Miracle-dz/UniFAR-FTUs huggingface dataset:
FingertipCubes: an inexpensive D.I.Y wearable for 6-DoF per fingertip pose estimation using a single RGB camera

It is natural to use our hands for interacting with a virtual world, but this remains to be widely available. The Leap Motion controller has brought 3D hand tracking to consumers, but its high cost prohibits its mass adoption, especially for users in developing countries. To facilitate mass adoption, we present a do-it-yourself wearable that has a material cost of only 1 US Dollar, and which coupled with a webcam, can provide 6-DoF(degrees of freedom) per fingertip tracking in real-time. We also propose a novel solution to the pose ambiguity problem of a single square planar fiducial marker in monocular view.
Generative Models for Simulating Mobility Trajectories

Mobility datasets are fundamental for evaluating algorithms pertaining to geographic information systems and facilitating experimental reproducibility. But privacy implications restrict sharing such datasets, as even aggregated location-data is vulnerable to membership inference attacks. Current synthetic mobility dataset generators attempt to superficially match a priori modeled mobility characteristics which do not accurately reflect the real-world characteristics. Modeling human mobility to generate synthetic yet semantically and statistically realistic trajectories is therefore crucial for publishing trajectory datasets having satisfactory utility level while preserving user privacy. Specifically, long-range dependencies inherent to human mobility are challenging to capture with both discriminative and generative models. In this paper, we benchmark the performance of recurrent neural architectures (RNNs), generative adversarial networks (GANs) and nonparametric copulas to generate synthetic mobility traces. We evaluate the generated trajectories with respect to their geographic and semantic similarity, circadian rhythms, long-range dependencies, training and generation time. We also include two sample tests to assess statistical similarity between the observed and simulated distributions, and we analyze the privacy tradeoffs with respect to membership inference and location-sequence attacks.
Life sciences in virtual reality: first-year students learning as creators

Students commencing tertiary studies in life sciences over the next decade are going to arrive at universities with a more advanced digital literacy and shifting expectations of how technology supports learning. This poses a challenge to traditional approaches to tertiary science education. As the increasingly digitally literate students arrive, and the barrier to entry to Virtual Reality (VR) drops, there is an opportunity to develop new learning activities in VR. Students may learn by using VR, but students can also explore complex scientific concepts by creating their own VR content, while developing digital and creation fluencies. We describe how first-year students explored biological science concepts with a series of learning activities based in content creation for VR. We outline the feedback students provided on these activities and how this feedback informed further development of the learning activities.
STUDIES ON TUBERCULIN FEVER

Evidence has been presented that the fever elicited by intravenous administration of old tuberculin (O.T.) in BCG-infected rabbits is a specific property of this hypersensitivity system and is probably not due to contamination of tuberculin with bacterial endotoxins. Daily injections of O.T. in sensitized animals resulted in a rapid tolerance to its pyrogenic effect. Tuberculin tolerance can be differentiated from that occurring with endotoxins and was invariably associated with the development of a negative skin test. The mechanism of this tolerance would thus appear to be desensitization. A circulating pyrogen found during tuberculin fever was indistinguishable in its biologic effects from endogenous pyrogens obtained in several other types of experimental fever. This material produced fevers in normal recipients and therefore may be clearly differentiated from O.T. itself which was pyrogenic only to sensitized animals. Since the titer of serum pyrogen was directly proportional to the degree of fever induced by injection of O.T. in the donor animals, a causal relation is suggested. On the basis of these findings, it is postulated that tuberculin fever is due to a circulating endogenous pyrogen released by a specific action of O.T. on sensitized cells of the host.
Dynamic Network 3 -- 0 FIFA Rankings: Replacing an inaccurate, biased, and exploitable ranking system

We explore the advantages of representing international football results as a directed network in order to give each team a rank. Two network-based models — Static and Dynamic — are constructed and compared with the FIFA Rankings. The Dynamic Model outperforms the FIFA Rankings in terms of World Cup predictive accuracy, while also removing continental bias and reducing the vulnerability of the FIFA Rankings to exploitation. ar X iv :1 80 6. 08 25 9v 2 [ ph ys ic s. so cph ] 3 0 N ov 2 01 8
A variational U-Net for motion retargeting

In this paper, we present a novel motion retargeting system by using the deep autoencoder combining the Deep Convolution Inverse Graphics Network (DC-IGN) ([Kulkarni et al. 2015]) and the U-Net ([Long et al. 2015]) to produce high-quality human motion. The retargeted motion is fully-automatically and naturally generated from the given input motion and bone length ratios. To validate the proposed motion retargeting system, we conduct several experiments and achieve more accuracy and less computational burden when compared with the conventional motion retargeting approach and other neural network architectures.
Kernel Learning Method on Riemannian Manifold with Geodesic Distance Preservation

Riemannian manifold recently has widely exploited in pattern recognition, data analysis, and machine learning. In this paper, a novel kernel learning method, based on preserving geodesic distance on Riemannian manifold, is proposed. In our approach, the features of data first are extracted by an covariance descriptor, and represented as covariance matrices (SPD matrices). Then an logarithm operation is used to transfer them to column vectors. Different from general doing, we introduce a parameterized Mahalanobis distance to define the distance between two column vectors, aiming to make it equal to the geodetic distance between two corresponding data on Riemannian manifold. Furthermore, an initialization kernel matrix is obtained from the learned Mahalanobis distance matrix by a linear transformation, and with it the Bregman optimization algorithm is applied to find the optimal kernel matrix, in which the distance in kernel space is equal to the geodetic distance on Riemannian manifold. We implement experiments on texture data sets to demonstrate the benefits of the proposed method.
Parallelizing greedy for submodular set function maximization in matroids and beyond

We consider parallel, or low adaptivity, algorithms for submodular function maximization. This line of work was recently initiated by Balkanski and Singer and has already led to several interesting results on the cardinality constraint and explicit packing constraints. An important open problem is the classical setting of matroid constraint, which has been instrumental for developments in submodular function maximization. In this paper we develop a general strategy to parallelize the well-studied greedy algorithm and use it to obtain a randomized (1 / 2 − є)-approximation in O( log2(n) / 2 ) rounds of adaptivity. We rely on this algorithm, and an elegant amplification approach due to Badanidiyuru and Vondrák to obtain a fractional solution that yields a near-optimal randomized ( 1 − 1/e − є )-approximation in O( log2(n) / є3 ) rounds of adaptivity. For non-negative functions we obtain a ( 3−2√2 − є )-approximation and a fractional solution that yields a ( 1 / e − є)-approximation. Our approach for parallelizing greedy yields approximations for intersections of matroids and matchoids, and the approximation ratios are comparable to those known for sequential greedy.
RFIDesk: an interactive surface for multi-touch and rich-ID stackable tangible interactions

This work introduces RFIDesk, an interactive surface that enables both multi-touch and rich-ID stackable tangible interactions. By using ultra-high frequency (UHF) radio-frequency identification (RFID) technology, the RFIDesk can effectively identify the elements of a stack. Furthermore, this system integrates capacitive multi-touch sensing based on indium tin oxide (ITO) to effectively detect touch events while preserving the interface transparency, thus enabling rich visual feedback to be displayed under the stackable objects. The interference between the two sensing technologies is resolved by applying time-division multiplexing sampling. We use a tangible tower-defense game to demonstrate the interaction possibilities of this system.
A Ketogenic Diet Improves Cognition and Has Biochemical Effects in Prefrontal Cortex That Are Dissociable From Hippocampus

Age-related cognitive decline has been linked to a diverse set of neurobiological mechanisms, including bidirectional changes in proteins critical for neuron function. Importantly, these alterations are not uniform across the brain. For example, the hippocampus (HPC) and prefrontal cortex (PFC) show distinct patterns of dysfunction in advanced age. Because higher cognitive functions require large–scale interactions across prefrontal cortical and hippocampal networks, selectively targeting an alteration within one region may not broadly restore function to improve cognition. One mechanism for decline that the PFC and HPC share, however, is a reduced ability to utilize glucose for energy metabolism. Although this suggests that therapeutic strategies bypassing the need for neuronal glycolysis may be beneficial for treating cognitive aging, this approach has not been empirically tested. Thus, the current study used a ketogenic diet (KD) as a global metabolic strategy for improving brain function in young and aged rats. After 12 weeks, rats were trained to perform a spatial alternation task through an asymmetrical maze, in which one arm was closed and the other was open. Both young and aged KD-fed rats showed resilience against the anxiogenic open arm, training to alternation criterion performance faster than control animals. Following alternation testing, rats were trained to perform a cognitive dual task that required working memory while simultaneously performing a bi-conditional association task (WM/BAT), which requires PFC–HPC interactions. All KD-fed rats also demonstrated improved performance on WM/BAT. At the completion of behavioral testing, tissue punches were collected from the PFC for biochemical analysis. KD-fed rats had biochemical alterations within PFC that were dissociable from previous results in the HPC. Specifically, MCT1 and MCT4, which transport ketone bodies, were significantly increased in KD-fed rats compared to controls. GLUT1, which transports glucose across the blood brain barrier, was decreased in KD-fed rats. Contrary to previous observations within the HPC, the vesicular glutamate transporter (VGLUT1) did not change with age or diet within the PFC. The vesicular GABA transporter (VGAT), however, was increased within PFC similar to HPC. These data suggest that KDs could be optimal for enhancing large-scale network function that is critical for higher cognition.
Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

Traffic forecasting is a particularly challenging application of spatiotemporal forecasting, due to the time-varying traffic patterns and the complicated spatial dependencies on road networks. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, Traffic Graph Convolutional Long Short-Term Memory Neural Network (TGC-LSTM), to learn the interactions between roadways in the traffic network and forecast the network-wide traffic state. We define the traffic graph convolution based on the physical network topology. The relationship between the proposed traffic graph convolution and the spectral graph convolution is also discussed. An L1-norm on graph convolution weights and an L2-norm on graph convolution features are added to the model’s loss function to enhance the interpretability of the proposed model. Experimental results show that the proposed model outperforms baseline methods on two real-world traffic state datasets. The visualization of the graph convolution weights indicates that the proposed framework can recognize the most influential road segments in real-world traffic networks.
Detecting Offensive Content in Open-domain Conversations using Two Stage Semi-supervision

As open-ended human-chatbot interaction becomes commonplace, sensitive content detection gains importance. In this work, we propose a two stage semi-supervised approach to bootstrap large-scale data for automatic sensitive language detection from publicly available web resources. We explore various data selection methods including 1) using a blacklist to rank online discussion forums by the level of their sensitiveness followed by randomly sampling utterances and 2) training a weakly supervised model in conjunction with the blacklist for scoring sentences from online discussion forums to curate a dataset. Our data collection strategy is flexible and allows the models to detect implicit sensitive content for which manual annotations may be difficult. We train models using publicly available annotated datasets as well as using the proposed large-scale semi-supervised datasets. We evaluate the performance of all the models on Twitter and Toxic Wikipedia comments testsets as well as on a manually annotated spoken language dataset collected during a large scale chatbot competition. Results show that a model trained on this collected data outperforms the baseline models by a large margin on both in-domain and out-of-domain testsets, achieving an F1 score of 95.5% on an out-of-domain testset compared to a score of 75% for models trained on public datasets. We also showcase that large scale two stage semi-supervision generalizes well across multiple classes of sensitivities such as hate speech, racism, sexual and pornographic content, etc. without even providing explicit labels for these classes, leading to an average recall of 95.5% versus the models trained using annotated public datasets which achieve an average recall of 73.2% across seven sensitive classes on out-of-domain testsets.
Active Learning in Recommendation Systems with Multi-level User Preferences

While recommendation systems generally observe user behavior passively, there has been an increased interest in directly querying users to learn their specific preferences. In such settings, considering queries at different levels of granularity to optimize user information acquisition is crucial to efficiently providing a good user experience. In this work, we study the active learning problem with multi-level user preferences within the collective matrix factorization (CMF) framework. CMF jointly captures multi-level user preferences with respect to items and relations between items (e.g., book genre, cuisine type), generally resulting in improved predictions. Motivated by finite-sample analysis of the CMF model, we propose a theoretically optimal active learning strategy based on the Fisher information matrix and use this to derive a realizable approximation algorithm for practical recommendations. Experiments are conducted using both the Yelp dataset directly and an illustrative synthetic dataset in the three settings of personalized active learning, cold-start recommendations, and noisy data -- demonstrating strong improvements over several widely used active learning methods.
Fast Activation Function Approach for Deep Learning Based Online Anomaly Intrusion Detection

The piecewise-linear activation functions such as ReLU become the catalyst that revolutionizes the training of the deep neural networks. Common nonlinear activation functions used in neural networks such as the tanh and the sigmoid activation functions su? er from saturation during training. The saturation behavior causes the problem of vanishing stochastic gradient decent. We propose a fast activation function, namely the Adaptive Linear Function (ALF) to increase the convergence speed and accuracy of the deep leaning structure for real-time applications. The ALF reduces the saturation effects caused by the soft activation functions and the vanishing gradient caused by the negative values of the ReLU. We evaluate the training method for an online anomaly intrusion detection system using Deep Belief Network (DBN) and simulating four bench mark datasets. The activation function increases the convergence speed of the DBN, with the entire training time reduced 80% compared to the sigmoid, ReLU, and tanh activation functions. The method achieves an accuracy rate of 98.59% on the total 10% KDDCUP'99 test dataset, 96.2% on the NSL-KDD dataset, 98.4% on the Kyoto dataset, and 96.57% on the CSIC HTTP dataset. The proposed activation function outperformed the results obtained when any of the three activation functions—sigmoid, ReLu, or tanh— was used on the test stream of the four datasets. Furthermore, the DBN structure outperforms state-of-the-art networks such as the Stacked Sparse AutoEncoder Based Extreme Learning Machine (SSAELM) in both accuracy and convergence speed
Diversity of Iron Oxidizers in Groundwater-Fed Rapid Sand Filters: Evidence of Fe(II)-Dependent Growth by Curvibacter and Undibacterium spp.

Although earlier circumstantial observations have suggested the presence of iron oxidizing bacteria (IOB) in groundwater-fed rapid sand filters (RSF), ferrous iron (Fe(II)) oxidation in this environment is often considered a chemical process due to the highly oxic and circumneutral pH conditions. The low water temperature (5–10°C), typical of groundwaters, on the other hand, may reduce the rates of chemical Fe(II) oxidation, which may allow IOB to grow and compete with chemical Fe(II) oxidation. Hence, we hypothesized that IOB are active and abundant in groundwater-fed RSFs. Here, we applied a combination of cultivation and molecular approaches to isolate, quantify, and confirm the growth of IOB from groundwater-fed RSFs, operated at different influent Fe(II) concentrations. Isolates related to Undibacterium and Curvibacter were identified as novel IOB lineages. Gallionella spp. were dominant in all waterworks, whereas Ferriphaselus and Undibacterium were dominant at pre-filters of waterworks receiving groundwaters with high (>2 mg/l) Fe(II) concentrations. The high density and diversity of IOB in groundwater-fed RSFs suggest that neutrophilic IOB may not be limited to oxic/anoxic interfaces.
TIFTI: A Framework for Extracting Drug Intervals from Longitudinal Clinic Notes

Oral drugs are becoming increasingly common in oncology care. In contrast to intravenous chemotherapy, which is administered in the clinic and carefully tracked via structure electronic health records (EHRs), oral drug treatment is self-administered and therefore not tracked as well. Often, the details of oral cancer treatment occur only in unstructured clinic notes. Extracting this information is critical to understanding a patient's treatment history. Yet, this a challenging task because treatment intervals must be inferred longitudinally from both explicit mentions in the text as well as from document timestamps. In this work, we present TIFTI (Temporally Integrated Framework for Treatment Intervals), a robust framework for extracting oral drug treatment intervals from a patient's unstructured notes. TIFTI leverages distinct sources of temporal information by breaking the problem down into two separate subtasks: document-level sequence labeling and date extraction. On a labeled dataset of metastatic renal-cell carcinoma (RCC) patients, it exactly matched the labeled start date in 46% of the examples (86% of the examples within 30 days), and it exactly matched the labeled end date in 52% of the examples (78% of the examples within 30 days). Without retraining, the model achieved a similar level of performance on a labeled dataset of advanced non-small-cell lung cancer (NSCLC) patients.
Improved Crowding Distance for NSGA-II

Non-dominated sorting genetic algorithm II (NSGA-II) does well in dealing with multi-objective problems. When evaluating validity of an algorithm for multi-objective problems, two kinds of indices are often considered simultaneously, i.e. the convergence to Pareto Front and the distribution characteristic. The crowding distance in the standard NSGA-II has the property that solutions within a cubic have the same crowding distance, which has no contribution to the convergence of the algorithm. Actually the closer to the Pareto Front a solution is, the higher priority it should have. In the paper, the crowding distance is redefined while keeping almost all the advantages of the original one. Moreover, the speed of converging to the Pareto Front is faster. Finally, the improvement is proved to be effective by applying it to solve nine Benchmark problems.
Constructing Trivariate B-splines with Positive Jacobian by Pillow Operation and Geometric Iterative Fitting

The advent of isogeometric analysis has prompted a need for methods to generate Trivariate B-spline Solids (TBS) with positive Jacobian. However, it is difficult to guarantee a positive Jacobian of a TBS since the geometric pre-condition for ensuring the positive Jacobian is very complicated. In this paper, we propose a method for generating TBSs with guaranteed positive Jacobian. For the study, we used a tetrahedral (tet) mesh model and segmented it into sub-volumes using the pillow operation. Then, to reduce the difficulty in ensuring a positive Jacobian, we separately fitted the boundary curves and surfaces and the sub-volumes using a geometric iterative fitting algorithm. Finally, the smoothness between adjacent TBSs is improved. The experimental examples presented in this paper demonstrate the effectiveness and efficiency of the developed algorithm.
Chronic Overexpression of Bradykinin in Kidney Causes Polyuria and Cardiac Hypertrophy

Acute intra-renal infusion of bradykinin increases diuresis and natriuresis via inhibition of vasopressin activity. However, the consequences of chronically increased bradykinin in the kidneys have not yet been studied. A new transgenic animal model producing an excess of bradykinin by proximal tubular cells (KapBK rats) was generated and submitted to different salt containing diets to analyze changes in blood pressure and other cardiovascular parameters, urine excretion, and composition, as well as levels and expression of renin-angiotensin system components. Despite that KapBK rats excrete more urine and sodium, they have similar blood pressure as controls with the exception of a small increase in systolic blood pressure (SBP). However, they present decreased renal artery blood flow, increased intrarenal expression of angiotensinogen, and decreased mRNA expression of vasopressin V1A receptor (AVPR1A), suggesting a mechanism for the previously described reduction of renal vasopressin sensitivity by bradykinin. Additionally, reduced heart rate variability (HRV), increased cardiac output and frequency, and the development of cardiac hypertrophy are the main chronic effects observed in the cardiovascular system. In conclusion: (1) the transgenic KapBK rat is a useful model for studying chronic effects of bradykinin in kidney; (2) increased renal bradykinin causes changes in renin angiotensin system regulation; (3) decreased renal vasopressin sensitivity in KapBK rats is related to decreased V1A receptor expression; (4) although increased renal levels of bradykinin causes no changes in mean arterial pressure (MAP), it causes reduction in HRV, augmentation in cardiac frequency and output and consequently cardiac hypertrophy in rats after 6 months of age.