Miracle-dz/UniFAR-FTUs huggingface dataset:
Behavioral Analysis of the Cryptoprocta Ferox in an Ex-Situ Condition

Fossas (Cryptoprocta ferox) are the top, endemic predators of Madagascar. Their populations are dwindling and are reported as “Vulnerable” by IUCN. In an effort to combat this, zoological facilities have placed the species on a captive breeding program. This study focuses on concerns one of those zoos had about their captive C. ferox. The purpose of this study was to collect activity budgets on the C. ferox to determine if there is a change in behavior due to construction occurring around the C. ferox’s habitat, analyze behavior changes during estrus, determine any behavior changes, as well as addressing other concerns staff members of the zoo had. Some of those concerns included how often stereotypic behaviors were displayed and how much time C. ferox spent observing the male C. ferox next to her habitat. All visible behaviors were recorded and analyzed by finding frequencies of the observed behaviors, while comparing those to time periods where there was a presence or absence of construction and estrus. This study’s findings showed that the subject did display behavior changes during estrus and did not display any major behavior changes in response to a presence of construction occurring. Since all activity budgets were collected, this study analyzed other factors including usage of enrichment and stereotypic behaviors. This study showcases the variety of behaviors a female C. ferox may exhibit in ex-situ conditions while also providing proposals to discourage unwanted behaviors and enhance animal welfare.
Visual Prediction of Driver Behavior in Shared Road Areas

We propose a framework to analyze and predict vehicles behavior within shared road segments like intersections or at narrow passages. The system first identifies critical interaction regions based on topological knowledge. It then checks possible colliding trajectories from the current state of vehicles in the scene, defined by overlapping occupation times in road segments. For each possible interaction area, it analyzes the behavioral profile of both vehicles. Depending on right of way and (unpredictable) behavior parameters, different outcomes are expected and will be tested against input. The interaction between vehicles is analyzed over a short time horizon based on an initial action from one vehicle and the reaction by the other. The vehicle to yield most often performs the first action and the response of the opponent vehicle is measured after a reaction time. The observed reaction is classified by attention, if there was a reaction at all, and the collaboration of the opponent vehicle, whether it helps to resolve the situation or hinders it. The output is a classification of behavior of involved vehicles in terms of active participation in the interaction and assertiveness of driving style in terms of collaborative or disruptive behavior. The additional knowledge is used to refine the prediction of intention and outcome of a scene, which is then compared to the current status to catch unexpected behavior. The applicability of the concept and ideas of the approach is validated on scenarios from the recent Intersection Drone (inD) data set.
Estimating Static and Dynamic Brain Networks by Kulback-Leibler Divergence from fMRI Data

Representing brain activities by networks is very crucial to understand various cognitive states. This study proposes a novel method to estimate static and dynamic brain networks using Kulback-Leibler divergence. The suggested brain networks are based on the probability distributions of voxel intensity values measured by functional Magnetic Resonance Images (fMRI) recorded while the subjects perform a predefined cognitive task, called complex problem solving. We investigate the validity of the estimated brain networks by modeling and analyzing the different phases of complex problem solving process of human brain, namely planning and execution phases. The suggested computational network model is tested by a classification schema using Support Vector Machines. We observe that the network models can successfully discriminate the planning and execution phases of complex problem solving process with more than 90% accuracy, when the estimated dynamic networks, extracted from the fMRI data, are classified by Support Vector Machines.
Magnifying Spontaneous Facial Micro Expressions for Improved Recognition

Building an effective automatic micro expression recognition (MER) system is becoming increasingly desirable in computer vision applications. However, it is also very challenging given the fine-grained nature of the expressions to be recognized. Hence, we investigate if amplifying micro facial muscle movements as a pre-processing phase, by employing Eulerian Video Magnification (EVM), can boost performance of Local Phase Quantization with Three Orthogonal Planes (LPQ-TOP) to achieve improved facial MER across various datasets. In addition, we examine the rate of increase for recognition to determine if it is uniform across datasets using EVM. Ultimately, we classify the extracted features using Support Vector Machines (SVM). We evaluate and compare the performance with various methods on seven different datasets namely CASME, CAS(ME)2, CASME2, SMIC-HS, SMIC-VIS, SMIC-NIR and SAMM. The results obtained demonstrate that EVM can enhance LPQ-TOP to achieve improved recognition accuracy on the majority of the datasets.
A Wearable Pedestrian Localization and Gait Identification System Using Kalman Filtered Inertial Data

In this article, we introduce a pedestrian dead reckoning (PDR)-based navigation device that does not require global navigation satellite system (GNSS) signals or beacons and works with an inertial measurement unit (IMU) mounted on its waist belt. The system identifies the individual by their walking pattern to use the proper gains in the computations, estimates the attitude by applying an unscented Kalman filter, and finally derives the position in three dimensions with the help of a step detection algorithm. The experimental results show an outdoor 4.7-km walk resulting in an error of 0.96%.
AV-SLAM: Autonomous Vehicle SLAM with Gravity Direction Initialization

Simultaneous localization and mapping (SLAM) algorithms aimed for autonomous vehicles (AVs) are required to utilize sensor redundancies specific to AVs and enable accurate, fast and repeatable estimations of pose and path trajectories. In this work, we present a combination of three SLAM algorithms that utilize a different subset of available sensors such as inertial measurement unit (IMU), a gray-scale mono-camera, and a Lidar. Also, we propose a novel acceleration-based gravity direction initialization (AGI) method for the visual-inertial SLAM algorithm. We analyze the SLAM algorithms and initialization methods for pose estimation accuracy, speed of convergence and repeatability on the KITTI odometry sequences. The proposed VI-SLAM with AGI method achieves relative pose errors less than 2%, convergence in half a minute or less and convergence time variability less than 3s, which makes it preferable for AVs.
Cloud Attenuation Statistical Model for Satellite-Based FSO Communications

This letter addresses the attenuation statistics due to the clouds on satellite-based free-space optical communications. Specifically, we present a novel distribution model of monthly cloud attenuation for several regions in Japan. To this purpose, a five-year reanalysis meteorological database, namely, ERA-Interim, produced from the European Center for Medium-Range Weather Forecast (ECMWF), is investigated. The cloud liquid water content (CLWC) obtained from the ECMWF ERA-interim database is used to determine the cloud attenuation. The curve-fitting method is performed to derive the probability density function (PDF) of cloud attenuation. The results show that the log-normal distribution best models the PDF of monthly cloud attenuation.
A Riemannian Framework for Detecting Stimulus-Relevant Fiber Pathways

Functional MRI based on blood oxygenation level-dependent (BOLD) contrast is well established as a neuroimaging technique for detecting neural activity in the cortex of the human brain. Recent studies have shown that variations of BOLD signals in white matter are also related to neural activities both in resting state and under functional loading. We develop a comprehensive framework of detecting task-specific fiber pathways. We not only study fiber tracts as open curves with different physical features (shape, scale, orientation and position), but also incorporate the BOLD signals associated with them to find stimulus-relevant pathways. Specifically, we propose a novel Riemannian metric, which is a weighted sum of distances in product space of shapes and functions. This metric provides both a cost function for registration and a proper distance for comparison. Experimental results on real data have shown that we can cluster fiber pathways correctly by evaluating correlations between BOLD signals and stimuli, temporal variations and power spectra of them.
Broadband Small-Aperture High-Gain Ridge Horn Antenna Array Element

This letter presents the design and characterization of an octave-bandwidth, small-aperture, linearly polarized, high-gain horn antenna operating in the 6–12 GHz band for active arrays. The aperture size is limited by the waveguide feed cutoff at lower frequencies and by grating lobes at higher frequencies. The radiating structure is composed of an E-plane flared horn with an exponential double ridge fed by a low-loss waveguide-to-microstrip transition, eliminating the need for additional connections between the antenna elements and the beamforming and amplification circuits. The horn is loaded with an artificial dielectric with 1 $< \epsilon _{\mathrm{r,eff}}< $ 2.2, which lowers the cutoff frequency and improves phase distribution over the aperture. The lens and feed loading structure are designed together to improve matching without degrading the radiation pattern. A prototype is fabricated in brass using split-block machining, and the lens is machined from 16 parallel dielectric slabs. The measured performance is in agreement with simulations.
Limitations and Implementation Strategies of Interstage Matching in a 6-W, 28–38-GHz GaN Power Amplifier MMIC

In this article, we summarize the theoretical matching boundaries and show the limitations they implicate for real-world amplifier design. Starting with a common schematic prototype, we investigate the question of how to realize its electrical response in a densely routed, massively parallelized layout. To that end, we develop a comprehensive study on the application of space-mapping techniques toward the design of high-power amplifiers (HPAs). We derive three reference design procedures and compare their performance in terms of convergence, speed, and practicality when laying out a densely routed HPA interstage matching network. Subsequently, we demonstrate the usefulness of the study by designing the networks of a compact three-stage eight-way wideband HPA in the Ka-band. The processed monolithic microwave integrated circuit features a 1-dB large-signal bandwidth of more than 11 GHz (a fractional bandwidth of 32.8%) and thus covers most of the Ka-band with an output power exceeding 6 W in 3 dB of gain compression. This demonstrates the highest combination of power and bandwidth to date using a reactively matched topology in the Ka-band.
DualBox: Generating BBox Pair with Strong Correspondence via Occlusion Pattern Clustering and Proposal Refinement

Despite the rapid development of pedestrian detection, the problem of dense pedestrian detection is still unsolved, especially the upper limit of Recall caused by Non-Maximum-Suppression (NMS). Out of this reason, R2NMS [1] is proposed to simultaneously detect full and visible body bounding boxes, by replacing the full body BBoxes with less occluded visible body BBoxes in the NMS algorithm, achieving a higher recall. However, the P-RPN and P-RCNN modules proposed in R2NMS for simultaneous high quality full and visible body prediction require non-trivial positive/negative assigning strategies for anchor BBoxes. To simplify the prerequisites and improve the utility of R2NMS, we incorporate clustering analysis into the learning of visible body proposals from full body proposals. Furthermore, to reduce the computation complexity caused by the large number of potential visible body proposals, we introduce a novel occlusion pattern prediction branch on top of the R-CNN module (i.e. F-RCNN) to select the best matched visible proposals for each full body proposals and then feed them into another R-CNN module (i.e. V-RCNN). Incorporated with R2NMS, our DualBox model can achieve competitive performance while only requires few hyper-parameters. We validate the effectiveness of the proposed approach on the CrowdHuman [2] and CityPersons [3] datasets. Experimental results show that our approach achieves promising performance for detecting both non-occluded and occluded pedestrians, especially heavily occluded ones.
Unveiling Groups of Related Tasks in Multi - Task Learning

A common approach in multi-task learning is to encourage the tasks to share a low dimensional representation. This has led to the popular method of trace norm regularization, which has proved effective in many applications. In this paper, we extend this approach by allowing the tasks to partition into different groups, within which trace norm regularization is separately applied. We propose a continuous bilevel optimization framework to simultaneously identify groups of related tasks and learn a low dimensional representation within each group. Hinging on recent results on the derivative of generalized matrix functions, we devise a smooth approximation of the upper-level objective via a dual forward-backward algorithm with Bregman distances. This allows us to solve the bilevel problem by a gradient-based scheme. Numerical experiments on synthetic and benchmark datasets support the effectiveness of the proposed method.
Statistical Strategies to Capture Correlation Between Overshooting Effect and Propagation Delay Time in Nano-CMOS Inverters

In this paper, we model statistical correlation between overshooting effect and propagation delay time in nano-CMOS technology considering the influence of intrinsic parameter fluctuations caused by discreteness of charge and granularity of matter. The impact of input slew rate, output capacitive load, and supply voltage on this statistical correlation is comprehensively studied. Moreover, we propose two alternative approaches which are capable of reproducing the statistical correlation as well as mean and standard deviation of both propagation delay time and overshoot voltage. We evaluate the accuracy of these alternative approaches against accurate Monte-Carlo simulations. It is shown that the statistical correlations are almost preserved using these alternative approaches.
A Temporal-Spatial Spectrum Prediction Using the Concept of Homotopy Theory for UAV Communications

As unmanned aerial vehicles (UAVs) have been widely employed, the spectrum resource for UAV communication becomes scarce. The UAVs may communicate based on spectrum sharing, requiring efficient spectrum prediction to improve the performance of spectrum sensing. However, since the existing prediction methods are generally designed to predict the spectrum states in the next moment at the current location, they can not be directly applied to UAVs which rapidly move to the next location when obtaining the prediction result. In another word, the UAVs require to predict the spectrum in the next moment at the next location. This is a method which can predict spectrum in the joint temporal and spatial dimensions. The main challenge is the historical data of the next location is usually not possible to be obtained in advance by the UAVs. This paper proposes a new prediction approach which firstly estimates the historical data of the next location based on the homotopy theory (HT), and then performs the prediction based on the hidden Markov model (HMM), referring to the HT-HMM based prediction. Experimental results show that the HT-HMM based approach is able to efficiently predict the spectrum states of the next location.
Deep Next-Best-View Planner for Cross-Season Visual Route Classification

This paper addresses the problem of active visual place recognition (VPR) from a novel perspective of long-term autonomy. In our approach, a next-best-view (NBV) planner plans an optimal action-observation-sequence to maximize the expected cost-performance for a visual route classification task. A difficulty arises from the fact that the NBV planner is trained and tested in different domains (times of day, weather conditions, and seasons). Existing NBV methods may be confused and deteriorated by the domain-shifts, and require significant efforts for adapting them to a new domain. We address this issue by a novel deep convolutional neural network (DNN) -based NBV planner that does not require the adaptation step. Our main contributions in this paper are summarized as follows: (1) We present a novel domain-invariant NBV planner that is specifically tailored for DNN-based VPR. (2) We formulate the active VPR as a POMDP problem and present a feasible solution to address the inherent intractability. Specifically, the probability distribution vector (PDV) output by the available DNN is used as a domain-invariant observation model without the need to retrain it. (3) We verify efficacy of the proposed approach through challenging cross-season VPR experiments, where it is confirmed that the proposed approach clearly outperforms the previous single-view-based or multi-view-based VPR in terms of VPR accuracy and action-observation-cost.
Passive Sonar Target Classification Using Deep Generative $\beta $-VAE

The intrinsic complexity associated with passive sonar data makes the task of target recognition extremely challenging. The conventional classifier architectures based on hand-engineered feature transforms often fail miserably to disentangle the high-dimensional non-linear structures in the observed target records. Although the modern deep learning algorithms through hierarchical feature learning yield acceptable success rates, they often require tremendous amounts of data when trained in a supervised manner. An unsupervised generative framework utilizing a variational autoencoder (VAE) is proposed in this work in order to create better disentangled representations for the downstream classification task. The disentanglement is further enforced using a hyperparameter <inline-formula><tex-math notation="LaTeX">$\beta $</tex-math></inline-formula>. For the purpose of better segregating the spectro-temporal features, an intermediate non-linearly scaled time-frequency representation is also employed in conjunction with <inline-formula><tex-math notation="LaTeX">$\beta $</tex-math></inline-formula>-VAE. Experimental analysis of various classifier configurations yields encouraging results in terms of data efficiency and classification accuracy on target records collected from various locations of the Indian Ocean.
Rank-based ordinal classification

Differently from the regular classification task, in ordinal classification there is an order in the classes. As a consequence not all classification errors matter the same: a predicted class close to the groundtruth one is better than predicting a farther away class. To account for this, most previous works employ loss functions based on the absolute difference between the predicted and groundtruth class labels. We argue that there are many cases in ordinal classification where label values are arbitrary (for instance $1 \ldots C$, being $C$ the number of classes) and thus such loss functions may not be the best choice. We instead propose a network architecture that produces not a single class prediction but an ordered vector, or ranking, of all the possible classes from most to less likely. This is thanks to a loss function that compares groundtruth and predicted rankings of these class labels, not the labels themselves. Another advantage of this new formulation is that we can enforce consistency in the predictions, namely, predicted rankings come from some unimodal vector of scores with mode at the groundtruth class. We compare with the state of the art ordinal classification methods, showing that ours attains equal or better performance, as measured by common ordinal classification metrics, on three benchmark datasets. Furthermore, it is also suitable for a new task on image aesthetics assessment, i.e. most voted score prediction. Finally, we also apply it to building damage assessment from satellite images, providing an analysis of its performance depending on the degree of imbalance of the dataset.
ODCC: A Dynamic Star Spots Extraction Method for Star Sensors

In this article, we consider the problem of extracting star spots for traditional star sensors under highly dynamic conditions. Under this setting, it is difficult to effectively extract star spots with a low signal-to-noise ratio (SNR), resulting in a failure to estimate the attitude of star sensors. We propose a method named optimal directional connected component (ODCC) for this task. According to the dynamic characteristics of star sensors, we propose an image enhancement method that can adaptively estimate the directions of star spots and integrate the star image so that the SNR of the star spots is increased. This aids in searching for spots with a low SNR. According to the imaging characteristics of a star, we model the imaging region of a star as a rectangle and then creatively cast the problem of extracting spots into a problem of estimating the endpoints of the rectangle. A maximum likelihood estimation method based on the Gaussian mixture model is proposed to determine the region of the star spot. Experiments demonstrate the extraction capability and accuracy of the method. In conclusion, this method has a promising application value for improving the dynamic performance of traditional star sensors.
Efficient Distributed Learning for Large-Scale Expectile Regression With Sparsity

High-dimensional datasets often display heterogeneity due to heteroskedasticity or other forms of non-location-scale covariance effects. When the size of datasets becomes very large, it may be infeasible to store all of the high-dimensional datasets on one machine, or at least to keep the datasets in memory. In this paper, we consider penalized expectile regression using smoothly clipped absolute deviation (SCAD) and adaptive LASSO penalties, which can effectively detect the heteroskedasticity of high-dimensional data. We propose a communication-efficient approach for distributed sparsity learning, where observations are randomly partitioned across machines. By selecting the appropriate tuning parameters, we show that the proposed estimators display oracle properties. Extensive numerical experiments on both synthetic and real data validate the theoretical results and demonstrate the superior performance of our proposed method.