Miracle-dz/UniFAR-FTUs huggingface dataset:
A Common Topic Transfer Learning Model for Crossing City POI Recommendations

With the popularity of location-aware devices (e.g., smart phones), large amounts of location-based social media data (i.e., user check-in data) are generated, which stimulate plenty of works on personalized point of interest (POI) recommendations using machine learning techniques. However, most of the existing works could not recommend POIs in a new city to a user where the user and his/her friends have never visited before. In this paper, we propose a common topic transfer learning graphical model–the common-topic transfer learning model (CTLM)–for crossing-city POI recommendations. The proposed model separates the city-specific topics (or features) of each city from the common topics (or features) shared by all cities, to enable the users’ real interests in the source city to be transferred to the target city. By doing so, the ill-matching problem between users and POIs from different cities can be well addressed by preventing the real interests of users from being influenced by the city-specific features. Furthermore, we incorporate the spatial influence into our proposed model by introducing the regions’ accessibility. As a result, the co-occurrence patterns of users and POIs are modeled as the aggregated result from these factors. To evaluate the performance of the CTLM, we conduct extensive experiments on Foursquare and Twitter datasets, and the experimental results show the advantages of CTLM over the state-of-the-art methods for the crossing-city POI recommendations.
Enhancing the Stiffness Perception of Tangible Objects in Mixed Reality Using Wearable Haptics

This paper studies the combination of tangible objects and wearable haptics for improving the display of stiffness sensations in virtual environments. Tangible objects enable to feel the general shape of objects, but they are often passive or unable to simulate several varying mechanical properties. Wearable haptic devices are portable and unobtrusive interfaces able to generate varying tactile sensations, but they often fail at providing convincing stiff contacts and distributed shape sensations. We propose to combine these two approaches in virtual and augmented reality (VR/AR), becoming able of arbitrarily augmenting the perceived stiffness of real/tangible objects by providing timely tactile stimuli at the fingers. We developed a proof-of-concept enabling to simulate varying elasticity/stiffness sensations when interacting with tangible objects by using wearable tactile modules at the fingertips. We carried out a user study showing that wearable haptic stimulation can well alter the perceived stiffness of real objects, even when the tactile stimuli are not delivered at the contact point. We illustrated our approach both in VR and AR, within several use cases and different tangible settings, such as when touching surfaces, pressing buttons and pistons, or holding an object. Taken together, our results pave the way for novel haptic sensations in VR/AR by better exploiting the multiple ways of providing simple, unobtrusive, and low-cost haptic displays.
Hosting Capacity Improvement Unlocked by Control Strategies for Photovoltaic and Battery Storage Systems

High penetration of photovoltaic installation may challenge the reliability of grid operation due to the occurrence of critical grid states. Hence, grid relieving control strategies, such as active power curtailment or reactive power provision, have been introduced for photovoltaic systems in technical guidelines. The integration of battery storages in combination with such systems is considered as a promising alternative to support the grid. Though, a grid supporting effect does not emerge intrinsically, but requires an appropriate strategy for battery charging. This paper presents a simulation study which compares several decentralized control strategies for PV and battery storage systems. The control strategies are assessed in terms of their capability to improve the grid hosting capacity for photovoltaic systems. In particular, the implementation of an advanced strategy for battery charging based on Model Predictive Control is proposed and evaluated comparatively.
Recurrent Reversible Cognitive Impairment in a Cluster Headache Patient: A Case Report

Cluster headache (CH) is a disabling primary headache disorder sometimes dubbed the “suicide headache. ” CH is characterized by a severe or very severe unilateral head pain lasting from 15 to 180 minutes with a frequency ranging from once every other day to 8 times daily, accompanied by ipsilateral autonomic symptoms and/or restlessness or agitation. Behavior during attacks may range to include not just restlessness but a variety of complex, even violent, self-injurious behaviors and even suicide attempts. CH has been previously associated with psychiatric comorbidities such as depression, anxiety disorders, and aggressiveness. However, little is known about cognition in CH patients. Here we report a patient presenting with progressive worsening of cognitive functions and behavioral modifications during CH bouts. An extensive neuropsychological assessment, including global cognition indexes, verbal and visual memory, attention, executive and visuospatial function, language, and depression, was performed during 2 active CH periods, and during a pain-free period. Selective deficits in attention and executive functions were found only during the active CH bouts. We describe a 50-year-old man with a medical history of hypertension presenting with a 6-year history of episodic CH. He described strictly right-sided episodes of severe pain occurring in the periorbital area, accompanied by ipsilateral lacrimation, conjunctival injection, eyelid edema, and nasal congestion. The attacks had prominent psychiatric features with agitation and violent self-injurious behaviors. The pain occurred only during the daytime. The onset was never at night. The attacks usually lasted from 30 minutes to 2 hours when untreated, occurring with a frequency of one every other day, for nearly a month once a year. Attacks responded to subcutaneous sumatriptan 6 mg and high-flow inhaled oxygen. A trial with quietapine (up to 100 mg/die) was added to a From the IRCCS Institute of Neurological Sciences of Bologna, UOC Clinica Neurologica, Bologna, Italy (V. Favoni, L. Sambati, F. Oppi, M. Stanzani Maserati, S. Cevoli, and G. Pierangeli); Department of Biomedical and NeuroMotor Sciences (DiBiNeM), Alma Mater Studiorum University of Bologna, Italy (V. Favoni, L. Sambati, and G. Pierangeli).
Concerto: A Program Parallelization, Orchestration and Distribution Infrastructure

The important step in Program Parallelization, is identifying the pieces of the given program, that can be run concurrently, on separate processing elements. The parallel pieces once identified, need to be hoisted and executed remotely, and the results combined. This is a complex process, usually referred to as Program Orchestration and Distribution, and the details are closely tied to the target architecture of the parallel machine. Program Distribution in a Shared Memory Parallel Computer, is comparatively simpler, and involves structuring the parallel pieces, as separate threads, with synchronization provided as needed, and just scheduling the threads, on the various processors. On the contrary, Program Orchestration on a Distributed Machine, such as a Cluster is more involved, and requires explicit message passing, with the help of Send and Receive primitives, to share variables between the parallel subprograms, which are running on separate machines. Concerto is a Parallelization, Orchestration and Distribution Framework, and is a component of our larger Program Transformation and Parallelization Solution. The parallel architectures targeted, include both Shared Memory Multicomputer and Distributed Memory Multicomputer. However, the focus of this paper, is mainly on the class of Distributed Memory Parallel Machines. Here we look at issues involved in Program Distribution, and provide a high level design of Concerto, our solution to the problem, along with the Program Parallelization and Distribution Algorithm. Majority of the existing Program Distribution Solutions, require user annotations to identify parallel pieces of code and data, which can be a cumbersome process, from the programmer perspective. However in Concerto, the Distribution and Mapping process is entirely automated, requires no user directives and is based solely on Dependence and Flow analysis of the sequential program.
Studying the Sense of Embodiment in VR Shared Experiences

In this paper, we explore the influence of sharing a virtual environment with another user on the sense of embodiment in virtual reality. For this aim, we conducted an experiment where users were immersed in a virtual environment while being embodied in an anthropomorphic virtual representation of themselves. To evaluate the influence of the presence of another user, two situations were studied: either users were immersed alone, or in the company of another user. During the experiment, participants performed a virtual version of the well-known whac-a-mole game, therefore interacting with the virtual environment, while sitting at a virtual table. Our results show that users were significantly more “efficient” (i.e., faster reaction times), and accordingly more engaged, in performing the task when sharing the virtual environment, in particular for the more competitive tasks. Also, users experienced comparable levels of embodiment both when immersed alone or with another user. These results are supported by subjective questionnaires but also through behavioural responses, e.g. users reacting to the introduction of a threat towards their virtual body. Taken together, our results show that competition and shared experiences involving an avatar do not influence the sense of embodiment, but can increase user engagement. Such insights can be used by designers of virtual environments and virtual reality applications to develop more engaging applications.
Remaining useful life prediction for non-Markovian degrading systems with multiple working conditions

Considering the reliability and operational security of some large-scale industrial systems such as blast furnaces, generator sets, and gas turbines, how to provide an accurate remaining useful life (RUL) prediction result for the concerned equipments has become an increasingly issue in recent years. It is worth noting that a real degrading system may contain more than one working condition, leading to relatively large errors with the traditional one-phase degradation models. Different from most of the existing works, we mainly concentrate on analyzing the non-Markovian property of complicated performance degradation processes, and present a novel fractional Brownian motion (FBM) based multi-phase degradation model incorporating with the uncertain abrupt jump. All the change-points are detected simultaneously by the wild binary segmentation (WBS) procedure, while the Hurst exponent is estimated according to a specific Wavelet-based algorithm. To make full use of the statistical similarity from the same batch of equipments, the population model parameters and the individual ones are identified step by step through a hierarchical method. Furthermore, an approximated closed-form solution of the RUL distribution is derived utilizing the theory of first hitting time (FHT). A numerical example subject to three working conditions finally verifies the effectiveness of the proposed method.
Framework for Detecting Metamorphic Malware Based on Opcode Feature Extraction

Malware is a PC program that is intended to enter and hinder PCs without proprietor's consent. There are different malware types such as key loggers, logic bomb, viruses, rootkits, Trojans, spywares, ransom wares, worms, backdoors, bots, etc. Metamorphic malwares are the malware type which can undergo change at runtime such that they change their code and signature's to bypass the security. Malware's are written in such a way that they get mutated frequently but the original algorithm remains the same. To distinguish a malware, systems like conduct based location, signature based identification and machine learning based procedures are utilized. The mark based discovery framework falls flat in the event that it experiences another obscure malware. In the event of conduct based identification framework, if the antivirus program distinguishes any odd conduct over web then it will produce alert flag, yet there is a high false positive rate as it is inclined to arrange kind records as malware. Machine learning methodology is proposed to detect the malware where feature extraction is based on opcodes in the executable file of the malware. Opcodes of the malware can be extracted into a text file using disassemblers like IDA pro. As success rate of a machine learning algorithm depends on correctness of the features extracted, a robust approach to extract the features is proposed. This model is expected to give success rate of more than 96% in detection of malware
Topological Overview on Solid-state Transformer Traction Technology in High-speed Trains

The modern trend towards high-speed trains (HST) with distributed propulsion systems, demands high efficiency and high-power density traction systems. Line frequency transformers (LFTs) in railway traction systems are heavy and bulky, quite often necessitating power density to be compromised to achieve maximum efficiency of typically 90–92%. The advancements in power converter topologies, power switching devices and magnetic materials makes it possible to substitute massive LFTs with a new technology called solid-state transformers (SST) (also known as power electronic transformers (PET) or medium frequency transformers (MFT)) traction technology. This technology enables high power density systems with comparatively lower noise emissions which provide essential functionality without compromising efficiency. However, there are still major challenges to overcome associated with power converter connection on the high-voltage (HV) side, architecture modification and the compactness of the transformer design. This paper reviews the existing architectures and also introduces the new research possibilities especially in the power conversion stages, and the power switching devices. Finally, the design guide lines for high-power converters are presented.
Decentralized PHY/MAC Design for the Uplink of Multi-Packet Reception Wireless Networks

Decentralized PHY/MAC techniques for the uplink of Multi-Packet Reception (MPR) schemes are of great interest in future wireless communication systems (e.g., IEEE 802.11ax). In MPR schemes the average number of successful received packets depends on the radio propagation effects, the number of nodes simultaneously transmitting and the capture threshold that characterizes the PHY-layer MPR technology. In this paper we adopt a MPR capture model capable of describing the average number of successful packets simultaneously received. The capture model is then used in an optimal reservation-based medium access control (MAC), designed to operate in a decentralized way through a random access policy. This work describes the PHY/MAC cross-layer design, as well as the rationale required to understand its optimality. While the main objective of the proposed MAC is to achieve the optimal number of simultaneous transmissions that maximize the PHY-layer performance, a cost due to the MAC reservation period must also be considered in the optimization process. A model of the PHY/MAC performance is derived, which allows the optimization of the parameters that regulate the nodes’ random access. Different simulation results are described and compared with numerical results obtained with the proposed PHY/MAC model. The paper ends up discussing possible solutions to implement the proposed methodology in practical wireless networks formed by a high number of competing nodes.
Rendering of Pressure and Textures Using Wearable Haptics in Immersive VR Environments

Haptic systems have only recently started to be designed with wearability in mind. Compact, unobtrusive, inexpensive, easy-to-wear, and lightweight haptic devices enable researchers to provide compelling touch sensations to multiple parts of the body, significantly increasing the applicability of haptics in many fields, such as robotics, rehabilitation, gaming, and immersive systems. In this respect, wearable haptics has a great potential in the fields of virtual and augmented reality. Being able to touch virtual objects in a wearable and unobtrusive way may indeed open new exciting avenues for the fields of haptics and VR. This work presents a novel wearable haptic system for immersive virtual reality experiences. It conveys the sensation of touching objects made of different materials, rendering pressure and texture stimuli through a moving platform and a vibrotactile abbrv-doi-hyperref-narrowmotor. The device is composed of two platforms: one placed on the nail side of the finger and one in contact with the finger pad, connected by three cables. One small servomotor controls the length of the cables, moving the platform towards or away from the fingertip. One voice coil actuator, embedded in the platform, provides vibrotactile stimuli to the user.
Miniaturization of Wideband Loose Coupled (10dB) BLC Using Dual Transmission Lines

This paper presents a design of miniaturized wideband branch line coupler (BLC) with loose coupling of 10dB using dual transmission lines as a feed network. The dual transmission line technique provides a size reduction of 27%. The size of the coupler is further reduced by folding the high impedance section (shunt quarter wavelength) of the coupler, as a result of which the overall size reduction of around 44% with FBW of 58.3% is achieved for the coupled output of 10 0.5dB. The proposed BLC is design on an Arlon substrate of dielectric constant r=3.5, thickness h=0.5 mm and loss tangent of 0.002.
Layer-weakening feature fusion network for remote sensing detection

The remote sensing detection is a challenging issue for scale diversity, diversity of views, small objects, and sophisticated light and shadow backgrounds. In this paper, inspired by the state-of-the-art detection framework FPN [6], we propose a novel approach for constructing a feature fusion mode that optimizes feature context utilization in detection, calling our resulting system LFFN for Layer-weakening Feature Fusion Network. We explore the inherent relevance of different layers to the final decision, as well as the incentives of higher-level features to lower-level features. Based on the experiments on the remote sensing dataset from Google Earth, our approach has proved effective and practical for remote sensing detection, achieving 89% mAP which is 4.1% higher than that of FPN, running at 4 to 6 FPS.
Numerical Optimal Control With Periodicity Constraints in the Presence of Invariants

Periodic optimal control problems (POCPs) based on dynamic models holding invariants are often problematic to treat using standard numerical methods. The difficulty stems from a failure of standard constraint qualifications and typically hinders the convergence of the numerical solver, or even defeats it. Optimization problems having weak constraint qualifications can be treated using dedicated solvers, at the price of a more involved algorithmic. In this paper, we analyze the constraint qualification of POCPs holding invariants, and propose three simple and computationally inexpensive modifications of the formulation that allow for a recovery of linear independence constraint qualification, while not affecting the second-order sufficient conditions for optimality. Hence, the resulting POCP can be tackled via standard solvers, without special treatment. The application of these approaches is detailed for the case of POCPs holding index-reduced differential-algebraic equations and representations of the $\mathrm{SO}\left(3\right)$ Lie group.
The Tryptophan decarboxylase 1 Gene From Aegilops variabilis No.1 Regulate the Resistance Against Cereal Cyst Nematode by Altering the Downstream Secondary Metabolite Contents Rather Than Auxin Synthesis

Cereal cyst nematode (CCN, Heterodera avenae) is a most important pathogen of wheat and causes tremendous yield loss annually over the world. Since the lack of resistance materials among wheat cultivars, identification and characterization of the resistance-related genes from the relatives of wheat is a necessary and efficient way. As a close relative of wheat with high resistance against CCN, Aegilops variabilis No.1 is believed to be a valuable source for wheat breeding against this devastating disease. However so far, very few resistance-associated genes have been characterized from this species. In this study, we present that the tryptophan decarboxylase genes from Ae. variabilis No.1 (AeVTDC1 and AeVTDC2) were both induced by CCN juveniles at the early stage of resistance response (30 h post-inoculation), with AeVTDC1 more sensitive to CCN infection than AeVTDC2. Silencing of AeVTDC1 led to compromised immunity to CCN with more CCN intrusion into roots; while overexpression AeVTDC1 in Nicotiana tabacum dramatically enhanced the resistance of plants by reducing the knots formed on roots. Metabolism analysis showed that the contents of secondary metabolites with activity of resistance to varied pathogens correlated with the expression level of AeVTDC1 in both Ae. variabilis No.1 and the transgenic tobacco plants. In addition, the content of IAA was not affected by either silencing or overexpressing of AeVTDC1. Hence, our research provided AeVTDC1 a valuable target that mediates resistance to CCN and root knot nematode (RKN, Meloidogyne naasi) without influencing the auxin biosynthesis.
CrowdSLAM: Toward Large-Scale 3D Structure from Crowd-Captured Images

Large-scale 3D structure reconstruction is a complex task. This area has attracted increasing interest in recent years in both the research community and the industry. Google, for example, used Lidar technology and a fleet of vehicles to build 3D scans of big cities such as San Francisco or New York. However, this task remains limited to a few cities and companies due to cost and access to technology. In order to extend 3D reconstruction to cities and to large-scale buildings worldwide, it is necessary to use a more affordable technology. In this work, we present a framework for large-scale 3D structure reconstruction using crowd-captured images. We use images captured by smart-phones for the reconstruction of large 3D structures. Geo-localization data is used to group images based on their relative position to each other. We develop a combined SLAM-SFM algorithm to build 3D blocks based on clusters of closest images. Geo-localization is also used to repeatedly register closest 3D blocks and form a large-scale 3D structure. The proposed approach permits the reconstruction of large structures using images captured by one or more individuals. Additionally, the proposed model allows for an incremental reconstruction by exploiting new images to build missing blocks and refine the large structure. The obtained results are promising and show the efficiency of the proposed CrowdSLAM framework for large-scale 3D structure reconstruction.