Miracle-dz/UniFAR-FTUs huggingface dataset:
Associated Multi-Hazards in the British Isles

Hazards such as heatwaves, droughts and floods are often associated with persistent 17 weather patterns. Atmosphere-Ocean General Circulation Models (AOGCMs) are important tools 18 for evaluating projected changes in extreme weather. Here, we demonstrate that 2-day weather 19 pattern persistence, derived from the Lamb Weather Types (LWTs) objective scheme, is a useful 20 concept for both investigating climate risks from multi-hazard events as well as for assessing 21 AOGCM realism. This study evaluates the ability of a Coupled Model Intercomparison Project 22 Phase 5 (CMIP5) multi-model sub-ensemble of 10 AOGCMs at reproducing seasonal LWTs 23 persistence and frequencies over the British Isles (BI). Changes in persistence are investigated under 24 two Representative Concentration Pathways (RCP8.5 and RCP4.5) up to 2100. The ensemble broadly 25 replicates historical LWTs persistence observed in reanalyses (1971–2000). Future persistence and 26 frequency of summer anticyclonic LWT are found to increase, implying heightened risk of drought 27 and heatwaves. On the other hand, the cyclonic LWT decreases in autumn suggesting reduced 28 likelihood of flooding and severe gales. During winter, AOGCMs point to increased risk of 29 concurrent fluvial flooding-wind hazards by 2100, however, they also tend to over-estimate such 30 risks when compared to reanalyses. In summer, the strength of the nocturnal Urban Heat Island 31 (UHI) of London could intensify, enhancing the likelihood of combined heatwave-poor air quality 32 events. Further research is needed to explore other multi-hazards in relation to changing weather 33 pattern persistence and how best to communicate such threats to vulnerable communities. 34
Adherence to MRI Protocol Consensus Guidelines in Multiple Sclerosis Patients at Taif Hospitals

Background: multiple sclerosis (MS) is a chronic inflammatory disease that affects the central nervous system (CNS). Its diagnosis is characterized by foci of demyelination, disseminated over time and anatomical space, both clinically and radiologically. Consensus guidelines for the diagnosis of MS have been established to identify the proper sequences and imaging technique. Objective: this study aims to investigate the adherence of Taif hospitals to these consensus guidelines. Method: data were collected from the radiology departments of 3 main hospitals in Taif (King Abdul-Aziz Specialist Hospital, King Faisal Medical Complex, and Al-Hada Hospital for Armed Forces). The standard magnetic resonance imaging (MRI) protocol for MS in each hospital was reviewed using the picture archiving and communication system (PACS). The comparisons were based on the strength of the magnetic field, slice thickness, slice orientation, coverage, and the MR core sequences used. Results: each Taif hospital has its own protocol, which differ from consensus guidelines. The main difference is the hospitals’ lack of 3D sequences. Another difference is the lack of inversion recovery prepared T1 sequences. Finally, for follow-up patients, no contrast administration exists in Taif hospitals. Conclusion: this study shows some major differences in the MS protocols among Taif hospitals. This is probably due to poor awareness of the guidelines in the radiology community. Neuroradiologists at Taif hospitals should ensure the recommended protocol is followed.
Visualizing Vitriol: Hate Speech and Image Sharing in the 2020 Singaporean Elections (cid:63)

Online hate speech represents a damaging force to the health of digital discourse. While text-based research in this area has advanced signiﬁcantly, little work explicitly examines the visual components of online hate speech on social media platforms. This work empirically analyzes hate speech from a multimodal perspective by examining its association with image sharing in Twitter conversations during the 2020 Singaporean elections. We further link our ﬁndings to bot activity and potential information operations to discern the role of potential digital threats in heightening online conﬂicts. Higher levels of hate speech were detected in bot-rich communities which shared images depicting the in-cumbent Prime Minister and other contested elections in the Asia-Paciﬁc region. Implications of this work are discussed pertaining to advancing multimodal approaches in social cybersecurity more broadly.
Instance Segmentation of Point Clouds via Similarity Learning

We introduce a novel approach for instance segmentation of point clouds without using instance label information. Our method makes use of a form of similarity learning with semantic information. Specifically, we modify the loss function of the PointNet [38] neural network so that the network can learn to predict point features, where the point features are close to each other in feature space if the points belong to the same semantic category. We call this network trained with a modified loss function PointSimNet, for brevity. PointSimNet is trained on a set of point clouds of rooms where objects are grouped into different categories. After predicting features and semantic labels with PointSimNet, we perform spectral clustering to obtain instances. The spectral clustering method can be performed on features or coordinates. A similarity matrix, given by applying a Gaussian kernel to a matrix of feature distances or point coordinate distances, is used to find the optimal number of clusters using the eigengap heuristic. Spectral clustering is then performed on the similarity matrix to obtain the final set of instances. We also provide an optional step of constrained clustering where the user can guide the clustering to refine the segmentation results. In this step, the user provides the desired number of clusters and one or more constraints where the user identifies points that cannot be linked together. We show that our instance segmentation method provides 16 percentage points lower than a state-of-the-art method in terms of average precision, but outperforms the method by 20 percentage points according to an alternative definition of the average precision. We are able to achieve these results without requiring any instance information in the training set. We also demonstrate that the user can improve the accuracy of results by adding a small number of constraints.
Context Aware Group Activity Recognition

This paper addresses the task of group activity recognition in multi-person videos. Existing approaches decompose this task into feature learning and relational reasoning. Despite showing progress, these methods only rely on appearance features for people and overlook the available contextual information, which can play an important role in group activity understanding. In this work, we focus on the feature learning aspect and propose a two-stream architecture that not only considers person-level appearance features, but also makes use of contextual information present in videos for group activity recognition. In particular, we propose to use two types of contextual information beneficial for two different scenarios: pose context and scene context that provide crucial cues for group activity understanding. We combine appearance and contextual features to encode each person with an enriched representation. Finally, these combined features are used in relational reasoning for predicting group activities. We evaluate our method on two benchmarks, Volleyball and Collective Activity and show that joint modeling of contextual information with appearance features benefits in group activity understanding.
Learning Robust Helpful Behaviors in Two-Player Cooperative Atari Environments

We initiate the study of helpful behavior in the setting of two-player Atari games, suitably modiﬁed to provide cooperative incentives. Our main interest is to understand whether reinforcement learning can be used to achieve robust, helpful behavior— where one agent is trained to help a second, partner agent. Robustness requires the helpful AI to be able to cooperate effectively with a diverse set of partners. We study this question with both artiﬁcial partner agents as well as human participants (introducing a new, web-based framework for the study of human-with-AI behavior). We achieve positive results in both Space Invaders and Fall Down, as well as successful transfer to human partners, including with people who are asked to deliberately follow unexpected behaviors.
Cascading Failure Phenomenon in the Multi-Stage Hydraulically Fractured Wells

The phenomenon of cascading fracture failure during flowback and initial production from a horizontal multistage hydraulically fractured well is introduced, described, and investigated. First, a simplified analytical model of production from such well is built. This model allows evaluating a range of systems parameters through which the cascading failure evolves and performing a sensitivity study of this effect. Next, while keeping the physical model of the system relatively simple, the critical flow rates causing the motion of proppant pack in fractures are treated as random variables. This assumption brings the next level of sophistication to the model and allows demonstrating non-obvious effects. In particular, the well production rate losses due to cascading fracture failure are estimated. Finally, the proposed hypothesis is validated by conducting numerical simulations of flowback in the estimated conditions of cascading failure. As a practical outcome of this study, recommendations on the mitigation of the well productivity failure caused by cascading failure are formulated and discussed.
Comparing SIS–A Assessments Administered Using Face-to-Face and Virtual Interviewing Formats

The COVID-19 pandemic prompted Supports Intensity Scale—Adult Version (SIS–A) (Thompson et al., 2015)® interviewers from multiple U.S. states to conduct assessments using virtual interviews instead of face-to-face (f2f) interviews. To investigate the impact that administration mode had on SIS—A scores, SIS—A results from assessments conducted virtually during April/May of 2020 were compared to SIS—A results from assessments conducted f2f in the same jurisdictions during April/May of 2019. Although differences in the scores from the two time periods were detectable, with few exceptions the magnitude of the differences was small and not meaningful. Therefore, jurisdictions can have confidence in the results of SIS—A assessments that have been conducted virtually. However, future researchers should continue to investigate the impact assessment mode has on both the process and outcomes of
Gameful Learning: Designing with Motivation in Mind

: We introduce gameful learning , a pedagogical approach rooted in both motivation theory and principles of successful game design. The approach complements cognitively-based design approaches that are more typically employed in the Learning Sciences. This paper describes the roots of the pedagogy, its importance in supporting deep learning in the current educational context, its underpinnings in motivation, and core principles for design.
Velocity model evaluation through Born modeling and migration: a feasibility study

A way to quickly test many possible migration velocity models would be a valuable interpretation tool. Here, a modified Born modeling scheme is used to simulate a new, smaller dataset from an initial image, allowing for target-oriented migrations in a fraction of the time needed for a full migration of the original dataset. Furthermore, the simulated dataset is migrated with a generalized source function derived from the original prestack image, preserving important velocity information that would be lost if a standard wavelet were used as the source function. While the method is currently limited to analysis of a single reflector, initial tests on a simple 2D synthetic model indicate that this method can accurately and efficiently produce images comparable to full standard migrations.
Cross-sectional scanning probe microscopy on GaAs: Tip-induced band bending, buried acceptors and adsorbed molecules

This thesis presents low temperature scanning probe experiments performed in the cross-sectional geometry (X-SPM) on GaAs samples. In particular, three topics have been addressed. First, the GaAs(110) surface has been utilized as a substrate to adsorb iron-II-phthalocyanine molecules. The molecules were probed from scanning tunneling microscopy (STM) and spectroscopy (STS) and were found to be only weakly perturbed by the substrate. This is in analogy to molecules decoupled from a metallic sample by an ultrathin insulating layer and hence offers the possibility to combine single-molecule functionality with the rich versatility of semiconductor physics. Second, we have exploited the possibility to tune molecular resonances via the adsorption position of the individual molecules across an epitaxialy grown heterostructure, where the relative positions of molecular resonances, Fermi level and vacuum level can be tuned with respect to each other. Third, we have used a combination of STM/STS and Kelvin probe force spectroscopy (KPFS) to solve the long-standing problem of tip-induced band bending usually present in STM/STS on semiconducting surfaces due to the penetration of the electrostatic field into the interior of the sample. With this combination of experimental techniques, we revisited individual shallow acceptors buried below the GaAs(110) surface, and found the acceptor-induced enhanced conductance to be present similarly within multiple band bending regimes.
Catalytic hydrogenation of an aromatic sulfonyl chloride into thiophenol

Catalytic Hydrogenation of an Aromatic Sulfonyl Chl oride into Thiophenol. (May 2008) Nicolas Julien Rouckout, B.S., Universite Pierre et Marie Curie, Paris VI; M.S., Universite Pierre et Marie Curie, Paris VI Co-Chairs of Advisory Committee: Dr. Rayford G. Ant hony Dr. Gilbert F. Froment The catalytic hydrogenation of an aromatic sulfonyl chloride was investigated in continuous and semi-batch mode processes using a Ro binson-Mahoney stationary basket reactor. A complete experimental unit was designed an built. The operating and analytical procedures have been developed and the m ethodologies to gather the kinetic data have been described. Hydrogenation reactions w ere conducted at a reaction pressure of 364.7 psia, at three different reaction temperat ures: 85 °C, 97 °C and 110 °C, at five different residence times: 0.6 (only at 110 °C), 1. 0, . 5, 2.0, 3.1 hr, with the hydrogen to the aromatic sulfonyl chloride molar ratio: 8.0 mol /mol and hydrogen to argon molar ratio: 3.0 mol/mol. Intrinsic reaction rates of the reacting species were obtained on the surface of a commercial 1 wt% palladium on charcoal atalyst. The conversion and molar yield profiles of the reac ting species with respect to process time suggest a deactivation of the 1 wt % p alladium on charcoal catalyst. Kinetic data collected in a continuous process mode show th at the catalyst is deactivated during an experiment when the process time equal to two to three times the residence time of the liquid within the reactor. XRD analysis shows t hat the active sites are blocked and an amorphous layer was formed on the surface of the pa lladium catalyst. Semi-Batch mode experimental data were obtained at 110 °C after 8 h ours of reaction time for several aromatic sulfonyl chlorides.
CHEMICAL REACTIVITY AND FLOW PROPERTIES OF ASPHALTS MODIFIED BY METAL COMPLEX-INDUCED REACTIONS WITH ATMOSPHERIC OXYGEN (WITH DISCUSSION)

A modified paving composition is being evaluated by a number of state and federal highway transportation agencies. The modification consists of pretreatment of the asphalt with an asphalt-soluble metal complex such as manganese. The objectives of this study were to (1) determine chemical changes in the asphalt resulting from the metal-induced asphalt reaction with atmospheric oxygen and compare these changes with those that occur during air oxidative aging of unmodified asphalt and (2) compare the flow properties of the modified, cured asphalt with those of unmodified asphalt and unmodified asphalt that has been age-hardened by air oxidation.
Multi‐resource allocation in cloud data centers: A trade‐off on fairness and efficiency

Fair allocation has been studied intensively in both economics and computer science. Many existing mechanisms that consider fairness of resource allocation focus on a single resource. With the advance of cloud computing that centralizes multiple types of resources under one shared platform, multi‐resource allocation has come into the spotlight. In fact, fair/efficient multi‐resource allocation has become a fundamental problem in any shared computer system. The widely used solution is to partition resources into bundles that contain fixed amounts of different resources, so that multiple resources are abstracted as a single resource. However, this abstraction cannot satisfy different demands from heterogeneous users, especially on ensuring fairness among users competing for resources with different capacity limits. A promising approach to this problem is dominant resource fairness (DRF), which tries to equalize each user's dominant share (share of a user's most highly demanded resource, that is, the largest fraction of any resource that the user has required for a task), but this method may still suffer from significant loss of efficiency (i.e., some resources are underused). This article develops a new allocation mechanism based on DRF aiming to balance fairness and efficiency. We consider fairness not only in terms of a user's dominant resource, but also in another resource dimension which is secondarily desired by this user. We call this allocation mechanism 2‐dominant resource fairness (2‐DF). Then, we design a non‐trivial on‐line algorithm to find a 2‐DF allocation and extend this concept to k‐dominant resource fairness (k‐DF).
Accident Externality of Driving: Evidence from the London Congestion Charge

This paper estimates the marginal accident externality of driving in Central London by exploiting variation in traffic flow induced by the London Congestion Charge Zone using an instrumental variable approach. The charge attributed to a 9.4% reduction in traffic flow, which resulted in a less than proportional 6.0% and 7.6% decrease in accidents and slight injuries, and a 6.5% increase in serious injuries/fatalities. Our preferred estimates indicate that the accident, slight injuries and serious injuries/fatalities rate elasticities with respect to traffic flow are −0.36, −0.19 and −1.65, respectively. These estimates imply that the marginal external benefit of road safety from an additional kilometer driven is approximately £0.16. The marginal accident externality is positive, as the marginal driver along congested roads decreases the risk and severity of traffic collisions for other road users by slowing others down and increasing awareness.
MONOCULAR 3D HUMAN BODY RECONSTRUCTION

Monocular 3D human reconstruction is a very relevant problem due to numerous applications to the entertainment industry, e-commerce, health care, mobile-based AR/VR platforms, etc. However, it is severely ill-posed due to self-occlusions from complex body poses and shapes, clothing obstructions, lack of surface texture, background clutter, single view, etc. Conventional approaches address these challenges by using different sensing systems marker-based, marker-less multi-view cameras, inertial sensors, and 3D scanners. Although effective, such methods are often expensive and have limited wide-scale applicability. In an attempt to produce scalable solutions, a few have focused on fitting statistical body models to monocular images, but are susceptible to the costly optimization process. Recent efforts focus on using data-driven algorithms such as deep learning to learn priors directly from data. However, they focus on template model recovery, rigid object reconstruction, or propose paradigms that don’t directly extend to recovering personalized models. To predict accurate surface geometry, our first attempt was VolumeNet, which predicted a 3D occupancy grid from a monocular image. This was the first of its kind model for non-rigid human shapes at that time. To circumvent the ill-posed nature of this problem (aggravated by an unbounded 3D representation), we follow the ideology of providing maximal training priors with our unique training paradigms, to enable testing with minimal information. As we did not impose any body-model based constraint, we were able to recover deformations induced by free-form clothing. Further, we extended VolumeNet to PoShNet by decoupling Pose and Shape, in which we learn the volumetric pose first, and use it as a prior for learning the volumetric shape, thereby recovering a more accurate surface. Although volumetric regression enables recovering a more accurate surface reconstruction, they do so without an animatable skeleton. Further, such methods yield reconstructions of low resolution at higher computational cost (regression over the cubic voxel grid) and often suffer from an inconsistent topology via broken or partial body parts. Hence, statistical body models become a natural choice to offset the ill-posed nature of this problem. Although theoretically, they are low dimensional, learning such models has been challenging due to the complex non-linear mapping from the image to the relative axis-angle representation. Hence, most solutions rely on different projections of the underlying mesh (2D/3D keypoints, silhouettes, etc.). To simplify the learning process, we propose the CR framework that uses classification as a prior for guiding the regression’s learning process. Although recovering personalized models with high-resolution meshes isn’t a possibility in this space, the framework shows that learning such template models can be difficult without additional supervision.
A pr 2 01 8 Discrete discontinuous basis projection method for large-scale electronic structure calculations

We present an approach to accelerate real-space electronic structure methods several fold, without loss of accuracy, by reducing the dimension of the discrete eigenproblem that must be solved. To accomplish this, we construct an efficient, systematically improvable, discontinuous basis spanning the occupied subspace and project the real-space Hamiltonian onto the span. In calculations on a range of systems, we find that accurate energies and forces are obtained with 8–25 basis functions per atom, reducing the dimension of the associated real-space eigenproblems by 1–3 orders of magnitude.
Calibrating CNNs for Lifelong Learning

We present an approach for lifelong/continual learning of convolutional neural networks (CNN) that does not suffer from the problem of catastrophic forgetting when moving from one task to the other. We show that the activation maps generated by the CNN trained on the old task can be calibrated using very few calibration parameters, to become relevant to the new task. Based on this, we calibrate the activation maps produced by each network layer using spatial and channel-wise calibration modules and train only these calibration parameters for each new task in order to perform lifelong learning. Our calibration modules intro-duce signiﬁcantly less computation and parameters as compared to the approaches that dynamically expand the network. Our approach is immune to catastrophic forgetting since we store the task-adaptive calibration parameters, which contain all the task-speciﬁc knowledge and is exclusive to each task. Further, our approach does not require storing data samples from the old tasks, which is done by many replay based methods. We perform extensive experiments on multiple benchmark datasets (SVHN, CIFAR, ImageNet, and MS-Celeb), all of which show substan-tial improvements over state-of-the-art methods (e.g., a 29% absolute increase in accuracy on CIFAR-100 with 10 classes at a time). On large-scale datasets, our approach yields 23.8% and 9.7% absolute increase in accuracy on ImageNet-100 and MS-Celeb-10K datasets, respectively, by employing very few (0.51% and 0.35% of model parameters) task-adaptive calibration parameters.