Miracle-dz/UniFAR-FTUs huggingface dataset:
An Effective Dynamic Spatio-temporal Framework with Multi-Source Information for Traffic Prediction

Traffic prediction is necessary not only for management departments to dispatch vehicles but also for drivers to avoid congested roads. Many traffic forecasting methods based on deep learning have been proposed in recent years, and their main aim is to solve the problem of spatial dependencies and temporal dynamics. In this paper, we propose a useful dynamic model to predict the urban traffic volume by combining fully bidirectional LSTM, the more complex attention mechanism, and the external features, including weather conditions and events. First, we adopt the bidirectional LSTM to obtain temporal dependencies of traffic volume dynamically in each layer, which is different from the hybrid methods combining bidirectional and unidirectional ones; second, we use a more elaborate attention mechanism to learn short-term and long-term periodic temporal dependencies; and finally, we collect the weather conditions and events as the external features to further improve the prediction precision. The experimental results show that the proposed model improves the prediction precision by approximately 3-7 percent on the NYC-Taxi and NYC-Bike datasets compared to the most recently developed method, being a useful tool for the urban traffic prediction.
FAME: 3D Shape Generation via Functionality-Aware Model Evolution

We introduce a modeling tool which can evolve a set of 3D objects in a functionality-aware manner. Our goal is for the evolution to generate large and diverse sets of plausible 3D objects for data augmentation, constrained modeling, as well as open-ended exploration to possibly inspire new designs. Starting with an initial population of 3D objects belonging to one or more functional categories, we evolve the shapes through part recombination to produce generations of hybrids or crossbreeds between parents from the heterogeneous shape collection. Evolutionary selection of offsprings is guided both by a functional plausibility score derived from functionality analysis of shapes in the initial population and user preference, as in a design gallery. Since cross-category hybridization may result in offsprings not belonging to any of the known functional categories, we develop a means for functionality partial matching to evaluate functional plausibility on partial shapes. We show a variety of plausible hybrid shapes generated by our functionality-aware model evolution, which can complement existing datasets as training data and boost the performance of contemporary data-driven segmentation schemes, especially in challenging cases. Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels. At the same time, unexpected yet functional object prototypes can emerge during open-ended exploration owing to structure breaking when evolving a heterogeneous collection.
SimpleMKKM: Simple Multiple Kernel K-means

We propose a simple yet effective multiple kernel clustering algorithm, termed simple multiple kernel k-means (SimpleMKKM). It extends the widely used supervised kernel alignment criterion to multi-kernel clustering. Our criterion is given by an intractable minimization-maximization problem in the kernel coefficient and clustering partition matrix. To optimize it, we re-formulate the problem as a smooth minimization one, which can be solved efficiently using a reduced gradient descent algorithm. We theoretically analyze the performance of SimpleMKKM in terms of its clustering generalization error. Comprehensive experiments on 11 benchmark datasets demonstrate that SimpleMKKM outperforms state of the art multi-kernel clustering alternatives.
VectorNet: Encoding HD Maps and Agent Dynamics From Vectorized Representation

Behavior prediction in dynamic, multi-agent systems is an important problem in the context of self-driving cars, due to the complex representations and interactions of road components, including moving agents (e.g. pedestrians and vehicles) and road context information (e.g. lanes, traffic lights). This paper introduces VectorNet, a hierarchical graph neural network that first exploits the spatial locality of individual road components represented by vectors and then models the high-order interactions among all components. In contrast to most recent approaches, which render trajectories of moving agents and road context information as bird-eye images and encode them with convolutional neural networks (ConvNets), our approach operates on the primitive vector representation. By operating on the vectorized high definition (HD) maps and agent trajectories, we avoid lossy rendering and computationally intensive ConvNet encoding steps. To further boost VectorNet's capability in learning context features, we propose a novel auxiliary task to recover the randomly masked out map entities and agent trajectories based on their context. We evaluate VectorNet on our in-house behavior prediction benchmark and the recently released Argoverse forecasting dataset. Our method achieves on par or better performance than the competitive rendering approach on both benchmarks while saving over 70% of the model parameters with an order of magnitude reduction in FLOPs. It also obtains state-of-the-art performance on the Argoverse dataset.
Perturbed gradient descent with occupation time

This paper develops further the idea of perturbed gradient descent, by adapting perturbation with the history of state via the notation of occupation time for saddle points. The proposed algorithm PGDOT is shown to converge at least as fast as perturbed gradient descent (PGD) algorithm, and is guaranteed to avoid getting stuck at saddle points. The analysis is corroborated by experimental results.
The Dynamics of Quantum Correlations of Two Qubits in a Common Environment

This paper is a continuation of our previous paper [8], in which we have studied the dynamics of quantum correlations of two qubits embedded each into its own disordered multiconnected environment. We modeled the environment by random matrices of large size allowing for a possibility to describe meso- and even nanoenvironments. In this paper we also study the dynamics of quantum correlations of two qubits but embedded into a common environment which we also model by random matrices of large size. We obtain the large size limit of the reduced density matrix of two qubits. We then use an analog of the Bogolyubov-van Hove (also known as the Born-Markov) approximation of the theory of open systems and statistical mechanics. The approximation does not imply in general the Markovian evolution in our model but allows for sufficiently detailed analysis both analytical and numerical of the evolution of several widely used quantifiers of quantum correlation, mainly entanglement. We find a number of new patterns of qubits dynamics comparing with the case of independent environments studied in [8] and displaying the role of dynamical (indirect, via the environment) correlations in the enhancing and diversification of qubit evolution. Our results, (announced in [9]), can be viewed as a manifestation of the universality of certain properties of the decoherent qubit evolution which have been found previously in various exact and approximate versions of two-qubit models with macroscopic bosonic environment.
Evaluating Sparse Interpretable Word Embeddings for Biomedical Domain

Word embeddings have found their way into a wide range of natural language processing tasks including those in the biomedical domain. While these vector representations successfully capture semantic and syntactic word relations, hidden patterns and trends in the data, they fail to offer interpretability. Interpretability is a key means to justification which is an integral part when it comes to biomedical applications. We present an inclusive study on interpretability of word embeddings in the medical domain, focusing on the role of sparse methods. Qualitative and quantitative measurements and metrics for interpretability of word vector representations are provided. For the quantitative evaluation, we introduce an extensive categorized dataset that can be used to quantify interpretability based on category theory. Intrinsic and extrinsic evaluation of the studied methods are also presented. As for the latter, we propose datasets which can be utilized for effective extrinsic evaluation of word vectors in the biomedical domain. Based on our experiments, it is seen that sparse word vectors show far more interpretability while preserving the performance of their original vectors in downstream tasks.
On Estimating the Primary Sex Ratio from Incomplete Data

Bertram, B. C. R. 1975. Social factors influencing reproduction in wild lions. J. Zool. 117:463-482. deCalesta, D. S. , J. G. Nagy, and J. A. Bailey. 1975. Starving and refeeding mule deer. J. Wildl. Manage. 39:663-669. Duke-Elder, S. 1958. System of ophthalmology. Vol. I. The eye in evolution. Kimpton, London. Estes, R. D. , and J. Goddard. 1967. Prey selection and hunting behavior of the African wild dog. J. Wildl. Manage. 31:52-70. Hirth, D. H. , and D. R. McCullough. 1977. Evolution of alarm signals in ungulates with special reference to white-tailed deer. Am. Nat. 111:31-42. Kolenosky, G. B. 1972. Wolf predation on wintering deer in east-central Ontario. J. Wildl. Manage. 36:357-368. Kruuk, H. 1972. The spotted hyena: a study of predation and social behavior. University of Chicago Press, Chicago. Marler, P., and W. J. Hamilton, III. 1966. Mechanisms of animal behavior. Wiley, New York. Mattfeld, G. F. 1973. The effect of snow on the energy expenditure of walking white-tailed eer. Trans. 30th Northwest Fish Wildl. Conf. 30:327-343. Mautz, W. W. 1978. Sledding on a brushy hillside: the fat cycle in deer. Wildl. Soc. Bull. 6:88-90. Mech, L. D. 1977. Productivity, mortality, and population trends of wolves in northeastern Minnesota. J. Mammal. 58:559-574. Moen, A. N. 1973. Wildlife ecology. Freeman, San Francisco. 1976. Energy conservation by white-tailed deer in the winter. Ecology 57:192-198. Nellis, C. H. , S. P. Wetmore, and L. B. Keith. 1972. Lynx-prey interactions in central Alberta. J. Wildl. Manage. 36:320-329. Schaller, G. B. 1972. The Serengetti lion. University of Chicago Press, Chicago. Smythe, N. 1970. On the existence of "pursuit invitation" signals in mammals. Am. Nat. 104:491-494. 1977. The function of mammalian alarm advertising: social signals or pursuit invitation? Am. Nat. 111:191-194. Van Ballenberghe, V. , and L. D. Mech. 1975. Weights, growth, and survival of timber wolf pups in Minnesota. J. Mammal. 56:44-63. Wolfe, M. L. , and D. L. Allen. 1973. Continued studies of the status, socialization, and relationships of Isle Royale wolves, 1967 to 1970. J. Mammal. 54:611-634.
A Multi-Dimensional Big Data Storing System for Generated COVID-19 Large-Scale Data using Apache Spark

The ongoing outbreak of coronavirus disease (COVID-19) had burst out in Wuhan China, specifically in December 2019. COVID-19 has caused by a new virus that had not been identified in human previously. This was followed by a widespread and rapid spread of this epidemic throughout the world. Daily, the number of the confirmed cases are increasing rapidly, number of the suspect increases, based on the symptoms that accompany this disease, and unfortunately number of the deaths also increase. Therefore, with these increases in number of cases around the world, it becomes hard to manage all these cases information with different situations; if the patient either injured or suspect with which symptoms that appeared on the patient. Therefore, there is a critical need to construct a multi-dimensional system to store and analyze the generated large-scale data. In this paper, a Comprehensive Storing System for COVID-19 data using Apache Spark (CSS-COVID) is proposed, to handle and manage the problem caused by increasing the number of COVID-19 daily. CSS-COVID helps in decreasing the processing time for querying and storing COVID-19 daily data. CSS-COVID consists of three stages, namely, inserting and indexing, storing, and querying stage. In the inserting stage, data is divided into subsets and then index each subset separately. The storing stage uses set of storing-nodes to store data, while querying stage is responsible for handling the querying processes. Using Apache Spark in CSS-COVID leverages the performance of dealing with large-scale data of the coronavirus disease injured whom increase daily. A set of experiments are applied, using real COVID-19 Datasets, to prove the efficiency of CSS-COVID in indexing large-scale data.
Hypergraph Learning with Line Expansion

Previous hypergraph expansions are solely carried out on either vertex level or hyperedge level, thereby missing the symmetric nature of data co-occurrence, and resulting in information loss. To address the problem, this paper treats vertices and hyperedges equally and proposes a new hypergraph formulation named the \emph{line expansion (LE)} for hypergraphs learning. The new expansion bijectively induces a homogeneous structure from the hypergraph by treating vertex-hyperedge pairs as "line nodes". By reducing the hypergraph to a simple graph, the proposed \emph{line expansion} makes existing graph learning algorithms compatible with the higher-order structure and has been proven as a unifying framework for various hypergraph expansions. We evaluate the proposed line expansion on five hypergraph datasets, the results show that our method beats SOTA baselines by a significant margin.
Epitaxial growth and band structure of antiferromagnetic Mott insulator CeOI

The van der Waals material CeOI is predicted to be a layered antiferromagnetic Mott insulator by DFT+U calculation. We successfully grow the CeOI films down to monolayer on graphene/6H-SiC(0001) substrate by using molecular beam epitaxy. Films are studied by {\it in-situ} scanning tunneling microscopy and spectroscopy, which shows a band gap of 4.4 eV. A metallic phase with composition unidentified also exists. This rare earth oxyhalide adds a new member to the two-dimensional magnetic materials.
Probabilistic Multi-Step-Ahead Short-Term Water Demand Forecasting with Lasso

Water demand is a highly important variable for operational control and decision making. Hence, the development of accurate forecasts is a valuable field of research to further improve the efficiency of water utilities. Focusing on probabilistic multi-step-ahead forecasting, a time series model is introduced, to capture typical autoregressive, calendar and seasonal effects, to account for time-varying variance, and to quantify the uncertainty and path-dependency of the water demand process. To deal with the high complexity of the water demand process a high-dimensional feature space is applied, which is efficiently tuned by an automatic shrinkage and selection operator (lasso). It allows to obtain an accurate, simple interpretable and fast computable forecasting model, which is well suited for real-time applications. The complete probabilistic forecasting framework allows not only for simulating the mean and the marginal properties, but also the correlation structure between hours within the forecasting horizon. For practitioners, complete probabilistic multi-step-ahead forecasts are of considerable relevance as they provide additional information about the expected aggregated or cumulative water demand, so that a statement can be made about the probability with which a water storage capacity can guarantee the supply over a certain period of time. This information allows to better control storage capacities and to better ensure the smooth operation of pumps. To appropriately evaluate the forecasting performance of the considered models, the energy score (ES) as a strictly proper multidimensional evaluation criterion, is introduced. The methodology is applied to the hourly water demand data of a German water supplier.
Cusp Size Variability of the Maxillary Molariform Teeth

The morphological differences of tooth crown components among the maxillary molariform teeth were investigated by means of an odontometric method. The second deciduous molar (dm2), and the first and second permanent molars (M1 and M2) were measured on 159 dental casts taken from Japanese males. Measurements taken on the tooth crown included the mesiodistal and buccolingual crown diameters, and the diameters of the four main cusps, the paracone, protocone, metacone and hypocone. In terms of overall crown sizes, dm2 was the smallest of the three teeth. The mesial cusps, the paracone and protocone, were significantly larger in the permanent molars (M1 and M2) than in the dm2 (P < 0.01). The distal cusps, the metacone and hypocone, were significantly larger in the M1 than in the dm2 and M2 (P < 0.01), and were similar in size in the dm2 and M2. The paracone was the most stable of the four main cusps in size. As for relative cusp size, the protocone increased distally from dm2 to M2, but no significant difference was found in the paracone. It is concluded that paracone size is proportionally stable regardless of overall tooth size.
Characterizing EoR foregrounds: a study of the Lockman Hole region at 325 MHz

One of the key science goals for the most sensitive telescopes, both current and upcoming, is the detection of the redshifted 21-cm signal from the Cosmic Dawn and Epoch of Reionization. The success of detection relies on accurate foreground modeling for their removal from data sets. This paper presents the characterization of astrophysical sources in the Lockman Hole region. Using 325 MHz data obtained from the GMRT, a $6^\circ \times 6^\circ$ mosaiced map is produced with an RMS reaching 50 $\mu$Jy $\mathrm{beam}^{-1}$. A source catalog containing 6186 sources is created, and the Euclidean normalized differential source counts have been derived from it, consistent with previous observations as well as simulations. A detailed comparison of the source catalog is also made with previous findings - at both lower and higher frequencies. The angular power spectrum (APS) of the diffuse Galactic synchrotron emission is determined for three different galactic latitudes using the Tapered Gridded Estimator. The values of the APS lie between $\sim$1 mK$^2$ to $\sim$100 mK$^2$. Fitting a power law of the form $A\ell^{-\beta}$ gives values of $A$ and $\beta$ varying across the latitudes considered. This paper demonstrates, for the first time, the variation of the power-law index for diffuse emission at very high galactic locations. It follows the same trend that is seen at locations near the galactic plane, thus emphasizing the need for low-frequency observations for developing better models of the diffuse emission.
It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations

Training on only perfect Standard English corpora predisposes pre-trained neural networks to discriminate against minorities from non-standard linguistic backgrounds (e.g., African American Vernacular English, Colloquial Singapore English, etc.). We perturb the inflectional morphology of words to craft plausible and semantically similar adversarial examples that expose these biases in popular NLP models, e.g., BERT and Transformer, and show that adversarially fine-tuning them for a single epoch significantly improves robustness without sacrificing performance on clean data.
A Light Signalling Approach to Node Grouping for Massive MIMO IoT Networks

Massive MIMO is one of the leading technologies for connecting very large numbers of energy-constrained nodes, as it offers both extensive spatial multiplexing and large array gain. A challenge resides in partitioning the many nodes into groups that can communicate simultaneously such that the mutual interference is minimized. Here we propose node partitioning strategies that do not require full channel state information, but rather are based on nodes’ respective directional channel properties. In our considered scenarios, these typically have a time constant that is far larger than the coherence time of the channel. We developed both an optimal and an approximation algorithm to partition users based on directional channel properties, and evaluated them numerically. Our results show that both algorithms, despite using only these directional channel properties, achieve similar performance in terms of the minimum signal-to-interference-plus-noise ratio for any user, compared with a reference method using full channel knowledge. In particular, we demonstrate that grouping nodes with related directional properties is to be avoided. We hence realize a simple partitioning method, requiring minimal information to be collected from the nodes, and in which this information typically remains stable over the long term, thus promoting the system’s autonomy and energy efficiency.
NOTES ON THE SONG OF BOLUA TURKIYAE AND ON THE PHYLOGENY OF THE GENUS BOLUA (ORTHOPTERA, TETTIGONINIIDAE, TETTIGONINIINAE)

Data on the song and stridulatory organs of the monotypic genus Bolua Unal (Orthoptera, Tettigoniinae) are presented. Based on new material, descriptions of the genus and the species are supplemented. The phylogenetic relationships of the genus are evaluated using both song and morphological characters, for which illustrations are provided. The genus Pachytrachis Uvarov is assumed to be the closest relative of Bolua.
ConvoKit: A Toolkit for the Analysis of Conversations

This paper describes the design and functionality of ConvoKit, an open-source toolkit for analyzing conversations and the social interactions embedded within. ConvoKit provides an unified framework for representing and manipulating conversational data, as well as a large and diverse collection of conversational datasets. By providing an intuitive interface for exploring and interacting with conversational data, this toolkit lowers the technical barriers for the broad adoption of computational methods for conversational analysis.
#lockdown: network-enhanced emotional profiling at the times of COVID-19

The COVID-19 pandemic forced countries all over the world to take unprecedented measures like nationwide lockdowns. To adequately understand the emotional and social repercussions, a large-scale reconstruction of how people perceived these unexpected events is necessary but currently missing. We address this gap through social media by introducing MERCURIAL (Multi-layer Co-occurrence Networks for Emotional Profiling), a framework which exploits linguistic networks of words and hashtags to reconstruct social discourse describing real-world events. We use MERCURIAL to analyse 101,767 tweets from Italy, the first country to react to the COVID-19 threat with a nationwide lockdown. The data were collected between 11th and 17th March, immediately after the announcement of the Italian lockdown and the WHO declaring COVID-19 a pandemic. Our analysis provides unique insights into the psychological burden of this crisis, focussing on: (i) the Italian official campaign for self-quarantine (#iorestoacasa}), (ii) national lockdown (#italylockdown), and (iii) social denounce (#sciacalli). Our exploration unveils evidence for the emergence of complex emotional profiles, where anger and fear (towards political debates and socio-economic repercussions) coexisted with trust, solidarity, and hope (related to the institutions and local communities). We discuss our findings in relation to mental well-being issues and coping mechanisms, like instigation to violence, grieving, and solidarity. We argue that our framework represents an innovative thermometer of emotional status, a powerful tool for policy makers to quickly gauge feelings in massive audiences and devise appropriate responses based on cognitive data.