Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Object Detection and Pose Estimation in General Belief Space

3D object detection and pose estimation is an indispensable task for service robots, however, due to the challenges in everyday environments where occlusion are observed frequently, it has been recognised that object detection and localisation cannot be achieved using observation from a single viewpoint. Active object detection, recognising the targeting objects via active maneuver, is a solution towards improving the recognition probability and minimising the estimation uncertainty through fully utilising the mobility of the robotic platform. Active perception, proposed by Bajcsy[2], is defined as the problem of intelligent control strategies applied to the data acquisition process which will depend on the current state of data interpretation including recognition. Active object detection and pose estimation, as a subset of active perception, has been investigated intensively in recently years. Atanasov and et al. [1] introduced Viewpoint-Pose tree (VP-Tree) which provides not only object category but also its relative pose and the active hypothesis testing problem is addressed by a pointbased POMDP algorithm. Potthast and et al. [4] combined next-best-view planning and feature selection into one online active object detection system. Based on simple features and detectors, their algorithm enables the quadrotor to detect the targeting object more robustly. In this paper, discretisation on state and control spaces are not required and similar as Indelman’s work[3], based on the probabilistic representation of the general belief, the next-best-view are further decided via optimising the objective function parameterised by the general belief, thus enabling the planning in the continuous domain. We follow the maximum likelihood observation assumption and construct the objective function which consolidates uncertainties of the robot and the poses of detected objects, object detection confidences and control consumption. The simulation results demonstrates the effectiveness of the proposed approach. The conclusion and future work are discussed in section. IV.
Compressed Sensing With Nonlinear Observations and Related Nonlinear Optimization Problems

Nonconvex constraints are valuable regularizers in many optimization problems. In particular, sparsity constraints have had a significant impact on sampling theory, where they are used in compressed sensing and allow structured signals to be sampled far below the rate traditionally prescribed. Nearly, all of the theory developed for compressed sensing signal recovery assumes that samples are taken using linear measurements. In this paper, we instead address the compressed sensing recovery problem in a setting where the observations are nonlinear. We show that, under conditions similar to those required in the linear setting, the iterative hard thresholding algorithm can be used to accurately recover sparse or structured signals from few nonlinear observations. Similar ideas can also be developed in a more general nonlinear optimization framework. In the second part of this paper, we therefore present related result that shows how this can be done under sparsity and union of subspaces constraints, whenever a generalization of the restricted isometry property traditionally imposed on the compressed sensing system holds.
Finding non-redundant multi-word events on Twitter

Twitter is a pervasive technology, with hundreds of millions of users serving as sensors that provide eyewitness accounts of events on the ground. In case of popular events, these sensors start to broadcast news by tweeting to their followers, and to the world. Within minutes these tweets can attract attention and also serve as a primary information source for traditional media. Given a huge set of tweets, the key questions are: (1) How can we detect informative events in general? (2) How can we distinguish relevant events from others? In this paper we tackle these challenges with a statistical model for detecting events by spotting significant frequency deviations of the words' frequency over time. Besides single word events, our model also accounts for events composed of multiple co-occurring words, thus, providing much richer information. Our statistical process is complemented with an optimization algorithm to extract only non-redundant events, overall, providing the user with a succinct summary of the current events. We used our model to analyze 24 million geotagged tweets that have been sent in the US from April 9 to April 22, 2013 - the time period of the Boston marathon bombing - and we show that our approach can create multi-word events that efficiently summarize real-world events.
HEART RATE VARIABILITY ANALYSIS: A REVIEW

Biological signals are nonlinear and non periodic in nature. The signals show self-similarity in various scales of time. Heart rate variability is a noisy time series having self-similar and self affine characteristics. Its analysis is commonly used in accessing the autonomic nervous system of the human body and in diagnosing of cardiac status and other parameters related to ANS in both normal and pathological conditions. The last two decades have shown that there is significant relationship between the Autonomic nervous system (ANS) and cardiovascular mortality, including sudden cardiac death. Easy and non-invasive way to measure has popularized its use. Fractal dimension is one of the robust methods in characterizing complex time series. Non linear time series methods are meant for extracting invariant of the dynamics of system that is a unique characteristic of the system. Unlike Euclidean dimension, fractal dimension is a fractal number that is characteristic of the statistical property and dynamics of the EKG. This paper reviews various methods used for the analysis of HRV and comparing HRV parameters in variouspathological conditions.
Semi-Supervised MEC Clustering Algorithm on Maximized Central Distance

In the field of pattern recognition, the traditional supervised learning methods and unsupervised learning methods are not always suitable for the practical applications. In some applications, the data obtained is neither no-information-given nor all-information-given. In addition, the data obtained usually contains some noises due to many interference factors in practical collection procedure and these noises are of great influence on the traditional clustering methods. In order to overcome the two problems mentioned above, based on the classical Maximal Entropy Clustering (MEC), we propose a semi-supervised MEC algorithm based on the maximized central distance and the compensation term for membership, i.e., CM-sSMEC algorithm. The experimental results on benchmarking UCI data sets show that it has a better performance than the traditional unsupervised clustering method.
Creating relationships with persons with moderate to severe dementia

The study describes how relationships are created with persons with moderate to severe dementia. The material comprises 24 video sequences of Relational Time (RT) sessions, 24 interviews with persons with dementia and eight interviews with professional caregivers. The study method was Constructivist Grounded Theory. The categories of ‘Assigning time’, ‘Establishing security and trust’ and ‘Communicating equality’ were strategies for arriving at the core category, ‘Opening up’, which was the process that led to creating relationships. Both parties had to contribute to create a relationship; the professional caregiver controlled the process, but the person with dementia permitted the caregiver's overtures and opened up, thus making the relationship possible. Interpersonal relationships are significant to enhancing the well-being of persons with dementia. Small measures like RT that do not require major resources can open paths to creating relationships.
Investigation of the Effect of Plasma Polymerized Siloxane Coating for Enzyme Immobilization and Microfluidic Device Conception

This paper describes the impact of a physical immobilization methodology, using plasma polymerized 1,1,3,3, tetramethyldisiloxane, on the catalytic performance of β-galactosidase from Aspergillus oryzae in a microfluidic device. The β-galactosidase was immobilized by a polymer coating grown by Plasma Enhanced Chemical Vapor Deposition (PEVCD). Combined with a microchannel patterned in the silicone, a microreactor was obtained with which the diffusion through the plasma polymerized layer and the hydrolysis of a synthetic substrate, the resorufin-β-d-galactopyranoside, were studied. A study of the efficiency of the immobilization procedure was investigated after several uses and kinetic parameters of immobilized β-galactosidase were calculated and compared with those of soluble enzyme. Simulation and a modelling approach were also initiated to understand phenomena that influenced enzyme behavior in the physical immobilization method. Thus, the catalytic performances of immobilized enzymes were directly influenced by immobilization conditions and particularly by the diffusion behavior and availability of substrate molecules in the enzyme microenvironment.
PKUICST at TREC 2015 Microblog Track: Query-biased Adaptive Filtering in Real-time Microblog Stream

This paper describes our approaches to real-time ﬁlter-ing task including push notiﬁcations on a mobile phone scenario and periodic email digest scenario in the TREC 2015 Microblog track. In the push notiﬁcations on a mobile phone scenario, we apply an adaptive timely query-biased ﬁltering framework which utilizes two effective scores to estimate the relevance of tweets. External evidences are well incorporated in our approach with Web-based query expansion technique. In the periodic email digest scenario, we apply pseudo-relevance feedback using language model and similarly we adopt an adaptive dynamic query-biased ﬁltering method to choose the novel representative tweets. Besides, the re-sults of scenario periodic email digest can promote the performance of scenario push notiﬁcations since we utilize shared global relevance threshold. Experimental re-sults show that our adaptive query-biased ﬁltering meth-ods achieve good performance with respect to ELG and nCG metrics for push notiﬁcations scenario. In addition, our systems for scenario periodic email digest also obtain convincing nDCG scores.
Concept drift detection based on equal density estimation

An important problem that remains in online data mining systems is how to accurately and efficiently detect changes in the underlying distribution of large data streams. The challenge for change detection methods is to maximise the accumulative effect of changing regions with unknown distribution, while at the same time providing sufficient information to describe the nature of the changes. In this paper, we propose a novel change detection method based on the estimation of equal density regions, with the aim of overcoming the issues of instability and inefficiency that underlie methods of predefined space partitioning schemes. Our method is general, nonparametric and requires no prior knowledge of the data distribution. A series of experiments demonstrate that our method effectively detects concept drift in single dimension as well as high dimension data, and is also able to explain the change by locating the data points that contribute most to the change. The detection result is guaranteed by statistical tests.
Dependence of a human squamous carcinoma and associated paraneoplastic syndromes on the epidermal growth factor receptor pathway in nude mice.

Increased levels of epidermal growth factor receptor (EGFR) have been shown on squamous cell carcinomas. Recently, we described a squamous cell carcinoma (MH-85) derived from the oral cavity which was associated with several paraneoplastic syndromes including hypercalcemia and cachexia. This tumor induced the same paraneoplastic syndromes in nude mice (BALB/c, nu/nu, male, 4-6 weeks old). Scatchard analysis revealed that there are two classes of EGFR in MH-85. The dissociation constant and number of binding sites for the high affinity receptors were 38 pM and 5 x 10(4)/cell, respectively, and 2.2 nM and 6 x 10(5) cell, respectively, for the low affinity receptors. Growth of MH-85 in culture was stimulated by epidermal growth factor (EGF) and inhibited by monoclonal antibody 108 to human EGFR, which recognizes the extracellular domain of the EGF receptor. Surgical removal of submandibular glands from male nude mice resulted in a dramatic decrease in plasma EGF levels and a significant reduction of tumor growth, hypercalcemia, and cachexia. When EGF (5 micrograms/mouse, every 2 days for 6 weeks, i.p.) was administered to these sialoadenectomized animals, tumor growth increased, with a parallel increase in hypercalcemia. When monoclonal antibody 108 (1 mg/mouse, i.p.) was given 1, 5, and 10 days after MH-85 tumor implantation, tumor formation was retarded, which resulted in delayed onset of hypercalcemia and cachexia. Moreover, when the antibody was injected 6 times in nude mice exhibiting large tumors and profound hypercalcemia and cachexia, there was a striking decrease in tumor growth, which was accompanied with a reversal of hypercalcemia and cachexia. These results indicate that growth of the human squamous cell carcinoma MH-85 is dependent on the EGFR pathway and that subsequent development of hypercalcemia and cachexia is dependent on tumor growth. They also suggest that agents which interfere with the EGFR pathway may have therapeutic potential as anticancer agents in some human tumors.
The Mira variable S Orionis: relationships between the photosphere, molecular layer, dust shell, and SiO maser shell at 4 epochs

Aims. We present the first multi-epoch study that includes concurrent mid-infrared and radio interferometry of an oxygen-rich Mira star. Methods. We obtained mid-infrared interferometry of S Ori with VLTI/MIDI at four epochs in December 2004, February/March 2005, November 2005, and December 2005. We concurrently observed $v=1$, $J=1{-}0$ (43.1 GHz), and $v=2, J=1{-}0$ (42.8 GHz) SiO maser emission toward S Ori with the VLBA in January, February, and November 2005. The MIDI data are analyzed using self-excited dynamic model atmospheres including molecular layers, complemented by a radiative transfer model of the circumstellar dust shell. The VLBA data are reduced to the spatial structure and kinematics of the maser spots. Results. The modeling of our MIDI data results in phase-dependent continuum photospheric angular diameters of 9.0 ± 0.3 mas (phase 0.42), 7.9 ± 0.1 mas (0.55), 9.7 ± 0.1 mas (1.16), and 9.5 ± 0.4 mas (1.27). The dust shell can best be modeled with Al 2 O 3 grains using phase-dependent inner boundary radii between 1.8 and 2.4 photospheric radii. The dust shell appears to be more compact with greater optical depth near visual minimum ($\tau_V\sim 2.5$), and more extended with lower optical depth after visual maximum ($\tau_V\sim 1.5$). The ratios of the 43.1 GHz/42.8 GHz SiO maser ring radii to the photospheric radii are 2.2 ± 0.3/2.1 ± 0.2 (phase 0.44), 2.4 ± 0.3/2.3 ± 0.4 (0.55), and 2.1 ± 0.3/1.9 ± 0.2 (1.15). The maser spots mark the region of the molecular atmospheric layers just beyond the steepest decrease in the mid-infrared model intensity profile. Their velocity structure indicates a radial gas expansion. Conclusions. S Ori shows significant phase-dependences of photospheric radii and dust shell parameters. Al 2 O 3 dust grains and SiO maser spots form at relatively small radii of ~$1.8{-}2.4$ photospheric radii. Our results suggest increased mass loss and dust formation close to the surface near the minimum visual phase, when Al 2 O 3 dust grains are co-located with the molecular gas and the SiO maser shells, and a more expanded dust shell after visual maximum. Silicon does not appear to be bound in dust, as our data show no sign of silicate grains.
Design of Covariance Functions using Inter-Domain Inducing Variables

We introduced the Gaussian Process Convolution Model (GPCM) in [1], a timeseries model for stationary signals based on the convolution between a continuoustime white-noise process and a continuous-time linear filter drawn from Gaussian process. The GPCM is, conditionally, itself a Gaussian process with a nonparametric kernel defined in a probabilistic fashion. Learning is achieved using a variational free-energy approach based on inter-domain inducing variables that summarise the (posterior) continuous-time linear filter and the driving white-noise process. However, the inter-domain transformation in [1] considers local averages of the noise process and therefore requires a large number inducing variables to represent underlying functions of complex spectra. In this paper, we develop an alternative transformation operating directly in the frequency domain, that retains the same modelling and predictive power as the original but requires fewer inducing variables and, consequently, has a reduced training time. The proposed approach is validated in a spectrum estimation task on a real-world time series. Note: this is a short version of [1] with additional results. For more technical details and experiments please refer to the original paper.
Type Ia Supernovae: Burning and Detonation in the Distributed Regime

A simple, semianalytic representation is developed for nuclear burning in Type Ia supernovae in the special case where turbulent eddies completely disrupt the flame. The speed and width of the "distributed" flame front are derived. For the conditions considered, the burning front can be considered as a turbulent flame brush composed of corrugated sheets of well-mixed flames. These flames are assumed to have a quasi-steady state structure similar to the laminar flame structure, but controlled by turbulent diffusion. Detonations cannot appear in the system as long as distributed flames are still quasi-steady state, but this condition is violated when the distributed flame width becomes comparable to the size of the largest turbulent eddies. When this happens, a transition to detonation may occur. For current best estimates of the turbulent energy, the most likely density for the transition to detonation is in the range (0.5-1.5) × 107 g cm-3.
The concurrency workbench: a semantics-based tool for the verification of concurrent systems

The Concurrency Workbench is an automated tool for analyzing networks of finite-state processes expressed in Milner's Calculus of Communicating Systems. Its key feature is its breadth: a variety of different verification methods, including equivalence checking, preorder checking, and model checking, are supported for several different process semantics. One experience from our work is that a large number of interesting verification methods can be formulated as combinations of a small number of primitive algorithms. The Workbench has been applied to the verification of communications protocols and mutual exclusion algorithms and has proven a valuable aid in teaching and research.
Interpretation of the Siklos solutions as exact gravitational waves in the anti-de Sitter universe

The Siklos class of solutions of Einstein's field equations is investigated by analytical methods. By studying the behaviour of free particles we reach the conclusion that the spacetimes represent exact gravitational waves propagating in the anti-de Sitter universe. The presence of a negative cosmological constant implies that the `background' space is not asymptotically flat and requires `rotating' reference frames in order to fully simplify and view the behaviour of nearby test particles. The Kaigorodov spacetime, which is the simplest representative of the Siklos class, is analysed in more detail. It is argued that it may serve as a `cosmological' analogue of the well known homogeneous pp-waves in the flat universe.
Relationship Between Markers of Insulin Resistance, Markers of Adiposity, HbA1c, and Cognitive Functions in a Middle-Aged Population–Based Sample: the MONA LISA Study

OBJECTIVE To determine the relationship between markers of insulin resistance (fasting insulin and homeostasis model assessment of insulin resistance), markers of adiposity (BMI, waist circumference, and body fat), HbA1c, and cognitive performances in a middle-aged population–based sample free of diabetes. RESEARCH DESIGN AND METHODS Our study sample consisted of 1,172 people aged 35–64 years (49% women), free of diabetes, and recruited between 2005 and 2007 in the MONA LISA survey. Cognitive functions (memory, attention, and processing speed) were evaluated by neuropsychological tests: word-list learning test, digit symbol substitution test (DSST), word fluency test, and Stroop Test. Multiple logistic regressions were used to estimate the relationship between cognitive performance and metabolic markers. We serially adjusted for age, sex, education, and occupational status (model A), additionally for income, smoking, alcohol consumption, sedentarity, and psychotropic substance use (model B), and finally, included variables linked to the metabolic syndrome (hypertension, dyslipidemia, vascular disease, and C-reactive protein) and depression (model C). RESULTS Elevated markers of adiposity were associated with poor cognitive performance in tests evaluating processing speed. The probability of being in the lowest quartile of each test was nearly doubled for participants in the upper quartile of BMI, compared with those in the lowest one [BMI, adjusted odds ratio (OR) 2.18, P = 0.003 (DSST), and OR 2.09, P = 0.005 (Stroop Test)]. High HbA1c was associated with poor cognitive performance in DSST (adjusted OR 1.75, P = 0.037). Waist circumference was linked to poor cognitive performance in men but not in women. CONCLUSIONS Poor cognitive performance is associated with adiposity and hyperglycemia in healthy middle-aged people.
Bias in the prediction of genetic gain due to mass and half-sib selection in random mating populations

The prediction of gains from selection allows the comparison of breeding methods and selection strategies, although these estimates may be biased. The objective of this study was to investigate the extent of such bias in predicting genetic gain. For this, we simulated 10 cycles of a hypothetical breeding program that involved seven traits, three population classes, three experimental conditions and two breeding methods (mass and half-sib selection). Each combination of trait, population, heritability, method and cycle was repeated 10 times. The predicted gains were biased, even when the genetic parameters were estimated without error. Gain from selection in both genders is twice the gain from selection in a single gender only in the absence of dominance. The use of genotypic variance or broad sense heritability in the predictions represented an additional source of bias. Predictions based on additive variance and narrow sense heritability were equivalent, as were predictions based on genotypic variance and broad sense heritability. The predictions based on mass and family selection were suitable for comparing selection strategies, whereas those based on selection within progenies showed the largest bias and lower association with the realized gain.