Miracle-dz/UniFAR-FTUs huggingface dataset:
Unsupervised Deep Transform Learning

We introduce deep transform learning - a new tool for deep learning. Deeper representation is learnt by stacking one transform after another. The model is akin to a feedforward neural network. The first layer learns the transform and features from the input training samples. Subsequent layers use the features (after activation) from the previous layers as training input. However, this explanation is only given for intuitive understanding; the ensuing problem is not solved in a greedy fashion. All the layers are solved jointly. Experiments have been carried out with other state-of-the-art. Results on classification and clustering show that our proposed technique is better than all the said techniques, at least on the benchmark datasets compared on.
Effects of macroalgae loss in an Antarctic marine food web: applying extinction thresholds to food web studies

Antarctica is seriously affected by climate change, particularly at the Western Antarctic Peninsula (WAP) where a rapid regional warming is observed. Potter Cove is a WAP fjord at Shetland Islands that constitutes a biodiversity hotspot where over the last years, Potter Cove annual air temperatures averages increased by 0.66 °C, coastal glaciers declined, and suspended particulate matter increased due to ice melting. Macroalgae are the main energy source for all consumers and detritivores of Potter Cove. Some effects of climate change favor pioneer macroalgae species that exploit new ice-free areas and can also decline rates of photosynthesis and intensify competition between species due to the increase of suspended particulate matter. In this study, we evaluated possible consequences of climate change at Potter Cove food web by simulating the extinction of macroalgae and detritus using a topological approach with thresholds of extinction. Thresholds represent the minimum number of incoming links necessary for species’ survival. When we simulated the extinctions of macroalgae species at random, a threshold of extinction beyond 50% was necessary to obtain a significant number of secondary extinctions, while with a 75% threshold a real collapse of the food web occurred. Our results indicate that Potter Cove food web is relative robust to macroalgae extinction. This is dramatically different from what has been found in other food webs, where the reduction of 10% in prey intake caused a disproportionate increase of secondary extinctions. Robustness of the Potter Cove food web was mediated by omnivory and redundancy, which had an important relevance in this food web. When we eliminated larger-biomass species more secondary extinctions occurred, a similar response was observed when more connected species were deleted, yet there was no correlation between species of larger-biomass and high-degree. This similarity could be explained because both criteria involved key species that produced an emerging effect on the food web. In this way, large-biomass and high-degree species could be acting as source for species with few trophic interactions or low redundancy. Based on this work, we expect the Potter Cove food web to be robust to changes in macroalgae species caused by climate change until a high threshold of stress is reached, and then negative effects are expected to spread through the entire food web leading to its collapse.
A Multi-Camera Deep Neural Network for Detecting Elevated Alertness in Drivers

We present a system for the detection of elevated levels of driver alertness in driver-facing video captured from multiple viewpoints. This problem is important in automotive safety as a helpful feedback signal to determine driver engagement and as a means of automatically flagging anomalous driving events. We generated a dataset of videos from 25 participants overseeing an hour each of driving sequences in a simulator consisting of a mixture of normal and near-miss driving events. Our proposed system consists of a deep neural network which fuses information from three driver-facing cameras to estimate moments of elevated driver alertness. A novel aspect of the system is that it learns to actively re-weight the importance of camera inputs depending on their content. We demonstrate that this approach is not only resilient to dropped or occluded frames, but also has significantly improved performance compared to a system trained on any single stream.
New Area Record for the AES Combined S-Box/Inverse S-Box

The AES combined S-box/inverse S-box is a single construction that is shared between the encryption and decryption data paths of the AES. The currently most compact implementation of the AES combined S-box/inverse S-box is Canright's design, introduced back in 2005. Since then, the research community has introduced several optimizations over the S-box only, however the combined S-boxlinverse S-box received little attention. In this paper, we propose a new AES combined S-boxlinverse S-box design that is both smaller and faster than Canright's design. We achieve this goal by proposing to use new tower field and optimizing each and every block inside the combined architecture for this field. Our complexity analysis and ASIC implementation results in the CMOS STM 65nm and NanGate 15nm technologies show that our design outperforms the counterparts in terms of area and speed.
Discrete-Time Equivalent Homogeneous Differentiators

This paper proposes an entirely new discrete-time realization of an arbitrary order robust exact differentiator. Its construction relies on the redesign of the differentiator in the discrete-time domain by means of a non-linear eigenvalue placement. The resulting algorithm is consistent with the continuous-time algorithm and preserves the best possible asymptotic accuracies known from the continuous-time differentiator. In contrast to the existing discretization schemes, the proposed schemes are exact in the sense that in the unperturbed case the differentiators ensure vanishing estimation errors. Limit cycles typically present in the error state variables enforced by the forward Euler discretized algorithm are avoided and the precision is insensitive to an overestimation of the gains.
Control and Management of a Connected Car Using SDN/NFV, Fog Computing and YANG data models

There are several use cases that claim the need for a connected car. Among them there is the need for connectivity between vehicles and information sources, or V2V and V2X exchanges for accident prevention. In order to cope with the need for novel applications running on top of an interconnected network, the concept of fog computing appears as a realistic solution for both intra-car and inter-car data processing and decision making. This paper describes the proposed architecture and experimental evaluation of an innovative proof-of-concept (PoC) for a connected car, modeled with YANG, which can be remotely controlled using SDN/NFV and fog computing technologies. As an example, the remote control of the car might be based on a service application running on a fog node, which can be located close to a road side unit (RSU). We also propose a fog architecture in order to enable cooperative perception between connected cars. Finally, the performance evaluation uses a RESTCONF server installed in a Raspberry Pi aboard of a small car. This server is responsible for the sensors and actuators of the car and allows for its remote control from a user terminal (e.g., a smartphone, tablet, or laptop) and through the fog node, running a control application as a service.
Hippocampal CA1 gamma power predicts the precision of spatial memory judgments

Significance Spatial memory is critical to every facet of our lives. The hippocampus is known to play a prominent role in the formation and retrieval of spatial memories; however, the exact mechanisms remain elusive. Using electrophysiology recordings from presurgical epilepsy patients implanted with depth electrodes, we measured high-frequency neural activity from the CA1 region of the hippocampus and the dorsolateral prefrontal cortex. We found associations between increased high-frequency activity and the precision of spatial memory retrieval. These data suggest that local processing within the CA1 subfield and the dorsolateral prefrontal cortex underlie the quality of retrieved spatial memories. The hippocampus plays a critical role in spatial memory. However, the exact neural mechanisms underlying high-fidelity spatial memory representations are unknown. We report findings from presurgical epilepsy patients with bilateral hippocampal depth electrodes performing an object-location memory task that provided a broad range of spatial memory precision. During encoding, patients were shown a series of objects along the circumference of an invisible circle. At test, the same objects were shown at the top of the circle (0°), and patients used a dial to move the object to its location shown during encoding. Angular error between the correct location and the indicated location was recorded as a continuous measure of performance. By registering pre- and postimplantation MRI scans, we were able to localize the electrodes to specific hippocampal subfields. We found a correlation between increased gamma power, thought to reflect local excitatory activity, and the precision of spatial memory retrieval in hippocampal CA1 electrodes. Additionally, we found a similar relationship between gamma power and memory precision in the dorsolateral prefrontal cortex and a directional relationship between activity in this region and in the CA1, suggesting that the dorsolateral prefrontal cortex is involved in postretrieval processing. These results indicate that local processing in hippocampal CA1 and dorsolateral prefrontal cortex supports high-fidelity spatial memory representations.
Bayesian Anisotropic Gaussian Model for Audio Source Separation

In audio source separation applications, it is common to model the sources as circular-symmetric Gaussian random variables, which is equivalent to assuming that the phase of each source is uniformly distributed. In this paper, we introduce an anisotropic Gaussian source model in which both the magnitude and phase parameters are modeled as random variables. In such a model, it becomes possible to promote a phase value that originates from a signal model and to adjust the relative importance of this underlying model-based phase constraint. We conduct Bayesian inference of the model through the derivation of an expectation-maximization algorithm for estimating the parameters. Experiments conducted on realistic music songs for a monaural source separation task, in an scenario where the variance parameters are assumed known, show that the proposed approach outperforms state-of-the-art techniques.
Spoken Language Understanding without Speech Recognition

While conventional approaches to spoken language understanding involve cascading a speech recognizer with a language understanding system, in this paper, we describe a novel approach for deriving semantics directly from the speech signal without the need for an explicit speech recognition step. We evaluate this approach in the context of a customer care dialog system and demonstrate its effectiveness in comparison to the conventional approach.
Realistic Procedural Plant Modeling from Multiple View Images

In this paper, we describe a novel procedural modeling technique for generating realistic plant models from multi-view photographs. The realism is enhanced via visual and spatial information acquired from images. In contrast to previous approaches that heavily rely on user interaction to segment plants or recover branches in images, our method automatically estimates an accurate depth map of each image and extracts a 3D dense point cloud by exploiting an efficient stereophotogrammetry approach. Taking this point cloud as a soft constraint, we fit a parametric plant representation to simulate the plant growth progress. In this way, we are able to synthesize parametric plant models from real data provided by photos and 3D point clouds. We demonstrate the robustness of the proposed approach by modeling various plants with complex branching structures and significant self-occlusions. We also demonstrate that the proposed framework can be used to reconstruct ground-covering plants, such as bushes and shrubs which have been given little attention in the literature. The effectiveness of our approach is validated by visually and quantitatively comparing with the state-of-the-art approaches.
A Pilot Study of Four Intraosseous Blood Transfusion Strategies.

BACKGROUND Intraosseous (IO) access is used by military first responders administering fluids, blood, and medications. Current IO transfusion strategies include gravity, pressure bags, rapid transfusion devices, and manual push-pull through a three-way stopcock. In a swine model of hemorrhagic shock, we compared flow rates among four different IO blood transfusion strategies. METHODS Nine Yorkshire swine were placed under general anesthesia. We removed 20 to 25mL/kg of each animal's estimated blood volume using flow of gravity. IO access was obtained in the proximal humerus. We then autologously infused 10 to 15mL/kg of the animal's estimated blood volume through one of four randomly assigned treatment arms. RESULTS The average weight of the swine was 77.3kg (interquartile range, 72.7kg-88.8kg). Infusion rates were as follows: gravity, 5mL/min; Belmont rapid infuser, 31mL/min; single-site pressure bag, 78mL/min; double-site pressure bag, 103mL/min; and push-pull technique, 109mL/min. No pulmonary arterial fat emboli were noted. CONCLUSION The optimal IO transfusion strategy for injured Servicemembers appears to be single-site transfusion with a 10mL to 20mL flush of normal saline, followed immediately by transfusion under a pressure bag. Further study, powered to detect differences in flow rate and clinical complications. is required.
Few-Shot Semantic Segmentation with Prototype Learning

Semantic segmentation assigns a class label to each image pixel. This dense prediction problem requires large amounts of manually annotated data, which is often un-available. Few-shot learning aims to learn the pattern of a new category with only a few annotated examples. In this paper, we formulate the few-shot semantic segmentation problem from 1-way (class) to N -way (classes). Inspired by few-shot classiﬁcation, we propose a generalized framework for few-shot semantic segmentation with an alternative training scheme. The framework is based on prototype learning and metric learning. Our approach outperforms the baselines by a large margin and shows comparable performance for 1-way few-shot semantic segmentation on PASCAL VOC 2012 dataset.
There's No Place Like Home: Visual Teach and Repeat for Emergency Return of Multirotor UAVs During GPS Failure

Redundant navigation systems are critical for safe operation of UAVs in high-risk environments. Since most commercial UAVs almost wholly rely on GPS, jamming, interference, and multi-pathing are real concerns that usually limit their operations to low-risk environments and visual line-of-sight. This letter presents a vision-based route-following system for the autonomous, safe return of UAVs under primary navigation failure such as GPS jamming. Using a Visual Teach and Repeat framework to build a visual map of the environment during an outbound flight, we show the autonomous return of the UAV by visually localizing the live view to this map when a simulated GPS failure occurs, controlling the vehicle to follow the safe outbound path back to the launch point. Using gimbal-stabilized stereo vision and inertial sensing alone, without reliance on external infrastructure, Visual Odometry and localization are achieved at altitudes of 5–25 m and flight speeds up to 55 km/h. We examine the performance of the visual localization algorithm under a variety of conditions and also demonstrate closed-loop autonomy along a complicated 450 m path.
Efficient Approach Determination for Fake Biometric Detection

Biometric systems provide one of the best options for implementing security requirements however they are susceptible to some of the threats such as fraudulent attacks. A reliable and efficient solution is required to overcome this risk. Liveness detection is one of the mechanisms that can be applied to protect against these kind of attacks effectively. A software based protection approach is presented to prevent fraudulent access attempts. The proposed solution is software based. It computes different image quality measures extracted from one image. It also proves to be useful in multisensor scenario. An optimum combination of Image Quality Measures will be determined to make the system more efficient without compromising the accuracy.
Planning Ergonomic Sequences of Actions in Human-Robot Interaction

In this paper, we define the problem of human-robot collaboration as a combined task and motion planning problem which is extended to the multi-agent case (human and robot). Our proposed approach allows us to explicitly take into account ergonomic cost, synchrony and concurrency of behavior in an optimization formulation. We show simulated results as well as an experiment with a real robot combined with a user study. Results show that optimizing over a sequence of actions leads to more ergonomic situations.
Vision-Based Global Localization Using Ceiling Space Density

Service robots are becoming a reality across homes. Still, the range of applications supported by such robots remains tied to their ability to self-localize in the environment. Man-made constructions often have a documented blueprint which can be used as input information for robot localization and smart home applications. However, home environments commonly include movable objects and furniture, which can make localization a complicated task, especially for forward-facing horizontal range-finders. In this paper, we present a new and effective global localization approach for home environments which adapts the notion of free space density to a camera pointing to the ceiling. We exploit the available blueprint information, as well as evidence that ceiling vision can provide robust localization information, even in the presence of occlusions. We perform real-world experiments using a robotic vacuum cleaner equipped with an upward-facing camera in two different apartments across multiple trajectories and compare the proposed method with competing approaches. Our solution shows superior localization results using maps where neither furniture or movable objects are not modeled.
Research on Auto Sales Forecast Based on Online Reviews-Take R Brand Automobile as an Example

The rapid development of automobile industry has led to the blind expansion of the production capacity of various auto companies, which has brought serious potential overcapacity. Accurate forecaste of car sales is not only of great importance to automobile manufacturers and distributors in setting up strategy and target. It also has a certain influence on the good growth of China's automobile market and the healthy development of the national economy. This article takes R brand automobile as the research object, collects the related online reviews data, through the natural language processing and the text emotion model, the online reviews data is processed quantitatively. The sales forecasts are made by combining the internal historical sales data of the automobile company with the online reviews of R brand and the sales and reviews data of competitive brands. Compared with the benchmark model, it is found that the sales forecasting model which has added the sentiment variables and the influence factors of the competitive brands improves the fitting ability of the data.
Morphology of mitochondria in spatially restricted axons revealed by cryo-electron tomography

Neurons project axons to local and distal sites and can display heterogeneous morphologies with limited physical dimensions that may influence the structure of large organelles such as mitochondria. Using cryo-electron tomography (cryo-ET), we characterized native environments within axons and presynaptic varicosities to examine whether spatial restrictions within these compartments influence the morphology of mitochondria. Segmented tomographic reconstructions revealed distinctive morphological characteristics of mitochondria residing at the narrowed boundary between presynaptic varicosities and axons with limited physical dimensions (~80 nm), compared to mitochondria in non-spatially restricted environments. Furthermore, segmentation of the tomograms revealed discrete organizations between the inner and outer membranes, suggesting possible independent remodeling of each membrane in mitochondria at spatially restricted axonal/varicosity boundaries. Thus, cryo-ET of mitochondria within axonal subcompartments reveals that spatial restrictions do not obstruct mitochondria from residing within them but limited available space can influence their gross morphology and the organization of the inner and outer membranes. These findings offer new perspectives on the influence of physical and spatial characteristics of cellular environments on mitochondrial morphology and highlights the potential for remarkable structural plasticity of mitochondria to adapt to spatial restrictions within axons.
Verification of Consensus Building Support System on Large-Scale Social Experiment where Celebrities Participate in Discussion

We have developed the consensus building support system called COLLAGREE on the Web with facilitator support function. If discussions are conduct by free participation on the Web, celebrity may participate. Therefore, in this work, we conduct large scale social experiments using support systems where celebrities participate. The purpose of this work is to verify the effectiveness and problems of consensus building support system in discussion where celebrities participate. IN the large-scale social experiments, we clarified that new threads by celebrities' increases the number of reply and approval number by about 3 times compared to other participants. Also, we clarified that opinion by celebrities did not little affect voting results. Furthermore, we clarified that participants were not influenced by celebrity opinions, but were influenced by other participants.