Miracle-dz/UniFAR-FTUs huggingface dataset:
Visual inspection of railroad tracks.

In this dissertation, we have developed computer vision methods for measurement of rail gauge, and reliable identification and localization of structural defects in railroad tracks. The rail gauge is the distance between the innermost sides of the two parallel steel rails. We have developed two methods for evaluation of rail gauge. These methods were designed for different hardware setups: the first method works with two pairs of unaligned video cameras while the second method works with depth maps generated by paired laser range scanners. We have also developed a method for detection of rail defects such as damaged or missed rail fasteners, tie clips, and bolts, based on correlation and MACH filters. Lastly, to make our algorithms perform in real-time, we have developed the GPU based library for parallel computation of the above algorithms. Rail gauge is the most important measurement for track maintenance, because deviations in gauge indicate where potential defects may exist. We have developed a vision-based method for rail gauge estimation from a pair of industrial laser range scanners. In this approach, we start with building a 3D panorama of the rail out of a stack of input scans. After the panorama is built, we apply FIR circular filtering and Gaussian smoothing to the panorama buffer to suppress the noise component. In the next step we attempt to segment the rail heads in the panorama buffer. We employ the method which detects railroad crossings or forks in the panorama buffer. If they are not present, we find the rail edge using robust line fit. If they are present we use an alternative way: we predict the rail edge positions using Kalman filter. In the next step, common to both fork/crossings conditions, we find the adjusted positions of rail edges using additional iv clustering in the vicinity of the edge. We approximate rail head surface by the third degree polynomial and then fit two plane surfaces to find the exact position of the rail edge. Lastly, using rail edge information, we calculate the rail gauge and smooth it with 1D Gaussian filter. We have also developed a vision-based method to estimate the rail gauge from a pair of unaligned high shutter speed calibrated cameras. In this approach, the first step is to accurately detect the rail in each of the two non-overlapping synchronous images from the two cameras installed on the data collection cart by building an edge …
Learning Backchanneling Behaviors for a Social Robot via Data Augmentation from Human-Human Conversations

: Backchanneling behaviors on a robot, such as nodding, can make talking to a robot feel more natural and engaging by giving a sense that the robot is actively listening. For backchanneling to be effective, it is important that the timing of such cues is appropriate given the humans’ conversational behaviors. Recent progress has shown that these behaviors can be learned from datasets of human-human conversations. However, recent data-driven methods tend to overﬁt to the human speakers that are seen in training data and fail to generalize well to previously unseen speakers. In this paper, we explore the use of data augmentation for effective nodding behavior in a robot. We show that, by augmenting the input speech and visual features, we can produce data-driven models that are more robust to unseen features without collecting additional data. We analyze the efﬁcacy of data-driven backchanneling in a realistic human-robot conversational setting with a user study, showing that users perceived the data-driven model to be better at listening as compared to rule-based and random baselines.
Long-Term Persistence of IgG Antibodies in recovered COVID-19 individuals at 18 months and the impact of two-dose BNT162b2 (Pfizer-BioNTech) mRNA vaccination on the antibody response

This era of emerging variants needs a thorough evaluation of data on the long-term efficacy of immune responses in vaccinated as well as recovered individuals, to understand the overall evolution of the pandemic. In this study, we aimed to assess the dynamics of IgG titers over 18 months in 34 patients from the Umbria region in Italy, who had a documented history of COVID-19 infection in March 2020, and then compared the impact of two-dose BNT162b2 (Pfizer-BioNTech) vaccination on the antibody titers of these patients with the ones who did not receive any dose of vaccine. This is the longest observation (March 2020-September 2021) for the presence of antibodies against SARS-CoV-2 in recovered individuals along with the impact of 2 dose-BNT162b2 vaccination on the titers. Fixed-effect regression models were used for statistical analysis which could be also used to predict future titer trends. At 18 months, 97% participants tested positive for anti-NCP hinting towards the persistence of infection-induced immunity even for the vaccinated individuals. Our study findings demonstrate that while double dose vaccination boosted the IgG titers in recovered individuals 161 times, this boost was relatively short-lived. The unvaccinated recovered individuals, in contrast, continued to show a steady decline but detectable antibody levels. Further studies are required to re-evaluate the timing and dose regimen of vaccines for an adequate immune response in recovered individuals.
Risk for Mould Growth on Hemp-Lime at Different Relative Humidity

Microbial growth often thrives in humid conditions, at high relative humidity. Moulds are complex organisms; many types of mould are able to survive strong variations in humidity and temperature, such as those on building façades. For some building materials a critical relative humidity is determined, which functions as a theoretical threshold; at this (or lower) relative humidity microbial growth will likely not occur. Hemp-lime is a building material that consists of hemp shiv (the woody core parts of the hemp stem) and building lime. It is a material that can be used for walls, and even though it has been used for more than 20 years, thusfar little is known about its critical moisture levels for microbial growth. The aim of this research was therefore to determine at what relative humidity microbial growth occurs on carbonated hemp-lime material, and to study if there is a protective influence of a carbonated lime binder on the hemp shiv. The objective was to study microbial growth on hemp shiv, hemp-lime and on hemp with a thin layer of lime at three relative humidity (75 %, 85 % and 95 %) and at two different temperatures (15°C and 23°C); conditions that could occur naturally in a hemp-lime façade exposed to high rain loads in a northern European climate. Hemp shiv seems to have a relatively low resistance to microbial growth, similar to that of wood. However, because the hemp is protected by lime it can withstand much higher relative humidity without microbial growth occurring on the material. The critical moisture level for hemp-lime seemed to occur between 75 and 85 % RH, while the material was completely without microbial growth at 75 % RH. The lime had a protective effect on the hemp and acted as a mould inhibitor, both over time and with varying temperature and humidity.
Evaluation and Management After Acute Left-Sided Colonic Diverticulitis

BACKGROUND The value of interventions used after acute colonic diverticulitis is unclear. PURPOSE To evaluate postdiverticulitis colonoscopy and interventions to prevent recurrent diverticulitis. DATA SOURCES MEDLINE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Embase, CINAHL, and ClinicalTrials.gov from 1 January 1990 through 16 November 2020. STUDY SELECTION Comparative studies of interventions of interest reporting critical or important outcomes, and larger single-group studies to evaluate prevalence of colonoscopy findings and harms. DATA EXTRACTION 6 researchers extracted study data and risk of bias. The team assessed strength of evidence. DATA SYNTHESIS 19 studies evaluated colonoscopy. Risk for prevalent colorectal cancer (CRC) compared with the general population is unclear. Based on low-strength evidence, long-term CRC diagnosis is similar with or without colonoscopy. High-strength evidence indicates that risk for prevalent CRC is higher among patients with complicated diverticulitis and colonoscopy complications are rare. Based on high-strength evidence, mesalamine does not reduce recurrence risk (6 randomized controlled trials [RCTs]). Evidence on other nonsurgical interventions is insufficient. For patients with prior complicated or smoldering or frequently recurrent diverticulitis, elective surgery is associated with reduced recurrence (3 studies; high strength). In 19 studies, serious surgical complications were uncommon. LIMITATIONS Few RCTs provided evidence. Heterogeneity of treatment effect was not adequately assessed. CONCLUSION It is unclear whether patients with recent acute diverticulitis are at increased risk for prevalent CRC, but those with complicated diverticulitis are at increased risk. Mesalamine is ineffective in preventing recurrence; other nonsurgical treatments have inadequate evidence. Elective surgery reduces recurrence in patients with prior complicated or smoldering or frequently recurrent diverticulitis, but it is unclear which of these patients may benefit most. PRIMARY FUNDING SOURCE Agency for Healthcare Research and Quality and American College of Physicians. (PROSPERO: CRD42020151246).
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer

: Modern Reinforcement Learning (RL) algorithms are not sample-efﬁcient to train on multi-step tasks in complex domains, impeding their wider deployment in the real world. We address this problem by leveraging the insight that RL models trained to complete one set of tasks can be re-purposed to complete related tasks when given just a handful of demonstrations. Based upon this insight, we propose See-SPOT-Run (SSR), a new computational approach to robot learning that enables a robot to complete a variety of real robot tasks in novel problem domains without task-speciﬁc training. SSR uses pretrained RL models to create vectors that represent model, task, and action relevance in demonstration and test scenes. SSR then compares these vectors via our Cycle Consistency Distance (CCD) metric to determine the next action to take. SSR completes 58% more task steps and 20% more trials than a baseline few-shot learning method that requires task-speciﬁc training. SSR also achieves a four order of magnitude improvement in compute efﬁciency and a 20% to three order of magnitude improvement in sample efﬁciency compared to the baseline and to training RL models from scratch. To our knowledge, we are the ﬁrst to address multi-step tasks from demonstration on a real robot without task-speciﬁc training, where both the visual input and action space output are high dimensional. Code is available in the supplement.
LinkMan: hyperlink-driven misbehavior detection in online security forums

How can we detect and analyze hyperlink-driven misbehavior in online forums? Online forums contain enormous amounts of user-generated content, with threads and comments frequently supplemented by hyperlinks. These hyperlinks are often posted with malicious intention and we refer to this as 'hyperlink-driven misbehavior'. We present LinkMan, a systematic suite of capabilities, to detect and analyze hyperlink-driven misbehavior in online forums. We take a unique perspective focusing on hyperlink sharing practices of the users to spot misbehavior. LinkMan can categorize these hyperlinks as: a) phishing, b) spamming, and b) promoting malicious products. Our approach consists of three high-level phases: (a) extracting hyperlinks from the textual data, (b) identifying misbehaving hyperlinks, and (c) modeling the behavioral patterns of hyperlink sharing, where we identify key hyperlinks and analyze the collaboration dynamics of hyperlink sharing. In addition, we implement our approach as a powerful and easy-to-use open platform for practitioners. We apply LinkMan to spot misbehavior from three online security forums, where we expect the users to be more security-aware. We show that our approach works very well in terms of retrieving and classifying hyperlinks compared to previous solutions. Furthermore, we find non-trivial and often systematic misbehavior: (a) we find a total of 637 misbehaving hyperlinks, and (b) we identify 30 colluding groups of users in terms of promoting hyperlinks. Our work is a significant step towards mining online forums and detecting misbehaving users comprehensively.
Human Cortical Serotonin 2A Receptor Occupancy by Psilocybin Measured Using [11C]MDL 100,907 Dynamic PET and a Resting-State fMRI-Based Brain Parcellation

Psilocybin (a serotonin 2A, or 5-HT2A, receptor agonist) has shown preliminary efficacy as a treatment for mood and substance use disorders. The current report utilized positron emission tomography (PET) with the selective 5-HT2A receptor inverse agonist radioligand [11C]MDL 100,907 (a.k.a. M100,907) and cortical regions of interest (ROIs) derived from resting-state functional connectivity-based brain parcellations in 4 healthy volunteers (2 females) to determine regional occupancy/target engagement of 5-HT2A receptors after oral administration of a psychoactive dose of psilocybin (10 mg/70 kg). Average 5-HT2A receptor occupancy across all ROIs was 39.5% (± 10.9% SD). Three of the ROIs with greatest occupancy (between 63.12 and 74.72% occupancy) were within the default mode network (subgenual anterior cingulate and bilateral angular gyri). However, marked individual variability in regional occupancy was observed across individuals. These data support further investigation of the relationship between individual differences in the acute and enduring effects of psilocybin and the degree of regional 5-HT2A receptor occupancy.
An insight into network structure measures and number of driver nodes

Control of complex networks is one of the most challenging open problems within network science. One view says that we can only claim to fully understand a network if we have the ability to influence or control it and predict the results of the employed control mechanisms. The area of control and controllability has progressed notably in the past ten years with several frameworks proposed namely, structural, exact, and physical. With continuing advancement in the area, the need to develop effective and efficient control methods that provide robust control is increasingly critical. The ultimate responsibility for controlling the network lies with the set of driver nodes that, according to the classical definition of the control theory of complex systems, can steer the network from any given state to a desired final state. To be able to develop better control mechanisms, we need to understand the relationship between different network structures and the number of driver nodes needed to control a given structure. This will allow understanding of which networks might be easier to control and the resources needed to control them. In this paper, we present a systematic study that builds an understanding of how network profiles (random (R), small-world (SW), scale-free (SF)) influence the number of driver nodes needed for control. Additionally, we also consider real social networks and identify their driver nodes set to further expand the discussion. We mean to find a correlation between network structure measures and number of driver nodes. Our results show that there is in fact a strong relationship between these.
Space Shuttle Rbar Pitch Maneuver for Thermal Protection System Inspection

A new Space Shuttle orbital maneuver developed by NASA will allow the International Space Station (ISS) crew to perform a high resolution photo survey of the lower surface thermal protection system (TPS) tiles to screen for ascent debris damage. The Rbar Pitch Maneuver (RPM) was conceived in the months following the breakup of the Space Shuttle Columbia and has been approved for use in shuttle missions beginning with STS-114. The RPM, though simple in concept, had to overcome numerous design challenges in order to demonstrate a safe and practicable operational plan. The issues resolved by the NASA mission operations and engineering team included limitations on crew and sensor visibility, maneuvers conducted about an unstable orbital stationkeeping position, inherently unstable attitude dynamics, thruster plume impingement on the ISS, shuttle propellant limitations, and a very tight set of lighting and geometry constraints for the inspection photography. The history of the RPM development is outlined, with special emphasis on key GN&C issues.
The BiasChecker: how biased are social media searches?

Social media searches are frequently employed by users to keep them up to date about ongoing events and learn broadly about public opinion on topics that are unfamiliar to them. Nevertheless, there are rising concerns about the results returned that can reinforce users' existing biases - the inclination to one opinion over another. This paper introduces a tool, called BiasChecker, that contributes to the check for bias in search results on a social media platform. BiasChecker follows a distributed and extendable architecture that allows us to simulate users following and unfollowing accounts, search for different polarised topics in a concurrent manner and measure bias. It may be applied to multiple social media platforms. The proposed tool takes into account several factors that can interfere with the detection of bias, e.g., the cross-over effect, geolocation, IP address, and language.
Domain adaptation based on the measure of kernel-product maximum mean discrepancy

Transfer learning is an important branch of machine learning, focusing on applying what has been learned in the old field to new problems. Maximum mean discrepancy (MMD) is used in most existing works to measure the difference between two distributions by applying a single kernel. Recent works exploit linear combination of multiple kernels and need to learn the weight of each kernel. Because of the singleness of single-kernel and the complexity of multiple-kernel, we propose a novel kernel-product maximum mean discrepancy (DA-KPMMD) approach. We choose the product of linear kernel and Gaussian kernel as the new kernel. Specifically, we reduce differences in the marginal and conditional distribution simultaneously between source and target domain by adaptively adjusting the importance of the two distributions. Further, the within-class distance is minimizing to differentiate samples of different classes. We conduct cross-domain classification experiments on three image datasets and experimental results show the superiority of DA-KPMMD compared with several domain adaptation methods. CCS CONCEPTS • Computing methodologies • Machine learning • Machine learning approaches • Kernel methods
Meta-analysis of the black soldier fly (Hermetia illucens) microbiota based on 16S rRNA gene amplicon sequencing

Black soldier fly larvae (BSFL) belong to the most widely reared insect species as an alternative protein source at industrial scale. Bacteria in the larval gut can provide benefits for the animal, though some bacteria can also be pathogenic for the insect. Accurate characterization of the BSFL microbiota is important for the production of BSFL in terms of yield and microbiological safety. In this study, 16S ribosomal RNA gene sequence data sets from 11 studies were re-analysed to gain better insights in the BSFL gut microbiota, potential factors that influence their composition, and differences between the gut and the whole larvae microbiota. A core gut microbiota was found consisting of members of Enterococcus, Klebsiella, Morganella, Providencia, and Scrofimicrobium. Further, the factors “Study”, “Age” and “Feed” significantly affected the microbiota gut composition. When compared to whole larvae, a significantly lower number of observed zero-radius Operational Taxonomic Units and a lower diversity was found for gut samples, suggesting that the larvae harboured additional microbes on their cuticle or in the insect body. Universal choices in insect sample type, primer selection and bio-informatics can strengthen future meta-analyses and improve our understanding of the BSFL gut microbiota towards the optimization of insect production.
Exploring the Compositional Space of High-Entropy Alloys for Cost-Effective High-Temperature Applications

High-entropy alloys (HEAs) are nearly equimolar multi-principal element alloys, exhibiting exceptional thermal and mechanical properties at extreme conditions such as high-temperatures and stresses. Since the first discovery and early conceptualization of conventional HEAs nearly two decades ago, HEAs with far-from-equimolar compositions have attracted substantial interest to provide a broader range of material properties and to adjust price fluctuations and availability of commodities. Here, we present a first-principles investigation of non-equimolar chromium-manganese-iron-cobalt-nickel (CrMnFeCoNi) HEAs and effects of molybdenum (Mo) and niobium (Nb) substitutions on cost, phase stability and solubility, and mechanical and thermal performance up to 1000 K operational temperature. Virtual-crystal approximation is used to expediently approximate random solid solutions at the disordered mean-field limit. Using multi-objective metaheuristics built on a first-principles database, golden compositions are predicted for thermally well-insulated components and effective heat sinks. Replacing Co with Fe lowers commodity costs without hindering phase stability and solubility. Lower Ni concentration leads to lower thermal conductivity, indicating better thermal insulation, while reducing Mn concentration significantly increases the thermal conductivity, indicating better performing heat sinks. Moving away from equimolar ratios commonly increases the thermal expansion coefficient, which could generate higher thermal stresses. Nb and Mo substitution always lead to substantially higher commodity cost and density but with an increment in the mechanical performance due to solid-solution hardening. However, alloying with Mo and Nb is the only compositional space that reduces the thermal conductivity and thermal expansion coefficient.
Truth and travesty intertwined: a case study of #SSR counterpublic campaign

Twitter has emerged as a prominent social media platform for activism and counterpublic narratives. The counterpublics leverage hashtags to build a diverse support network and share content on a global platform that counters the dominant narrative. This paper applies the framework of connective action on the counter-narrative campaign over the cause of death of #SushantSinghRajput. We combine descriptive network, modularity, and hashtag based topical analysis to identify three major mechanisms underlying the campaign: generative role taking, hashtag-based narratives and formation of alignment network towards a common cause. Using the case study of #SushantSinghRajput, we highlight how connective action framework can be used to identify different strategies adopted by counterpublics for the emergence of connective action.