Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoor scene segmentation with semantic cuboids

Indoor scene understanding is important to service robots, and scene segmentation is part of this work. This paper has proposed a method of semantic cuboids to partition a single RGB-D image. Semantic cuboids is generated by combining semantic labels and cuboid. Semantic labels are assigned by Markov random field based on super-pixels. And cuboids are constructed by graph method in a scene. Experiments on real data show that the proposed framework obtains accurate semantic cuboids in indoor environments, and completes computational tasks in real time. This method executes faster and adds more semantic information to cuboid than linear matching approach of H Jiang. The results provide useful and precise information of indoor cuboids for service robots.
An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants

Maximum likelihood parameter estimation and sampling from Bayesian posterior distributions are problematic when the probability density for the parameter of interest involves an intractable normalising constant which is also a function of that parameter. In this paper, an auxiliary variable method is presented which requires only that independent samples can be drawn from the unnormalised density at any particular parameter value. The proposal distribution is constructed so that the normalising constant cancels from the Metropolis-Hastings ratio. The method is illustrated by producing posterior samples for parameters of the Ising model given a particular lattice realisation. Copyright 2006, Oxford University Press.
Data parallel Haskell: a status report

We describe the design and current status of our effort to implement the programming model of nested data parallelism into the Glasgow Haskell Compiler. We extended the original programming model and its implementation, both of which were first popularised by the NESL language, in terms of expressiveness as well as efficiency. Our current aim is to provide a convenient programming environment for SMP parallelism, and especially multicore architectures. Preliminary benchmarks show that we are, at least for some programs, able to achieve good absolute performance and excellent speedups.
Analyzing Probability Vectors for Named Entity Statistical Machine Transliteration

ABSTRACT Machine transliteration systems are classified as either Rule-based methods or statistical methods. A rule-based method focuses on transliterating names using lots of human-made rules set. These systems are simple to implement but require huge amount of language expertise. In statistical methods, the importance is given in converting transliteration problem into a classification problem and employs a statistical model to solve this classification problem. Though these methods don’t require expert knowledge of Language model, they need large amounts of bilingual data and good algorithm for training. Currently, basic Markov Chain Model (MM), Extended Markov Chain (EMC), Hidden Markov Model (HMM), Conditional Random Fields (CRF), Decision Tree (DT), Maximum Entropy Markov Model (MEMM) and Support Vector Machine (SVM) are the popular statistical approaches used by many researchers across the globe. This paper focuses on mathematical analysis of different statistical approaches used in machine transliteration of named entity which would be beneficial for many upcoming researchers to know the mathematics used behind the curtains.
Traffic-Known Urban Vehicular Route Prediction Based on Partial Mobility Patterns

Travel route analysis and prediction are essential for the success of many applications in Vehicular Ad-hoc Networks (VANETs). Yet it is quit challenging to make accuracy route prediction for general vehicles in urban settings due to several practical issues such as very complicated traffic networks, the highly dynamic real-time traffic conditions and their interaction with drivers’ route selections. In this paper, we undertake a systematic study on the vehicular route prediction in urban environments where the traffic conditions on complicated road networks keep changing from time to time. Inspired by the observation that a vehicle often has its own route selection flavor when traversing between its sources and destinations, we define a mobility pattern as a consecutive series of road segment selections that exhibit frequent appearance along all the itineraries of the vehicle. We further leverage Variable-order Markov Models (VMMs) to mine mobility patterns from the real taxi GPS trace data collected in Shanghai. In addition, considering the tremendous impact of dynamic traffic conditions to the accuracy of route prediction, we deploy multiple VMMs differentiating different traffic conditions in daytime. Our extensive trace-driven simulation results show that notable patterns can be mined from routes of common vehicles though they usually have no constraints when selecting routes. Given a specific taxi, around 40% next road segments are predictable using our model with a confidence weight of 60%. With multiple VMMs a high route prediction accuracy is achievable from the real traffic trace.
A Real Time Eye-Motion Monitoring System

A real time image based system is presented that is able to measure eye movement related quantities at high frame rate. The system's hardware component allows for high frame rate image acquisition under near infrared constant lighting conditions, whereas the software component is capable of performing real time image processing to determine the values of the required eye-related parameters. The measured quantities include blink parameters, eye-lid parameters and pupil diameter. The robustness of the proposed system is tested for different types of iris color, as well as under normal lighting conditions and near Infra-red light. The results are validated by off-line observer based inspection of the video sequences.
Hybrid- and complex-type N-glycans are not essential for Newcastle disease virus infection and fusion of host cells

N-linked glycans are composed of three major types: high-mannose (Man), hybrid or complex. The functional role of hybrid- and complex-type N-glycans in Newcastle disease virus (NDV) infection and fusion was examined in N-acetylglucosaminyltransferase I (GnT I)-deficient Lec1 cells, a mutant Chinese hamster ovary (CHO) cell incapable of synthesizing hybrid- and complex-type N-glycans. We used recombinant NDV expressing green fluorescence protein or red fluorescence protein to monitor NDV infection, syncytium formation and viral yield. Flow cytometry showed that CHO-K1 and Lec1 cells had essentially the same degree of NDV infection. In contrast, Lec2 cells were found to be resistant to NDV infection. Compared with CHO-K1 cells, Lec1 cells were shown to more sensitive to fusion induced by NDV. Viral attachment was found to be comparable in both lines. We found that there were no significant differences in the yield of progeny virus produced by both CHO-K1 and Lec1 cells. Quantitative analysis revealed that NDV infection and fusion in Lec1 cells were also inhibited by treatment with sialidase. Pretreatment of Lec1 cells with Galanthus nivalis agglutinin specific for terminal α1-3-linked Man prior to inoculation with NDV rendered Lec1 cells less sensitive to cell-to-cell fusion compared with mock-treated Lec1 cells. Treatment of CHO-K1 and Lec1 cells with tunicamycin, an inhibitor of N-glycosylation, significantly blocked fusion and infection. In conclusion, our results suggest that hybrid- and complex-type N-glycans are not required for NDV infection and fusion. We propose that high-Man-type N-glycans could play an important role in the cell-to-cell fusion induced by NDV.
Attention Engagement and Cognitive State Analysis for Augmented Reality Text Display Functions

Human eye gaze has recently been used as an effective input interface for wearable displays. In this paper, we propose a gaze-based interaction framework for optical see-through displays. The proposed system can automatically judge whether a user is engaged with virtual content in the display or focused on the real environment and can determine his or her cognitive state. With these analytic capacities, we implement several proactive system functions including adaptive brightness, scrolling, messaging, notification, and highlighting, which would otherwise require manual interaction. The goal is to manage the relationship between virtual and real, creating a more cohesive and seamless experience for the user. We conduct user experiments including attention engagement and cognitive state analysis, such as reading detection and gaze position estimation in a wearable display towards the design of augmented reality text display applications. The results from the experiments show robustness of the attention engagement and cognitive state analysis methods. A majority of the experiment participants (8/12) stated the proactive system functions are beneficial.
Adaptive Sliding Mode Based Loss-Free Resistor for Power-Factor Correction Application

The emulation of virtual resistance finds various applications in the power processing industry. This paper proposes a novel notion of adaptive sliding mode based loss free resistor (ASLFR). This is achieved by allowing the input power of the power-out power-in (POPI) system to vary, in order to accommodate the load demands. In this paper, the concept is illustrated for power-factor correction (PFC) applications. The ASLFR is used to achieve the dual purpose of harmonics-free rectification along with excellent system response under load and line transients. The scheme serves itself as an efficient single-stage PFC solution. A generic mathematical formulation of the scheme is presented, which can be used for different converters. Then on, a boost topology, operating in continuous conduction mode, is chosen to demonstrate theoretical developments and to showcase the effectiveness of the scheme. The robustness of the proposed controller to any line or load variation is established. A fast voltage recovery with almost no undershoot/overshoot is achieved at transients by using the proposed controller. Additionally, a qualitative analysis is provided to demonstrate the expediency of the proposed ASLFR. The theoretical claims are well supported by simulation as well as experimental results.
Instance-Based Non-standard Inferences in EL with Subjective Probabilities

For practical ontology-based applications representing and reasoning with probabilities is an essential task. For Description Logics with subjective probabilities reasoning procedures for testing instance relations based on the completion method have been developed. In this paper we extend this technique to devise algorithms for solving nonstandard inferences for EL and its probabilistic extension Prob-EL c : computing the most specific concept of an individual and finding explanations for instance
The Fukushima Accident and Public Perceptions About Nuclear Power Around the Globe – A Challenge & Response Model

ABSTRACT We examine the impact of the Fukushima accident (March 2011) on global public perceptions of nuclear power. We contrast conceptually and empirically two models, an event & effect (EE) model [Kim, Y. , Kim, M. , & Kim, W. (2013). Effect of Fukushima nuclear disaster on global public acceptance of nuclear energy. Energy Policy, 61, 822–828] and our own challenge & response (CR) model. We replicate Kim et al. (2013), who modelled retrospective opinion changes on a set of “objective” predictors, using historical opinion data 1996–2016 for 23+ countries. The EE model shows little explanatory power for opinion shifts beyond nuclear dependency in the energy mix. We argue that individual and societal responses to nuclear accidents are constrained by cultural memories, and introduce the alternative CR model. Memory, both individual and collective, is primarily adaptive and makes available schematic information to deal with uncertain and novel situations. The CR model explains better the responses to Fukushima with memory factors of “Past Responses to Nuclear Incidents”, of “Nuclear Renaissance” and “Long-term levels of Acceptance”. We are able to typify 23 countries according to their characteristic pattern of cultural memory and their Fukushima responses.
Initializing Blind Adaptive Equalizers Using A-Priori Environmental Knowledge

This paper addresses the problem of initializing blind adaptive equalizers. Due to the absence of information, the commonly used strategy is single spike initialization. Usually, this means starting the adaptive process far from the minimum mean-squared error, leading to a slow convergence. Blind equalization methods additionally suffer from local minima of the cost function in which the adaptive process might get trapped. These local minima, however, correspond to a large amount of residual intersymbol interference. Therefore, proper initialization might help to overcome both problems. We propose to incorporate coarse environmental knowledge to obtain an estimate of the channel impulse response. This estimate is then used to initialize the equalizer. Tests with two widely used blind equalization methods and experimental data confirm our claim.
Detection of Candida in Concentrated Oral Rinse Cultures by Real-Time PCR

ABSTRACT The incidence of oral candidosis has increased in recent years, largely as a result of the emergence of human immunodeficiency virus infection and the more widespread use of immunosuppressive chemotherapy. This development has been associated with a need for more reliable methods for the detection of Candida. The present study assessed the performance of a real-time PCR and two block-based PCRs for the detection of Candida in 193 concentrated oral rinse culture (CRC) specimens. A total of 102 CRC specimens were positive by culture for Candida; and 96, 90, and 75 of these were also positive by real-time, N18-specific, and internal transcribed spacer (ITS)-specific PCRs, respectively. The five false-negative results by the real-time PCR were all non-Candida albicans positive by culture. Of the 91 culture-negative CRC specimens, 20, 41, and 44 were positive by the real-time PCR and the N18- and ITS-specific PCRs, respectively. All three PCRs detected fungal DNA in 8 culture-negative CRC specimens, with a further 30 being positive by two of the three PCRs. A total of 32 CRC specimens were Candida free by all methods. In summary, a real-time PCR that provides a sensitive, specific, and rapid alternative technique for detection of Candida in the mouth is described.
CONVOLVED ORTHOGONAL EXPANSIONS FOR UNCERTAINTY PROPAGATION: APPLICATION TO RANDOM VIBRATION PROBLEMS

Physical nonlinear systems are typically characterized with n-fold convolution of the Green’s function, e.g., nonlinear oscillators, inhomogeneous media, and scattering theory in continuum and quantum mechanics. A novel stochastic computation method based on orthogonal expansions of random fields has been recently proposed [1]. In this study, the idea of orthogonal expansion is formalized as the so-called nth-order convolved orthogonal expansion (COE) method, especially in dealing with random processes in time. Although the paper is focused on presentation of the properties of the convolved random basis processes, examples are also provided to demonstrate application of the COE method to random vibration problems. In addition, the relation to the classical Volterra-type expansions is discussed
Morphological study of the palatal gingiva of the maxillary first molar in the type 2 diabetes mellitus model rat.

SUMMARY We studied morphological changes at the maxillary first molar in a model rat for type 2 spontaneous diabetes mellitus (DM), the Goto-Kazizaki (GK) rat, vs. the normal 8-week-old Wistar rat. Serial frontal sections of the gingiva of the maxilla with the bone were prepared from the rats. Image analyses, performed on light micrographs of the hematoxylin-eosin stained specimens, allowed comparison of the thickness of the keratinized, granular, prickle, and basal layers. In addition, the cell population of the granular and prickle layers and the cross-sectional area of the connective tissue beneath the mucosal epithelium were examined. The thickness of the capillary of the maxillary first molar was determined by image analysis of scanning electron micrographs of microvascular corrosion cast specimens. We found that the thickness of the keratinized, granular, and prickle layers was significantly higher in the DM vs. normal group, as were the cell population of the granular and prickle layers. In contrast, the cross-sectional area of the connective tissue beneath the mucosal epithelium, and the thickness of the capillary were significantly lower in the DM vs. normal sections. Therefore, we consider that the DM-associated hyperglycemia causes hypertrophy of the mucosal epithelium, atrophy of the connective tissue beneath the mucosal epithelium, and microangiopathy of the capillary of the palatal gingiva of the maxillary first molar in the GK rat.
Effects of the NMDA receptor antagonist MK-801 on female reproduction and juvenile hormone biosynthesis in the cricket Gryllus bimaculatus and the butterfly Bicyclus anynana

SUMMARY Apart from regulating insect development, juvenile hormones (JHs) play an important role in insect reproduction, where they initiate vitellogenin synthesis and regulate the uptake of yolk by the ovary. JH synthesis is a tightly regulated process controlled by neurons and peptidergic neurosecretory cells. One of the known stimulatory regulators of JH biosynthesis is glutamate, and its N-methyl-d-aspartate (NMDA) receptor has been recently found in the cockroach Diploptera punctata. In this study we demonstrate a strong reduction in reproductive output in the tropical butterfly Bicyclus anynana and the Mediterranean field cricket Gryllus bimaculatus caused by the NMDA receptor antagonist MK-801. Such inhibiting effects on reproduction could be overruled by the application of JH mimics. In G. bimaculatus, MK-801 inhibits in vitro JH biosynthesis in the corpora allata and reduces in vivo JH haemolymph titres in a dose-dependent manner. These results suggest that JH biosynthesis in the corpora allata is at least in part controlled by an NMDA receptor with Ca2+ as a second level messenger. Based on our findings we consider NMDA receptor antagonists as important tools for manipulating juvenile hormone biosynthesis and therefore for gaining a better understanding of the mechanistic basis of reproduction.
A robust image copy-move forgery detection based on mixed moments

In order to improve the poor robustness of the image region copy-move forgery detection algorithm for the subsequent processing, this paper proposes a new blind detection method based on the mixed moments. Firstly, using Gaussian pyramid transform to extract the low-frequency information from the image and dividing the low frequency part into overlapping blocks; Secondly, the eigenvector of block composed by the exponenti-fourier moments and histogram moments is lexicographically sorted; Thirdly, positioning tampered region precisely and quickly according to the Euclidean distance and space distance. The experimental results show that this method can successfully detect the forged part with translation, rotation, scaling and mixed operation tamper when the image is changed by brightness variation and contrast adjustment.
User-Oriented Materialized View Selection

The problem of materialized view selection has been long researched, and many approaches have been proposed to deal with this issue. However, all the methods proposed to date strive toward improving the overall query performance, instead of being user-oriented. In this paper, we propose a new user-oriented method, called SOMES (user- oriented materialized view selection), aiming at achieving better performance for view selection problem. SOMES takes into account query characteristics of different users, in which, users are classified into different groups according to their query characteristics, and various user groups are provided with their own windows, user view windows containing the views involved in their own query process. Experimental results show that our method can achieve desirable performance improvements over other methods such as BPUS and FPUS.
A Class-AB CMOS Variable Gain Amplifier for Low-Voltage Applications

A compact class-AB variable gain amplifier has been proposed. The amplifier structure is based on two-stage architecture comprising a linear transconductor cascaded by a class-AB current amplifier-based feedback transimpedance amplifier. The major advantage of such VGA circuit is its ability to offer a good degree of signal linearity without sacrificing the original advantages of its predecessor on both aspects of the power consumption and circuit complexity. Superiority of the proposed VGA has been confirmed by circuit simulation employing 0.18mum standard CMOS technology in designing a 10-MHz VGA under 0.5-V voltage supply with MOS's minimum threshold voltage of 0.43V while draining static power consumption less than 25muW.
Loss Functions For Improved On-Policy Control

We introduce and empirically evaluate two novel online gradientbased reinforcement learning algorithms with function approximation – one model based, and the other model free. These algorithms come with the possibility of having non-squared loss functions which is novel in reinforcement learning, and seems to come with empirical advantages. We further extend a previous gradient based algorithm to the case of full control, by using generalized policy iteration. Theoretical properties of these algorithms are studied in a companion paper.
The cognitive organization of music knowledge: a clinical analysis

Despite much recent interest in the clinical neuroscience of music processing, the cognitive organization of music as a domain of non-verbal knowledge has been little studied. Here we addressed this issue systematically in two expert musicians with clinical diagnoses of semantic dementia and Alzheimer’s disease, in comparison with a control group of healthy expert musicians. In a series of neuropsychological experiments, we investigated associative knowledge of musical compositions (musical objects), musical emotions, musical instruments (musical sources) and music notation (musical symbols). These aspects of music knowledge were assessed in relation to musical perceptual abilities and extra-musical neuropsychological functions. The patient with semantic dementia showed relatively preserved recognition of musical compositions and musical symbols despite severely impaired recognition of musical emotions and musical instruments from sound. In contrast, the patient with Alzheimer’s disease showed impaired recognition of compositions, with somewhat better recognition of composer and musical era, and impaired comprehension of musical symbols, but normal recognition of musical emotions and musical instruments from sound. The findings suggest that music knowledge is fractionated, and superordinate musical knowledge is relatively more robust than knowledge of particular music. We propose that music constitutes a distinct domain of non-verbal knowledge but shares certain cognitive organizational features with other brain knowledge systems. Within the domain of music knowledge, dissociable cognitive mechanisms process knowledge derived from physical sources and the knowledge of abstract musical entities.