Miracle-dz/UniFAR-FTUs huggingface dataset:
Extinction at the Galactic Center Using Near- and Mid-infrared Broadband Photometry: A Twist on the Rayleigh–Jeans Color Excess Method

We present an extinction map of the inner ∼15′ by 16′ of the Galactic center (GC) with map pixels measuring 5″ × 5″ using integrated light color measurements in the near- and mid-infrared. We use a variant of the Rayleigh–Jeans color excess (RJCE) method first described by Majewski et al. as the basis of our work, although we have approached our problem with a Bayesian mindset and dispensed with point-source photometry in favor of surface photometry, turning the challenge of the extremely crowded field at the GC into an advantage. Our results show that extinction at the GC is not inconsistent with a single power-law coefficient, β = 2.03 ± 0.06, and compare our results with those using the red clump (RC) point-source photometry method of extinction estimation. We find that our measurement of β and its apparent lack of spatial variation are in agreement with prior studies, despite the bimodal distribution of values in our extinction map at the GC with peaks at 5 and 7.5 mag. This bimodal nature of extinction is likely due to the infrared dark clouds that obscure portions of the inner GC field. We present our extinction law and map of the GC region using the point-source catalog of infrared sources compiled by DeWitt et al. The dereddening is limited by the error in the extinction measurement (typically 0.6 mag), which is affected by the size of our map pixels and is not fine-grained enough to separate out the multiple stellar populations present toward the GC.
Deep Learning for Stock Market Prediction

The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.
SNEAP: A Fast and Efficient Toolchain for Mapping Large-Scale Spiking Neural Network onto NoC-based Neuromorphic Platform

Spiking neural network (SNN), as the third generation of artificial neural networks, has been widely adopted in vision and audio tasks. Nowadays, many neuromorphic platforms support SNN simulation and adopt Network-on-Chips (NoC) architecture for multi-cores interconnection. However, a large volume and run-time communication on the interconnection has a significant effect on performance of the platform. In this paper, we propose a toolchain called SNEAP (Spiking NEural network mAPping toolchain) for mapping SNNs to neuromorphic platforms with multi-cores, which aims to reduce the energy and latency brought by spike communication on the interconnection. SNEAP includes two key steps: partitioning the SNN to reduce the spikes communicated between partitions, and mapping the partitions of SNN to the NoC to reduce average hop of spikes under the constraint of hardware resources. SNEAP effectively reduces the energy and latency on the NoC-based neuromorphic platform and spend less time than other toolchains. The experimental results show that SNEAP can achieve average 418X reduction in end-to-end execution time, and reduce energy consumption and spike latency, on average, by 23% and 51% respectively, compared with SpiNeMap.
On the Search Efficiency of Parallel Lévy Walks on ${\mathbb Z}^2$

Motivated by the \emph{Levy flight foraging hypothesis} -- the premise that the movement of various animal species searching for food resembles a \emph{Levy walk} -- we study the hitting time of parallel Levy walks on the infinite 2-dimensional grid. Levy walks are characterized by a parameter $\alpha \in(1,+\infty)$, that is the exponent of the power law distribution of the time intervals at which the moving agent randomly changes direction. In the setting we consider, called the ANTS problem (Feinerman et al. PODC 2012), $k$ independent discrete-time Levy walks start simultaneously at the origin, and we are interested in the time $h_{k,\ell}$ before some walk visits a given target node on the grid, at distance $\ell$ from the origin. In this setting, we provide a comprehensive analysis of the efficiency of Levy walks for the complete range of the exponent $\alpha$. For any choice of $\alpha$, we observe that the \emph{total work} until the target is visited, i.e., the product $k \cdot h_{k,\ell}$, is at least $\Omega(\ell^2)$ with constant probability. Our main result is that the right choice for $\alpha$ to get optimal work varies between $1$ and $3$, as a function of the number $k$ of available agents. For instance, when $k = \tilde \Theta(\ell^{1-\epsilon})$, for some positive constant $\epsilon < 1$, then the unique optimal setting for $\alpha$ lies in the \emph{super-diffusive} regime $(2,3)$, namely, $\alpha = 2+\epsilon$. Our results should be contrasted with various previous works in the continuous time-space setting showing that the exponent $\alpha = 2$ is optimal for a wide range of related search problems on the plane.
LiDAR-Based Online 3D Video Object Detection With Graph-Based Message Passing and Spatiotemporal Transformer Attention

Existing LiDAR-based 3D object detectors usually focus on the single-frame detection, while ignoring the spatiotemporal information in consecutive point cloud frames. In this paper, we propose an end-to-end online 3D video object detector that operates on point cloud sequences. The proposed model comprises a spatial feature encoding component and a spatiotemporal feature aggregation component. In the former component, a novel Pillar Message Passing Network (PMPNet) is proposed to encode each discrete point cloud frame. It adaptively collects information for a pillar node from its neighbors by iterative message passing, which effectively enlarges the receptive field of the pillar feature. In the latter component, we propose an Attentive Spatiotemporal Transformer GRU (AST-GRU) to aggregate the spatiotemporal information, which enhances the conventional ConvGRU with an attentive memory gating mechanism. AST-GRU contains a Spatial Transformer Attention (STA) module and a Temporal Transformer Attention (TTA) module, which can emphasize the foreground objects and align the dynamic objects, respectively. Experimental results demonstrate that the proposed 3D video object detector achieves state-of-the-art performance on the large-scale nuScenes benchmark.
Preserving Statistical Privacy in Distributed Optimization

We present a distributed optimization protocol that preserves statistical privacy of agents’ local cost functions against a passive adversary that corrupts some agents in the network. The protocol is a composition of a distributed “<italic>zero-sum</italic>” obfuscation protocol that obfuscates the agents’ local cost functions, and a standard non-private distributed optimization method. We show that our protocol protects the statistical privacy of the agents’ local cost functions against a passive adversary that corrupts up to <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula> arbitrary agents as long as the communication network has (<inline-formula> <tex-math notation="LaTeX">$t+1$ </tex-math></inline-formula>)-vertex connectivity. The “<italic>zero-sum</italic>” obfuscation protocol preserves the sum of the agents’ local cost functions and therefore ensures accuracy of the computed solution.
Introducing Anisotropic Minkowski Functionals and Quantitative Anisotropy Measures for Local Structure Analysis in Biomedical Imaging.

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p < 10-4). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications.
DNA methylation profiling to predict overall survival risk in gastric cancer: development and validation of a nomogram to optimize clinical management

DNA methylation has been reported to serve an important role in the carcinogenesis and development of gastric cancer. Our aim was to systematically develop an individualized prediction model of the survival risk combing clinical and methylation factors in gastric cancer. A univariate Cox proportional risk regression analysis was used to identify the prognosis-associated methylation sites based on the differentially expressed methylation sites between early and advanced gastric cancer group, then we applied least absolute shrinkage and selection operator (LASSO) Cox regression model to screen candidate methylation sites. Subsequently, multivariate Cox proportional risk regression analysis was conducted to identify predictive signature according to the candidate sites. Relative operating characteristic curve (ROC) analysis manifested that an 11-methylation signature exhibited great predictive efficiency for 1-, 3-, 5-year survival events. Patients in the low-risk group classified according to 11-methylation signature-based risk score yield significantly better survival than that in high-risk group. Moreover, Cox regression analysis combing methylation-based risk score and other clinical factors indicated that 11-methylation signature served as an independent risk factor. The predictive value of risk score was validated in the testing dataset. In addition, a nomogram was constructed and the ROC as well as calibration plots analysis demonstrated the good performance and clinical application of the nomogram. In conclusion, the result suggested the 11-DNA methylation signature may be potentially independent prognostic marker and functioned as a significant tool for guiding the clinical prediction of gastric cancer patients' overall survival.
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning

Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics. Such approaches find TSP solutions of good quality but require additional procedures such as beam search and sampling to improve solutions and achieve state-of-the-art performance. However, few studies have focused on improvement heuristics, where a given solution is improved until reaching a near-optimal one. In this work, we propose to learn a local search heuristic based on 2-opt operators via deep reinforcement learning. We propose a policy gradient algorithm to learn a stochastic policy that selects 2-opt operations given a current solution. Moreover, we introduce a policy neural network that leverages a pointing attention mechanism, which unlike previous works, can be easily extended to more general k-opt moves. Our results show that the learned policies can improve even over random initial solutions and approach near-optimal solutions at a faster rate than previous state-of-the-art deep learning methods.
Fundamental differences in the radio properties of red and blue quasars: insight from the LOFAR Two-metre Sky Survey (LoTSS)

Red quasi-stellar objects (QSOs) are a subset of the luminous end of the cosmic population of active galactic nuclei (AGNs), most of which are reddened by intervening dust along the line of sight towards their central engines. In recent work from our team, we developed a systematic technique to select red QSOs from the Sloan Digital Sky Survey, and demonstrated that they have distinctive radio properties using the Faint Images of the Radio Sky at Twenty centimetres radio survey. Here we expand our study using low-frequency radio data from the LOFAR Two-metre Sky Survey (LoTSS). With the improvement in depth that LoTSS offers, we confirm key results: Compared to a control sample of normal ‘blue’ QSOs matched in redshift and accretion power, red QSOs have a higher radio detection rate and a higher incidence of compact radio morphologies. For the first time, we also demonstrate that these differences arise primarily in sources of intermediate radio loudness: Radio-intermediate red QSOs are × 3 more common than typical QSOs, but the excess diminishes among the most radio-loud systems and the most radio-quiet systems in our study. We develop Monte Carlo simulations to explore whether differences in star formation could explain these results, and conclude that, while star formation is an important source of low-frequency emission among radio-quiet QSOs, a population of AGN-driven compact radio sources is the most likely cause for the distinct low-frequency radio properties of red QSOs. Our study substantiates the conclusion that fundamental differences must exist between the red and normal blue QSO populations.
Post-operative wound complications following emergency and elective abdominal surgeries

The post-operative wound complications can be defined as any negative outcome as perceived either by the surgeon or by the patient. These complications can be encountered after any surgery, but the key to success is the early detection and the prompt management. Surgical site infections and wound and tissue dehiscence are wellknown postoperative complications in abdominal surgery. The severity of these complications embraces mild cases needing local wound care and antibiotics to serious cases with multiple reoperations and a high mortality rate. In most cases, such complications prolong hospitalization, with a substantial increase in cost of care. ABSTRACT
Past and recent contributions to indefinite sublinear elliptic problems

We review the indefinite sublinear elliptic equation $-\Delta u=a(x)u^{q}$ in a smooth bounded domain $\Omega\subset\mathbb{R}^{N}$, with Dirichlet or Neumann homogeneous boundary conditions. Here $0<q<1$ and $a$ is continuous and changes sign, in which case the strong maximum principle does not apply. As a consequence, the set of nonnegative solutions of these problems has a rich structure, featuring in particular both dead core and/or positive solutions. Overall, we are interested in sufficient and necessary conditions on $a$ and $q$ for the existence of positive solutions. We describe the main results from the past decades, and combine it with our recent contributions. The proofs are briefly sketched.
Many-body localization of zero modes

The celebrated Dyson singularity signals the relative delocalization of single-particle wave functions at the zero-energy symmetry point of disordered systems with a chiral symmetry. Here we show that analogous zero modes in interacting quantum systems can fully localize at sufficiently large disorder, but do so less strongly than nonzero modes, as signifed by their real-space and Fock-space localization characteristics. We demonstrate this effect in a spin-1 Ising chain, which naturally provides a chiral symmetry in an odd-dimensional Hilbert space, thereby guaranteeing the existence of a many-body zero mode at all disorder strengths. In the localized phase, the bipartite entanglement entropy of the zero mode follows an area law, but is enhanced by a system-size-independent factor of order unity when compared to the nonzero modes. Analytically, this feature can be attributed to a specific zero-mode hybridization pattern on neighboring spins. The zero mode also displays a symmetry-induced even-odd and spin-orientation fragmentation of excitations, characterized by real-space spin correlation functions, which generalizes the sublattice polarization of topological zero modes in noninteracting systems, and holds at any disorder strength.
The Nature of Ultra-diffuse Galaxies in Distant Massive Galaxy Clusters: A370 in the Hubble Frontier Fields

We report the discovery of ultra-diffuse galaxies (UDGs) in Abell 370 (A370; z = 0.375). We find 46 UDGs in A370 from the images of the Hubble Frontier Fields. Most UDGs are low-luminosity red sequence galaxies, while a few of them are blue UDGs. We estimate the abundance of UDGs in A370, N(UDG) = 644 ± 104. Combining these results with those of Abell S1063 (z = 0.348) and Abell 2744 (z = 0.308), we derive a mean radial number density profile of UDGs in the three clusters. The number density profiles of UDGs and bright galaxies show a discrepancy in the central region of the clusters: the profile of UDGs shows a flattening as clustercentric distance decreases, while that of bright galaxies shows a continuous increase. This implies that UDGs are prone to disruption in the central region of the clusters. The relation between the abundance of UDGs and virial masses of their host systems is described by a power law with an index of nearly one: for M200 > 1013 M⊙. We estimate approximately dynamical masses of UDGs using the fundamental manifold method and find that most UDGs have dwarf-like masses (M200 < 1011M⊙). This implies that most UDGs have a dwarf-like origin and a small number of them could be failed L* galaxies. These results suggest that multiple origins may contribute to the formation and evolution of UDGs in massive galaxy clusters.
The fate of disc galaxies in IllustrisTNG clusters

We study the stellar morphological evolution of disc galaxies within clusters in the TNG50 and TNG100 runs from the IllustrisTNG simulation suite. We select satellites of masses $10^{9.7} \leq M_{*,z=0}/\text{M}_{\odot} \leq 10^{11.6}$ residing in clusters of masses $10^{14} \lesssim M_{\text{200c,z=0}}/\text{M}_{\odot} \leq 10^{14.6}$ at $z=0$ and that were discs at accretion according to a kinematic morphology indicator (the circularity fraction). These are traced from the time of accretion to $z=0$ and compared to a control sample of central galaxies mass-matched at accretion. Most cluster discs become non-discy by $z=0$, in stark contrast with the control discs, of which a significant fraction remains discy over the same timescales. Cluster discs become non-discy accompanied by gas removal and star formation quenching, loss of dark matter and little growth or a loss of stellar mass. In contrast, control discs transform while also losing gas mass and quenching, but growing significantly in dark matter and stellar mass. Most cluster satellites change morphologies on similar timescales regardless of stellar mass, in $\sim0.5-4$ Gyr after accretion. Cluster discs that experienced more numerous and closer pericentric passages show the largest change in morphology. Morphological change in all cases requires the presence of a gravitational perturbation to drive stellar orbits to non-discy configurations, along with gas removal/heating to prevent replenishment of the disc through continued star-formation. For cluster discs, the perturbation is impulsive tidal shocking at pericentres and not tidal stripping of outer disc stellar material, whereas for control discs, a combination of mergers and AGN feedback appears to be the key driving force behind morphological transformations.
Transmission Helium Ion Microscopy of Graphene

We compare transmission He ion microscopy (HIM) to transmission electron microscopy (TEM) of graphene support films. We present spot transmission patterns that compare with scattering and range of ions in materials (SRIM) predictions, and show examples of scanning He$^+$ transmission images, based on integrated camera intensity. We also consider the potential for coherent HIM scattering.
THE IMPACT OF FOREIGN OWNERSHIP ON PROFITABILITY OF VIETNAMESE FIRMS LISTED ON HO CHI MINH STOCK EXCHANGE

The study examines the effect of foreign ownership on firm profitability in the Vietnamese context by using panel data of 161 listed firms on Ho Chi Minh Stock Exchange during the period of 2007 to 2014. This paper employs pooled OLS model, FEM and REM to investigate the relationship between foreign ownership and firm profitability. The study finds that foreign ownership has Ushaped relationship with firm profitability. Empirical results reveal that firm profitability declines with an increase of foreign ownership up to about 25.7% and when foreign ownership goes beyond this level, profitability increases. The results indicate that although foreign ownership may associates with low profitability at first, it would enhance firm profitability at a certain level.