Miracle-dz/UniFAR-FTUs huggingface dataset:
Ontology-Driven Hierarchical Deep Learning for Fashion Recognition

We present an automatic approach for large-scale fashion recognition, given an image without any kind of annotation. We formulate the problem as a hierarchical deep learning (HDL) algorithm which can: (i) integrate the deep CNNs to learn more discriminative high-level features for fashion image representations of both coarse-grained and fine-grained classes at different levels of the fashion ontology tree; (ii) leverage multi-task learning and inter-task relationship constraint to train more discriminative classifiers for the nodes on the fashion ontology; (iii) use back propagation to simultaneously refine both the relevant node classifiers and the deep CNNs according to a joint objective function; and (iv) accelerate the fashion retrieval process via path-based classification. The experimental results have verified the effectiveness and efficiency of our proposed algorithm on both classification and retrieval performance.
Enrichment of extracellular vesicles from human synovial fluid using size exclusion chromatography

ABSTRACT As a complex biological fluid, human synovial fluid (SF) presents challenges for extracellular vesicle (EV) enrichment using standard methods. In this study of human SF, a size exclusion chromatography (SEC)-based method of EV enrichment is shown to deplete contaminants that remain after standard ultracentrifugation-based enrichment methods. Specifically, considerable levels of serum albumin, the high-density lipoprotein marker, apolipoprotein A-I, fibronectin and other extracellular proteins and debris are present in EVs prepared by differential ultracentrifugation. While the addition of a sucrose density gradient purification step improved purification quality, some contamination remained. In contrast, using a SEC-based approach, SF EVs were efficiently separated from serum albumin, apolipoprotein A-I and additional contaminating proteins that co-purified with high-speed centrifugation. Finally, using high-resolution mass spectrometry analysis, we found that residual contaminants which remain after SEC, such as fibronectin and other extracellular proteins, can be successfully depleted by proteinase K. Taken together, our results highlight the limitations of ultracentrifugation-based methods of EV isolation from complex biological fluids and suggest that SEC can be used to obtain higher purity EV samples. In this way, SEC-based methods are likely to be useful for identifying EV-enriched components and improving understanding of EV function in disease.
Floating-Point Data Compression Using Improved GFC Algorithm

Compression is widely used in both scientific research and industry. The most common use is that people compress the backup data and infrequently used data to save spaces. Compression is significantly meaningful for big data because it will save a lot of resources with the help of a good compression algorithm. There are two criteria for a good compression algorithm—compression ratio and time consumption. GFC is one of the fastest compression algorithms with a mediocre compression ratio, which is designed for real-time compression with the help of Graphics Processing Units (GPU). This paper introduces three methods to increase the speed of GFC algorithm by using the clzll function, removing if-else statements, and using multi-GPUs. The first and third methods improve the original algorithm performance. However, the if-elseremoval method cannot always guarantee better results. The final compression speed is more than 1,000 gigabits/s, which is much faster than 75 gigabits/s—the original GFC algorithm speed.
MIMO channel estimation based sparse VB-SAGE algorithm for wireless devices

Today, the Smartphone and wireless devices are increasingly solicited for providing new services in fields of video conferencing, mobile voice services, including other services. To satisfy this requirement, researches on optimizing channel estimation are developed. This thesis propose a new channel estimation based sparse variational Bayesian (VB) for space-alternating generalized expectation maximization (SAGE) algorithm with high resolution estimation on parameters of relevant multipath components in the response of frequency and spatially selective wireless channels. The new proposed algorithm extends the classical SAGE algorithm in two respects: the minimizing the variational free energy, and a perfect possibility to implement jointly the estimation of the number of relevant multipath components and the estimation of the component parameters. Finally, Simulation performances confirm an outperform of synthetic single-input-multiple-output (SIMO) time-invariant channel system.
Discontinuous Galerkin method for an elliptic problem with nonlinear Newton boundary conditions in a polygon

The paper is concerned with the analysis of the discontinuous Galerkin method (DGM) for the numerical solution of an elliptic boundary value problem with a nonlinear Newton boundary condition in a two-dimensional polygonal domain. Using the monotone operator theory it is possible to prove the existence and uniqueness of the exact weak solution and the approximate DG solution. The main attention is paid to the study of error estimates. To this end, the regularity of the weak solution is investigated and it is shown that due to the singular boundary points, the solution looses regularity in a vicinity of these points. It comes out that the error estimation depends essentially on the opening angle of the corner points and the nonlinearity in the boundary term. It also depends on the parameter defining the nonlinear behaviour of the Newton boundary condition. At the end of the paper some computational experiments are presented.
The Symplectic Camel and Poincaré Superrecurrence: Open Problems

Poincaré's Recurrence Theorem implies that any isolated Hamiltonian system evolving in a bounded Universe returns infinitely many times arbitrarily close to its initial phase space configuration. We discuss this and related recurrence properties from the point of view of recent advances in symplectic topology which have not yet reached the Physics community. These properties are closely related to Emergent Quantum Mechanics since they belong to a twilight zone between classical (Hamiltonian) mechanics and its quantization.
Convolutional neural network with pair-wise pure dependence for sentence classification

Sentence classification has always been a crucial research topic in Natural Language Processing (NLP). Classical bag-of-words based models have a major limitation: the contextual information between words, which is the key to form meaningful semantic entities, is missing. Moreover, the semantic entities are not necessarily limited to syntactically valid phrases or named entities, but can be high-order association (also referred to as high-order dependence) patterns. To address this issue, in this paper, we propose PPD-CNN, a convolutional neural network (CNN) architecture with Pair-wise Pure Dependence (PPD) for sentence classification. Compared with the traditional CNN, our PPD-CNN (1) combines PPD pattern which is a couple of dependence words as strong un-separable high-level semantic entity and (2) extracts multi-granular semantic information, which treats PPD pattern as an input channel to capture the whole features and the original sentence as another input channel through variable-size convolution filters to catch all kinds of local features. With this design, our PPD-CNN can model the contextual information, which is important for grasping the word sense. The experimental results show that our approach significantly outperforms a wide range of baselines and state-of-the-art methods.
Distributed Network Center and Size Estimation

A fully distributed algorithm for estimating the center and the radius of the smallest sphere that contains a wireless sensor network is proposed. The center finding problem is formulated as a convex optimization problem in summation form by using a soft-max approximation to the maximum function. Diffusion adaptation method is used where states of nodes converge to the estimated center distributively. Then, distributed max consensus is used to compute the radius. The proposed algorithm is fully distributed in the sense that each node in the network only needs to know its own location and nodes do not need to be pre-labeled. The algorithm works for any connected graph structure. The performance of the proposed algorithm is analyzed and it is shown that there is a tradeoff: a larger design parameter results in a more accurate center estimation but also makes the convergence speed of the distributed algorithm slower. It is also shown that the proposed algorithm can also be used to estimate the center and radius of the (not necessarily location-based) data at sensor nodes in a distributed way, thereby providing information and insights about the global data at each sensor. Simulation results corroborating the theory are also provided.
Identification of a Moving Object's Velocity and Range With a Static-Moving Camera System

In this technical note, a nonlinear observer strategy is proposed to asymptotically identify the velocity and range of the feature points on a moving object using a static-moving camera system. Specifically, the camera system consists of a static camera and a moving camera. The observers are developed in a sequential way. First, nonlinear observers are designed to identify the scaled velocity of the feature points and the angular velocity of the moving camera. Second, based on the estimated velocities, the range of the feature points is identified by a reduced order estimator. Homography-based techniques are used in the auxiliary state construction and the observer design. With the estimated scaled velocity and range, the motion and structure of the object can be obtained with respect to both cameras. The proposed approach does not require the motion constraint or a priori geometric knowledge of the moving object. Furthermore, Lyapunov-based analysis is used to prove that the estimators asymptotically identify the velocity and range of the feature points. The performance of the proposed strategy is demonstrated by simulation results.
Dilated 4 × 4 Hybrid MZI-SOA Switch with Relaxed Active Bias Control

Increasingly complex and high capacity optical switch fabrics are being proposed and demonstrated for high-bandwidth data streams in datacentres [1]. Mach-Zehnder interferometer (MZI) and semiconductor optical amplifier (SOA) based switches have received much attention owing to their nanosecond response time which is essential for packet switching [2]. However, the scalability of MZI switches is inherently limited by insertion loss and accumulated crosstalk, whilst the scalability of SOA switches is restricted by accumulated noise and distortion.
HDNN-CF: A hybrid deep neural networks collaborative filtering architecture for event recommendation

Along with the rise of Event-Based Social Networks (EBSNs), event recommendation has become an increasing important problem. However, unlike recommending usual items, such as movies or music, event recommendation suffers from severe cold-start problem, because most events in EBSNs are typically short-lived and registered by only a few users. Additionally, the available feedbacks for events are implicit feedbacks. In this work, we propose a Hybrid Deep Neural Networks Collaborative Filtering Architecture (HDNN-CF) that collaboratively makes use of the events' semantic information and users' implicit feedbacks for event recommendation. Specifically, we extend state-of-the-art method AutoRec to model implicit feedbacks by proposing Probabilistic AutoRec (PAutoRec). We collaboratively train a Stacked Denoise AutoEncoder (SDAE) to learn the deep representation of the semantic information and a PAutoRec to collaborative filter based on implicit feedbacks. Extensive experiments on a real large scale dataset Meetup show that HDNN-CF significantly outperforms state-of-the-art methods by 10% on recall of top 30 recommendations.
Initial study on evolving state space neural networks (eSSNN)

This paper presents initial research on the novel neural network evolution algorithm for modelling dynamic systems called evolving state space neural network (eSSNN) algorithm. It also introduces a state space neural network (SSNN) for modelling non-linear dynamic systems using neural network, derived from state space representation. The evolution is done by inserting influence signal polynomial to the evolution edge in the SSNN in order to reduce state estimation error. The inserting is done automatically, where every combination of influence signal and potential evolution edge are tested in order to find the combination with largest error reduction. SSNN Model was created for 3 synthetic systems, where model simulations showed promising results for further research, including control design based on SSNN.
Active fuzzy rule induction

The use of rule based learners has been highly motivated all these years because of their inherent properties of interpretability and comprehensibility, leading to the construction of user friendly exported models by keeping pace with propositional logic. Besides this, their ability to operate under efficient time complexity allows us to occupy it under Active Learning schemes that integrate the human factor as an oracle into their learning kernel so as to tackle with the scarcity of existing labeled examples over several scientific fields. Upon this assumption, a recently proposed fuzzy rule based learner has been combined with a suitable query strategy for mining, with both robust and fast enough ability, unlabeled instances that facilitate the improvement of the learning behavior of the whole classification method. Rigorous experiments have been executed, proving the rightness of our ambition.
Wi-Eye: Tracking Urban Private Vehicles with Inter-Vehicle Communications and Sparse Video Surveillance Cameras

Due to the sparse distribution of video surveillance cameras and low installation rate of dash-mounted online telematics systems, tracking precise trajectories of urban private vehicles is a challenging task. Previous studies on vehicle tracking are mostly concerned with recovering trajectories with low- sampling rate GPS coordinates or captured surveillance information by identifying road traffic patterns from this information. Nevertheless, to the best of our knowledge, none of them have considered using the vehicle encounter information to enhance the sampling rate as well as the time-varying and in-group characteristics of vehicle traffic patterns, let alone to achieve vehicle tracking. With this insight, we divide all vehicles into clusters with a Canopy and K- means combined algorithm for different time periods and use an exponential distribution to approximate the time that vehicles from one cluster encounter a fixed Wi-Fi hotspot in the future during a given time period and at a specific location. Based on these preliminary results, we propose a novel approach to select the optimal data packet transmission scheme between encountered vehicles to transfer vehicle encounter information to the server as much and quickly as possible, and then calculate the trajectories of all private vehicles accurately. We evaluate our solution via real-world private vehicles and road surveillance system datasets. Experimental results demonstrate that our approach outperforms other solutions in terms of the accuracy ratio of vehicle tracking.
A powered ankle prothesis driven by EHA technique

There are many limitations in passive ankle prosthesis which are widely used so far. Meanwhile succeed commercial product has not been push out yet although the study of powered ankle prosthesis is ongoing. One of the main reason is that it is difficult to integrate the mechanism with limited weight, which have enough instantaneous power to promote the body forward. In this paper, a powered ankle prosthesis driven by EHA (Electro-Hydraulic Actuator) technique is proposed to solve this problem. It is mainly composed of peripheral mechanism and embedded hydraulic system which contains accumulator, plunger pump, motor, solenoid valve, miniature hydraulic circuit. The prototype with this structure is similar in quality and volume to a real ankle and is verified to match the true ankle performance.
Restraining Strategy of the Stone–Wales Defect Effect on Graphene Nanoribbon MOSFETs

In this letter, armchair graphene nanoribbon (aGNR) metal-oxide-semiconductor field effect transistors with stone–wales (SW) defects are investigated by the first-principles simulation and nonequilibrium Green’s function method. The on-state currents of the devices are found to be significantly reduced by the defects, leading to obvious deviations in GNR technology. Therefore, we propose a restraining strategy using foreign decorations to enhance the local conductivities. The most stable decoration positions for atoms (such as F, N, and B) are found by searching the lowest energy point in the SW regions. After the decorations, more energy states can participate in the conductions, and the state localizations are also alleviated. The simulation results reveal that the decorations can remarkably restrain the SW defect effects in GNR transport and generate more than tenfold ON-state current improvements.
Multi-source heterogeneous data fusion

As the exponential growth of data in internet era, there comes the big data era. Big data fusion creates huge values that makes it a research hotspot. However, in big data era, data shows characters of large volume, velocity, veracity and especially variety which is also called heterogeneity. Multiple different sources of data lead to data heterogeneity. Multi-source heterogeneous data brings opportunities and challenges to big data fusion. This paper introduces big data fusion and methods for heterogeneous data fusion, especially focus on the application of deep learning methods in multi-source heterogeneous data fusion. Challenges of dealing with multi-source heterogeneous data fusion is also discussed.
Fast Vision-Based Pedestrian Traffic Light Detection

Detection of pedestrian traffic light is very important for the visually impaired. However, fast but accurate vision-based detection is not an easy task due to the complexity of background and illumination. In this paper, a fast vision-based detection system is designed. In the designed system, the background filter is applied to identify the candidate regions of pedestrian traffic lights. And the cascade classifier obtained by the Adaboost algorithm based on the multi-layer features is used to detect the pedestrian traffic lights. Testing results verifies the effectiveness of the designed system.
Influence of the Cladding Structure in PMMA mPOFs Mechanical Properties for Strain Sensors Applications

This paper presents a dynamic mechanical analysis of a microstructured polymer optical fiber (mPOF). The fiber material is polymethyl methacrylate, which is widely available commercially. The DMA is made by means of sequential strain cycles produced with an oscillatory load with controlled frequency to obtain the variation of Young’s modulus with respect to temperature, frequency, and humidity for mPOFs with two-, three-, and five-ring hexagonal microstructured cladding. Results show that the three different cladding structures have similar Young’s modulus on the stress-strain tests performed. Furthermore, the three-ring structure presents the lowest Young’s Modulus variation with temperature among the samples tested, whereas the five-ring structure presents a Young’s Modulus variation with frequency 25% lower than the two- and three-rings cladding structures. Regarding the humidity sensitivity, the two-ring structure presented a 30% lower Young’s Modulus variation for a 25% humidity increase. The results obtained provide guidelines for the cladding structure choice for strain or stress sensors applications when low cross-sensitivity with temperature, humidity, and frequency is desired.
Exploring and understanding web search behavior with human activities

Nowadays, web search has become an important approach to retrieving information. Incorporating context factors such as device type and location to improve search performance has attracted more and more attentions. However, impacts of offline human activities (i.e., background activity) on web search haven't been fully explored yet, although intuitively, some of them exist. To reduce this gap, we examine user's web search behavior during activities of different themes by studying a 9-day Internet access logs in a large convention center, as well as web search preferences with respect to other factors, such as device type, query language, in these activities. Our results show that, the background activity causes a large proportion of activity theme related queries, while it has little influence on theme irrelevant ones. In addition, even if in the same activity context, differences in query language, device type, device vendor and search engine lead to various changes in web search behavior.
The Arrowhead Framework applied to energy management

Energy management in buildings can provide massive benefits in financial and energy saving terms. It is possible to optimize energy usage with smart grid techniques, where the benefits are enhanced when the energy consumer can trade the energy on energy markets, since it forces energy providers to compete with each other on the energy price. However, two hurdles oppose this approach: the devices providing control over appliances do not interoperate with each other; and energy markets limit trading activities to large quantities of energy, thus impeding access for small consumers. This work considers using the FlexOffer (FO) concept to allow the consumer to express its energy needs, and FO-related mechanisms to aggregate energy requests into quantities relevant for energy markets. Moreover, the presented system, named FlexHousing, is based on the Arrowhead Framework — a framework that simplifies design and implementation of distributed applications by means of normalizing communication via services — and exploits its Service Oriented mechanisms to provide device interoperability. The implemented FlexHousing system uses multi-level FO aggregation to empower either the final user, for example the owner of an apartment, to manage its own energy by defining their flexibilities, or to offload this responsibility to an energy manager who takes care of all the apartments in a building or set of buildings.
Smart services: A case study on smarter public safety by a mobile app for University of São Paulo

The University of São Paulo has faced public safety issues a long the years. Due to its size preventive surveillance by the campus security guard cannot be effective all the times. In order to bring a safer environment to its public of more than 60,000 daily users, a smart public safety system is being developed. This is a complex system, spread throughout all the University's campuses. It is composed of a smart surveillance cameras system, a back office system with a workflow engine and a mobile application within a collaborative concept. The smart cameras system is being deployed and the mobile application together with the back office system is being used this past year with satisfactory results. The mobile application is the user entry point to report several security and campus maintenance related issues that are automatically directed to the responder team for immediate action in the case of security or enters an automated workflow engine in the case of campus maintenance. This paper presents the structure created towards achieving a smarter public safety environment, details of the implementation, presents statistical data collected by the system showing its effectiveness and concludes showing the improvements introduced in the university community safety and welfare.