XQC and CSR constraints on strongly interacting dark matter with spin and velocity dependent cross sections

Dark matter that interacts strongly with baryons can avoid the stringent dark matter direct detection constraints, because, like baryons, they are likely to be absorbed when traversing the rocks, leading to a suppressed flux in deep underground labs. Such strongly interacting dark matter, however, can be probed by dark matter experiments or other experiments operated on the ground level or in the atmosphere. In this paper we carry out systematic analysis of two of these experiments, XQC and CSR, to compute the experimental constraints on the strongly interacting dark matter in the following three scenarios: (1) spin-independent and spin-dependent interactions; (2) different velocity dependent cross sections; (3) different dark matter mass fractions. Some of the scenarios are first analyzed in the literature. We find that the XQC exclusion region has some non-trivial dependencies on the various parameters and the limits in the spin-dependent case is quite different from the spin-independent case. A peculiar region in the parameter space, where the XQC constraint disappears, is also found in our Monte Carlo simulations. This occurs in the case where the interaction cross section is proportional to the square of the velocity. We further compare our XQC and CSR limits to other experimental constraints, and find that a large parameter space is allowed by various experiments if the dark matter mass fraction is sufficiently small, $f_\chi\lesssim 10^{-4}$.Comment: 26 pages, 13 figures. v2: added references, more CMB and Lyman-alpha constraints, conclusion unchanged. v3: journal versio

Fine-Grained Car Detection for Visual Census Estimation

Targeted socioeconomic policies require an accurate understanding of a country's demographic makeup. To that end, the United States spends more than 1 billion dollars a year gathering census data such as race, gender, education, occupation and unemployment rates. Compared to the traditional method of collecting surveys across many years which is costly and labor intensive, data-driven, machine learning driven approaches are cheaper and faster--with the potential ability to detect trends in close to real time. In this work, we leverage the ubiquity of Google Street View images and develop a computer vision pipeline to predict income, per capita carbon emission, crime rates and other city attributes from a single source of publicly available visual data. We first detect cars in 50 million images across 200 of the largest US cities and train a model to predict demographic attributes using the detected cars. To facilitate our work, we have collected the largest and most challenging fine-grained dataset reported to date consisting of over 2600 classes of cars comprised of images from Google Street View and other web sources, classified by car experts to account for even the most subtle of visual differences. We use this data to construct the largest scale fine-grained detection system reported to date. Our prediction results correlate well with ground truth income data (r=0.82), Massachusetts department of vehicle registration, and sources investigating crime rates, income segregation, per capita carbon emission, and other market research. Finally, we learn interesting relationships between cars and neighborhoods allowing us to perform the first large scale sociological analysis of cities using computer vision techniques.Comment: AAAI 201

Une erreur peut en cacher une autre : les apprenants chinois du français L2 face aux problèmes de la morphologie écrite du genre

Dans la zone de la morphologie écrite, l’acquisition du système du genre représente un lieu de difficulté majeure. L’objectif de notre travail est de décrire la réalisation de la morphologie écrite du genre chez les apprenants chinois du français L2. Nous examinerons les données issues d’une tâche écrite semi-spontanée des apprenants chinois, en nous attachant plus particulièrement à leur profil développemental, et les tendances langagières spécifiques à ce groupe d’apprenants face aux problèmes de morphologie du genre. En outre, une analyse des erreurs commises par les apprenants, ainsi que les explications possibles pour interpréter ces erreurs seront également présentée

Intensity Mapping Functions For HDR Panorama Imaging: Weighted Histogram Averaging

It is challenging to stitch multiple images with different exposures due to possible color distortion and loss of details in the brightest and darkest regions of input images. In this paper, a novel intensity mapping algorithm is first proposed by introducing a new concept of weighted histogram averaging (WHA). The proposed WHA algorithm leverages the correspondence between the histogram bins of two images which are built up by using the non-decreasing property of the intensity mapping functions (IMFs). The WHA algorithm is then adopted to synthesize a set of differently exposed panorama images. The intermediate panorama images are finally fused via a state-of-the-art multi-scale exposure fusion (MEF) algorithm to produce the final panorama image. Extensive experiments indicate that the proposed WHA algorithm significantly surpasses the related state-of-the-art intensity mapping methods. The proposed high dynamic range (HDR) stitching algorithm also preserves details in the brightest and darkest regions of the input images well. The related materials will be publicly accessible at https://github.com/yilun-xu/WHA for reproducible research.Comment: 11 pages, 5 figure

Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US

The United States spends more than $1B each year on initiatives such as the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed half a decade. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may provide a cheaper and faster alternative. Here, we present a method that determines socioeconomic trends from 50 million images of street scenes, gathered in 200 American cities by Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22M automobiles in total (8% of all automobiles in the US), was used to accurately estimate income, race, education, and voting patterns, with single-precinct resolution. (The average US precinct contains approximately 1000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a 15-minute drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next Presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographic trends may effectively complement labor-intensive approaches, with the potential to detect trends with fine spatial resolution, in close to real time.Comment: 41 pages including supplementary material. Under review at PNA

Study on Greenway Plant Landscape Based on Bird Habitat Conservation - A Case Study of Wenyu River - North Canal Greenway in Beijing

In recent years, rapid urbanization is leading to a sharp decrease of bird diversity in city. The plant landscape in the greenway plays an important role in habitat conservation. This paper aims to explore the effects of plant landscape planning for the bird habitat conservation in urban greenway, and to study the design methods of greenway plant landscapes based on bird habitats conservation. Wenyu River - North Canal, a river located in the east of Beijing with uninterrupted green spaces along the coast, has the potential to become the migration channel for migratory birds. Dongjiao Wetland Park is an important node. At the macro level, the program investigated the vegetation pattern of Wenyu River-North Canal by using GIS technology and analyzed the distribution and ecological connectivity of different bird habitat types in the greenway. The results show that along the Wenyu River-North Canal, the distribution of habitats for some bird groups is uneven and some habitat types are poorly connected. At the micro level, a field study was conducted in Dongjiao Wetland Park in combination with actual projects, in which the forest form distribution and plant species composition were analyzed and the bird biotope was mapped. The results show that in the Dongjiao Wetland Park, the plant community is dominated by arbor-herb type; evergreen plants, shrubs and food plants are lacking; grasslands habitats and wetlands habitats were small and the area disturbed by human is large. According to the analysis results, aiming at bird habitat conservation, a vegetation landscape optimization plan of Wenyu River-North Canal Greenway and a plant landscape reconstruction design of the Northern Park of Dongjiao Wetland Park were proposed, including protecting important habitat patches, optimizing plant community structure and selecting plant species