Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Correlated Holistic Metric Learning for Sketch-Based 3D Shape Retrieval

How to effectively retrieve desired 3D models with simple queries is a long-standing problem in computer vision community. The model-based approach is quite straightforward but nontrivial, since people could not always have the desired 3D query model available by side. Recently, large amounts of wide-screen electronic devices are prevail in our daily lives, which makes the sketch-based 3D shape retrieval a promising candidate due to its simpleness and efficiency. The main challenge of sketch-based approach is the huge modality gap between sketch and 3D shape. In this paper, we proposed a novel deep correlated holistic metric learning (DCHML) method to mitigate the discrepancy between sketch and 3D shape domains. The proposed DCHML trains two distinct deep neural networks (one for each domain) jointly, which learns two deep nonlinear transformations to map features from both domains into a new feature space. The proposed loss, including discriminative loss and correlation loss, aims to increase the discrimination of features within each domain as well as the correlation between different domains. In the new feature space, the discriminative loss minimizes the intra-class distance of the deep transformed features and maximizes the inter-class distance of the deep transformed features to a large margin within each domain, while the correlation loss focused on mitigating the distribution discrepancy across different domains. Different from existing deep metric learning methods only with loss at the output layer, our proposed DCHML is trained with loss at both hidden layer and output layer to further improve the performance by encouraging features in the hidden layer also with desired properties. Our proposed method is evaluated on three benchmarks, including 3D Shape Retrieval Contest 2013, 2014, and 2016 benchmarks, and the experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.
Combining Harmony-Based and Novelty-Based Approaches for Structural Segmentation

This paper describes a novel way to combine a well-proven method of structural segmentation through novelty detection with a recently introduced method based on harmonic analysis. The former system works by looking for peaks in novelty curves derived from self-similarity matrices. The latter relies on the detection of key changes and on the differences in prior probability of chord transitions according to their position in a structural segment. Both approaches are integrated into a probabilistic system that jointly estimates keys, chords and structural boundaries. The novelty curves are herein used as observations. In addition, chroma profiles are used as features for the harmony analysis. These observations are then subjected to a constrained transition model that is musically motivated. An information theoretic justification of this model is also given. Finally, an evaluation of the resulting system is performed. It is shown that the combined system improves the results of both constituting components in isolation.
Trajectory following for legged robots

While robust trajectory following is a well-studied problem on mobile robots, the question of how to track accurately a trajectory on a humanoid robot remains open. This paper suggests a closed-loop trajectory tracking strategy aimed at humanoid robots. Compared to approaches from mobile robotics, this control scheme takes into account footsteps alteration, equilibrium constraints and singularities avoidance for humanoids. It provides a robust way to execute long and/or precise motion with the ability of correcting on-line preplanned trajectories in a very reactive manner. Results have been validated on the HRP-2 humanoid platform.
A pure red luminescent β-carboline-substituted biphenylmethyl radical: photophysics, stability and OLEDs

Luminescent radicals, whose emission comes from the doublet exited state, have potential application in the field of organic optoelectronics. However, few radicals show luminescence at room temperature. Herein, a new pure red-emissive biphenyl-type (N-pyrido[3,4-b]indolyl)bis(2,4,6-trichlorophenyl)methyl radical (PyID-BTM) is designed and synthesized, in which the carbazole moiety of CzBTM (a previously reported biphenylmethyl radical by our group) is successfully replaced by β-carboline. Its photophysical properties, including the ground and excited states, and the radiative and non-radiative processes are systematically investigated. The photoluminescence quantum efficiency (ϕ = 19.5%) of PyID-BTM is ten times higher than that of CzBTM (ϕ = 2.0%) in cyclohexane. The crystal structure, magnetic properties, photostability and electroluminescence performance of PyID-BTM are studied. An optimized OLED device using PyID-BTM as an emissive dopant showed pure red emission with CIE coordinates of (0.649,0.317) and a maximum EQE of 2.8%, and the formation ratio of doublet excitons was up to 70%. This study provides a new approach for designing high-performance luminescence biphenylmethyl radicals for applications in organic electroluminescence.
Path Optimization for Humanoid Walk Planning - An Efficient Approach

This paper deals with humanoid walk planning in cluttered environments. It presents a heuristic and efficient optimization method that takes as input a path computed for the robot bounding box, and produces a path where a discrete set of configurations is reoriented using an A* search algorithm. The resulting trajectory is realistic and time-optimal. This method is validated in various scenarios on the humanoid robot HRP-2.
Z-Glyph: Visualizing outliers in multivariate data

Outlier analysis techniques are extensively used in many domains such as intrusion detection. Today, even with the most advanced statistical learning techniques, human judgment still plays an important role in outlier analysis tasks due to the difficulty of defining and collecting outlier examples. This work seeks to tackle this problem by introducing a new visualization design, “Z-Glyph,” a family of glyphs designed to facilitate human judgment in outlier analysis of multivariate data. By employing a location-scale transformation, a Z-Glyph represents the “normal” data using regular shapes (e.g. straight line and circle), such that the abnormal data can be revealed when deviating from the regular shapes. Extensive controlled experiment and case studies based on real-world datasets indicate the superior performance of the Z-Glyph family, compared with the baselines, suggesting that the proposed design is able to leverage human perceptional features with statistical characterization. This study contributes to a more fundamental understanding about designing visual representations for revealing outliers in multivariate data, which can be applied as a building block in many domain-specific anomaly detection applications.
Verbal Shadowing and Visual Interference in Spatial Memory

Spatial memory is thought to be organized along experienced views and allocentric reference axes. Memory access from different perspectives typically yields V-patterns for egocentric encoding (monotonic decline in performance along with the angular deviation from the experienced perspectives) and W-patterns for axes encoding (better performance along parallel and orthogonal perspectives than along oblique perspectives). We showed that learning an object array with a verbal secondary task reduced W-patterns compared with learning without verbal shadowing. This suggests that axes encoding happened in a verbal format; for example, by rows and columns. Alternatively, general cognitive load from the secondary task prevented memorizing relative to a spatial axis. Independent of encoding, pointing with a surrounding room visible yielded stronger W-patterns compared with pointing with no room visible. This suggests that the visible room geometry interfered with the memorized room geometry. With verbal shadowing and without visual interference only V-patterns remained; otherwise, V- and W-patterns were combined. Verbal encoding and visual interference explain when W-patterns can be expected alongside V-patterns and thus can help in resolving different performance patterns in a wide range of experiments.
Comparative evaluation of user interfaces for robot-assisted laser phonomicrosurgery

This research investigates the impact of three different control devices and two visualization methods on the precision, safety and ergonomics of a new medical robotic system prototype for assistive laser phonomicrosurgery. This system allows the user to remotely control the surgical laser beam using either a flight simulator type joystick, a joypad, or a pen display system in order to improve the traditional surgical setup composed by a mechanical micromanipulator coupled with a surgical microscope. The experimental setup and protocol followed to obtain quantitative performance data from the control devices tested are fully described here. This includes sets of path following evaluation experiments conducted with ten subjects with different skills, for a total of 700 trials. The data analysis method and experimental results are also presented, demonstrating an average 45% error reduction when using the joypad and up to 60% error reduction when using the pen display system versus the standard phonomicrosurgery setup. These results demonstrate the new system can provide important improvements in terms of surgical precision, ergonomics and safety. In addition, the evaluation method presented here is shown to support an objective selection of control devices for this application.
Data Encryption Based Image Steganography: A Review

- The steganography system which uses an image as the cover, there are several techniques to conceal information inside cover-image (a) Spatial Domain and, (b) Frequency Domain. The spatial domain techniques manipulate the cover-image pixel bit values to embed the secret information. The secret bits are written directly to the cover image pixel bytes. In Frequency domain first images are transformed into the frequency domain and then messages are embeddeding the transform coefficients. Consequently, the spatial domain techniques are simple and easy to implement. This paper reviews few such techniques which focus on image steganography using various data encryption techniques.
Assessing Risk and Sources of Heavy Metals in a Tropical River Basin: A Case Study of the Selangor River, Malaysia

Heavy metal concentration has become a major concern for water quality in rivers. Rapid urbanization and industrialization contribute to heavy metal concentrations in river water. This study aims to investigate the distribution, source, and environmental risk of heavy metals in the Selangor River basin in Malaysia. A total of 132 water samples were collected from 11 sampling stations on a monthly basis over a one-year period. Thirteen heavy metals were analyzed using inductively coupled plasma optical emission spectroscopy (ICP-OES). In order to identify the sources of heavy metals along the river basin, multivariate statistical techniques like principal component analysis (PCA) and cluster analysis (CA) were performed. It was found that As, Mn, and Fe exceeded the admissible limits of the Malaysian National Standard Water Quality (NSDWQ) at several of the sampling stations. Heavy metal pollution index (HPI) was below the critical pollution index value of 100. Statistical analyses showed that potential sources of heavy metals are land-based, thereby implying that former tin mining and industries in the surrounding area are the most likely sources. Anthropogenic metal concentrations were found to be low in the Selangor River, indicating that it has yet to be burdened by pollution of heavy metals.
Parallel evolution of Batesian mimicry supergene in two Papilio butterflies, P. polytes and P. memnon

Female-limited polymorphisms underlying Batesian mimicry have evolved independently in two closely related butterfly species. Batesian mimicry protects animals from predators when mimics resemble distasteful models. The female-limited Batesian mimicry in Papilio butterflies is controlled by a supergene locus switching mimetic and nonmimetic forms. In Papilio polytes, recent studies revealed that a highly diversified region (HDR) containing doublesex (dsx-HDR) constitutes the supergene with dimorphic alleles and is likely maintained by a chromosomal inversion. In the closely related Papilio memnon, which exhibits a similar mimicry polymorphism, we performed whole-genome sequence analyses in 11 butterflies, which revealed a nearly identical dsx-HDR containing three genes (dsx, Nach-like, and UXT) with dimorphic sequences strictly associated with the mimetic/nonmimetic phenotypes. In addition, expression of these genes, except that of Nach-like in female hind wings, showed differences correlated with phenotype. The dimorphic dsx-HDR in P. memnon is maintained without a chromosomal inversion, suggesting that a separate mechanism causes and maintains allelic divergence in these genes. More abundant accumulation of transposable elements and repetitive sequences in the dsx-HDR than in other genomic regions may contribute to the suppression of chromosomal recombination. Gene trees for Dsx, Nach-like, and UXT indicated that mimetic alleles evolved independently in the two Papilio species. These results suggest that the genomic region involving the above three genes has repeatedly diverged so that two allelic sequences of this region function as developmental switches for mimicry polymorphism in the two Papilio species. The supergene structures revealed here suggest that independent evolutionary processes with different genetic mechanisms have led to parallel evolution of similar female-limited polymorphisms underlying Batesian mimicry in Papilio butterflies.
Event-Related Potential Study of P2 and N2 Components on Fast and Slow Responses in the Auditory Condensation Task

In tasks involving response choice based on certain stimulus-to-response mappings, at least two stages of information processing may be involved: (1) formation of sensory stimulus object representations leading to stimulus identification, and (2) application of stimulus-to-response mappings (i.e. “task rules”) to these representations leading to response selection. Most of the research done in this area involved simple reflex-like stimulus-to-response mappings, thus addressing mostly the perceptual aspect of decision making. Here we used the condensation task, which involves more complex stimulus-to-response mappings. Within each subject, we divided participants’ responses into four conditions depending upon performance speed and accuracy: fast correct, fast erroneous, slow correct and slow erroneous responses. We compared event-related potentials between these conditions. We found that P2 amplitude was related to performance accuracy, the effect being evident for fast but not for slow responses. N2 amplitude was increased for slow responses – both correct and erroneous. We suggest that fast errors result mostly from erroneous sensory representations that immediately become translated into actions in conditions of low motor threshold. On the contrary, slow responses happen in conditions of low executive attention, through reevaluation of sensory representations and invocation of cognitive control process via the mechanisms of response conflict detection.
Feature-based tracking of urethral motion in low-resolution trans-perineal ultrasound

This paper describes a novel algorithm for tracking the motion of the urethra from trans-perineal ultrasound. Our work is based on the structure-from-motion paradigm and therefore handles well structures with ill-defined and partially missing boundaries. The proposed approach is particularly well-suited for video sequences of low resolution and variable levels of blurriness introduced by anatomical motion of variable speed. Our tracking method identifies feature points on a frame by frame basis using the SURF detector/descriptor. Inter-frame correspondence is achieved using nearest-neighbor matching in the feature space. The motion is estimated using a non-linear bi-quadratic model, which adequately describes the deformable motion of the urethra. Experimental results are promising and show that our algorithm performs well when compared to manual tracking.
Investigating Cybersecurity Issues in Active Traffic Management Systems

Active traffic management (ATM) systems have been introduced by transportation agencies to manage recurrent and non-recurrent congestion. ATM systems rely on the interconnectivity of components made possible by wired and/or wireless networks. Unfortunately, this connectivity that supports ATM systems also provides potential system access points that results in vulnerability to cyberattacks. This is becoming more pronounced as ATM systems begin to integrate Internet of Things (IoT) devices. Hence, there is a need to rigorously evaluate ATM systems for cyberattack vulnerabilities, and explore design concepts that provide stability and graceful degradation in the face of cyberattacks. In this research, a prototype ATM system along with a real-time cyberattack monitoring system were developed for a 1.5-mile section of I-66 in Northern Virginia. The monitoring system detects deviation from expected operation of an ATM system by comparing lane control states generated by the ATM system with lane control states deemed most likely by the monitoring system. This comparison provides the functionality to continuously monitor the system for abnormalities that would result from a cyberattack. In the case of any deviation between two sets of states, the monitoring system displays the lane control states generated by the back-up data source. In a simulation experiment, the prototype ATM system and cyberattack monitoring system were subject to emulated cyberattacks. The evaluation results showed that the ATM system, when operating properly in the absence of attacks, improved average vehicle speed in the system to 60 mph (a 13% increase compared to the baseline case without ATM). However, when subject to cyberattack, the mean speed reduced by 15% compared to the case with the ATM system and was similar to the baseline case. This illustrates that the effectiveness of the ATM system was negated by cyberattacks. The monitoring system allowed the ATM system to revert to an expected state with a mean speed of 59 mph and reduced the negative impact of cyberattacks. These results illustrate the need to revisit ATM system design concepts as a means to protect against cyberattacks in addition to traditional system intrusion prevention approaches.
Early Factors Associated with the Development of Chronic Pain in Trauma Patients

Objective To identify factors, available at the time of trauma admission, associated with the development of chronic pain to allow testing of preventive approaches. Methods In a retrospective observational cohort study, we included all patients ≥ 18 years old admitted for injury in 57 adult trauma centers in the province of Quebec (Canada) between 2004 and 2014. Chronic pain was defined as follows: treated in a chronic pain clinic, diagnosed with chronic pain, or received at least 2 prescriptions of chronic pain medications 3 to 12 months postinjury. Results A total of 95,134 patients were retained for analysis. Mean age was 59.8 years (±21.7), and 52% were men. The causes of trauma were falls (63%) and motor vehicle accidents (22%). We identified 14,518 patients (15.3%; 95% CI: 15.1–15.5) who developed chronic pain. After controlling for confounding factors, the variables associated with chronic pain were spinal cord injury (OR = 3.9; 95% CI: 3.4–4.6), disc-vertebra trauma (OR = 1.6; 95% CI: 1.5–1.7), history of alcoholism (OR = 1.4; 95% CI: 1.2–1.7), history of anxiety (OR = 1.4; 95% CI: 1.2–1.5), history of depression (OR = 1.3; 95% CI: 1.1–1.4), and being female (OR = 1.3; 95% CI: 1.2–1.3). The area under the receiving operating characteristic curve derived from the model was 0.80. Conclusions We identified risk factors present on hospital admission that can predict trauma patients who will develop chronic pain. These factors should be prospectively validated.
Using mel-frequency audio features from footstep sound and spatial segmentation techniques to improve frame-based moving object detection

Moving object detection in video streams is a challenging and integral part of computer vision which is used in surveillance, traffic and site monitoring, and navigation. Compared with the background-based techniques, frame differencing technique is computationally inexpensive. However, frame differencing technique only detects the boundary of a moving object. Due to changing light conditions, shadows, poor contrast between object and background, and a slow-moving object, object detection rate from frame differencing technique reduces. This is because the number of noisy frames and frames with missing/partially detected object increases. Application of large kernel size morphological operations fails to remove noise as they might remove the boundary (or part) of a moving object. In this study, the authors propose a methodology to improve the frame differencing technique using footstep sound generated by a moving object. Audio recorded with the video system is processed and footstep sound is detected using audio features computed as mel-frequency cepstral coefficients. Number of frames within each footstep sound are counted and processed. Spatial segmentation is used to find the moving object in noisy frames. A missing or partially detected object is recovered by modelling an ellipse using a moving object from other neighbourhood frames.
Data-driven estimation of mutual information using frequency domain and its application to epilepsy

We consider the problem of estimating mutual information between dependent data, an important problem in many science and engineering applications. We propose a data-driven estimator of mutual information in this paper. The main novelty of our solution lies in transforming the data to frequency domain to make the problem tractable. We define a novel metric-mutual information in frequency (Ml-in-frequency)-to detect and quantify the dependence between two random processes across frequency using Cramer's spectral representation. Our solution calculates mutual information as a function of frequency to estimate the mutual information between the dependent data over time and validate its performance on linear and nonlinear models. We then use our MI-in-frequency metric to infer the cross-frequency coupling during epileptic seizures, by analyzing electrocorticographic recordings from a total of eleven seizures in four medial temporal lobe epilepsy patients.