Miracle-dz/UniFAR-FTUs huggingface dataset:
Seek and You Will Find: A New Optimized Framework for Efficient Detection of Pedestrian

Studies of object detection and localization, particularly pedestrian detection have received considerable attention in recent times due to its several prospective applications such as surveillance, driving assistance, autonomous cars, etc. Also, a significant trend of latest research studies in related problem areas is the use of sophisticated Deep Learning based approaches to improve the benchmark performance on various standard datasets. A trade-off between the speed (number of video frames processed per second) and detection accuracy has often been reported in the existing literature. In this article, we present a new but simple deep learning based strategy for pedestrian detection that improves this trade-off. Since training of similar models using publicly available sample datasets failed to improve the detection performance to some significant extent, particularly for the instances of pedestrians of smaller sizes, we have developed a new sample dataset consisting of more than 80K annotated pedestrian figures in videos recorded under varying traffic conditions. Performance of the proposed model on the test samples of the new dataset and two other existing datasets, namely Caltech Pedestrian Dataset (CPD) and CityPerson Dataset (CD) have been obtained. Our proposed system shows nearly 16% improvement over the existing state-of-the-art result.
The Nucleosynthetic Yields of Core-collapse Supernovae: Prospects for the Next Generation of Gamma-Ray Astronomy

Though the neutrino-driven convection model for the core-collapse explosion mechanism has received strong support in recent years, there are still many uncertainties in the explosion parameters—such as explosion energy, remnant mass, and end-of-life stellar abundances as initial conditions. Using a broad set of spherically symmetric core-collapse simulations we examine the effects of these key parameters on explosive nucleosynthesis and final explosion yields. The post-bounce temperature and density evolution of zero-age main-sequence 15, 20, and 25 solar mass progenitors are post-processed through the Nucleosynthesis Grid nuclear network to obtain detailed explosive yields. In particular, this study focuses on radio isotopes that are of particular interest to the next generation of gamma-ray astronomical observations: 43K, 47Ca, 44Sc, 47Sc, 48V, 48Cr, 51Cr, 52Mn, 59Fe, 56Co, 57Co, and 57Ni. These nuclides may be key in advancing our understanding of the inner workings of core-collapse supernovae by probing the parameters of the explosion engine. We find that the isotopes that are strong indicators of explosion energy are 43K, 47Ca, 44Sc, 47Sc, and 59Fe, those that are dependent on the progenitor structure are 48V, 51Cr, and 57Co, and those that probe neither are 48Cr, 52Mn, 57Ni, and 56Co. We discuss the prospects of observing these radionuclides in supernova remnants.
Physical properties of the CDFS X-ray sources through fitting spectral energy distributions

The physical parameters of galaxies and/or AGNs can be derived by fitting their multi-band spectral energy distributions (SEDs). By using CIGALE code, we perform multi-band SED fitting (from ultraviolet to infrared) for 791 X-ray sources (518 AGNs and 273 normal galaxies) in the 7 Ms Chandra Deep Field-south survey (CDFS). We consider the contributions from AGNs and adopt more accurate redshifts than published before. Therefore, more accurate star formation rates (SFRs) and stellar masses (M$_*$) are derived. We classify the 518 AGNs into type-I and type-II based on their optical spectra and their SEDs. Moreover, six AGN candidates are selected from the 273 normal galaxies based on their SEDs. Our main results are as follows: (1) the host galaxies of AGNs have larger M$_*$ than normal galaxies, implying that AGNs prefer to host in massive galaxies; (2) the specific star formation rates (sSFRs) of AGN host galaxies are different from those of normal galaxies, suggesting that AGN feedback may play an important role in the star formation activity; (3) we find that the fraction of optically obscured AGNs in CDFS decreases with the increase of intrinsic X-ray luminosity, which is consistent with previous studies;(4) the host galaxies of type-I AGNs tend to have lower M$_*$ than type-II AGNs, which may suggest that dust in the host galaxy may also contribute to the optical obscuration of AGNs.
Integral Quadratic Constraints for Asynchronous Sample-and-Hold Links

A model is proposed for a class of asynchronous sample-and-hold operators that is relevant to the analysis of embedded and networked systems. The model is parametrized by characteristics of the corresponding time-varying input–output delay. Uncertainty in the relationship between the timing of zero-order-hold update events at the output and the possibly aperiodic sampling events at the input means that the delay does not always reset to a fixed value. This is distinct from the well-studied synchronous case in which the delay intermittently resets to zero at output update times. The main result provides a family of integral quadratic constraints that covers the proposed model. To demonstrate an application of this result, stability and performance certificates are devised for an asynchronous sampled-data implementation of the feedback loop around given linear time-invariant continuous-time open-loop dynamics. Numerical examples are also presented.
Reynolds Dilatancy Coincides with Two Separate Transport Mechanisms in Sheared Amorphous Packings

Shearing granular materials induces non-affine displacements. Such non-affine displacements have been studied extensively, showing the relevance of local displacement measures to mechanical responses. A well known example is shear transformation zones as captured by the local deviation from affine deformation, $D^2_{min}$, and their relevance to failure and stress fluctuation. Here, we show that in a granular system sheared at constant volume there exists at least one additional mesoscopic transport mechanism that superimposes itself on top of local diffusive motion. We reveal this second transport mechanism via a diffusion tensor analysis and show that the trace of the diffusion tensor equals the classic $D^2_{min}$ when this second mesoscopic transport is corrected for. We suggest that this second transport mechanism is the microscopic driving mechanism for Reynolds dilatancy, which is emerging at the same time. The dilational transport mechanism is consistently observed over a wide range of volume fractions and even for particles with different friction coefficients.
Geometry Sharing Network for 3D Point Cloud Classification and Segmentation

In spite of the recent progresses on classifying 3D point cloud with deep CNNs, large geometric transformations like rotation and translation remain challenging problem and harm the final classification performance. To address this challenge, we propose Geometry Sharing Network (GS-Net) which effectively learns point descriptors with holistic context to enhance the robustness to geometric transformations. Compared with previous 3D point CNNs which perform convolution on nearby points, GS-Net can aggregate point features in a more global way. Specially, GS-Net consists of Geometry Similarity Connection (GSC) modules which exploit Eigen-Graph to group distant points with similar and relevant geometric information, and aggregate features from nearest neighbors in both Euclidean space and Eigenvalue space. This design allows GS-Net to efficiently capture both local and holistic geometric features such as symmetry, curvature, convexity and connectivity. Theoretically, we show the nearest neighbors of each point in Eigenvalue space are invariant to rotation and translation. We conduct extensive experiments on public datasets, ModelNet40, ShapeNet Part. Experiments demonstrate that GS-Net achieves the state-of-the-art performances on major datasets, 93.3% on ModelNet40, and are more robust to geometric transformations.
the preSent SItuatIon of taenIaSIS and cyStIcercoSIS In aSIa and the pacIfIc

Taenia solium cysticercosis is briefly overviewed. Atypical neurocysticercosis cases that lack specific information from clinical manifestation, neuroimaging, serology, or histopathologic examination require molecular identification of histopathologic specimens for obtaining of concrete evidence of infection with metacestodes of T. solium. The present situation of taeniasis and cysticercosis in Asia and the Pacific is overviewed based on our joint projects in Indonesia, China, and Thailand. Both T. saginata from beef and T. asiatica from the viscera of pigs occur sympatrically in China and in Thailand; whereas, such a sympatric occurrence is not confirmed in Indonesia, where the religious taboo about food is quite strict.
Promoting male refugees’ mental health after they have been granted leave to remain (refugee status)

Abstract There is existing evidence that suggests that refugees’ mental health is often impaired by pre-migratory stress, by their experience of being asylum seekers and also by their resettlement adversities; however, little is known about the specific mental health issues of male refugees. The current research is set in this context and it aims to explore multiple levels of stress that male refugees experience after they have been granted Leave to Remain (refugee status) in the U.K. A total of nine male refugees took part in the study; they were interviewed individually and the interview transcripts were analysed by the use of Thematic Analysis. The overall findings indicated that being granted Leave to Remain worsened participants’ mental health, as they had to face new challenges such as seeking employment and accommodation, as well as their general struggle to settle in the U.K. Participants indicated that the type of support available to them was quite poor. These findings are relevant in order to support specific intervention strategies to promote male refugees’ mental health.
TextNAS: A Neural Architecture Search Space tailored for Text Representation

Learning text representation is crucial for text classification and other language related tasks. There are a diverse set of text representation networks in the literature, and how to find the optimal one is a non-trivial problem. Recently, the emerging Neural Architecture Search (NAS) techniques have demonstrated good potential to solve the problem. Nevertheless, most of the existing works of NAS focus on the search algorithms and pay little attention to the search space. In this paper, we argue that the search space is also an important human prior to the success of NAS in different applications. Thus, we propose a novel search space tailored for text representation. Through automatic search, the discovered network architecture outperforms state-of-the-art models on various public datasets on text classification and natural language inference tasks. Furthermore, some of the design principles found in the automatic network agree well with human intuition.
Dissipative-type theories for Bjorken and Gubser flows

We use the dissipative-type theory (DTT) framework to solve for the evolution of conformal fluids in Bjorken and Gubser flows from isotropic initial conditions. The results compare well with both exact and other hydrodynamic solutions in the literature. At the same time, DTTs enforce the Second Law of thermodynamics as an exact property of the formalism, at any order in deviations from equilibrium, and are easily generalizable to more complex situations.
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks

The optimization of multilayer neural networks typically leads to a solution with zero training error, yet the landscape can exhibit spurious local minima and the minima can be disconnected. In this paper, we shed light on this phenomenon: we show that the combination of stochastic gradient descent (SGD) and over-parameterization makes the landscape of multilayer neural networks approximately connected and thus more favorable to optimization. More specifically, we prove that SGD solutions are connected via a piecewise linear path, and the increase in loss along this path vanishes as the number of neurons grows large. This result is a consequence of the fact that the parameters found by SGD are increasingly dropout stable as the network becomes wider. We show that, if we remove part of the neurons (and suitably rescale the remaining ones), the change in loss is independent of the total number of neurons, and it depends only on how many neurons are left. Our results exhibit a mild dependence on the input dimension: they are dimension-free for two-layer networks and depend linearly on the dimension for multilayer networks. We validate our theoretical findings with numerical experiments for different architectures and classification tasks.
Using coupling methods to estimate sample quality for stochastic differential equations

A probabilistic approach for estimating sample qualities for stochastic differential equations is introduced in this paper. The aim is to provide a quantitative upper bound of the distance between the invariant probability measure of a stochastic differential equation and that of its numerical approximation. In order to extend estimates of finite time truncation error to infinite time, it is crucial to know the rate of contraction of the transition kernel of the SDE. We find that suitable numerical coupling methods can effectively estimate such rate of contraction, which gives the distance between two invariant probability measures. Our algorithms are tested with several low and high dimensional numerical examples.
Using Small Domain Estimation to obtain better retrospective Age Period Cohort insights

Recent changes in housing costs relative to income are likely to affect people's propensity to Housing Affordability Stress (HAS), which is known to have a detrimental effect on a range of health outcomes. The magnitude of these effects may vary between subgroups of the population, in particular across age groups. Estimating these effect sizes from longitudinal data requires Small Domain Estimation (SDE) as available data is generally limited to small sample sizes. In this paper we develop the rationale for smoothing-based SDE using two case studies: (1) transitions into and out of HAS and (2) the mental health effect associated with HAS. We apply cross-validation to assess the relative performance of multiple SDE methods and discuss how SDE can be embedded into g-computation for causal inference.
Catalan words avoiding pairs of length three patterns

Catalan words are particular growth-restricted words counted by the eponymous integer sequence. In this article we consider Catalan words avoiding a pair of patterns of length 3, pursuing the recent initiating work of the first and last authors and of S. Kirgizov where (among other things) the enumeration of Catalan words avoiding a patterns of length 3 is completed. More precisely, we explore systematically the structural properties of the sets of words under consideration and give enumerating results by means of recursive decomposition, constructive bijections or bivariate generating functions with respect to the length and descent number. Some of the obtained enumerating sequences are known, and thus the corresponding results establish new combinatorial interpretations for them.
Study on the Knowledge of Human Papilloma Virus in Female University Students

Purpose: This study was performed to examine the knowledge of HPV in female university students. Methods: Participants consisted of 285 female university students(nursing major;153, others;132). A dichotomy with 20 items of HPV knowledge was developed by a researcher. Results: Reliability of the HPV knowledge tool was Cronbach' alpha . 87, and Split-half Guttman coefficient . 85. Correction rates of HPV knowledge by item ranged from 19.6 to 76.8%. There were no significant differences in HPV knowledge scores by major group or sex related characteristics. Conclusions: Accurate, university wide education regarding HPV knowledge for female university students should be done regardless of the major; nursing or not. Replication studies with different ages or sex groups and application of HPV education in relation to STI prevention and cervical cancer prevention are recommended.
Jacobian Adversarially Regularized Networks for Robustness

Adversarial examples are crafted with imperceptible perturbations with the intent to fool neural networks. Against such attacks, adversarial training and its variants stand as the strongest defense to date. Previous studies have pointed out that robust models that have undergone adversarial training tend to produce more salient and interpretable Jacobian matrices than their non-robust counterparts. A natural question is whether a model trained with an objective to produce salient Jacobian can result in better robustness. This paper answers this question with affirmative empirical results. We propose Jacobian Adversarially Regularized Networks (JARN) as a method to optimize the saliency of a classifier's Jacobian by adversarially regularizing the model's Jacobian to resemble natural training images. Image classifiers trained with JARN show improved robust accuracy compared to standard models on the MNIST, SVHN and CIFAR-10 datasets, uncovering a new angle to boost robustness without using adversarial training examples.
Lower AGN Abundance in Galaxy Clusters at z < 0.5

Most galaxies in clusters have supermassive black holes (SMBHs) at their center, and a fraction of those SMBHs show strong activity. These active galactic nuclei (AGNs) are an important probe of environmental dependence of galaxy evolution, intracluster medium, and cluster-scale feedback. We investigated AGN fraction in one of the largest samples of X-ray selected clusters from the ROSAT and their immediate surrounding field regions below z < 0.5. We found a lower average AGN fraction in clusters (2.37 ± 0.39)% than for the fields (5.12 ± 0.16)%. The lower AGN fractions in clusters were measured, after dividing the clusters into five redshift intervals between 0.0 and 0.5, in each redshift interval, and we found an increase in the fraction for both cluster and field galaxies with redshift below z < 0.5, which clearly indicates an environment and redshift dependence. We further divided the clusters into low-mass and high-mass objects using a mass cut at log10(M500/M⊙) = 13.5, finding comparable AGN fractions for both classifications, while a significantly higher AGN fraction in field. We also measured increasing AGN fractions with cluster-centric distance for all redshift bins, further confirming the environmental dependence of AGN activities. In addition, we did not find an obvious trend between AGN fraction and Sloan Digital Sky Survey Mr absolute magnitudes among different redshift bins. We conclude that the lower AGN fraction in clusters relative to fields indicate that factors, such as inefficient galaxy mergers and ram pressure stripping, cause a deficit of cold gas available in high-density regions to fuel the central SMBH. Clusters and fields in the present universe have lost more gas relative to their high-redshift counterparts resulting in a lower AGN fraction observed today.
Numerical solution of large scale Hartree-Fock-Bogoliubov equations

The Hartree–Fock–Bogoliubov (HFB) theory is the starting point for treating superconducting systems. However, the computational cost for solving large scale HFB equations can be much larger than that of the Hartree–Fock equations, particularly when the Hamiltonian matrix is sparse, and the number of electrons N is relatively small compared to the matrix size Nb. We first provide a concise and relatively self-contained review of the HFB theory for general finite sized quantum systems, with special focus on the treatment of spin symmetries from a linear algebra perspective. We then demonstrate that the pole expansion and selected inversion (PEXSI) method can be particularly well suited for solving large scale HFB equations. For a Hubbard-type Hamiltonian, the cost of PEXSI is at most 𝒪(Nb2) for both gapped and gapless systems, which can be significantly faster than the standard cubic scaling diagonalization methods. We show that PEXSI can solve a two-dimensional Hubbard-Hofstadter model with Nb up to 2.88 × 106, and the wall clock time is less than 100 s using 17 280 CPU cores. This enables the simulation of physical systems under experimentally realizable magnetic fields, which cannot be otherwise simulated with smaller systems.
Constraining the Dense Matter Equation of State with Joint Analysis of NICER and LIGO/Virgo Measurements

The Neutron Star Interior Composition Explorer collaboration recently published a joint estimate of the mass and the radius of PSR J0030+0451, derived via X-ray pulse-profile modeling. Raaijmakers et al. explored the implications of this measurement for the dense matter equation of state (EOS) using two parameterizations of the high-density EOS: a piecewise-polytropic model, and a model based on the speed of sound in neutron stars (NSs). In this work we obtain further constraints on the EOS following this approach, but we also include information about the tidal deformability of NSs from the gravitational wave signal of the compact binary merger GW170817. We compare the constraints on the EOS to those set by the recent measurement of a 2.14 M⊙ pulsar, included as a likelihood function approximated by a Gaussian, and find a small increase in information gain. To show the flexibility of our method, we also explore the possibility that GW170817 was a NS–black hole merger, which yields weaker constraints on the EOS.
On the persistence of lytic bacteriophages in vivo and its consequences for bacteriophage therapy

Bacteriophages are viruses infecting bacteria and archaea. Many phage species cause infections which lead to the certain death of the infected prokaryotic host cell and the release of a large batch of phage progeny, yet they have been able to stably coexist with their bacterial hosts over the eons in nature, as well as in the majority of laboratory experiments reported in the literature. This possibility of a stable coexistence between populations of bacteria and bacteriophages can be suspected to critically reduce the chances of a successful therapeutic application of bacteriophages for combatting pathogenic bacterial infections in vivo. Here, we extend an established differential equation model describing the interaction of bacteria, bacteriophages, and the host immune system, modelling different degrees of spatial heterogeneity of the host organism by introducing a scaling parameter which alters the encounter rates of the different cell populations. We demonstrate by rigorous mathematical analysis that, depending on the degree of spatial heterogeneity, the system will either converge to the desired state of bacterial and phage extinction, to a state of persisting bacterial infection with a completely eliminated bacteriophage population or to an equally undesirable state of a stable long-term bacteria-bacteriophage coexistence. We additionally provide numerical solutions of the model to illustrate the emerging dynamics and discuss some implications for bacteriophage therapy.