Miracle-dz/UniFAR-FTUs huggingface dataset:
Second-order Quantile Methods for Online Sequential Prediction ∗

We aim to design strategies for sequential decision making that adjust to the difﬁculty of the learning problem. We study this question both in the setting of prediction with expert advice, and for more general combinatorial decision tasks. We are not satisﬁed with just guaranteeing minimax regret rates, but we want our algorithms to perform significantly better on easy data. Two popular ways to formalize such adaptivity are second-order regret bounds and quantile bounds. The underlying notions of ‘easy data’, which may be paraphrased as “the learning problem has small variance” and “multiple decisions are useful”, are syner-getic. But even though there are sophisticated algorithms that exploit one of the two, no existing algorithm is able to adapt to both. In this paper we outline a new method for obtaining such adaptive algorithms, based on a potential function that aggregates a range of learning rates (which are essential tuning parameters). By choosing the right prior we construct efﬁcient algorithms and show that they reap both beneﬁts by proving the ﬁrst bounds that are both second-order and incorporate quantiles.
Improved Kernel Discriminative Common Vectors for Face Recognition

Kernel discriminative common vector is one of the most effective non-linear techniques for feature extraction from high dimensional data including images and text data. This paper presents a new algorithm called "Improved Kernel Discriminative Common Vector" (IKDCV) to further improves the overall performance of KDCV by integrating the boosting parameters and KDCV techniques. The proposed method possesses several appealing properties. First, like all Kernel methods, it handles non-linearity in a disciplined manner. Second by introducing the pair-wise class discriminant information into discriminant criterion, it further increases the classification accuracy. Third, by calculating significant discriminant information, within class scatter space, it also effectively deals with the Small Sample Size Problem. Fourth, it constitutes a strong ensemble based KDCV framework by taking advantage of the boosting parameters and KDCV techniques. This new method is applied on Extended YaleB face database and achieves better recognition performance by means of solving overlapping between classes. Experimental results demonstrate the promising performance of the proposed method as compared to the other linear and non-linear methods.
Agile Single- and Dual-Band All-Digital Transmitter Based on a Precompensated Tunable Delta–Sigma Modulator

In this paper, a new architecture for designing tunable single- and dual-band radio-frequency fully digital transmitters is proposed and validated. The proposed architecture excels the state of the art in terms of simplicity and flexibility. While its short critical path leverages the use of equivalent polyphase decomposition techniques to increase the global system's sampling frequency, the capability of changing the system's frequency response in real time enables its use in both single- and dual-band transmission scenarios. To mitigate a crosstalk in the dual-band scenario, a precompensation technique is also proposed. This novel concept has been successfully validated in a field-programmable gate array (FPGA) based transmitter. To validate both the proposed transmitter as well as the precompensation mechanism, spectrum and error-vector magnitude (EVM) measurements were obtained for two scenarios with a carrier frequency of 2.5 GHz: 1) single-band, using quadrature phase-shifting keying (QPSK), 16-quadrature amplitude modulation (QAM) and 64-QAM, with no intermediate frequency (IF), for different symbol rate (SR) values (from 3.125 up to 15.625 Msps) and 2) single- and dual-band, using QPSK and 16-QAM, with an SR of 3.125 Msps, for different IF values (from 2 up to 120 MHz). All the experimental results present EVM values below 2.6%, resulting in a well-defined constellation.
Smart cage using object detection

A dog is a pet that very popular in every household. Sometimes owner is lack of time to taking care of it or does not want to leave it with stool which might cause some smell and become dirty. Therefore, it is great if there are some things to take care of it, in case of owner has no time to make it happy and healthy or owner do not want to leave it look dirty. This paper uses image processing to detect a dog and excrement in a closed space (cage), with the intention of using the detection to control the cleaning of the space with an automatic conveyor when excrement is detected (and the dog is not on the conveyor).
Motion Trajectory Learning in the DFT-Coefficient Feature Space

Techniques for understanding video object motion activity are becoming increasingly important with the widespread adoption of CCTV surveillance systems. In this paper we propose a novel vision system for clustering and classification of object-based video motion clips using spatiotemporal models. Object trajectories are modeled as motion time series using the lowest order Fourier coefficients obtained by Discrete Fourier Transform. Trajectory clustering is then carried out in the DFT-coefficient feature space to discover patterns of similar object motion activity. The DFT coefficients are used as input feature vectors to a Self-Organising Map which can learn similarities between object trajectories in an unsupervised manner. Encoding trajectories in this way leads to efficiency gains over existing approaches that use discrete point-based flow vectors to represent the whole trajectory. Assuming the clusters of trajectory points are distributed normally in the coefficient feature space, we propose a simple Mahalanobis classifier for the detection of anomalous trajectories. Our proposed techniques are validated on three different datasets - Australian sign language, handlabelled object trajectories from video surveillance footage and real-time tracking data obtained in the laboratory. Applications to event detection and motion data mining for visual surveillance systems are envisaged.
Vaccine allergies

Currently, the increasing numbers of vaccine administrations are associated with increased reports of adverse vaccine reactions. Whilst the general adverse reactions including allergic reactions caused by the vaccine itself or the vaccine components, are rare, they can in some circumstances be serious and even fatal. In accordance with many IgE-mediated reactions and immediate-type allergic reactions, the primary allergens are proteins. The proteins most often implicated in vaccine allergies are egg and gelatin, with perhaps rare reactions to yeast or latex. Numerous studies have demonstrated that the injectable influenza vaccine can be safely administered, although with appropriate precautions, to patients with severe egg allergy, as the current influenza vaccines contain small trace amounts of egg protein. If an allergy is suspected, an accurate examination followed by algorithms is vital for correct diagnosis, treatment and decision regarding re-vaccination in patients with immediate-type reactions to vaccines. Facilities and health care professionals should be available to treat immediate hypersensitivity reactions (anaphylaxis) in all settings where vaccines are administered.
Whole Body Center of Mass Estimation with Portable Sensors: Using the Statically Equivalent Serial Chain and a Kinect

The trajectory of the whole body center of mass (CoM) is useful as a reliable metric of postural stability. If the evaluation of a subject-specific CoM were available outside of the laboratory environment, it would improve the assessment of the effects of physical rehabilitation. This paper develops a method that enables tracking CoM position using low-cost sensors that can be moved around by a therapist or easily installed inside a patient's home. Here, we compare the accuracy of a personalized CoM estimation using the statically equivalent serial chain (SESC) method and measurements obtained with the Kinect to the case of a SESC obtained with high-end equipment (Vicon). We also compare these estimates to literature-based ones for both sensors. The method was validated with seven able-bodied volunteers for whom the SESC was identified using 40 static postures. The literature-based estimation with Vicon measurements had a average error 24.9 ± 3.7 mm; this error was reduced to 12.8 ± 9.1 mm with the SESC identification. When using Kinect measurements, the literature-based estimate had an error of 118.4 ± 50.0 mm, while the SESC error was 26.6 ± 6.0 mm. The subject-specific SESC estimate using low-cost sensors has an equivalent performance as the literature-based one with high-end sensors. The SESC method can improve CoM estimation of elderly and neurologically impaired subjects by considering variations in their mass distribution.
Combine Person Name and Person Identity Recognition and Document Clustering for Chinese Person Name Disambiguation

This paper presents the HITSZ_CITYU system in the CIPS-SIGHAN bakeoff 2010 Task 3, Chinese person name disambiguation. This system incorporates person name string recognition, person identity string recognition and an agglomerative hierarchical clustering for grouping the documents to each identical person. Firstly, for the given name index string, three segmentors are applied to segment the sentences having the index string into Chinese words, respectively. Their outputs are compared and analyzed. An unsupervised clustering is applied here to help the personal name recognition. The document set is then divided into subsets according to each recognized person name string. Next, the sys-tem identifies/extracts the person identity string from the sentences based on lexicon and heuristic rules. By incorporating the recognized person identity string, person name, organization name and contextual content words as features, an agglomerative hierarchical clustering is applied to group the similar documents in the document subsets to obtain the final person name disambiguation results. Evaluations show that the proposed sys-tem, which incorporates extraction and clustering technique, achieves encouraging recall and good overall performance.
An Approach for Automated Safety Testing of Safety-Critical Software System Based on Safety Requirements

Safety is a very important property for safetycritical software systems. We introduce an approach to test safety-critical software systems based on safety requirement by using model-based testing. The model is extracted from the safety requirement and system under test. In our approach, we use model checking techniques to generate test cases. To find appropriate test cases in less time, we divide the states spaces of model into different subsets. By using the approach, we have designed an automatic testing framework and developed an automatic testing platform, which has been used for testing the safety of high speed train control system in china
Progesterone Upregulates Gene Expression in Normal Human Thyroid Follicular Cells

Thyroid cancer and thyroid nodules are more prevalent in women than men, so female sex hormones may have an etiological role in these conditions. There are no data about direct effects of progesterone on thyroid cells, so the aim of the present study was to evaluate progesterone effects in the sodium-iodide symporter NIS, thyroglobulin TG, thyroperoxidase TPO, and KI-67 genes expression, in normal thyroid follicular cells, derived from human tissue. NIS, TG, TPO, and KI-67 mRNA expression increased significantly after TSH 20 μUI/mL, respectively: 2.08 times, P < 0.0001; 2.39 times, P = 0.01; 1.58 times, P = 0.0003; and 1.87 times, P < 0.0001. In thyroid cells treated with 20 μUI/mL TSH plus 10 nM progesterone, RNA expression of NIS, TG, and KI-67 genes increased, respectively: 1.78 times, P < 0.0001; 1.75 times, P = 0.037; and 1.95 times, P < 0.0001, and TPO mRNA expression also increased, though not significantly (1.77 times, P = 0.069). These effects were abolished by mifepristone, an antagonist of progesterone receptor, suggesting that genes involved in thyroid cell function and proliferation are upregulated by progesterone. This work provides evidence that progesterone has a direct effect on thyroid cells, upregulating genes involved in thyroid function and growth.
Hybrid Inference Optimization for robust pose graph estimation

In this paper we introduce a new optimization algorithm for networks of switched nonlinear objectives and apply this to the important problem of pose graph estimation for robot localization and mapping. The key insight is to replace the linear solver typically used in Gauss-Newton style methods with hybrid inference over switched discrete/continuous linear Gaussian networks. Since exact inference in these networks is known to be NP-hard, we also propose an approximate inference algorithm for the linearized hybrid networks based on message passing. We apply the new algorithm to the problem of robust pose graph estimation in the presence of incorrect loop closures and compare against three recently published approaches to the same problem. Evaluation is performed on ten sequences from two different datasets and shows that our approach performs substantially better than the state of the art.
Maneuver recognition using probabilistic finite-state machines and fuzzy logic

This paper presents a general approach for recognition of driving maneuvers in advanced driver assistance systems (ADAS). Such systems often rely on the identification of driving maneuvers (overtaking, left turn at intersections, etc.) to improve the prediction of potential collisions or to trigger appropriate support for the driver. The proposed maneuver recognition approach combines a fuzzy rule base to model basic maneuver elements and probabilistic finite-state machines to capture all possible sequences of basic elements that constitute a driving maneuver. The proposed method is specifically tailored to ADAS requirements because of its low computational complexity, its flexibility and its straight-forward design based on easily comprehensible logical rules. In addition, we propose a suitable training method to optimize the fuzzy rule base. Our approach is evaluated on the recognition of turn maneuvers. Experiments on real data from a test vehicle demonstrate the feasibility of the proposed method.
Gas-Path Health Estimation for an Aircraft Engine Based on a Sliding Mode Observer

Aircraft engine gas-path health monitoring (GPHM) plays a critical role in engine health management (EHM). Among model-based approaches, the Kalman filter (KF) has been widely employed in GPHM. The main shortcoming of KF-based scheme lies in the lack of robustness against uncertainties. To enhance robustness, this paper describes a new GPHM architecture using a sliding mode observer (SMO). The convergence of the error system in uncertainty-existing circumstances is demonstrated, and the proposed method is developed to estimate components’ performance degradations regardless of modeling uncertainties. Simulations using a nonlinear model of a turbofan engine are presented, in which health monitoring problems are handled by the KF and the SMO, respectively. Results indicate the proposed approach possesses better diagnostic performance compared to the KF-based scheme, and the SMO shows its strong robustness and great potential to be applied to GPHM.
Immunocytochemical techniques reveal multiple, distinct cellular pools of PtdIns4P and PtdIns(4,5)P2

PtdIns4P is the major precursor for the synthesis of the multifunctional plasma membrane lipid, PtdIns(4,5)P2. Yet PtdIns4P also functions as a regulatory lipid in its own right, particularly at the Golgi apparatus. In the present study we define specific conditions that enable preservation of several organellar membranes for the immunocytochemical detection of PtdIns4P. We report distinct pools of this lipid in both Golgi and plasma membranes, which are synthesized by different PI4K (phosphatidylinositol 4-kinase) activities, and also the presence of PtdIns4P in cytoplasmic vesicles, which are not readily identifiable as PI4K containing trafficking intermediates. In addition, we present evidence that the majority of PtdIns4P resides in the plasma membrane, where it is metabolically distinct from the steady-state plasma membrane pool of PtdIns(4,5)P2.
Category-based grouping in working memory and multiple object tracking

ABSTRACT Two prominent cognitive capacity limitations are the maximal number of objects we can place in working memory (WM) and the maximal number of objects we can track in a display. Both are believed to have a numeric value of 3 or 4, which has led to the proposal that we have a general cognitive capacity, and that this capacity is most likely linked to limitations of how many objects we can attend simultaneously. Based on previous results showing that we can memorize more objects if they come from different categories than if they come from the same category (e.g., Feigenson & Halberda, 2008; Wong, Peterson, & Thompson, 2008; Wood, 2008), we compare how category-based grouping affects performance for WM and multiple object tracking (MOT). We present participants with either “pure” displays of either cars or faces, or with “mixed” displays of cars and faces. Overall, the effects of category are weak. In some analyses but not others, we replicate the mixed advantage for WM, albeit with a small effect size. In contrast, we observe a weak pure advantage for MOT tasks, at least in a meta-analysis of five experiments, but not in all experiments. Accordingly, WM and MOT tasks differed significantly in their sensitivity to category membership. We also find that WM is slightly better for faces than for cars, but that no such difference exists for MOT. We tentatively suggest that cognitive capacity limitations in different domains are at least partially due to the limitations of distinct mechanisms.
Personality traits, social capital, and entrepreneurial creativity: comparing green socioentrepreneurial intentions across Taiwan and Hong Kong

ABSTRACT Promoting green socioentrepreneurship is crucial for sustainable economic development in the Asia-Pacific region, particularly in highly urbanised areas such as Taiwan and Hong Kong. However, little research has focused on this topic. Therefore, we conducted two studies to analyse how personality traits, entrepreneurial creativity, and social capital affect green socioentrepreneurial intentions among university students in Taiwan and Hong Kong. The results of Study 1 confirmed the factor structures of the four scales used in this study. The results of Study 2 revealed that though the effects of predictor variables on the outcome variable were varied, the mediation models of entrepreneurial creativity across contexts were partially supported. The major differences between Taiwan and Hong Kong lay in the effects resulting from usefulness, openness, agreeableness, conscientiousness, and bonding social capital.
Ensemble and Modular Approaches for Face Detection: A Comparison

A new learning model based on autoassociative neural networks is developped and applied to face detection. To extend the detection ability in orientation and to decrease the number of false alarms, different combinations of networks are tested: ensemble, conditional ensemble and conditional mixture of networks. The use of a conditional mixture of networks allows to obtain state of the art results on different benchmark face databases.
Model Selection for Automated Architectural Reconstruction from Multiple Views

We describe progress in automatically fitting a plane plus modelled perturbation surface model to represent architectural scenes. There are two areas of novelty. The first is a method of fitting parametrized models in which the cost function is based on a combination of disparity and gradient extrema, both computed over multiple views. The second is the use of an evaluation criteria for model selection, learnt from training examples. We demonstrate the method on reconstructions of several college scenes from multiple images.
SS-ClusterTree: a subspace clustering based indexing algorithm over high-dimensional image features

The rapid growth in the volume of image and video data collections motivates the research of building an index structure in image information retrieval. Constructing an index in the image database poses a very challenging problem due to the facts of image databases containing data with high dimensions, and lack of domain knowledge. ClusterTree is an indexing approach representing clusters generated by any existing clustering approach and do not need any prior knowledge. It is a hierarchy of clusters and subcluster which incorporates the cluster representation into the index structure to achieve effective and efficient retrieval. However, one disadvantage of ClusterTree is that non-clustering data points are often ignored. These non-clustering data points might represent interesting targets in an image database. In this paper, we propose a modified ClusterTree structure(called SS-ClusterTree), which is based on subspace clustering. The SS-ClusterTree includes two kinds of leaf nodes, a cluster leaf node and a noise leaf node. When a new data item is added to the SS-ClusterTree, if it belongs to a cluster, it is inserted into the corresponding the cluster leaf node, otherwise into the noise leaf node. The noise leaf node will be split while its volume is more than a certain threshold. We present a novel updating technique which optimizes the internal structure of the SS-ClusterTree by utilizing the Newton's Universal Law of Gravitation. When a noise node is split, the attraction forces are calculated between every new node and its sibling nodes. These new nodes may be merged by their sibling nodes, if the attraction force between them is the most significant. Meanwhile the nodes intersecting boundaries are updated. This approach guarantees that the SS-ClusterTree always represents the current dataset structure, and helps to identify the pattern hiding in the newly added data. SS-ClusterTree can efficiently support the dynamic insertion and manage the dataset with non-clustering data, and is highly adaptive to any kind of cluster structure. Our experiment results also show that this index structure is effective and efficient.
Probabilistic multi-tensor estimation using the Tensor Distribution Function

Diffusion weighted magnetic resonance (MR) imaging is a powerful tool that can be employed to study white matter microstructure by examining the 3D displacement profile of water molecules in brain tissue. By applying diffusion-sensitized gradients along a minimum of 6 directions, second-order tensors can be computed to model dominant diffusion processes. However, conventional DTI is not sufficient to resolve crossing fiber tracts. A number of high-angular resolution schemes with greater than 6 gradient directions have been employed to address this issue. In this paper, we introduce the tensor distribution function (TDF), a probability function defined on the space of symmetric positive definite matrices. Here, fiber crossing is modeled as an ensemble of Gaussian diffusion processes with weights specified by the TDF once this optimal TDF is determined, the diffusion orientation distribution function (ODF) can easily be computed by analytic integration of the resulting displacement probability function.