa must focus on the ‘instructional core’ in Foundation Phase classrooms, i.e. “actual interactions between teachers, students, and content in the classroom” (City et al., 2009). All other factors are important only to the extent that they impact on these three components.
Phylogenetic signal and rate of evolutionary change in language structures

Within linguistics, there is an ongoing debate about whether some language structures remain stable over time, which structures these are and whether they can be used to uncover the relationships between languages. However, there is no consensus on the definition of the term ‘stability’. I define ‘stability’ as a high phylogenetic signal and a low rate of change. I use metric D to measure the phylogenetic signal and Hidden Markov Model to calculate the evolutionary rate for 171 structural features coded for 12 Japonic, 2 Koreanic, 14 Mongolic, 11 Tungusic and 21 Turkic languages. To more deeply investigate the differences in evolutionary dynamics of structural features across areas of grammar, I divide the features into 4 language domains, 13 functional categories and 9 parts of speech. My results suggest that there is a correlation between the phylogenetic signal and evolutionary rate and that, overall, two-thirds of the features have a high phylogenetic signal and over a half of the features evolve at a slow rate. Specifically, argument marking (flagging and indexing), derivation and valency appear to be the most stable functional categories, pronouns and nouns the most stable parts of speech, and phonological and morphological levels the most stable language domains.
Host preferences inhibit transmission from potential superspreader host species

Host species that are particularly abundant, infectious and/or infected tend to contribute disproportionately to symbiont (parasite or mutualist) maintenance in multi-host systems. Therefore, in a facultative multi-host system where two host species had high densities, high symbiont infestation intensities and high infestation prevalence, we expected interspecific transmission rates to be high. Instead, we found that interspecific symbiont transmission rates to caged sentinel hosts were an order of magnitude lower than intraspecific transmission rates in the wild. Using laboratory experiments to decompose transmission rates, we found that opportunities for interspecific transmission were frequent, where interspecific and intraspecific contact rate functions were statistically indistinguishable. However, most interspecific contacts did not lead to transmission events owing to a previously unrecognized transmission barrier: strong host preferences. During laboratory choice experiments, the symbiont preferred staying on or dispersing to its current host species, even though the oligochaete symbiont is a globally distributed host generalist that can survive and reproduce on many snail host species. These surprising results suggest that when managing symbiont transmission, identifying key host species is still important, but it may be equally important to identify and manage transmission barriers that keep potential superspreader host species in check.
Introduction to the special issue dedicated to Michael J. Duff FRS on the occasion of his 70th birthday

This special feature, dedicated to Michael J. Duff FRS on the occasion of his 70th birthday, concerns topics in 'Quantum gravity, branes and M-theory'. These three intertwining subjects have been central to Duff's work; indeed many of his contributions have come to define significant aspects of what we actually mean by these terms. From the discovery of Weyl anomalies to recognising superstrings in 10 dimensions as a special case of membranes in an 11-dimensional M-theory, Duff's insights have shaped major developments across these themes. So it is an apposite setting for such a celebration and we are delighted to be able to include in this collection contributions from many of the pioneers of quantum gravity, branes and M-theory. The breadth of these topics has placed little constraint on the multiplicity of ideas appearing in these pages, from astrophysical black holes to chaotic condensed matter. Again, this is fitting as Duff's scientific remit spans a remarkable diversity of motifs, from the fundamentals of M-theory to entanglement in quantum information.
Two New Species of the Lycophyte Genus Selaginella (Selaginellaceae) from China, with Notes on the Phylogenetic Positions of Related Species

Abstract Two new species of lycophytes, Selaginella obovata and S. zhangii, were discovered in southeastern Yunnan, China. Selaginella obovata is characterized by the prostrate habit and relatively small size of plant, obovate median leaves, and short, dorsiventral strobili, and is not apparently similar to any known species. Phylogenetic analyses of plastid sequences (rbcL, atpI, psbA) indicated that S. obovata is closely allied to S. bisulcata, which is also supported by spore morphology, and that these two taxa together with S. pennata constitute a well-supported monophyletic group. Selaginella zhangii is comparable to S. rolandi-principis in their broad stems and shape and arrangement of vegetative leaves; but the former differs from the latter in its prostrate habit and dorsiventral strobili. The results of phylogenetic analyses indicated that S. zhangii is allied to S. heterostachys in the S. heterostachys–S. ciliaris clade, while S. rolandi-principis is distantly placed in the S. doederleinii–S. involvens clade. Finally, we provide comprehensive descriptions, photographs of living plants, line drawings, and scanning electron micrographs of spores to facilitate their recognition.
A Revised Subgeneric Classification of the Neotropical Genus Zapoteca (Caesalpinioideae; Fabaceae)

Abstract The subgeneric classification of the neotropical legume genus Zapoteca is revised based on an analysis of the nuclear ETS and ITS, and the plastid trnL-trnF DNA regions. Zapoteca subgenus Ravenia is included in Z. subgenus Zapoteca, Z. quichoi is transferred from Z. subgenus Amazonica to Z. subgenus Zapoteca, Z. sousae is included in Z. subgenus Zapoteca, and the new subgenus Zapoteca subgenus Subamazonica is described to accommodate the species Z. filipes and Z. scutellifera. A key to the subgenera is presented.
Numerical Evaluation for Spacer Vane Effects on Flow and Heat Transfer of Water at Supercritical Pressure in Annular Channel

ABSTRACT In the nuclear fuel structure, most spacers are constructed with vanes that increase turbulence flow mixing downstream of the spacer and therefore enhance the heat transfer rate. The objective of this work is numerical evaluation of the effects of a spacer without a vane and a spacer with a vane (hereinafter referred to as spacer/spacer with vane) on the flow and heat transfer of water at supercritical pressure downstream to the spacer of the annular channel. In this study, computational fluid dynamics (CFD) models of the annular channel have been developed considering spacer/spacer with vane. Experimental data for the heated annular channel have been used to validate the same CFD model (as the geometry used for the experiment) using the CFD code ANSYS Fluent. The CFD results show good agreement with the experimental data used, and hence, the developed CFD models of the annular channel that consider spacer/spacer with vane can be simulated with adequate precision for the flow and heat transfer downstream to the spacer. The effects of spacer/spacer with vane on heat transfer and flow behavior of water have been studied with numerical simulations for the following parameters: mass fluxes of 500 and 1000 kg/m2·s, heat flux of 400 kW/m2, pressure of 25 MPa, and inlet water temperature of 350°C. The results obtained through the simulations show that the spacer with vane has a remarkable influence on flow and heat transfer downstream to the spacer vane against spacer without a vane in an annular channel. Raising the flow velocity is an effective approach to reduce wall temperature and enhance the heat transfer in the channel. The range of the spacer effect in the enhancement of heat transfer is observed from X/D = 0 to 45 in the downstream direction. In addition, the simulation results for the Nusselt number ratio of the present CFD models have been compared with correlation data established by several researchers in a downstream direction to the spacer/spacer with vane, and qualitatively proper agreement has been found.