Miracle-dz/UniFAR-FTUs huggingface dataset:
Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group.

OBJECTIVE Because of the pressure for timely, informed decisions in public health and clinical practice and the explosion of information in the scientific literature, research results must be synthesized. Meta-analyses are increasingly used to address this problem, and they often evaluate observational studies. A workshop was held in Atlanta, Ga, in April 1997, to examine the reporting of meta-analyses of observational studies and to make recommendations to aid authors, reviewers, editors, and readers. PARTICIPANTS Twenty-seven participants were selected by a steering committee, based on expertise in clinical practice, trials, statistics, epidemiology, social sciences, and biomedical editing. Deliberations of the workshop were open to other interested scientists. Funding for this activity was provided by the Centers for Disease Control and Prevention. EVIDENCE We conducted a systematic review of the published literature on the conduct and reporting of meta-analyses in observational studies using MEDLINE, Educational Research Information Center (ERIC), PsycLIT, and the Current Index to Statistics. We also examined reference lists of the 32 studies retrieved and contacted experts in the field. Participants were assigned to small-group discussions on the subjects of bias, searching and abstracting, heterogeneity, study categorization, and statistical methods. CONSENSUS PROCESS From the material presented at the workshop, the authors developed a checklist summarizing recommendations for reporting meta-analyses of observational studies. The checklist and supporting evidence were circulated to all conference attendees and additional experts. All suggestions for revisions were addressed. CONCLUSIONS The proposed checklist contains specifications for reporting of meta-analyses of observational studies in epidemiology, including background, search strategy, methods, results, discussion, and conclusion. Use of the checklist should improve the usefulness of meta-analyses for authors, reviewers, editors, readers, and decision makers. An evaluation plan is suggested and research areas are explored.
Occupancy Map Initialization Tessellated Sphere Initialization Occlusion Detection Projection to the tessellated sphere Best Viewpoint Tracking Move to Best Viewpoint

The problem of finding a next best viewpoint for 3D modeling or scene mapping has been explored in computer vision over the last decade. This paper tackles a similar problem, but with different characteristics. It proposes a method for dynamic next best viewpoint recovery of a target point while avoiding possible occlusions. Since the environment can change, the method has to iteratively find the next best view with a global understanding of the free and occupied parts. We model the problem as a set of possible viewpoints which correspond to the centers of the facets of a virtual tessellated hemisphere covering the scene. Taking into account occlusions, distances between current and future viewpoints, quality of the viewpoint and joint constraints (robot arm joint distances or limits), we evaluate the next best viewpoint. The proposal has been evaluated on 8 different scenarios with different occlusions and a short 3D video sequence to validate its dynamic performance.
Renormalization: an advanced overview

We present several approaches to renormalization in QFT: the multi-scale analysis in perturbative renormalization, the functional methods \`a la Wetterich equation, and the loop-vertex expansion in non-perturbative renormalization. While each of these is quite well-established, they go beyond standard QFT textbook material, and may be little-known to specialists of each other approach. This review is aimed at bridging this gap.
3 D Pose Estimation and 3 D Model Retrieval for Objects in the Wild Supplementary Material

Fig. 2 shows additional qualitative results for 3D model retrieval from ShapeNet [1] given previously unseen images from Pascal3D+ [9] validation data for all twelve categories. Our approach predicts accurate 3D poses and 3D models for objects of different categories. Fig. 1 presents further 3D model alignment results for object detections which are not fully accurate. We significantly improve the alignment between the object in the image and an RGB rendering of our retrieved 3D model by taking advantage of our predicted 6-DoF pose and 3-DoF dimensions compared to just using a 3-DoF viewpoint.
Source : Target : 1 2 3 4

For machine translation to tackle discourse phenomena, models must have access to extrasentential linguistic context. There has been recent interest in modelling context in neural machine translation (NMT), but models have been principally evaluated with standard automatic metrics, poorly adapted to evaluating discourse phenomena. In this article, we present hand-crafted, discourse test sets, designed to test the models’ ability to exploit previous source and target sentences. We investigate the performance of recently proposed multi-encoder NMT models trained on subtitles for English to French. We also explore a novel way of exploiting context from the previous sentence. Despite gains using BLEU, multi-encoder models give limited improvement in the handling of discourse phenomena: 50% accuracy on our coreference test set and 53.5% for coherence/cohesion (compared to a non-contextual baseline of 50%). A simple strategy of decoding the concatenation of the previous and current sentence leads to good performance, and our novel strategy of multiencoding and decoding of two sentences leads to the best performance (72.5% for coreference and 57% for coherence/cohesion), highlighting the importance of target-side context.
Single-View 3D Object Reconstruction from Shape Priors in Memory

Existing methods for single-view 3D object reconstruction directly learn to transform image features into 3D representations. However, these methods are vulnerable to images containing noisy backgrounds and heavy occlusions because the extracted image features do not contain enough information to reconstruct high-quality 3D shapes. Humans routinely use incomplete or noisy visual cues from an image to retrieve similar 3D shapes from their memory and reconstruct the 3D shape of an object. Inspired by this, we propose a novel method, named Mem3D, that explicitly constructs shape priors to supplement the missing information in the image. Specifically, the shape priors are in the forms of "image-voxel" pairs in the memory network, which is stored by a well-designed writing strategy during training. We also propose a voxel triplet loss function that helps to retrieve the precise 3D shapes that are highly related to the input image from shape priors. The LSTM-based shape encoder is introduced to extract information from the retrieved 3D shapes, which are useful in recovering the 3D shape of an object that is heavily occluded or in complex environments. Experimental results demonstrate that Mem3D significantly improves reconstruction quality and performs favorably against state-of-the-art methods on the ShapeNet and Pix3D datasets.
Monitoring of the energy scale in the KATRIN neutrino experiment

The question of the absolute mass scale of neutrinos is of particular interest for particle physics, astrophysics, and cosmology. The KATRIN experiment (KArlsruhe TRItium Neutrino experiment) aims to address the effective electron antineutrino mass from the shape of the tritium β-spectrum with an unprecedented sensitivity of 0.2 eV/c. One of the major systematic effects concerns the experimental energy scale, which has to be stable at the level of only a few parts in a million. For its calibration and monitoring the monoenergetic electrons emitted in the internal conversion of γ-transition of the metastable isotope Kr will be extensively applied. The aim of this thesis is to address the problem of KATRIN energy scale distortions and its monitoring in detail. The source of electrons based on Kr embedded in a solid as well as the source based on gaseous Kr are studied. Based on the experimental results an approach for the continuous stability monitoring is proposed.
Tilted Dirac cone on W(110) protected by mirror symmetry

Topologically nontrivial states reveal themselves in strongly spin-orbit coupled systems by Dirac cones. However, their appearance is not a sufficient criterion for a topological phase. In topological insulators, where these states protect surface metallicity, they are straightforwardly assigned based on bulk-boundary correspondence. On metals, where these states are suspected to have tremendous impact as well, e.g., in catalysis, their topological protection is difficult to assess due to the lacking band gap and the frequent assignment to topological properties appears unjustified. Here, we discover by angle-resolved photoemission a state with the dispersion of a Dirac cone at a low-symmetry point of W(110). Our ab initio calculations predict this feature with a linear band crossing and high spin polarization. However, instead of being born by topology, the states arise from Rashba split bands and do not fundamentally depend on the opening of a spin-orbit gap. On the other hand, we find that the [001] mirror plane protects the band crossing point and renormalizes the dispersion towards a Dirac-cone shape. In this sense, the discovered state is the metal counterpart of the surface state of a topological crystalline insulator. The Dirac cone is tilted due to its origin in an accidental band crossing away from high symmetry points. Tilted Dirac cones have recently been predicted for twoand three-dimensional materials and were observed in three-dimensional Weyl semimetals. Accordingly, the protection and renormalization by mirror symmetry uncovered here are a potentially much wider spread phenomenon which does not require topological properties. Our results also indicate why the massive gapless crossing predicted for topological crystalline insulators has never been observed.
Northumbria Research

This article explores the ways in which memory and humiliation can shape the social engagement of persistently violent men. Drawing upon field data from two of our previous ethnographic studies conducted in the North East of England, we hope to make a few basic points about the importance of emotion and memory as constitutive and dynamic components in the core of identity. Focusing on the emotional ‘feelings’ of humiliation and regret, we will outline how violent incidents or verbal challenges from earlier stages of the individual’s life-course can be drawn upon, both directly and indirectly, as motivational and justificatory instruments in potentially violent interactions in the here and now. The intention is to propose what might be important psychosocial elements of Bourdieu’s concept of habitus and how an exposition of these elements might further our understanding of subjective violence.
Solving the "false positives" problem in fraud prediction

In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction. False positives plague the fraud prediction industry. It is estimated that only 1 in 5 declared as fraud are actually fraud and roughly 1 in every 6 customers have had a valid transaction declined in the past year. To address this problem, we use the Deep Feature Synthesis algorithm to automatically derive behavioral features based on the historical data of the card associated with a transaction. We generate 237 features (>100 behavioral patterns) for each transaction, and use a random forest to learn a classifier. We tested our machine learning model on data from a large multinational bank and compared it to their existing solution. On an unseen data of 1.852 million transactions, we were able to reduce the false positives by 54% and provide a savings of 190K euros. We also assess how to deploy this solution, and whether it necessitates streaming computation for real time scoring. We found that our solution can maintain similar benefits even when historical features are computed once every 7 days.
Multilingual Culture-Independent Word Analogy Datasets

Word analogy task evaluates word embeddings, based on analagous word pairs (eg. "Paris - France" should be equivalent to "Rome - Italy", "son - daughter" should be equivalent to "brother - sister"). The dataset has been inspired by Mikolov's analogy test set in English (http://download.tensorflow.org/data/questions-words.txt). It was first written for Slovenian and then partially translated and partially done from scratch for the other languages (Croatian, Finnish, Estonian, Swedish, Latvian, Lithuanian, Russian and English). The analogy dataset is composed of fifteen categories, five semantical and ten syntactical. Each dataset has about 19,000 entries. In addition to nine monolingual datasets (one for each language), we also composed 72 cross-lingual datasets (one for each language pair), where one half of the entry (one analogy, eg. "mother-father") is in one language and the other half of the entry (eg. "sister-brother") is in another language.
Multi-Modal Data Augmentation for End-to-end ASR

We present a new end-to-end architecture for automatic speech recognition (ASR) that can be trained using \emph{symbolic} input in addition to the traditional acoustic input. This architecture utilizes two separate encoders: one for acoustic input and another for symbolic input, both sharing the attention and decoder parameters. We call this architecture a multi-modal data augmentation network (MMDA), as it can support multi-modal (acoustic and symbolic) input. The MMDA architecture attempts to eliminate the need for an external LM, by enabling seamless mixing of large text datasets with significantly smaller transcribed speech corpora during training. We study different ways of transforming large text corpora into a symbolic form suitable for training our MMDA network. Our best MMDA setup obtains small improvements on CER and achieves 8-10\% relative WER improvement on the WSJ data set.
Capacity and optimal resource allocation for fading broadcast channels - Part I: Ergodic capacity

For pt.I see ibid., vol.47, no.3, p.1083-1102 (2002). We study three capacity regions for fading broadcast channels and obtain their corresponding optimal resource allocation strategies: the ergodic (Shannon) capacity region, the zero-outage capacity region, and the capacity region with outage. In this paper, we derive the outage capacity regions of fading broadcast channels, assuming that both the transmitter and the receivers have perfect channel side information. These capacity regions and the associate optimal resource allocation policies are obtained for code division (CD) with and without successive decoding, for time division (TD), and for frequency division (FD). We show that in an M-user broadcast system, the outage capacity region is implicitly obtained by deriving the outage probability region for a given rate vector. Given the required rate of each user, we find a strategy which bounds the outage probability region for different spectrum-sharing techniques. The corresponding optimal power allocation scheme is a multiuser generalization of the threshold-decision rule for a single-user fading channel. Also discussed is a simpler minimum common outage probability problem under the assumption that the broadcast channel is either not used at all when fading is severe or used simultaneously for all users. Numerical results for the different outage capacity regions are obtained for the Nakagami-m (1960) fading model.
A weighted multiple-feature fusion classifier for hyperspectral images with limited training samples

ABSTRACT In this paper, a novel weighted multiple-feature classifier based on sparse representation and locally dictionary collaborative representation (WMSLC) is put forward to improve the limited training samples’ hyperspectral image classification performance. The WMSLC method mainly includes the following steps. Firstly, Spectral feature, Extended Multi-attribute Profile (EMAP) feature and Gabor feature are applied as the multiple feature to describe the hyperspectral image from spectral and different spatial aspects. And weights are utilized to adjust the multiple-feature’s proportions to improve the efficiency of the classification. Secondly, a trade-off is given between different regularization residuals, sparse representation residuals and locally dictionary collaborative representation residuals. Here, the locally adaptive dictionary is implemented to reduce the irrelevant atoms to improve the classification performance. Finally, the test sample is assigned to the class, which has the minimal residuals. Experimental results on two real hyperspectral data sets (Indian Pines and Pavia University) demonstrate that the proposed WMSLC method outperforms several corresponding well-known classifiers when very limited numbers of training samples are available.
Numerical Modeling and Experimental Testing of a Pendulum Wave Energy Converter (PeWEC)

The research activities described in the present work aims to develop a pendulum converter (PeWEC: Pendulum Wave Energy Converter) for the Mediterranean Sea, where waves are shorter, thus with a higher frequency. In particular, the Pantelleria Island site wave climate is assumed as reference. The research activities started from the preliminary investigation of the working principle validity in the case of the Mediterranean Sea wave characteristics, taking into account a 1:45 scale prototype. The numerical model reliability and the success of experimental tests motivated the design and development of a 1:12 scaled device, useful for a deeper investigation of the technology capabilities and performances. Globally, the technology readiness level (TRL) was increased from 1 to 4. Important effort were focused in the development of a reliable model-based design and optimization methodology for the investigation of a full scale configuration. The latter was widely used to identify a preliminary full scale configuration and to assess the economic viability of the PeWEC technology in the Mediterranean Sea context. Results were benchmarked against the ISWEC pilot plant, deployed in 2015, in Pantelleria Island. One of the major outcomes of this analysis is a detailed overview of the advantages and drawbacks of an active (ISWEC) and a passive (PeWEC) technology, together with some guidelines for the improvement of this technology.
Gaussian process for human motion modeling: A comparative study

We evaluate recent Gaussian process (GP)-based manifold learning methods for human motion modeling, including our recently proposed joint gait and pose manifolds (JGPMs). Unlike most GP algorithms that involve either one latent variable or multiple independent variables in separate latent spaces, JGPMs define two variables jointly and explicitly in one latent space to represent a collection of gait data from different individuals. We develop a model validation technique to examine these GP-based algorithms in terms of their capability of motion interpolation, extrapolation, filtering, and recognition. Experimental results on both CMU Mocap and Brown HumanEva datasets show the superiority of JGPMs over existing GP algorithms for human motion modeling.
Are you a Good Employee or Simply a Good Guy ? In uence Costs and Contract Design

We develop a principal-agent model with a moral hazard problem in which the principal has access to a hard signal (the level of output) and a soft signal (the supervision signal) about the agents level of e¤ort. We show that the agents ability to manipulate the soft signal increases the cost of implementing the e¢ cient equilibrium, leading to wage compression when the inuence cost is privately incurred by the agent. When manipulation activities negatively a¤ect the agents productivity through the level of output, the design of inuence-free contracts that deter manipulation may lead to high-powered incentives. This result implies that high-productivity workers face incentive schemes that are more sensitive to hard evidence than those faced by their low-productivity counterparts. In that context, the principal will tolerate inuence for low-productivity workers but not for high-productivity workers. We also nd that in the case of productivity-based costs, it may be optimal for the principal not to supervise the agent, even if supervision is costless (JEL D23, D82).
Visual Robot Tracking and Identification using Deep LSTM Networks Hafez Farazi and

Collaborative robots working on a common task are necessary for many applications. One of the challenges for achieving collaboration in a team of robots is mutual tracking and identification. We present a novel pipeline for online visionbased detection, tracking and identification of robots with a known and identical appearance. Our method runs in realtime on the limited hardware of the observer robot. Unlike previous works addressing robot tracking and identification, we use a data-driven approach based on recurrent neural networks to learn relations between sequential inputs and outputs. We formulate the data association problem as multiple classification problems. A deep LSTM network was trained on a simulated dataset and fine-tuned on small set of real data. Experiments on two challenging datasets, one synthetic and one real, which include long-term occlusions, show promising results.
The analyticity and exponential decay of a Stokes-wave coupling system with viscoelastic damping in the variational framework

In this paper, we study a fluid-structure interaction model of Stokes-wave equation coupling system with Kelvin-Voigt type of damping. We show that this damped coupling system generates an analyticity semigroup and thus the semigroup solution, which also satisfies variational framework of weak solution, decays to zero at exponential rate.
Activation of matrix metalloproteinases following antiA b immunotherapy ; implications for microhemorrhage occurrence

Background: Anti-Ab immunotherapy is a promising approach to the prevention and treatment of Alzheimer’s disease (AD) currently in clinical trials. There is extensive evidence, both in mice and humans that a significant adverse event is the occurrence of microhemorrhages. Also, vasogenic edema was reported in phase 2 of a passive immunization clinical trial. In order to overcome these vascular adverse effects it is critical that we understand the mechanism(s) by which they occur. Methods: We have examined the matrix metalloproteinase (MMP) protein degradation system in two previously published anti-Ab immunotherapy studies. The first was a passive immunization study in which we examined 22 month old APPSw mice that had received anti-Ab antibodies for 1, 2 or 3 months. The second is an active vaccination study in which we examined 16 month old APPSw/NOS2-/mice treated with Ab vaccination for 4 months. Results: There is a significant activation of the MMP2 and MMP9 proteinase degradation systems by anti-Ab immunotherapy, regardless of whether this is delivered through active vaccination or passive immunization. We have characterized this activation by gene expression, protein expression and zymography assessment of MMP activity. Conclusions: Since the MMP2 and MMP9 systems are heavily implicated in the pathophysiology of intracerbral hemorrhage, these data may provide a potential mechanism of microhemorrhage due to immunotherapy. Increased activity of the MMP system, therefore, is likely to be a major factor in increased microhemorrhage occurrence.