Miracle-dz/UniFAR-FTUs huggingface dataset:
ImPosIng: Implicit Pose Encoding for Efficient Camera Pose Estimation

We propose a novel learning-based formulation for camera pose estimation that can perform relocalization accurately and in real-time in city-scale environments. Camera pose estimation algorithms determine the position and orientation from which an image has been captured, using a set of geo-referenced images or 3D scene representation. Our new localization paradigm, named Implicit Pose Encoding (ImPos-ing), embeds images and camera poses into a common latent representation with 2 separate neural networks, such that we can compute a similarity score for each image-pose pair. By evaluating candidates through the latent space in a hierarchical manner, the camera position and orientation are not directly regressed but incrementally refined. Compared to the representation used in structure-based relocalization methods, our implicit map is memory bounded and can be properly explored to improve localization performances against learning-based regression approaches. In this paper, we describe how to effectively optimize our learned modules, how to combine them to achieve real-time localization, and demonstrate results on diverse large scale scenarios that significantly outperform prior work in accuracy and computational efficiency.
Communication-Efficient Adaptive Federated Learning

Federated learning is a machine learning training paradigm that enables clients to jointly train models without sharing their own localized data. However, the implementation of federated learning in practice still faces numerous challenges, such as the large communication overhead due to the repetitive server-client synchronization and the lack of adaptivity by SGD-based model updates. Despite that various methods have been proposed for reducing the communication cost by gradient compression or quantization, and the federated versions of adaptive optimizers such as FedAdam are proposed to add more adaptivity, the current federated learning framework still cannot solve the aforementioned challenges all at once. In this paper, we propose a novel communication-efficient adaptive federated learning method (FedCAMS) with theoretical convergence guarantees. We show that in the nonconvex stochastic optimization setting, our proposed FedCAMS achieves the same convergence rate of $O(\frac{1}{\sqrt{TKm}})$ as its non-compressed counterparts. Extensive experiments on various benchmarks verify our theoretical analysis.
Rigorous formalism for unconventional symmetry breaking in Fermi liquid theory and its application to nematicity in FeSe

Unconventional symmetry breaking due to nonlocal order parameters has attracted considerable attention in many strongly correlated metals. Famous examples are the nematic order in Fe-based superconductors and the star-of-David charge density order in kagome metals. Such exotic symmetry breaking in metals is a central issue of modern condensed matter physics, while its theoretical foundation is still unclear in comparison with the well-established theory of superconductivity. To overcome this diﬃculty, here we introduce the “form factor” that generalizes the nonlocal order parameter into the Luttinger-Ward (LW) Fermi liquid theory. We then construct a rigorous formalism of the “density-wave equation” that gives the thermodynamically stable form factor, similarly to the superconducting-gap equation. In addition, a rigorous expression of the Ginzburg-Landau free-energy for the unconventional order is presented to calculate various thermodynamic properties. In the next stage, we apply the derived formalism to a typical Fe-based superconductor FeSe, by using the one-loop LW function that represents the free-energy gain due to the interference among paramagnons. The following key experiments are naturally explained: (i) Lifshitz transition (=disappearance of an electron-pocket) due to the bond+orbital order below T c . (ii) Curie-Weiss behavior of the nematic susceptibility at higher T , and the deviation from the Curie-Weiss behavior at lower T near the nematic quantum-critical-point. (iii) Scaling relation of the speciﬁc heat jump at T c , ∆ C/T c ∝ T b c with b ∼ 3. (Note that b = 0 in the BCS theory.) These results lead to a con-clusion that the nematicity in FeSe is the bond+orbital order due to the “paramagnon interference mechanism”. The present theory paves the way for solving various unconventional phase transition systems.
Text Detection on Technical Drawings for the Digitization of Brown-field Processes

This paper addresses the issue of autonomously detecting text on technical drawings. The detection of text on technical drawings is a critical step towards autonomous production machines, especially for brown-field processes, where no closed CAD-CAM solutions are available yet. Automating the process of reading and detecting text on technical drawings reduces the effort for handling inefficient media interruptions due to paper-based processes, which are often todays quasi-standard in brown-field processes. However, there are no reliable methods available yet to solve the issue of automatically detecting text on technical drawings. The unreliable detection of the contents on technical drawings using classical detection and object character recognition (OCR) tools is mainly due to the limited number of technical drawings and the captcha-like structure of the contents. Text is often combined with unknown symbols and interruptions by lines. Additionally, due to intellectual property rights and technical know-how issues, there are no out-of-the box training datasets available in the literature to train such models. This paper combines a domain knowledge-based generator to generate realistic technical drawings with a state-of-the-art object detection model to solve the issue of detecting text on technical drawings. The generator yields artificial technical drawings in a large variety and can be considered as a data augmentation generator. These artificial drawings are used for training, while the model is tested on real data. The authors show that artificially generated data of technical drawings improve the detection quality with an increasing number of drawings.
The Impact of Large-Scale Renewable Energy on Grid Small-Signal and Frequency Stability: Modelling, Analysis, and Control

The Impact of Large-Scale Renewable Energy on Grid Small-Signal and Frequency Stability: Modelling, Analysis, and Control Jae-Woong Shim Graduate School School of Electrical and Electronic Engineering Yonsei University, Seoul, South Korea School of Electrical and Information Engineering The University of Sydney, Sydney, NSW, Australia This dissertation intends to discuss the influence of renewable energy sources (RES) on the stability of power system. Although integration of renewable brings incalculable optimistic aspects, the inherent variability of RES concerning fluctuation of active power may exhibit adverse influence on stability issues regarding small-signal and frequency stability point of views. Thus, this dissertation discusses those two stability perspectives.
Implicit n-grams Induced by Recurrence

Although self-attention based models such as Transformers have achieved remarkable successes on natural language processing (NLP)tasks, recent studies reveal that they have limitations on modeling sequential transformations (Hahn, 2020), which may promptre-examinations of recurrent neural networks (RNNs) that demonstrated impressive results on handling sequential data. Despite manyprior attempts to interpret RNNs, their internal mechanisms have not been fully understood, and the question on how exactly they capturesequential features remains largely unclear. In this work, we present a study that shows there actually exist some explainable componentsthat reside within the hidden states, which are reminiscent of the classical n-grams features. We evaluated such extracted explainable features from trained RNNs on downstream sentiment analysis tasks and found they could be used to model interesting linguistic phenomena such as negation and intensification. Furthermore, we examined the efficacy of using such n-gram components alone as encoders on tasks such as sentiment analysis and language modeling, revealing they could be playing important roles in contributing to the overall performance of RNNs. We hope our findings could add interpretability to RNN architectures, and also provide inspirations for proposing new architectures for sequential data.
Reducing the Sign Problem with Line Integrals

We present a novel strategy to strongly reduce the severity of the sign problem, using line integrals along paths of changing imaginary action. Highly oscillating regions along these paths cancel out, decreasing their contributions. As a result, sampling with standard Monte-Carlo techniques becomes possible in cases that otherwise require methods taking advantage of complex analysis, such as Lefschetz-thimbles or Complex Langevin. We lay out how to write down an ordinary differential equation for the line integrals. As an example of its usage, we apply the results to a 1d quantum mechanical anharmonic oscillator with a $x^4$ potential in real time, finite temperature.
Two-variable fibrations, factorisation systems and 
$\infty $
 -categories of spans

Abstract We prove a universal property for $\infty $ -categories of spans in the generality of Barwick’s adequate triples, explicitly describe the cocartesian fibration corresponding to the span functor, and show that the latter restricts to a self-equivalence on the class of orthogonal adequate triples, which we introduce for this purpose. As applications of the machinery we develop, we give a quick proof of Barwick’s unfurling theorem, show that an orthogonal factorisation system arises from a cartesian fibration if and only if it forms an adequate triple (generalising work of Lanari), extend the description of dual (co)cartesian fibrations by Barwick, Glasman and Nardin to two-variable fibrations, explicitly describe parametrised adjoints (extending work of Torii), identify the orthofibration classifying the mapping category functor of an $(\infty ,2)$ -category (building on work of Abellán García and Stern), formally identify the unstraightenings of the identity functor on the $\infty $ -category of $\infty $ -categories with the (op)lax under-categories of a point, and deduce a certain naturality property of the Yoneda embedding (answering a question of Clausen).
Genotypic and functional properties of early infant HIV-1 Genotypic and functional properties of early infant HIV-1 envelopes envelopes

Background: Understanding the properties of HIV-1 variants that are transmitted from women to their infants is crucial to improving strategies to prevent transmission. In this study, 162 full-length envelope ( env ) clones were generated from plasma RNA obtained from 5 HIV-1 Clade B infected mother-infant pairs. Following extensive genotypic and phylogenetic analyses, 35 representative clones were selected for functional studies. Results: Infant quasispecies were highly homogeneous and generally represented minor maternal variants, consistent with transmission across a selective bottleneck. Infant clones did not differ from the maternal in env length, or glycosylation. All infant variants utilized the CCR5 co-receptor, but were not macrophage tropic. Relatively high levels (IC 50 ≥ 100 μ g/ml) of autologous maternal plasma IgG were required to neutralize maternal and infant viruses; however, all infant viruses were neutralized by pooled sera from HIV-1 infected individuals, implying that they were not inherently neutralization-resistant. All infant viruses were sensitive to the HIV-1 entry inhibitors Enfuvirtide and soluble CD4; none were resistant to Maraviroc. Sensitivity to human monoclonal antibodies 4E10, 2F5, b12 and 2G12 varied. Conclusions: This study provides extensive characterization of the genotypic and functional properties of HIV-1 env shortly after transmission. We present the first detailed comparisons of the macrophage tropism of infant and maternal env variants and their sensitivity to Maraviroc, the only CCR5 antagonist approved for therapeutic use. These findings may have implications for improving approaches to prevent mother-to-child HIV-1 transmission.
Indemnity Benefit Duration and Obesity

Motivation. There is mounting evidence of obesity contributing to the cost of workers compensation. Longitudinal studies by Duke University (Ostbye, Dement, and Krause [5]) of its own employees—and by Johns Hopkins University (Pollack et al. [7]) of employees of a multi-site U.S. aluminum manufacturing company— point to substantially higher odds of injury for workers in the highest obesity category. Further, a 2011 Gallup survey (Witters and Agrawal [9]) of 109,875 full-time employees found that obese employees account for a disproportionately high number of missed workdays, thus causing a significant loss in economic output. Finally, an NCCI study (Laws and Schmid [4]) of workers compensation claims established that where claimants are assigned a comorbidity code indicating obesity, the medical costs of the claim are a multiple of what is observed otherwise. In the following study, using a methodology similar to the one employed by Laws and Schmid [4], but accounting for a possible immortal time bias, it is shown that the indemnity benefit duration of claimants with an obesity-related comorbidity indicator is a multiple of what is observed for comparable non-obese claimants. Method. The study makes use of a matched-pairs research design, where obese claims are matched with comparable non-obese claims in the data set. Exact matching applies to all claim characteristics, except age at injury, where proximity matching is employed. The set of matched pairs is then analyzed using a semiparametric Bayesian Weibull proportional hazard model, the nonparametric component of which accounts for the possible nonlinear influence of age. Aside from age, an indicator variable signifying obesity is the only covariate in the model—this is because net of these two covariates (and duration, which serves as the dependent variable), the claims within each set of matched pairs are identical. The model is estimated by means of MCMC (Markov chain Monte Carlo simulation). Results. The study shows that, based on Temporary Total and Permanent Total indemnity benefit payments, the duration of obese claimants is more than five times the duration of non-obese claimants, after controlling for primary ICD-9 code, injury year, U.S. state, industry, gender, and age. When Permanent Partial benefits are counted toward indemnity benefit duration as well, this multiple climbs to more than six. Availability. The model was implemented in R (cran.r-project.org/) using the sampling platform JAGS (Just Another Gibbs Sampler, mcmc-jags.sourceforge.net/). JAGS was linked to R by means of the R package rjags (cran.r-project.org/web/packages/rjags/index.html).
Neuropsychiatric Treatment Saccadic eye movement applications for psychiatric disorders

Objective: The study presented here analyzed the patterns of relationship between oculomotor performance and psychopathology, focusing on depression, bipolar disorder, schizophrenia, attention-deficit hyperactivity disorder, and anxiety disorder. Methods: Scientific articles published from 1967 to 2013 in the PubMed/Medline, ISI Web of Knowledge, Cochrane, and SciELO databases were reviewed. Results: Saccadic eye movement appears to be heavily involved in psychiatric diseases covered in this review via a direct mechanism. The changes seen in the execution of eye movement tasks in patients with psychopathologies of various studies confirm that eye movement is associated with the cognitive and motor system. Conclusion: Saccadic eye movement changes appear to be heavily involved in the psychiatric disorders covered in this review and may be considered a possible marker of some disorders. The few existing studies that approach the topic demonstrate a need to improve the experimental paradigms, as well as the methods of analysis. Most of them report behavioral variables (latency/ reaction time), though electrophysiological measures are absent.
Event-Based Finite-Time Neural Control for Human-in-the-Loop UAV Attitude Systems

This article focuses on the event-based finite-time neural attitude consensus control problem for the six-rotor unmanned aerial vehicle (UAV) systems with unknown disturbances. It is assumed that the six-rotor UAV systems are controlled by a human operator sending command signals to the leader. A disturbance observer and radial basis function neural networks (RBF NNs) are applied to address the problems regarding external disturbances and uncertain nonlinear dynamics, respectively. In addition, the proposed finite-time command filtered (FTCF) backstepping method effectively manages the issue of “explosion of complexity,” where filtering errors are eliminated by the error compensation mechanism. In addition, an event-triggered mechanism is considered to alleviate the communication burden between the controller and the actuator in practice. It is shown that all signals of the six-rotor UAV systems are bounded and the consensus errors converge to a small neighborhood of the origin in finite time. Finally, the simulation results demonstrate the effectiveness of the proposed control scheme.
Seizure Duration and Spread Dynamics in MRI-Defined Subtypes of Temporal Lobe Epilepsy

Background and Objectives MRI and PET imaging enables subgroups of temporal lobe epilepsy (TLE) to be defined on the basis of structural pathology. Few studies have examined the variation in electroclinical seizure spread patterns based on imaging findings. We performed a retrospective cohort study to investigate the electroclinical differences among 3 specific groups of TLE: MRI-negative PET-positive TLE (MRI-negative TLE), temporal lobe lesion TLE (lesional TLE), and unilateral hippocampal sclerosis TLE (HS-TLE). Methods Patients with an electroclinical diagnosis of TLE who had video-scalp EEG recordings of seizures were identified from the retrospective database of the Austin Comprehensive Epilepsy Program between 2005 and 2019. The cohort was further selected into the 3 defined groups based on imaging findings, using MRI and FDG-PET. Timings of clinical and electrographic seizure progression were measured, considering the onset, ipsilateral lobar spread, contralateral spread, and termination. Durations were compared between groups using linear mixed models with inclusion of demographic and clinical covariates. Results A total of 105 patients (137 seizures) were included, comprising 36 with MRI-negative TLE (54 seizures), 36 with lesional TLE (18 lateral vs 16 mesial lesions; 44 seizures), and 33 with HS-TLE (39 seizures). Seizure duration was similar between MRI-negative TLE and lesional TLE (mean 75.9 vs 71.7 seconds; p = 0.91). Further dividing lesional TLE into medial vs lateral temporal revealed no timing difference. However, the HS-TLE group had longer total seizure duration (114 seconds) compared with both MRI-negative TLE (p < 0.001) and lesional TLE (p < 0.001). Progression of electrographic spread also reflected this pattern, with involvement of extratemporal regions and then the contralateral hemisphere each taking significantly longer in HS-TLE. Discussion MRI-negative TLE appears electrographically similar to lesional TLE, whether mesial or lateral, in the duration of seizures and the timing of electrographic spread. Both appear electrographically different from HS-TLE, where propagation is slower, suggesting engagement of different epileptogenic networks or seizure suppression mechanisms. Classification of Evidence This study provides Class II evidence that the electroclinical features of seizures in HS-TLE are different than MRI-negative TLE and lesional TLE.
Ageing modifies acute resting blood pressure responses to incremental consumption of dietary nitrate: a randomised, cross-over clinical trial

Abstract Beetroot (BR) is a rich source of nitrate (NO3-) that has been shown to reduce blood pressure (BP). Yet, no studies have examined the vascular benefits of BR in whole-food form and whether the effects are modified by age. This study was a four-arm, randomised, open-label, cross-over design in twenty-four healthy adults (young n 12, age 27 ± 4 years, old n 12, age 64 ± 5 years). Participants consumed whole-cooked BR at portions of (NO3- content in brackets) 100 g (272 mg), 200 g (544 mg) and 300 g (816 mg) and a 200-ml solution containing 1000 mg of potassium nitrate (KNO3) on four separate occasions over a 4-week period (≥7-d washout period). BP, plasma NO3- and nitrite (NO2-) concentrations, and post-occlusion reactive hyperaemia via laser Doppler, were measured pre- and up to 5-h post-intervention. Data were analysed by repeated-measures ANOVA. Plasma NO2- concentrations were higher in the young v. old at baseline and post-intervention (P < 0·05). All NO3- interventions decreased systolic and diastolic BP in young participants (P < 0·05), whereas only KNO3 (at 240–300 min post-intake) significantly decreased systolic (–4·8 mmHg, −3·5 %, P = 0·024) and diastolic (–5·4 mmHg, −6·5 %, P = 0·007) BP in older participants. In conclusion, incremental doses of dietary NO3- reduced systolic and diastolic BP in healthy young adults whereas in the older group a significant decrease was only observed with the highest dose. The lower plasma NO2- concentrations in older participants suggest that there may be mechanistic differences in the production of NO from dietary NO3- in young and older populations.
Comprehensive molecular profiling of pancreatic ductal adenocarcinoma in FNA, biopsy, and resection specimens

Molecular testing to identify molecular alterations in pancreatic ductal adenocarcinoma (PDAC) has been increasingly requested because of potential therapeutic implications. In this study, we compared the performance of PDAC fine‐needle aspiration (FNA), fine‐needle biopsy (FNB), and resection specimens for comprehensive molecular analysis.
Family history tools for primary care: A systematic review

Abstract Background Many medical family history (FH) tools are available for various settings. Although FH tools can be a powerful health screening tool in primary care (PC), they are currently underused. Objectives This review explores the FH tools currently available for PC and evaluates their clinical performance. Methods Five databases were systematically searched until May 2021. Identified tools were evaluated on the following criteria: time-to-complete, integration with electronic health record (EMR) systems, patient administration, risk-assessment ability, evidence-based management recommendations, analytical and clinical validity and clinical utility. Results We identified 26 PC FH tools. Analytical and clinical validity was poorly reported and agreement between FH and gold standard was commonly inadequately reported and assessed. Sensitivity was acceptable; specificity was found in half of the reviewed tools to be poor. Most reviewed tools showed a capacity to successfully identify individuals with increased risk of disease (6.2–84.6% of high and/or moderate or increased risk individuals). Conclusion Despite the potential of FH tools to improve risk stratification of patients in PC, clinical performance of current tools remains limited as well as their integration in EMR systems. Twenty-one FH tools are designed to be self-administered by patients.