Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Calibration and Registration Method for Indoor Scene with Joint Depth and Color Camera

Traditional vision registration technologies require the design of precise markers or rich texture information captured from the video scenes, and the vision-based methods have high computational complexity while the hardware-based registration technologies lack accuracy. Therefore, in this paper, we propose a novel registration method that takes advantages of RGB-D camera to obtain the depth information in real-time, and a binocular system using the Time of Flight (ToF) camera and a commercial color camera is constructed to realize the three-dimensional registration technique. First, we calibrate the binocular system to get their position relationships. The systematic errors are fitted and corrected by the method of B-spline curve. In order to reduce the anomaly and random noise, an elimination algorithm and an improved bilateral filtering algorithm are proposed to optimize the depth map. For the real-time requirement of the system, it is further accelerated by parallel computing with CUDA. Then, the Cam.
Solvation and desolvation effects in protein folding: native flexibility, kinetic cooperativity and enthalpic barriers under isostability conditions

As different parts of a protein chain approach one another during folding, they are expected to encounter desolvation barriers before optimal packing is achieved. This impediment originates from the water molecule's finite size, which entails a net energetic cost for water exclusion when the formation of compensating close intraprotein contacts is not yet complete. Based on recent advances, we extend our exploration of these microscopic elementary desolvation barriers' roles in the emergence of generic properties of protein folding. Using continuum Gō-like Cα chain models of chymotrypsin inhibitor 2 (CI2) and barnase as examples, we underscore that elementary desolvation barriers between a protein's constituent groups can significantly reduce native conformational fluctuations relative to model predictions that neglected these barriers. An increasing height of elementary desolvation barriers leads to thermodynamically more cooperative folding/unfolding transitions (i.e., higher overall empirical folding barriers) and higher degrees of kinetic cooperativity as manifested by more linear rate-stability relationships under constant temperature. Applying a spatially non-uniform thermodynamic parametrization we recently introduced for the pairwise Cα potentials of mean force, the present barnase model further illustrates that desolvation is a probable physical underpinning for the experimentally observed high intrinsic enthalpic folding barrier under isostability conditions.
Review mining for estimating users' ratings and weights for product aspects

Fine-grained opinions are often buried in user reviews. The opinionated aspects may also be associated with different weights by reviewers to represent the aspects’ relative importance. As the opinions and weights provide valuable information about users’ preferences for products, they can facilitate the generation of personalised recommendations. However, few studies to date have investigated the three inter-connected tasks in a unified framework: aspect identification, aspect-based rating inference and weight estimation. In this paper, we propose a unified framework for performing the three tasks, which involves 1) identifying the product aspects mentioned in a review, 2) inferring the reviewer’s ratings for these aspects from the opinions s/he expressed in a review, and 3) estimating the reviewer’s weights for these aspects. The relationship among these three tasks is inherently dependent in that the output of one task adjusts the accuracy of another task. We particularly develop an unsupervised model to Collectively estimate Aspect Ratings and Weights (shorted as CARW), which performs all of the three tasks by enhancing each other mutually. We conduct experiments on three real-life datasets to evaluate the CARW model. Experimental results show that the proposed model can achieve better performance than the related methods regarding each task.
WILL: Wireless indoor localization without site survey

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past two decades. Most radio-based solutions require a process of site survey, in which radio signatures are collected and stored for further comparison and matching. Site survey involves intensive costs on manpower and time. In this work, we study unexploited RF signal characteristics and leverage user motions to construct radio floor plan that is previously obtained by site survey. On this basis, we design WILL, an indoor localization approach based on off-the-shelf WiFi infrastructure and mobile phones. WILL is deployed in a real building covering over 1600m2, and its deployment is easy and rapid since site survey is no longer needed. The experiment results show that WILL achieves competitive performance comparing with traditional approaches.
Practical kinematic and dynamic calibration methods for force-controlled humanoid robots

This paper presents methods and experimental results regarding the identification of kinematic and dynamic parameters of force-controlled biped humanoid robots. We first describe a kinematic calibration method to estimate joint angle sensor offsets. The method is practical in the sense that it only uses joint angle and link orientation sensors, which most humanoid robots are equipped with. The basic idea is to solve an optimization problem that represents a kinematic constraint that can be easily enforced, such as placing both feet flat on floor. We then present two methods to identify physically consistent mass and local center of mass parameters even when obtaining enough excitation is difficult, as is always the case in humanoid robots. We demonstrate by experiment that these methods give good identification results even when the regressor has a large condition number. Moreover, we show that gradient-based optimization performs better than the leasts-quare method in many cases.
Technical University of Iasi , Romania PNiPAM-FUNCTIONALIZED MESOPOROUS CARBON FOR THE ADSORPTION OF VITAMIN B 2

The present research reports the synthesis of poly-N-isopropylacrylamide / mesoporous carbon composites, prepared by radical polymerization of N-isopropylacrylamide inside mesoporous carbon pores functionalized with carboxylic groups. The deposition of poly-N-isopropylacrylamide on mesoporous carbon was confirmed by FT-IR spectroscopy, TEM, N2-sorption measurements. The mesoporous carbon material was found to adsorb vitamin B2 from aqueous solution at room temperature. The obtained composite, poly-N-isopropylacrylamide / mesoporous carbon, exhibited a higher adsorption capacity for vitamin B2 as compared to un-functionalized mesoporous carbon sample, due to hydrogen bonding between carbonyl groups of poly-Nisopropylacrylamide immobilized on the mesoporous carbon surface and amino groups of vitamin B2 molecules.
Comparison of the Walz Nomogram and Presence of Secondary Circulating Prostate Cells for Predicting Early Biochemical Failure after Radical Prostatectomy for Prostate Cancer in Chilean Men.

PURPOSE To determine the utility of secondary circulating prostate cells for predicting early biochemical failure after radical prostatectomy for prostate cancer and compare the results with the Walz nomagram. MATERIALS AND METHODS A single centre, prospective study of men with prostate cancer treated with radical prostatectomy between 2004 and 2014 was conducted, with registration of clinical-pathological details, total serum PSA pre-surgery, Gleason score, extracapsular extension, positive surgical margins, infiltration of lymph nodes, seminal vesicles and pathological stage. Secondary circulating prostate cells were obtained using differential gel centrifugation and assessed using standard immunocytochemistry with anti-PSA. Biochemical failure was defined as a PSA >0.2ng/ml, predictive values werecalculated using the Walz nomagram and CPC detection. RESULTS A total of 326 men participated, with a median follow up of 5 years; 64 had biochemical failure within two years. Extracapsular extension, positive surgical margins, pathological stage, Gleason score ≥ 8, infiltration of seminal vesicles and lymph nodes were all associated with higher risk of biochemical failure. The discriminative value for the nomogram and circulating prostate cells was high (AUC >0.80), predictive values were higher for circulating prostate cell detection, with a negative predictive value of 99%, sensitivity of 96% and specificity of 75%. CONCLUSIONS The nomagram had good predictive power to identify men with a high risk of biochemical failure within two years. The presence of circulating prostate cells had the same predictive power, with a higher sensitivity and negative predictive value. The presence of secondary circulating prostate cells identifies a group of men with a high risk of early biochemical failure. Those negative for secondary CPCs have a very low risk of early biochemical failure.
Online (cid:96) 1 -Dictionary Learning with Application to Novel Document Detection

Given their pervasive use, social media, such as Twitter, have become a leading source of breaking news. A key task in the automated identiﬁcation of such news is the detection of novel documents from a voluminous stream of text documents in a scalable manner. Motivated by this challenge, we introduce the problem of online (cid:96) 1 -dictionary learning where unlike traditional dictionary learning, which uses squared loss, the (cid:96) 1 -penalty is used for measuring the reconstruction error. We present an efﬁcient online algorithm for this problem based on alternating directions method of multipliers, and establish a sublinear regret bound for this algorithm. Empirical results on news-stream and Twitter data, shows that this online (cid:96) 1 -dictionary learning algorithm for novel document detection gives more than an order of magnitude speedup over the previously known batch algorithm, without any signiﬁcant loss in quality of results.
Everyday Consequences of Analytic Thinking

We review recent evidence revealing that the mere willingness to engage analytic reasoning as a means to override intuitive gut feelings is a meaningful predictor of key psychological outcomes in diverse areas of everyday life. For example, those with a more analytic thinking style are more skeptical about religious, paranormal, and conspiratorial concepts. In addition, analytic thinking relates to having less traditional moral values, making less emotional or disgust-based moral judgments, and being less cooperative and more rationally self-interested in social dilemmas. Analytic thinkers are even less likely to offload thinking to smartphone technology and may be more creative. Taken together, these results indicate that the propensity to think analytically has major consequences for individual psychology.
Treatment outcome for locally advanced non‐small‐cell lung cancer using TomoDirect plan and its characteristics compared to the TomoHelical plan

TomoDirect (TD) is an intensity‐modulated radiotherapy system that uses a fixed gantry angle instead of the rotational beam delivery used in the TomoHelical (TH) system. This study was performed (1) to evaluate the treatment outcome of the TD plan for locally advanced non‐small‐cell lung cancer (NSCLC) and (2) to compare the characteristics of TD plans with those of TH plans.
A Cloud robotics architecture to foster individual child partnership in medical facilities

Robots and automation systems have become a valuable partner in several facets of human life: from learning and teaching, to daily working, including health monitoring and assistance. So far, these appealing robot-based applications are restricted to conduct repetitive, yet useful, tasks due to the reduced individual robots’ capabilities in terms of processing and computation. This concern prevents current robots from facing more complex applications related to understanding hu- man beings and perceiving their subtle feelings. Such hardware limitations have been already found in the computer science field. In this domain, they are currently being addressed using a new resource exploitation model coined as cloud computing, which is targeted at enabling massive storage and computation using smartly connected and inexpensive commodity hardware. The purpose of this paper is to propose a cloud-based robotics architecture to effectively develop complex tasks related to hospitalized children assistance. More specifically, this paper presents a multi-agent learning system that combines machine learning and cloud computing using low-cost robots to (1) collect and perceive children status, (2) build a human-readable set of rules related to the child-robot relationship, and (3) improve the children experience during their stay in the hos- pital. Conducted preliminary experiments proof the feasibility of this proposal and encourage practitioners to work towards this direction.
Towards Self-supervised Face Labeling via Cross-modality Association

Face recognition has become the de facto authentication solution in a broad spectrum of applications, from smart buildings, to industrial monitoring and security services. However, in many of those real-world scenarios, tracking or identifying people with facial recognition is extremely challenging due to the variations in the environment such as lighting conditions, camera viewing angles and subject motion. For most of the state-of-the-art face recognition systems, they need to be trained on a large dataset containing a good variety of labelled face images to work well. However, collecting and manually labelling such datasets is difficult and time consuming, probably more so than developing the algorithms. In this paper, we propose a novel framework to automatically label user identities with their face images in smart spaces, exploiting the fact that the users tend to carry their smart devices while seen by the surveillance cameras. We evaluate our method on 10 users in a smart building setting, and the experimental results show that our method can achieve > 0.9 f1 score on average.
Subnational mobility and consumption-based environmental accounting of US corn in animal protein and ethanol supply chains

Significance Companies and society alike are increasingly concerned with environmental impacts across complex supply chains. Suppliers engaged in upstream intermediate transactions commonly contribute over 75% of the carbon and water impacts of products ultimately consumed by users. These impacts pose risks to downstream customer-facing companies in the form of firm image, supply disruptions, and regulatory action. Policymakers and nonprofit advocacy organizations are increasingly looking to engage actors across supply chains to encourage conservation and environmental impact reduction. Unfortunately, traceability across complex, heterogeneous supply hinders these efforts. We provide a method for estimating mobility of corn from farms through feed and fuel supply chains, making it possible to characterize the variable environmental impacts of US corn inputs into animal protein and ethanol production. Corn production, and its associated inputs, is a relatively large source of greenhouse gas emissions and uses significant amounts of water and land, thus contributing to climate change, fossil fuel depletion, local air pollutants, and local water scarcity. As large consumers of this corn, corporations in the ethanol and animal protein industries are increasingly assessing and reporting sustainability impacts across their supply chains to identify, prioritize, and communicate sustainability risks and opportunities material to their operations. In doing so, many have discovered that the direct impacts of their owned operations are dwarfed by those upstream in the supply chain, requiring transparency and knowledge about environmental impacts along the supply chains. Life cycle assessments (LCAs) have been used to identify hotspots of environmental impacts at national levels, yet these provide little subnational information necessary for guiding firms’ specific supply networks. In this paper, our Food System Supply-Chain Sustainability (FoodS3) model connects spatial, firm-specific demand of corn purchasers with upstream corn production in the United States through a cost minimization transport model. This provides a means to link county-level corn production in the United States to firm-specific demand locations associated with downstream processing facilities. Our model substantially improves current LCA assessment efforts that are confined to broad national or state level impacts. In drilling down to subnational levels of environmental impacts that occur over heterogeneous areas and aggregating these landscape impacts by specific supply networks, targeted opportunities for improvements to the sustainability performance of supply chains are identified.
Augmenting WSNs with Interoperable 802.15.4 Sensor Tags

The sensing capabilities of most sensor networks are fixed at the time of deployment. Adding new sensing capabilities to such networks is a costly and cumbersome process. We present Passive Sensor Tags, battery-free sensing devices that could be used to extend the sensing capabilities of an existing network. Sensor tags feature our new 802.15. 4 receiver design which is suitable for micro-power operation, making battery-free tags possible. Because our tags can both transmit and receive 802.15. 4 frames there is no need for any modification to the deployed hardware. We present preliminary measurements of transmission and reception range.
Bovine trophectoderm cells induced from bovine fibroblasts with induced pluripotent stem cell reprogramming factors

Thirteen independent induced bovine trophectroderm (iBT) cell lines were established by reprogramming bovine fetal liver‐derived fibroblasts after viral‐vector transduction with either six or eight factors, including POU5F1 (OCT4), KLF4, SOX2, MYC, NANOG, LIN28, SV40 large T antigen, and hTERT. Light‐ and electron‐microscopy analysis showed that the iBT cells had epithelial cell morphology typical of bovine trophectoderm cells. Reverse‐transcription‐PCR assays indicated that all of the cell lines expressed interferon‐tau (IFNT) at passages 1 or 2. At later passages (≥ passage 8), however, immunoblot and antiviral activity assays revealed that more than half of the iBT cell lines had stopped expressing IFNT. Messenger RNAs specific to trophectoderm differentiation and function were found in the iBT cell lines, and 2‐dimensional‐gel analysis for cellular proteins showed an expression pattern similar to that of trophectoderm cell lines derived from bovine blastocysts. Integration of some of the human reprogramming factors, including POU5F1, KLF4, SOX2, MYC, NANOG, and LIN28, were detected by PCR, but their transcription was mostly absent in the iBT cell lines. Gene expression assessment of endogenous bovine reprogramming factor orthologs revealed endogenous bLIN28 and bMYC transcripts in all; bSOX2 and bNANOG in none; and bKLF4 and bPOU5F1 in less than half of the iBT cell lines. These results demonstrate that bovine trophectoderm can be induced via reprogramming factor expression from bovine liver‐derived fibroblasts, although other fibroblast populations—e.g., derived from fetal thigh tissue—may produce similar results, albeit at lower frequencies.
Towards Accurate Deceptive Opinion Spam Detection based on Word Order-preserving CNN

As a mainly network of Internet naval activities, the deceptive opinion spam is of great harm. The identification of deceptive opinion spam is of great importance because of the rapid and dramatic development of Internet. The effective distinguish between positive and deceptive opinion plays an important role in maintaining and improving the Internet environment. Deceptive opinion spam is very short, varied type and content. In order to effectively identify deceptive opinion, expect for the textual semantics and emotional polarity that have been widely used in text analysis, we need to further summarize the deep features of deceptive opinion in order to characterize deceptive opinion effectively. In this paper, we use the traditional convolution neural network and improve it from the point of the word order by using the method called word order-preserving k-max pooling, which makes convolution neural network more suitable for text classification. The experiment can get better deceptive opinion spam detection.
SafePub: A Truthful Data Anonymization Algorithm With Strong Privacy Guarantees

Abstract Methods for privacy-preserving data publishing and analysis trade off privacy risks for individuals against the quality of output data. In this article, we present a data publishing algorithm that satisfies the differential privacy model. The transformations performed are truthful, which means that the algorithm does not perturb input data or generate synthetic output data. Instead, records are randomly drawn from the input dataset and the uniqueness of their features is reduced. This also offers an intuitive notion of privacy protection. Moreover, the approach is generic, as it can be parameterized with different objective functions to optimize its output towards different applications. We show this by integrating six well-known data quality models. We present an extensive analytical and experimental evaluation and a comparison with prior work. The results show that our algorithm is the first practical implementation of the described approach and that it can be used with reasonable privacy parameters resulting in high degrees of protection. Moreover, when parameterizing the generic method with an objective function quantifying the suitability of data for building statistical classifiers, we measured prediction accuracies that compare very well with results obtained using state-of-the-art differentially private classification algorithms.
A new feature selection scheme using data distribution factor for transactional data

A new efficient unsupervised feature selection method is proposed to handle transactional data. The proposed feature selection method introduces a new Data Distribution Factor (DDF) to select appropriate clusters. This method combines the compactness and separation together with a newly introduced concept of singleton item. This new feature selection method is computationally inexpensive and is able to deliver very promising results. Four datasets from UCI machine learning repository are used in this studied. The obtained results show that the proposed method is very efficient and able to deliver very reliable results.