Miracle-dz/UniFAR-FTUs huggingface dataset:
Compound Facial Expressions in 3 D Faces : Forced and Spontaneous

This work investigates Compound Facial Expression (CFE) recognition in 3D images captured in two domains: forced and spontaneous. While no public datasets containing 3D faces and explicit CFE annotation data is available, such expressions can be inferred from the active actions units (AUs) present in the face. An well-known 3D AU detector, based on Local Depth Binary Patterns, was studied to develop a method for automatically determining the AUs of the face, allowing for CFEs to be recognized. Benchmarks are conducted on two public 3D datasets: Bosphorus and BP4D-Spontaneous. Results confirm the efficacy of the proposed approach, exploiting the robustness to variations in lighting, head pose and changes in facial appearance found in 3D images. Experiments indicate that CFEs are more likely to be found in spontaneous basic expressions, rather than on forced expressions. To the best of our knowledge, this is the first time that CFEs are studied in 3D images.
Active L\'evy Matter: Anomalous Diffusion, Hydrodynamics and Linear Stability

Anomalous diffusion, manifest as a nonlinear temporal evolution of the position mean square displacement, and/or non-Gaussian features of the position statistics, is prevalent in biological transport processes. Likewise, collective behavior is often observed to emerge spontaneously from the mutual interactions between constituent motile units in biological systems. Examples where these phenomena can be observed simultaneously have been identified in recent experiments on bird flocks, fish schools and bacterial swarms. These results pose an intriguing question, which cannot be resolved by existing theories of active matter: How is the collective motion of these systems affected by the anomalous diffusion of the constituent units? Here, we answer this question for a microscopic model of active Levy matter -- a collection of active particles that perform superdiffusion akin to a Levy flight and interact by promoting polar alignment of their orientations. We present in details the derivation of the hydrodynamic equations of motion of the model, obtain from these equations the criteria for a disordered or ordered state, and apply linear stability analysis on these states at the onset of collective motion. Our analysis reveals that the disorder-order phase transition in active Levy matter is critical, in contrast to ordinary active fluids where the phase transition is, instead, first-order. Correspondingly, we estimate the critical exponents of the transition by finite size scaling analysis and use these numerical estimates to relate our findings to known universality classes. These results highlight the novel physics exhibited by active matter integrating both anomalous diffusive single-particle motility and inter-particle interactions.
Framework for Evaluating Feature Selection in Opinion Mining

Opinion mining is important in text mining applications in brand and product positioning, consumer attitude detection, customer relationship management and market research. Applications result in new generation companies, products for reputation management, online market perception and online content monitoring. Web expansion encourages users to contribute or express opinions through blogs, videos and social networking sites which provide information for sentiment analysis regarding a product or service. This study investigates various feature extraction methods performance and opinion mining classification algorithm. Evaluation is through the use of opinions from amazon.com with product reviews. Features extraction is from opinions using Term Document Frequency and Inverse Document Frequency (TDF×IDF). Feature transformation is through Principal Component Analysis (PCA) and kernel PCA. Feature selection techniques like Information Gain (IG), Mutual Information (MI) and Fisher Score select features. Extracted features are classified by Naïve Bayes, k Nearest Neighbour and Classification and Regression Trees (CART) classification algorithms.
Phenomenology analysis of duration inflation for tachyon field in loop quantum cosmology

Assuming that the e-folding number is just determined by the change of the scale factor, the tachyonic inflation theory in loop quantum cosmology (LQC) has been discussed. Considering the tachyon field with exponential potential and inverse quadratic potential, we find that the evolutionary pictures of super inflation are affected by the potentials and the initial conditions. However it cannot provide enough e-folding number, no matter which condition is chosen. Therefore a slow-rolling inflation is necessary. The e-folding number for slow-rolling inflation depends on the values of the parameter α of the exponential potential and the initial conditions. To get enough e-folding number, α should be small. Based on the slow-rolling inflation happens immediately when the super inflation ends, and the scale factor continuously grows during the whole inflation stage, we consider an e-folding number provided by the whole inflationary stage, and we find that it is easier to get enough e-folding number when the sc.
Automatic generation technology of Chinese multiple-choice items based on ontology

In order to solve the two problems that the automatic generation technology of Chinese multiple-choice items is facing, the meta-knowledge-point pattern and ontology are put forward based on a general English model of automatic generation system of multiplechoice items.In the meta-knowledge-point pattern, stems is generated from the structure of contents, while distractors can be obtained from the lexis of the same semantic field and the sense relations in ontology.The final experimental results show that the method effectively achieve the desired objectives.
Histopathologic Image Processing: A Review

Histopathologic Images (HI) are the gold standard for evaluation of some tumors. However, the analysis of such images is challenging even for experienced pathologists, resulting in problems of inter and intra observer. Besides that, the analysis is time and resource consuming. One of the ways to accelerate such an analysis is by using Computer Aided Diagnosis systems. In this work we present a literature review about the computing techniques to process HI, including shallow and deep methods. We cover the most common tasks for processing HI such as segmentation, feature extraction, unsupervised learning and supervised learning. A dataset section show some datasets found during the literature review. We also bring a study case of breast cancer classification using a mix of deep and shallow machine learning methods. The proposed method obtained an accuracy of 91% in the best case, outperforming the compared baseline of the dataset.
STOCHASTIC STELLAR CLUSTER INITIAL MASS FUNCTIONS: MODELS AND IMPACT ON INTEGRATED CLUSTER PARAMETER DETERMINATION

Stellar clusters are regularly used to study the evolution of their host galaxy. Except for a few nearby galaxies, these studies rely on the interpretation of integrated cluster properties, especially integrated photometry observed using multiple filters (i.e., the spectral energy distribution, SED). To allow interpretation of such observations, we present a large set of GALEV cluster models using the realistic approach of adopting stochastically sampled stellar initial mass functions. We provide models for a wide range of cluster masses (103–2 × 105 M☉), metallicities (−2.3 ⩽ [Fe/H] ⩽ +0.18 dex), foreground extinction, and 184 regularly used filters. We analyze various sets of stochastic cluster SEDs by fitting them with non-stochastic models, which is the procedure commonly used in this field. We identify caveats and quantify the fitting uncertainties associated with this standard procedure. We show that this can yield highly unreliable fitting results, especially for low-mass clusters.
Robust Proportional-Derivative Control on SO ( 3 ) for Transporting Quadrotor with Load Uncertainties

In this paper, a model of a transporting quadrotor with proportional-derivative (PD) control on Special Orthogonal-3 is presented. Quadrotor is a unique rotary wing UAV (Unmanned Aerial Vehicle) with special features such as VTOL (Vertical Take-Off and Landing) and hovering at desired altitude. Those features, make it beneficial for many applications such as military purposes, aerial photography, and transporting missions. Unfortunately, due its underactuated nature, while flying it can not be stabilized just from its mechanical structure. Even further, for transporting missions, it is a challenging problem to compensate the inertia perturbation due to the uncertainties of the payload and also input saturation. That is the reason why such an electrical control algorithm needs to be introduced here. Numerical simulation and results are presented to verify the effectiveness of the proposed PD control algorithm.
LDA based feature selection for spam filter

Spam filtering is a long-drawn research issue.More and more text categorization techniques are replanted for spam filtering.Latent Dirichlet Allocation(LDA) and other related topic models are increasingly popular tools for summarization,manifold discovery and other application in discrete data.The LDA is introduced into spam filtering as a feature selection tool.Combined the LDA with a simple centroid-based + kNN classifier,a test spam filter is got.The experiment result shows that the features selected by LDA outperform the baseline features selected by IG and MI,and the test filter is comparative to other filters.
A Physics-Based Estimation of Mean Curvature Normal Vector for Triangulated Surfaces

In this note, we derive an approximation for the mean curvature normal vector on vertices of triangulated surface meshes from the Young-Laplace equation and the force balance principle. We then demonstrate that the approximation expression from our physics-based derivation is equivalent to the discrete Laplace-Beltrami operator approach in the literature. This work, in addition to providing an alternative expression to calculate the mean curvature normal vector, can be further extended to other mesh structures, including non-triangular and heterogeneous meshes.
Determining the Droplet Size Classification of Hollow Cone Nozzles with Droplet Image Analysis

Considering the harmful effects of pesticides, it is necessary to apply them very precisely. The selection of nozzle in pesticide applications is a matter of particular attention. The size of the droplet is very important for fighting with disease and pest. Coarser droplets run-off from the plant while finer droplets are prone to drift. For this reason, it is necessary to determine the droplet diameter in order to reduce drift. Volume median diameter is commonly used for understanding droplet size in the spray pattern. Due to the direct effect of the size of the droplet, a large number of studies have been conducted on the nozzles. But there are still some missing parts. For this reason, every year the nozzle manufacturers put a new type of nozzle on the market. In this study, the volume median diameter of different sized (0.8 and 1.0) hollow cone nozzles widely used in Turkey at different pressures (6, 9, 12 bar) in constant temperature and relative humidity were determined by Oxford Visisizer-PDIA (Particle Droplet Image Analysis). It has been determined that the volume median diameter of these two sizes of nozzles, which are tested at lower pressures, is very close to each other.
Seeing lens imaging as a superposition of multiple views

In the conventional approach to lens imaging, rays are used to map object points to image points. However, many students want to think of the image as a whole. To answer this need, Kepler’s ray drawing is reinterpreted in terms of shifted camera obscura images. These images are uncovered by covering the lens with pinholes. Thus, lens imaging is seen as a superposition of sharp images from different viewpoints, so-called elemental images. This superposition is simulated with projectors, and with transparencies. Lens ray diagrams are constructed based on elemental images; the conventional construction method is included as a special case.
D2-brane in the Penrose limits of AdS{sub 4}xCP{sup 3}

We consider a D2-brane in the pp-wave backgrounds obtained from AdS{sub 4}xCP{sup 3} when electric and magnetic fields have been turned on. Upon fixing the light-cone gauge, light-cone Hamiltonian and Bogomolni-Prasad-Sommerfield configurations are obtained. In particular we study Bogomolni-Prasad-Sommerfield configurations with an electric dipole on the two-sphere giant and a giant graviton rotating in transverse directions. Moreover we show that the gauge field living on the D2-brane is replaced by a scalar field in the light-cone Hamiltonian. We also present a matrix model by regularizing (quantizing) 2-brane theory.
Applied Longitudinal Data Analysis Modeling Change And Event Occurrence

Thank you very much for downloading applied longitudinal data analysis modeling change and event occurrence. As you may know, people have look hundreds times for their favorite novels like this applied longitudinal data analysis modeling change and event occurrence, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some infectious virus inside their computer.
Fast outlier data mining algorithm based on cell in large datasets

The paper proposed a fast cell-based algorithm for outlier detection in large datasets(short for FOMABCLD). The algorithm applied cluster technique to preprocesse data,and placed data into the appropriate cells based on their values and indexed the non-empty cells with Cell Dimension-Tree.A majority part of data located in high density cells and had no nearness relationship with outliers is filtered,which avoided large useless computations.The experiment show that FOMABCLD can mine outlier data from large datasets fast and accurately,and the speed of detecting outliers is increased.
Chemical composition and evaluation of the antimicrobial activity of two essential oils

This study aimed to identify and quantify the chemical constituents and to evaluate the in vitro antimicrobial activity of essential oils from lemongrass (Cymbopogum flexuosus EOCF) and basil (Ocimum basilicum EOOB) against 24 isolates of Aeromonas spp. The main components of EOCF were α-citral (50.13%) and ß-citral (40.31%), while those of EOOB were linalool (53.35%) and eucalyptol (11.49%). The EOCF showed high inhibitory activity (≥ 195.31 μg mL-1), whereas EOOB showed moderate inhibitory activity (≥ 781.25 μg mL-1) for Aeromonas spp. Both essential oils have potential for application as antimicrobial agents, in particular EOCF.
Applications of Graph Embedding in Mesh Untangling

The subject of this thesis is mesh untangling through graph embedding, a method of laying out graphs on a planar surface, using an algorithm based on the work of Fruchterman and Reingold[1]. Meshes are a variety of graph used to represent surfaces with a wide number of applications, particularly in simulation and modelling. In the process of simulation, simulated forces can tangle the mesh through deformation and stress. The goal of this thesis was to create a tool to untangle structured meshes of complicated shapes and surfaces, including meshes with holes or concave sides. The goals of graph embedding, such as minimizing edge crossings align very well with the objectives of mesh untangling. I have designed and tested a tool which I named MUT (Mesh Untangling Tool) on meshes of various types including triangular, polygonal, and hybrid meshes. Previous methods of mesh untangling have largely been numeric or optimizationbased. Additionally, most untangling methods produce low quality graphs which must be smoothed separately to produce good meshes. Currently graph embedding techniques have only been used for smoothing of untangled meshes. I have developed a tool based on the Fruchterman-Reingold algorithm for force-directed layout[1] that effectively untangles and smooths meshes simultaneously using graph embedding techniques. It can untangle complicated meshes with irregular polygonal frames, internal holes, and other complications that previous methods struggle with. The MUT does this by using several different approaches: untangling the mesh in stages from the frame in and anchoring the mesh at corner points to stabilize the untangling.
Impulse control and expected suprema

Abstract We consider a class of impulse control problems for general underlying strong Markov processes on the real line, which allows for an explicit solution. The optimal impulse times are shown to be of a threshold type and the optimal threshold is characterised as a solution of a (typically nonlinear) equation. The main ingredient we use is a representation result for excessive functions in terms of expected suprema.
Evaluating Theories of Collaborative Cognition Using the Hawkes Process and a Large Naturalistic Data Set

People spontaneously collaborate to solve a common goal. What factors affect whether teams are successful? Due to lack of large-scale naturalistic data and methods for investigating scientific questions on such data, previous work has either focused on very concrete cases, such as surveys of business teams, or abstract cases, such as GridWorld games, where agents coordinate their movement so that each agent can get to their own goal without obstructing other agents. We propose a computational framework based on the multivariate Hawkes process and a novel algorithm for parameter estimation on large data sets. We demonstrate the potential of this method by applying it to a large database of programming teams, public GitHub repositories. We analyze factors known to influence team performance, such as leader organization style and team cognitive diversity, as well as other factors, such as the burstiness of effort, that are difficult to test using existing methods.
A Thermochemical Cryogenic Buffer Gas Beam Source of ThO for Measuring the Electric Dipole Moment of the Electron

The discovery of an electric dipole moment of the electron (eEDM) within a few orders of magnitude of the current best limit would reveal the existence of time-reversal symmetry (T ) violating physics beyond the Standard Model. Certain polar molecules with unpaired electron spins possess highly advantageous qualities for eEDM searches, and recent experiments using such species [10, 103] have pushed the frontier of eEDM searches into regimes of unprecedented sensitivity. By performing a spin-precession measurement on a beam of thorium monoxide (ThO), the ACME collaboration has shown that the eEDM is less than 10−28 e cm, the most stringent upper bound to date [10, 11]. This null result severely constrains many theoretically proposed T -violating mechanisms, and many of the theories that remain viable predict eEDMs within one or two orders of magnitude below this bound. In order to probe this tantalizing regime, we have developed a new cryogenic buffer gas beam (CBGB) source that exploits a high-temperature chemical reaction [56] between thorium and thorium dioxide to produce gas-phase ThO. For a single target over a single day, this source produces ThO fluxes nearly an order of magnitude higher on average than those produced by the laser-ablation-based CBGB used in our previous measurement [106]. Other beam properties, such as forward velocity, rotational temperature, and divergence have been measured and shown to be comparable to or only marginally less favorable than those of the ablation source. By enhancing the experiment’s achievable count rate, this new thermochemical beam source could improve the statistical sensitivity of a future iteration of the ACME eEDM measurement by up to a factor of 2.5. In this thesis, I discuss the background of and motivation for the new beam source, its design, and its characterization and optimization for use in a future eEDM measurement.
Controlled islanding of power networks based on graph reduction and spectral clustering

Intentional controlled islanding aims to split the power system into self-sustainable islands after a severe disturbance, but prior the uncontrolled network separation. Given its nature (i.e. last resort for blackout prevention), this emergency control technique must be adopted as quickly as possible. This paper proposes a computationally efficient method based on graph reduction and spectral clustering. The paper contributes by describing important details of the graph reduction process in the context of controlled islanding and by the formalisation of this process. Furthermore, it demonstrates how to adopt embedded graphs to enhance the Multiway Spectral Clustering graph partitioning. Finally, it is shown how to explicitly incorporate important cannot-link constrains between coherent generator groups into the islanding problem. The proposed method is detailed using the IEEE 39-bus test case. To evaluate the algorithm performance, the method is applied to realistically-sized PEGASE test networks
A Demonstration of Declarative XML Data Cleaning

We demonstrate XClean, a data cleaning system specifically geared towards cleaning XML data. XClean’sapproach isbased on aset of cleaning operators. Users may specify cleaning programs by combining operators using the declarative XClean/PL language, which is then compiled into XQuery. We plan to show XClean in action on several scenarios based on real-world data. A graphical user interface supports users in writing XClean/PL programs and guides them through the process to obtain the clean data.
Rendering techniques for multimodal data

Many different direct volume rendering methods have been developed to visualize 3D scalar fields on uniform rectilinear grids. However, little work has been done on rendering simultaneously various properties of the same 3D region measured with different registration devices or at different instants of time. The demand for this type of visualization is rapidly increasing in scientific applications such as medicine in which the visual integration of multiple modalities allows a better comprehension of the anatomy and a perception of its relationships with activity. This paper presents different strategies of Direct Multimodal Volume Rendering (DMVR). It is restricted to voxel models with a known 3D rigid alignment transformation. The paper evaluates at which steps of the render-ing pipeline must the data fusion be realized in order to accomplish the desired visual integration and to provide fast re-renders when some fusion parameters are modified. In addition, it analyzes how existing monomodal visualization al-gorithms can be extended to multiple datasets and it compares their efficiency and their computational cost.
Robust Image Hashing via Random Gabor Filtering and DWT

Image hashing is a useful multimedia technology for many applications, such as image authentication, image retrieval, image copy detection and image forensics. In this paper, we propose a robust image hashing based on random Gabor filtering and discrete wavelet transform (DWT). Specifically, robust and secure image features are extracted from the normalized image by Gabor filtering and a chaotic map called Skew tent map, and then are compressed via a single-level 2-D DWT. Image hash is finally obtained by concatenating DWT coefficients in the LL sub-band. Many experiments with open image datasets are carries out and the results illustrate that our hashing is robust, discriminative and secure. Receiver operating characteristic (ROC) curve comparisons show that our hashing is better than some popular image hashing algorithms in classification performance between robustness and discrimination.