Miracle-dz/UniFAR-FTUs huggingface dataset:
How Are Galaxies Assigned to Halos? Searching for Assembly Bias in the SDSS Galaxy Clustering

Clustering of dark matter halos has been shown to depend on halo properties beyond mass, such as halo concentration, a phenomenon referred to as halo assembly bias. Standard halo-occupation models (HOD) in large-scale structure studies assume that halo mass alone is sufficient in characterizing the connection between galaxies and halos. Modeling of galaxy clustering can face systematic effects if the number of galaxies within a halo is correlated with other halo properties. Using the Small MultiDark-Planck high-resolution N-body simulation and the clustering measurements of the Sloan Digital Sky Survey DR7 main galaxy sample, we investigate the extent to which the concentration dependence of halo occupation can be constrained. Furthermore, we study how allowing for the concentration dependence can improve our modeling of galaxy clustering. Our constraints on HOD with assembly bias suggest that satellite population is not correlated with halo concentration at fixed halo mass. At fixed halo mass, our constraints favor lack of correlation between the occupation of centrals and halo concentration in the most luminous samples (Mr < −21.5, −21), and modest correlation in the Mr < −20.5, −20, −19.5 samples. We show that, in comparison with abundance-matching mock catalogs, our findings suggest qualitatively similar but modest levels of the impact of halo assembly bias on galaxy clustering. The effect is only present in the central occupation and becomes less significant in brighter galaxy samples. Furthermore, by performing model comparison based on information criteria, we find that in most cases, the standard mass-only HOD model is still favored by the observations.
Towards Secure Fog Computing: A Survey on Trust Management, Privacy, Authentication, Threats and Access Control

Fog computing is an emerging computing paradigm that has come into consideration for the deployment of Internet of Things (IoT) applications amongst researchers and technology industries over the last few years. Fog is highly distributed and consists of a wide number of autonomous end devices, which contribute to the processing. However, the variety of devices offered across different users are not audited. Hence, the security of Fog devices is a major concern that should come into consideration. Therefore, to provide the necessary security for Fog devices, there is a need to understand what the security concerns are with regards to Fog. All aspects of Fog security, which have not been covered by other literature works, need to be identified and aggregated. On the other hand, privacy preservation for user’s data in Fog devices and application data processed in Fog devices is another concern. To provide the appropriate level of trust and privacy, there is a need to focus on authentication, threats and access control mechanisms as well as privacy protection techniques in Fog computing. In this paper, a survey along with a taxonomy is proposed, which presents an overview of existing security concerns in the context of the Fog computing paradigm. Moreover, the Blockchain-based solutions towards a secure Fog computing environment is presented and various research challenges and directions for future research are discussed.
Guiding Extractive Summarization with Question-Answering Rewards

Highlighting while reading is a natural behavior for people to track salient content of a document. It would be desirable to teach an extractive summarizer to do the same. However, a major obstacle to the development of a supervised summarizer is the lack of ground-truth. Manual annotation of extraction units is cost-prohibitive, whereas acquiring labels by automatically aligning human abstracts and source documents can yield inferior results. In this paper we describe a novel framework to guide a supervised, extractive summarization system with question-answering rewards. We argue that quality summaries should serve as document surrogates to answer important questions, and such question-answer pairs can be conveniently obtained from human abstracts. The system learns to promote summaries that are informative, fluent, and perform competitively on question-answering. Our results compare favorably with those reported by strong summarization baselines as evaluated by automatic metrics and human assessors.
Corticosteroid Injection Alone or Combined with Surgical Excision of Keloids versus Other Therapies Including Ionising Radiotherapy: A Systematic Review and Meta-Analysis of Randomised Controlled Trials

Keloid scars are difficult to manage and remain a therapeutic challenge. Corticosteroid therapy alone or ionising radiation (radiotherapy) alone or combined with surgery are first-line treatments, but the scientific justification for these treatments is unclear. The aim of this systematic review and meta-analysis of randomised controlled trials (RCTs) is to assess the effects of intralesional corticosteroid injection in treating keloids or preventing their recurrence after surgical removal. Searches for RCTs were conducted through the MEDLINE, EMBASE, EBSCO and Cochrane databases from January 1974 to September 2017. Two authors independently reviewed study eligibility, extracted data, analysed the results, and assessed methodological quality. Sixteen RCTs that included more than 814 patients were scrutinised. The quality of evidence for most outcomes was moderate to high. In 10 RCTs, corticosteroid intralesional injections were compared with 5-fluorouracil, etanercept, cryosurgery, botulinum toxin, topical corticosteroid under a silicone dressing, and radiotherapy. Corticosteroid intralesional injections were more effective than radiotherapy (RR 3.3, 95% CI: 1.4–8.1) but equipotent with the other interventions. In conjunction with keloid excision, corticosteroid treatment was compared with radiotherapy, interferon α-2b and verapamil. In two RCTs, there were fewer keloid recurrences (RR 0.43, 95% CI: 0.21–0.89) demonstrated with adjuvant radiotherapy than with corticosteroid injections. More high-quality, large-scale RCTs are required to establish the effectiveness of corticosteroids and other therapies in keloid management.
Towards Resisting Large Data Variations via Introspective Learning

Learning deep networks which can resist large variations between training and testing data are essential to build accurate and robust image classifiers. Towards this end, a typical strategy is to apply data augmentation to enlarge the training set. However, standard data augmentation is essentially a brute-force method which is inefficient, as it performs all the pre-defined transformations to every training sample. In this paper, we propose a principled approach to train networks with significantly improved resistance to large variations between training and testing data. This is achieved by embedding a learnable transformation module into the introspective network, which is a convolutional neural network (CNN) classifier empowered with generative capabilities. Our approach alternatively synthesizes pseudo-negative samples with learned transformations and enhances the classifier by retraining it with synthesized samples. Experimental results verify that our approach significantly improves the ability of deep networks to resist large variations between training and testing data and achieves classification accuracy improvements on several benchmark datasets, including MNIST, affNIST, SVHN, CIFAR-10 and miniImageNet.
ALMA survey of Class II protoplanetary disks in Corona Australis: a young region with low disk masses

Context. In recent years, the disk populations in a number of young star-forming regions have been surveyed with the Atacama Large Millimeter/submillimeter Array (ALMA). Understanding the disk properties and their correlation with the properties of the central star is critical to understanding planet formation. In particular, a decrease of the average measured disk dust mass with the age of the region has been observed, consistent with grain growth and disk dissipation. Aims. We aim to compare the general properties of disks and their host stars in the nearby (d = 160 pc) Corona Australis (CrA) star forming region to those of the disks and stars in other regions. Methods. We conducted high-sensitivity continuum ALMA observations of 43 Class II young stellar objects in CrA at 1.3 mm (230 GHz). The typical spatial resolution is ~0.3′′. The continuum fluxes are used to estimate the dust masses of the disks, and a survival analysis is performed to estimate the average dust mass. We also obtained new VLT/X-shooter spectra for 12 of the objects in our sample for which spectral type (SpT) information was missing. Results. Twenty-four disks were detected, and stringent limits have been put on the average dust mass of the nondetections. Taking into account the upper limits, the average disk mass in CrA is 6 ± 3 M⊕. This value is significantly lower than that of disks in other young (1–3 Myr) star forming regions (Lupus, Taurus, Chamaeleon I, and Ophiuchus) and appears to be consistent with the average disk mass of the 5–10 Myr-old Upper Sco. The position of the stars in our sample on the Herzsprung-Russel diagram however seems to confirm that CrA has an age similar to Lupus. Neither external photoevaporation nor a lower-than-usual stellar mass distribution can explain the low disk masses. On the other hand, a low-mass disk population could be explained if the disks were small, which could happen if the parent cloud had a low temperature or intrinsic angular momentum, or if the angular momentum of the cloud were removed by some physical mechanism such as magnetic braking. Even in detected disks, none show clear substructures or cavities. Conclusions. Our results suggest that in order to fully explain and understand the dust mass distribution of protoplanetary disks and their evolution, it may also be necessary to take into consideration the initial conditions of star- and disk-formation process. These conditions at the very beginning may potentially vary from region to region, and could play a crucial role in planet formation and evolution.
White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks

Adversarial examples are important for understanding the behavior of neural models, and can improve their robustness through adversarial training. Recent work in natural language processing generated adversarial examples by assuming white-box access to the attacked model, and optimizing the input directly against it (Ebrahimi et al., 2018). In this work, we show that the knowledge implicit in the optimization procedure can be distilled into another more efficient neural network. We train a model to emulate the behavior of a white-box attack and show that it generalizes well across examples. Moreover, it reduces adversarial example generation time by 19x-39x. We also show that our approach transfers to a black-box setting, by attacking The Google Perspective API and exposing its vulnerability. Our attack flips the API-predicted label in 42% of the generated examples, while humans maintain high-accuracy in predicting the gold label.
Sequence-to-Sequence Speech Recognition with Time-Depth Separable Convolutions

We propose a fully convolutional sequence-to-sequence encoder architecture with a simple and efficient decoder. Our model improves WER on LibriSpeech while being an order of magnitude more efficient than a strong RNN baseline. Key to our approach is a time-depth separable convolution block which dramatically reduces the number of parameters in the model while keeping the receptive field large. We also give a stable and efficient beam search inference procedure which allows us to effectively integrate a language model. Coupled with a convolutional language model, our time-depth separable convolution architecture improves by more than 22% relative WER over the best previously reported sequence-to-sequence results on the noisy LibriSpeech test set.
Protein Dynamics and Time Resolved Protein Crystallography at Synchrotron Radiation Sources: Past, Present and Future

The ultrabright and ultrashort pulses produced at X-ray free electron lasers (XFELs) has enabled studies of crystallized molecular machines at work under ‘native’ conditions at room temperature by the so-called time-resolved serial femtosecond crystallography (TR-SFX) technique. Since early TR-SFX experiments were conducted at XFELs, it has been largely reported in the literature that time-resolved X-ray experiments at synchrotrons are no longer feasible or are impractical due to the severe technical limitations of these radiation sources. The transfer of the serial crystallography approach to newest synchrotrons upgraded for higher flux density and with beamlines using sophisticated focusing optics, submicron beam diameters and fast low-noise photon-counting detectors offers a way to overcome these difficulties opening new and exciting possibilities. In fact, there is an increasing amount of publications reporting new findings in structural dynamics of protein macromolecules by using time resolved crystallography from microcrystals at synchrotron sources. This review gathers information to provide an overview of the recent work and the advances made in this filed in the past years, as well as outlines future perspectives at the next generation of synchrotron sources and the upcoming compact pulsed X-ray sources.
The emergence of sequence-dependent structural motifs in stretched, torsionally constrained DNA

The double-helical structure of DNA results from canonical base pairing and stacking interactions. However, variations from steady-state conformations result from mechanical perturbations in cells. These different topologies have physiological relevance but their dependence on sequence remains unclear. Here, we use molecular dynamics simulations to show that sequence differences result in markedly different structural motifs upon physiological twisting and stretching. We simulated overextension on four different sequences of DNA ((AA)12, (AT)12, (GG)12 and (GC)12) with supercoiling densities within the physiological range. We found that DNA denatures in the majority of stretching simulations, surprisingly including those with overtwisted DNA. GC-rich sequences were observed to be more stable than AT-rich, with the specific response dependent on base pair ordering. Furthermore, we found that (AT)12 forms stable periodic structures with non-canonical hydrogen bonds in some regions and non-canonical stacking in others, whereas (GC)12 forms a stacking motif of four base pairs independent of supercoiling density. Our results demonstrate that 20-30% DNA extension is sufficient for breaking B-DNA around and significantly above cellular supercoiling, and that the DNA sequence is crucial for understanding structural changes under mechanical stress. Our findings have important implications for the activities of protein machinery interacting with DNA in all cells.
Generalizing discrete convolutions for unstructured point clouds

Point clouds are unstructured and unordered data, as opposed to images. Thus, most of machine learning approaches, developed for images, cannot be directly transferred to point clouds. It usually requires data transformation such as voxelization, inducing a possible loss of information. In this paper, we propose a generalization of the discrete convolutional neural networks (CNNs) able to deal with sparse input point cloud. We replace the discrete kernels by continuous ones.The formulation is simple, does not set the input point cloud size and can easily be used for neural network design similarly to 2D CNNs. We present experimental results, competitive with the state of the art, on shape classification, part segmentation and semantic segmentation for large scale clouds.
Envelope Convection, Surface Magnetism, and Spots in A and Late B-type Stars

Weak magnetic fields have recently been detected in a number of A-type stars, including Vega and Sirius. At the same time, space photometry observations of A and late B-type stars from Kepler and TESS have highlighted the existence of rotational modulation of surface features akin to stellar spots. Here we explore the possibility that surface magnetic spots might be caused by the presence of small envelope convective layers at or just below the stellar surface, caused by recombination of H and He. Using 1D stellar evolution calculations and assuming an equipartition dynamo, we make simple estimates of field strength at the photosphere. For most models, the largest effects are caused by a convective layer driven by second helium ionization. While it is difficult to predict the geometry of the magnetic field, we conclude that the majority of intermediate-mass stars should have dynamo-generated magnetic fields of order a few Gauss at the surface. These magnetic fields can appear at the surface as bright spots and cause photometric variability via rotational modulation, which could also be widespread in A-stars. The amplitude of surface magnetic fields and their associated photometric variability are expected to decrease with increasing stellar mass and surface temperature, so that magnetic spots and their observational effects should be much harder to detect in late B-type stars.
Opportunities in Optimizing Car Door Weight

Car door is an important part that is used for the protection of passengers from side collisions and also for the comfort of entering and leaving the car. Car doors weigh around 2% of total car weight. Conventional car doors are made from steel as it is sturdy. But it also contributes to the increment of the car weight. As scientific researchers are getting more advanced, finding out new materials as a substitute for conventionally used steel becomes more evident. Advancement in manufacturing and joining technologies pave the way to encouraging us to search for new improvement opportunities in car door weight optimization. Side impact collision of a car is the most hazardous as it directly hits the driver which causes injuries or death all over the world. Vehicle is one the medium of the terribly hazardous crashes causing injuries and death annually around the word. In this research paper, the most important parameters including materials, loads, stresses and deformation were studied to find out opportunities of car door weight optimization. Reduction in car weight means less fuel consumption which enables automobile industries to reduce carbon emission levels of a car. But the safety of passengers is the main priority which demands a light-weight as well as strong material that can sustain collision impact. The aim of the report is to compare the structure of different materials used in car door with currently used material. The designing of the car door is done using computer-aided three-dimensional interactive application (CATIA) software. Impact analysis is conducted on the door using ANSYS Workbench software by varying the materials. S-glass epoxy UD alloys become promising substitutes because of their satisfactory mechanical properties and specific strength.
Impact on Inclusive Development of Information and Communication Technologies in Turkey

In this study, the impact on inclusive development of information and communication technologies in Turkey’s economy is analyzed. Information and communication technologies are represented by mobile phone penetration measured by mobile cellular subscriptions, and inclusive development is measured by the human development index (IHDI) adapted to inequality. The annual data used in this study covers the period 1990-2019. After examining the stationarity of the series of variables, the cointegration between variables was investigated using the ARDL approach. As a result of the ARDL test, a cointegration between inclusive development and information and communication technologies has been determined. Toda-Yamamoto causality test was conducted to find the direction of the relationship between variables. The findings obtained from the analysis of causality determined that it has an impact on inclusive development of information and communication technologies in Turkey.
Optical orientation and alignment of excitons in direct and indirect band gap (In,Al)As/AlAs quantum dots with type-I band alignment

The spin structure and spin dynamics of excitons in an ensemble of (In,Al)As/AlAs quantum dots (QDs) with type-I band alignment, containing both direct and indirect band gap dots, are studied. Time-resolved and spectral selective techniques are used to distinguish between the direct and indirect QDs. The exciton fine structure is studied by means of optical alignment and optical orientation techniques in magnetic fields applied in the Faraday or Voigt geometries. A drastic difference in emission polarization is found for the excitons in the direct QDs involving a $\Gamma$-valley electron and the excitons in the indirect QDs contributed by an $X$-valley electron. We show that in the direct QDs the exciton spin dynamics is controlled by the anisotropic exchange splitting, while in the indirect QDs it is determined by the hyperfine interaction with nuclear field fluctuations. The anisotropic exchange splitting is determined for the direct QD excitons and compared with model calculations.
How Spatially Concentrated Are Industrial Clusters?: A Meta-analysis

This study conducts a meta-analysis of empirical studies that have measured the spatial scale of industrial clustering. Two types of scales are examined: the peak scale (at which cluster effects are maximized) and the maximum reach (beyond which cluster effects are undetectable). We find that the scale varies significantly by the unit of analysis, industry sector, country of study, and the sources of cluster effects examined (e.g., knowledge spillovers, localization, and urbanization). Planners and policy makers should tailor the geographies embodied in cluster strategies to match the specific local needs and circumstances.
Randomization Tests for Peer Effects in Group Formation Experiments

Measuring the effect of peers on individuals' outcomes is a challenging problem, in part because individuals often select peers who are similar in both observable and unobservable ways. Group formation experiments avoid this problem by randomly assigning individuals to groups and observing their responses; for example, do first‐year students have better grades when they are randomly assigned roommates who have stronger academic backgrounds? In this paper, we propose randomization‐based permutation tests for group formation experiments, extending classical Fisher Randomization Tests to this setting. The proposed tests are justified by the randomization itself, require relatively few assumptions, and are exact in finite samples. This approach can also complement existing strategies, such as linear‐in‐means models, by using a regression coefficient as the test statistic. We apply the proposed tests to two recent group formation experiments.