Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Learning Pipeline for the Automatic cell type Assignment of scRNA-seq Data

The increasing number of single-cell transcriptomics and single-cell RNA sequencing studies are allowing for a deeper understanding of the molecular processes underlying the normal development of an organism, as well as the onset of pathologies. In this context, cell type annotation represents a crucial step for the analysis of single-cell RNA sequencing data, which is usually performed by means of time-consuming and possibly biased manual processes, carried out by expert biologists. Recently, alternative computational tools have been proposed to realize an automatic cell identification either based on supervised or unsupervised Machine Learning approaches. These methods typically exploit gene expression data of curated marker gene databases to associate gene expression profiles of single cells with a cell type. In this paper, we propose a novel fully-automatic computational pipeline, named single-cell Automatic Labeling of cell POpulations (scALPO), which leverages a Long Short-Term Memory Neural Network to assign the cell types. Specifically, scALPO can label the provided clusters by simply relying on marker genes rather than gene expressions. Our results, obtained by considering two different datasets, show that scALPO outperforms the most promising state-of-the-art approaches (i.e., SCSA and scType), achieving a cell type annotation more similar to the manually-created ground truth.
Unknown System Dynamics Estimator-Based Fast Nonsingular Terminal Sliding Mode Control for Omnidirectional Mobile Robot

This paper proposes a new control method for the omnidirectional mobile robot (OMR) system with unknown dynamics and disturbances. An unknown system dynamics estimator (USDE) is developed by introducing low-pass filter operations to estimate the unknown dynamics and external disturbances, and only one parameter of the USDE needs to be tuned. Moreover, a new fast nonsingular terminal sliding mode control (FNTSMC) scheme is developed to overcome the singularity and slow convergence rate of the terminal sliding mode control (TSMC) method. Finally, we combine the proposed USDE and FNTSMC schemes to design a feedback controller to guarantee tracking control accuracy of the omnidirectional mobile robot system. In addition, stability analysis of the proposed USED and FNTSMC methods is presented via the Lyapunov theory, and the performance of the proposed control scheme is verified by the simulation.
A Greedy Algorithm for Budgeted Multiple-Product Profit Maximization in Social Network

Due to the development of technology in recent years, the propagation of information has become very conve-nient. People can easily disseminate information, such as product advertisements on the Internet, and this way of attracting users on social networks is called viral marketing. Because companies usually launch a variety of similar products for consumers to choose from, finding the most potential influencer for products with different prices and a limited seed budget is a very hot topic. However, the purchasing ability of a consumer is generally assumed to be unrestricted in some related studies. This assumption does not conform to the business model in reality since the demand for a product is usually limited for a customer. Considering the above situation, we study a novel problem, called the Budgeted Multiple-Product Profit Maximization (BMPM) problem. We will find suitable seeds for multiple products to maximize the overall profit when the overall budget is limited. Therefore, we propose a greedy algorithm named BG and a new graph structure PWDAG to effectively solve the BMPM problem. Our experiments on real world datasets show that BG can maximize the overall profit with high efficiency and effectiveness.
Entity Extraction Algorithm of Hybrid Network based on Attention Mechanism in Smart City

With the continuous development of information and communication network technology, the number of devices in the network and the running data are increasing geometrically, which brings certain challenges to network configuration, operation and maintenance and management. At present, with the vigorous development of artificial intelligence technology, the concept of intelligent operation and maintenance came into being. The construction of hybrid network knowledge graph is an important part of intelligent operation and maintenance, and hybrid network entity extraction is a key step in its construction. Therefore, in order to effectively identify hybrid network entities, this paper proposes hybrid network entity extraction algorithm based on attention mechanism. Using the multi-head attention mechanism combined with the bidirectional long and short-term memory network and CRF, the accuracy of the neural network model is further improved. In order to verify the effectiveness of the method, we compare the improved algorithm in this paper with the existing classical algorithms and literature algorithms. The experiments show that the algorithm in this paper has certain effectiveness and superiority.
Deep Unfolding for Compressed Sensing with Denoiser

Recent years have witnessed increasingly more exercises and uses of deep unfolding network (DUN) in image compressed sensing (CS) due to its high performance and interpretability. However, the existing DUN does not make full use of more flexible regularization methods. Besides, the intermediate information generated during the iterations of the DUN, which is crucial for the quality improvement of image reconstruction, has been largely overlooked in the existing methods. To alleviate this problem, we propose a novel DUN for image CS with regularization by denoising which casts half quadratic splitting (HQS) algorithm into the neural network. Further, we design an information collection strategy to leverage the useful information generated during the iterations. The information is provided to the image denoiser of the proposed network, which could enhance the image processing ability of the denoiser. The extensive experiments demonstrate that the proposed method is more efficient and achieves state-of-the-art reconstruction quality.
Q-Learning-based Setting of Cell Individual Offset for Handover of Flying Base Stations

Flying base stations (FlyBSs) are widely used to improve coverage and/or quality of service for users in mobile networks. To ensure a seamless mobility of the FlyBSs among the static base stations (SBSs), an efficient handover mechanism is required. We focus on the handover of FlyBSs among SBSs and we dynamically adjust the cell individual offset (CIO) of the SBSs based on their load to increase the sum capacity of the users served by the FlyBSs while considering also a handover cost. Due to complexity of the defined problem and limited knowledge of other parameters required for conventional optimization methods, we adopt Q-learning to solve the problem. For Q-learning, we define a reward function reflecting the tradeoff between the capacity of users and the cost of performed handovers. The proposed Q-learning based approach converges promptly and increases the sum capacity of the users served by the FlyBSs by up to 23% for eight deployed FlyBSs comparing to state-of-the-art algorithms. At the same time, the number of handovers performed by the FlyBSs is notably reduced (up to 25%) by the proposal.
Making Sense of Heterogeneous Maritime Data

While an abundance of real-time maritime information exists and is readily available to monitoring authorities, there are still many instances in which ships are found to be engaged in dangerous or illegal activities. In order to prevent such activities, authorities employ Vessel Traffic Services systems since they promote safety at sea while also assisting in management of ports. In this paper we report on research done in cooperation with Denbridge Marine Ltd., a global provider of maritime solutions, and present an application integrated in a Vessel Tracking Services system that allows the detection of normal vessel activity as well as dangerous or illegal situations in real-time, using information from the Automatic Identification System, a radar sensor and other information. We use a set of phenomena representing maritime activities of interest in the language of Phenesthe, our Complex Event Processing engine, and detect them on real maritime data streams from the area of Liverpool, United Kingdom. We evaluate our application and show that our system is capable of detecting and visualising maritime activities on the map in real time. Finally, we study and demonstrate the significance of using data from the Automatic Identification System along with radar data for maritime monitoring.
A Digital-to-Time Converter Based on Crystal Oscillator Waveform Achieving 86-fs Jitter in 22-nm FD-SOI CMOS

In this paper, we propose a digital-to-time conversion (DTC) technique operating entirely in the sinusiodal waveform voltage domain of a crystal oscillator (XO) before the signal's final slicing into the time-domain of the programmably delayed clock, with a deterministic predistortion to further improve the linearity. Precise timing delay is obtained by simply adjusting the dc offset of the sine signal. The technique merges the functionality of DTC with XO generation, thus drastically reducing the power consumption, while offering wide range with fine resolution, low noise and high linearity. Fabricated in 22-nm FD-SOI CMOS, the prototype of XO+DTC consumes only 0.52 mW while achieving a 546 ps range with fine resolution of 266 fs. The rms jitter is only 86.6 fs at a frequency of 100 MHz.
Recast Subspace Pursuit-based Channel Estimation for Hybrid Beamforming NarrowBand Millimeter-Wave Massive MIMO Systems

Millimeter wave (mmWave) wireless communication systems with hybrid beamforming require reliable estimation of channel state information to perform optimally. Though the combined high-speed analog-to-digital converters (ADCs) and hybrid precoder/combiner architecture of this system ameliorate the problem of high-power consumption, they make channel estimation to become cumbersome. The paper proposed a channel estimation scheme that is based on a conventional subspace pursuit algorithm. The proposed estimator is named Recast Subspace Pursuit (ReSP)-based channel estimator. Besides taking advantage of the inherent sparsity in the mmWave Massive MIMO channel, the ReSP-based estimator also exploits the a-priori knowledge of channel matrix support-set as side information for the estimation procedure. Simulation results confirm that the proposed ReSP-based channel estimator can exhibit improved performance than some traditional and earlier proposed channel estimation schemes.
Design and Control of a SEA Driven Knee Exoskeleton for Walking Assistance

Hemiplegia is the most common sequelae of stroke, which will lead to the loss of mobility in patients. Rehabilitation robots can efficiently assist patients in performing rehabilitation training. This paper designed an exoskeleton based on a series elastic actuator (SEA) to assist patients with post-stroke hemiplegia for knee rehabilitation. Due to the use of SEA, the exoskeleton can achieve low impedance and precise force control. The mechanical limit can improve the safety of the device, and the bracket can reduce the wearing burden on the human body. Unlike traditional rigid drives, our design improves compliance of human-robot interaction. The spring can provide cushioning for increased safety when the patient suffers an accidental shock. In this paper, the design process is introduced and the exoskeleton model is analyzed. We implement the force control based on the integral separation PID algorithm and conduct experiments and analysis on the impact resistance of the exoskeleton.
Algorithms for Vertex-Weighted Matching in Graphs

A matching M in a graph is a subset of edges such that no two edges in M are incident on the same vertex. Matching is a fundamental combinatorial problem that has applications in many contexts: high-performance computing, bioinformatics, network switch design, web technologies, etc. Examples in the first context include sparse linear systems of equations, where matchings are used to place large matrix elements on or close to the diagonal, to compute the block triangular decomposition of sparse matrices, to construct sparse bases for the null space or column space of under-determined matrices, and to coarsen graphs in multi-level graph partitioning algorithms. In the first part of this thesis, we develop exact and approximation algorithms for vertex weighted matchings, an under-studied variant of the weighted matching problem. We propose three exact algorithms, three half approximation algorithms, and a two-third approximation algorithm. We exploit inherent properties of this problem such as lexicographical orders, decomposition into sub-problems, and the reachability property, not only to design efficient algorithms, but also to provide simple proofs of correctness of the proposed algorithms. In the second part of this thesis, we describe work on a new parallel half-approximation algorithm for weighted matching. Algorithms for computing optimal matchings are not amenable to parallelism, and hence we consider approximation algorithms here. We extend the existing work on a parallel half approximation algorithm for weighted matching and provide an analysis of its time complexity. We support the theoretical observations with experimental results obtained with MatchBoxP, toolkit designed and implemented in C++ and MPI using modern software engineering techniques. The work in this thesis has resulted in better understanding of matching theory, a functional public-domain software toolkit, and modeling of the sparsest basis problem as a vertex-weighted matching problem.
Knowledge-Embedded Deep Reinforcement Learning for Autonomous Network Decision-Making Algorithm

This paper proposes a multi-critic deep Reinforcement learning framework (MCDRL) and a knowledge-embedded multi-critic deep reinforcement learning(KEMCDRL) Decision-making method, the method can ensure users’ real-time QoS delay requirements. Compared with implementing the deep reinforcement learning algorithm directly in the communication system, this method can accelerate the convergence and guarantee the initial QoS performance of the system. Simulation results show that the design method can significantly reduce the convergence time compared with traditional deep reinforcement learning, and has nearly optimal decision delay compared with existing decision-making methods, which can actualize real-time decision-making in a time-varying channel environment.
Event-triggering-based leader-following consensus of second-order multi-agent systems with mismatched disturbances

In this paper, leader-following consensus of second-order multi-agent systems with mismatched disturbances and matched disturbances are discussed. First, a nonlinear disturbance observer is used for each follower to estimate disturbances. Then, distributed event-triggering strategy is designed and Zeno-behavior is excluded. Moreover, based on the disturbance estimation and distributed event-triggering strategy, distributed control law is proposed to guarantee that the states of followers are able to tract the state of single leader. Finally, the effectiveness of the theoretical results are verified by a simulation example.
M3Rec: Cross-Modal Context Enhanced Micro-Video Recommendation with Mutual Information Maximization

Personalized recommendation of micro-videos is crucial for many content sharing platforms. Micro-videos typically com-prise fruitful multimodal contents. Existing methods have limitations in learning multimodal representations: (i) They suffer from data sparsity problems as only rely on the inter-action prediction loss to train the whole model. (ii) They do not consider the correlation among multimodal contents into the representation learning. To tackle these limitations, we propose a cross-Modal context enhanced method via Mutual infoMax for Recommendation, termed M3Rec. Specifically, we design the cross-modal graph neural network to inter-change multimodal information to generate modality-aware representations. Then, based on the coupled modality con-text, we design the masked modality prediction (MMP) with three self-supervised objectives to learn the correlation among visual, textual, and acoustic contents empowered by the mu-tual information maximization principle. Finally, we enhance representations via self-supervised pre-training to boost rec-ommendation. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art methods.
Technological machines, construction resources, efficiency and safety

Understanding the direct relationship of the person, his activities Wednesday habitats requires not only new methodological approaches to research, but, first and foremost, clarify the existing scientific theories, their coherence with the development the environment Wednesday. A model of modernization of transport and technological machines as a factor in the realization of tasks of improving their effectiveness, safety and environmental performance. The main focus in addressing modifications of technological machines in use is considered lowering their material intensity, by adopting a more rugged and durable in endurance and wear materials. Modified methodology of estimation of residual resource of technical facilities by implementing technology upgrades for further reliable and safe operation. In the method a key factor is the effective and safe operation. Developed a calculation model of optimization of the resource in terms of assessing risk of system failure due to functional aging; optimization model of the resource, frequency of maintenance and wear limit of units in terms of assessing risk of occurrence of a system failure affecting safety. The solution of the question of resource upgrading to provide manufacturers, as an additional stage of the life cycle of construction machinery and means of complex mechanization. 1 Theoretical and practical background Until now, natural resources are used for human needs, and sometimes just subjected to destruction when addressing the varying industrial tasks. Hastiness action led to a different kind of man-made disasters, after which world powers began to pay attention to the consequences that entails flying human thought in an effort to not only learn, but unknown and conquer yourself in serve the surrounding wednesday and its phenomena. In its development humanity uses both external and internal reserves. Note that the external reserves are natural resources, usually not renewable, internal we can assume human intellectual capabilities are unlimited, and social society and the standard of living, to which is humanity, only confirm this fact. As a result of years of research, based on an in-depth analysis of the development of scientific knowledge is that the fundamental laws of nature and technology are simple in * Corresponding author: nssevr@yandex.ru
The Flying Rabbit: Using a drone as a pace-setter for youth middle-distance runners

: In middle-distance time-trial sports events like 1500-m run, good performance heavily depends on the pacing behaviour of the athlete. Going too fast too early can deplete the energy stores before the finish of the race, while holding back for too long can leave the athlete with left-over energy after the finish. Pacing is the result of a decision-making process regarding the distribution of effort over the expected duration of the race, which is under development during adolescence. The development of adequate pacing behaviour is therefore an important determining factor for future performance. Currently, very few tools are available to assist athletes with optimal pacing behaviour on the track. This study explores the use of a drone as a tool to help junior runners to better approximate their pacing plan in a 1500-m race. Eleven well-trained junior athletes performed four 1500-m races, while they tried to best approximate their pacing plan. Two races were Self-paced by the athletes, the other two were Drone-paced; a drone accompanied them on the track. For all races, split times, finish times and RPE scores were recorded. Pacing performance was compared between the Self-paced trials and the Drone-paced trials. The presence of a drone did not improve pacing performance in the first trials, while it did in the second trials. Over the course of the trials, pacing performance improved in the presence of a drone, while it worsened in the absence of a drone. RPE scores were not different between Self-paced and Drone-paced trials. This suggests that a drone can modulate pacing performance of junior runners.
On the existence of curves with maximal rank in Pn.

We work over an algebraically closed field of characteristic zero. Let ρ(ά, g, n):=(n -f 1) d — ng — n(n + 1) be the Brill-Noether number. From Brill-Noether theory i t is known that if ρ ̂ 0 there exists a unique irreducible component, P>(d, g, n), of H(d, g, n) (the Hubert scheme of smooth, connected curves of degree d, genus g in P) which has general moduli. This means that the functorial rational map from P(d, g, n) to the moduli scheme Mg of curves of genus g is dominant. Hence in the r nge ρ(ά, g, n)^0 we have a nice component of H(d, g, n). Then two problems arise naturally:
A Spatial Reasoning Framework for Commonsense Reasoning in Visually Intelligent Agents

Service robots are expected to reliably make sense of complex, fast-changing environments. From a cognitive standpoint, they need the appropriate reasoning capabilities and background knowledge required to exhibit human-like Visual Intelligence. In particular, our prior work has shown that i) commonsense reasoning is a necessary capability for Visual Intelligence and also that ii) commonsense reasoning crucially requires the ability to reason about spatial relations between objects in the world. In this paper, we first recap our approach to Visual Intelligence in robotics, which is based on a hybrid architecture integrating a deep learning component with commonsense reasoning. We then present a framework for spatial reasoning, which has been designed to support the commonsense reasoning component in our architecture. Differently from prior approaches to qualitative spatial reasoning in robotics, the proposed framework is robust to variations in the robot’s viewpoint and object orientation. In the paper, we also show how this formally-defined framework can be operationalised in an off-the-shelf spatial database.