Miracle-dz/UniFAR-FTUs huggingface dataset:
Government Debt Management: The Long and the Short of it

Our aim is to provide insights into some basic facts of US government debt management by introducing simple financial frictions in a Ramsey model of fiscal policy. We find that the share of short bonds in total U.S. debt is large, persistent, and highly correlated with total debt. A well known literature argues that optimal debt management should behave very differently: long term debt provides fiscal insurance, hence short bonds should not be issued and the position on short debt is volatile and negatively correlated with total debt. We show that this result hinges on the assumption that governments buy back the entire stock of previously issued long bonds each year, which is very far from observed debt management. We document how the U.S. Treasury rarely has repurchased bonds before 10 years after issuance. When we impose in the model that the government does not buy back old bonds the puzzle disappears and the optimal bond portfolio matches the facts mentioned above. The reason is that issuing only long term debt under no buyback would lead to a lumpiness in debt service payments, short bonds help offset this by smoothing out interest payments and tax rates. The same reasoning helps explain why governments issue coupon-paying bonds. Solving dynamic stochastic models of optimal policy with a portfolio choice is computationally challenging. A separate contribution of this paper is to propose computational tools that enable this broad class of models to be solved. In particular we propose two significant extensions to the PEA class of computational methods which overcome problems due to the size of the model. These methods should be useful to many applications with portfolio problems and large state spaces.
X4-RARE: Revisiting the X4CP32 Coarse-Grained Reconfigurable Architecture Model

To improve reconfigurable architectures' programmability, a CGRA named X4CP32 was proposed in early 2003. With two execution modes (programming execution mode and reconfigurable execution mode), X4CP32 offered an architectural model providing support to use the array through programming or configuration. In other words, a statical or dynamical procedure defines the processing elements' operations. This paper revisits this CGRA design using modern techniques and tools, notably the GEM5 simulator, the McPAT framework, and CACTI. Also, a hardware unit providing transparent and dynamic reconfiguration was incorporated. Furthermore, the design replaced an unknown and naive embedded microprocessor using RISC-V. Using a modern embedded processor, transparent and dynamic reconfiguration, and a thin array allows the design of a reconfigurable multicore able to execute up to 88% faster and with 55% less energy than a regular multicore. (Abstract)
Comparison of Synchronous Reluctance Motors and Permanent Magnet Synchronous Motors with Direct Liquid Cooling Arrangement

A comparison of Synchronous Reluctance Motors and Permanent Magnet Synchronous Motors with a Direct Liquid Cooling system is given. Both solutions have been analyzed, designed, and optimized considering the same constraints in terms of geometrical volume, current density, and phase voltage, for more equitable comparison. In addition, Direct Liquid Cooling introduces challenges in the selection of the number of conductors per slot and the number of slots because the slot dimensions need to satisfy an aspect ratio close to one and the adoption of a bigger conductor size. The preferred winding configuration is the concentrated one that simplifies the manufacturing process and, therefore, the cost; however, it was not possible to optimize the Synchronous Reluctance Motor with a concentrated winding arrangement achieving a competitive performance. Based on the results it is clear that mostly Permanent Magnet Synchronous Motors can exploit all the advantages of Direct Liquid Cooling, guaranteeing higher performance than Synchronous Reluctance Motors, especially the torque capability in flux weakening operation.
Spatial Transmit Diversity for Enhance Spatial Modulation with Transmit Antennas Grouping

In this paper, we propose a new design, called as spatial transmit diversity-achieving ESM (STD-ESM) with transmit antenna grouping, which further enhance the communication quality in MIMO system. In our STD-ESM, all available transmit antennas (TAs) are divided into multiple groups, each with one or two active antennas. In order to obtain the diversity gain from the transmitter, multiple versions of the signal symbol(s) from three different constellations are respectively modulated by the selected antenna index (AI) vectors on the active antenna(s) in each group. Furthermore, the transmit rate and the bit error probability (BEP) are provided. Finally, computer simulation results verify that a lower BEP performance can be achieved by the STD-ESM in comparison with existing spatial modulations.
Harnessing the Effects of Process Variability to Mitigate Aging in Cloud Servers

The increasing number of cores within a single chip enables more powerful cloud servers to exploit request-level parallelism better. However, this also leads to unforeseen temperature issues, which may accelerate aging and cause soft-errors or even failures. Therefore, smartly managing temperature has become critical and unpredictable due to the inherent process variability - since the temperature will vary across the cores regardless if they operate at the same operating frequency. Based on that, we propose a framework to optimize cloud servers’ lifetime, which automatically distributes the workload among the cores and applies DVFS based on the applications’ behavior and systems status.
Consequent-Pole Dual-Layer Axial Flux Magnetic Gear with Halbach Array

This paper aims to introduce a new structural design to increase the torque density in axial flux Magnetic Gears (MG). The proposed structure consists of an excitation winding and two rotor layers with consequent magnetic poles. The electromagnetic performance characteristic of the proposed dual-layer structure is analysed in Finite Element (FE) and compared against baseline single-layer structures. Simulation results indicate that the proposed solution enhances the torque density with the same Permanent Magnet (PM) volume and operating condition.
A Novel Rotary Frequency Multiplier Using the Third-Order Harmonic of Air-Gap Flux

This paper firstly proposes a rotary frequency multiplier for fractional frequency transmission system (FFTS). First, the frequency multiplier model is introduced including structure, principle and harmonic shape. The electromagnetic performance of harmonic shape PM structure and equal thickness PM structure are compared, including flux density, voltage quality, torque ripple, power factor, PM eddy loss. The results show that HS-type can improve the performance of rotary frequency multiplier, and the feasibility of the proposed rotary frequency multiplier structure used for frequency conversion in FFTS has been verified.
Research on Beamspace Channel Estimation Method Based on DISTA

Since the conventional Compressed Sensing (CS) algorithm in millimeter wave Massive Multi-Input Multi-Output (MIMO) system has the problem of low channel estimation accuracy, a deep learning based beamspace channel estimation method, Deep Iterative Shrinkage-Thresholding Algorithm (DISTA), is proposed. First, due to the sparsity of the beamspace channel, the beamspace channel estimation problem can be transformed into a sparse signal recovery problem; second, based on the iterative shrinkage threshold algorithm (ISTA), the channel state information (CSI) is sparsified using nonlinear transformation functions to replace the traditional manual transformation; finally, the iterative process of ISTA is expanded into a deep network, and the linear inverse transformation from the received pilot signal to the CSI is solved using the expanded network. The experimental results show that the proposed algorithm improves the NMSE performance gain by about 3 dB over the GM-LAMP algorithm when the signal-to-noise ratio (SNR) is 15 dB, and the algorithm accelerates the convergence speed compared with the conventional CS channel estimation algorithm.
Methods of Semantic Structured Search

In this paper, we consider a method of documentary search based on a weighted assessment of the results of semantic and structured search methods. The main advantages and disadvantages of both approaches were considered, and a mathematical model describing the operation of the search method was highlighted. Experiments to test the effectiveness of the developed model were also described, their progress, output and resulting metrics necessary for evaluating the results of the work were determined. To analyze the practical work of the proposed scheme, experiments were carried out on scientific documentation from different fields (mathematics, technology, biology, medicine, social sciences, etc.). The work of the system was studied both in the conditions of semantic proximity of the collection of documents, and in the case of a random distribution of the semantic meaning of the works. The results obtained during the experiments, on the one hand, confirmed the effectiveness of the implementation of the structured approach, and on the other hand, demonstrated a direct correlation between the search efficiency and the semantic diversity of the document collection.
Bone Age Assessment in the Carpal Region Based on the TW3-C Carpal Scoring Method

At present, the bone age assessment scheme based on a convolution neural network generally uses the whole hand, metacarpal finger bone, or multi-scale hand bone image as input, but there is less work to use a carpal bone image to predict bone age. A method of bone age assessment based on the TW3-C Carpal scoring method is proposed to address the less friendly prediction results of bone age for children in the younger age groups by the above scheme. To enhance the detection accuracy of the region of interest, the target detection network is improved by replacing the backbone network in its baseline network to make it more fully extract features and provide a good basis for the subsequent bone age assessment task. After data experiments, the improved detection network mAP@0.5 reaches 99% compared with the original target detection network, and the total loss rate decreases to 0.02, which is significantly lower than the loss value of general target detection algorithms, and the method effectively improves the ability of model detection. Finally, the mapping rules of the skeletal maturity class classification network and maturity curves yielded errors in bone age assessment all within 7 months.
Secure and Reliable Multipath Transmission Scheme Based on Interleaving Network Encoding

Multipath transmission has been widely studied in recent years to improve data transmission efficiency. However, there is still a lack of security standards specifically for multipath transmission. Network coding has inherent security and reliability properties, which provides a solution for secure and reliable transmission in multipath scenarios. This paper proposes a lightweight secure and reliable transmission scheme based on coding. Firstly, an interleaving coding scheme is proposed, which increases the difficulty of intercepting complete coding groups by dispersing sequentially transmitted packets into different coding groups on different paths. Additionally, the scheme enhances the data recovery capability in the event of consecutive packet losses. Furthermore, a coding scheme based on the integer field is designed to address the shortcomings of traditional coding methods in terms of security and efficiency. The evaluation proves that the proposed scheme is lightweight and improves reliability and transmission efficiency.
SLearn: A Case for Task Sampling Based Learning for Cluster Job Scheduling

The ability to accurately estimate job runtime properties allows a scheduler to effectively schedule jobs. State-of-the-art online cluster job schedulers use history-based learning, which uses past job execution information to estimate the runtime properties of newly arrived jobs. However, with fast-paced development in cluster technology (in both hardware and software) and changing user inputs, job runtime properties can change over time, which lead to inaccurate predictions. In this article, we explore the potential and limitation of real-time learning of job runtime properties, by proactively sampling and scheduling a small fraction of the tasks of each job. Such a task-sampling-based approach exploits the similarity among runtime properties of the tasks of the same job and is inherently immune to changing job behavior. Our analytical and experimental analysis of 3 production traces with different skew and job distribution shows that learning in space can be substantially more accurate. Our simulation and testbed evaluation on Azure of the two learning approaches anchored in a generic job scheduler using 3 production cluster job traces shows that despite its online overhead, learning in space reduces the average Job Completion Time (JCT) by 1.28×, 1.56×, and 1.32× compared to the prior-art history-based predictor. We further analyze the experimental results to give intuitive explanations to why learning in space outperforms learning in time in these experiments. Finally, we show how sampling-based learning can be extended to schedule DAG jobs and achieve similar speedups over the prior-art history-based predictor.
Fe-GCN: A 3D FeFET Memory Based PIM Accelerator for Graph Convolutional Networks

Graph convolutional network (GCN) has emerged as a powerful model for many graph-related tasks. In conventional von Neumann architectures, massive data movement and irregular memory access in GCN computation severely degrade the performance and computation efficiency. For GCN acceleration, processing-in-memory (PIM) is promising by reducing the data movement. However, with the emergence of large GCN computation tasks, existing 2D PIM GCN accelerators face the challenge of storing all the necessary data on chip due to the limited PIM memory capacity, resulting in unwanted external memory access and degradation of performance and energy efficiency. This paper presents Fe-GCN, a 3D PIM GCN accelerator with high memory density based on the ferroelectric field-effect transistor (FeFET) memory. Besides, to mitigate the impact of the increased latency of the 3D memory structure, several software-hardware co-optimizations are proposed. Furthermore, an edge merging technique is also proposed to increase the memory utilization for the 3D GCN mapping and computing. Experimental results show that Fe-GCN achieves on average 2,647x, 58x, 18x, and 35x speedup and 26,708x, 1,246x, 25x, and 57x energy efficiency improvement over CPU, GPU, the state-of-the-art accelerators based on RRAM PIM and ASIC, respectively.
Intelligent Scheduling Strategy for Heterogeneous Multi-terminal Access Tasks for Complex Cloud-Edge Collaborative Computing

With the rapid development of network and mobile devices, the era of ten thousands households interconnection is gradually coming. The variety of applications is endless, putting higher demands on computing resources as well as latency. As a result, the task scheduling model of cloud-edge collaboration has emerged. In the current environment, due to the limited resources of edge nodes and the expanding volume of users and user data, how to allocate tasks to the best nodes becomes the most critical issue. To address this problem, this paper proposes the use of ant colony algorithm to optimize the cloud-edge collaborative task scheduling. The pheromone cumulative positive feedback method is mainly used to reflect the tendency of tasks to select processing devices. First, the probability of a processing device being selected by a task is calculated by pheromones. Then, a random number is used to determine the allocation strategy for each round of the cycle. Finally, the pheromone concentration of the processing device is updated and the minimum resource consumption as well as the optimal task assignment strategy is derived. The implementation results show that the ant colony algorithm can improve the response time, reduce the resource consumption, and improve the efficiency of multi-objective task scheduling.
Comparative Research of One-Bit Feedback Algorithms Based on UAV Swarm

One-bit feedback algorithm is a commonly used phase synchronization algorithm in distributed beamforming, which can synchronize the phase of the signal emitted from distributed transmitting unit at the receiving end, thereby maximizing the received signal strength and achieving the purpose of long-distance transmission. Four one-bit feedback algorithms are introduced: one-bit feedback algorithm, one-bit feedback algorithm with variable directional perturbation, one-bit feedback algorithm using two time slots and one-bit feedback algorithm exploiting cumulative positive feedback information. The system model of distributed beamforming is constructed based on UAV swarm. Under the ideal condition and additive white Gaussian noise, the convergence rate of different algorithms is obtained, the performance of these algorithms is summarized, and the principle and the effect of different improvement methods are analyzed. The results indicate that one-bit feedback algorithm exploiting cumulative positive feedback information has the best performance under both experimental conditions, however, under the influence of strong noise, the three improved algorithms lose their robustness.
Deep ensemble learning and transfer learning methods for classification of senescent cells from nonlinear optical microscopy images

The success of chemotherapy and radiotherapy anti-cancer treatments can result in tumor suppression or senescence induction. Senescence was previously considered a favorable therapeutic outcome, until recent advancements in oncology research evidenced senescence as one of the culprits of cancer recurrence. Its detection requires multiple assays, and nonlinear optical (NLO) microscopy provides a solution for fast, non-invasive, and label-free detection of therapy-induced senescent cells. Here, we develop several deep learning architectures to perform binary classification between senescent and proliferating human cancer cells using NLO microscopy images and we compare their performances. As a result of our work, we demonstrate that the most performing approach is the one based on an ensemble classifier, that uses seven different pre-trained classification networks, taken from literature, with the addition of fully connected layers on top of their architectures. This approach achieves a classification accuracy of over 90%, showing the possibility of building an automatic, unbiased senescent cells image classifier starting from multimodal NLO microscopy data. Our results open the way to a deeper investigation of senescence classification via deep learning techniques with a potential application in clinical diagnosis.
On the Performance Analysis of Cooperative Active Round-trip Ranging and Passive Hyperbolic Localization for UAV Surveillance

Unmanned aerial vehicles (UAVs) have been widely employed in all spheres of society, yet unreasonable usage pose a threat to the public, making UAV surveillance an increasingly pressing concern. Focusing on the requirement of high-accuracy localization for UAVs, this paper proposes a Cooperative Active round-trip ranging and Passive hyperbolic Localization (CAPL) framework. In particular, we derive the detailed expression of Cramér–Rao lower bound (CRLB) for CAPL, which contains the position-related information provided by both the active round-trip time of arrival (RT-TOA) and the passive time difference of arrival (TDOA) parameters. Then, we theoretically investigate the performance of CAPL in terms of accuracy increment, far-field localization, and the effect of signal characteristics. Numerous simulation results are provided to validate that CAPL has significant advantages over only passive hyperbolic localization, especially for solving the height of UAVs in the far-field.
Research on the Application of Autopilot Technology

The research of automatic driving technology has become more and more important in the automotive industry. This will have an impact on the use of automatic driving technology. In this paper, the possible determinants and regulatory factors that affect the use of autonomous driving technology are studied, and the measurement model of the influencing factors of the use of autonomous driving technology is established.
Comparative Study Between Finite Control Set Model Predictive Control and Digital Sliding-Mode Control for the Reduction of Current Harmonics in Six-Phase PMSM Drives

Asymmetrical six-phase permanent magnet synchronous machines (PMSMs) drives are a promising candidate for electric transportation systems, offering inherent fault tolerance. However, one of the key challenges in developing high-performance control strategies for these systems is suppressing undesirable circulating currents that increase the machine losses. Hence, this paper compares two non-linear control strategies, namely finite control set model predictive control (FCS-MPC) and digital sliding-mode control (DSMC), to address this issue. Besides providing an excellent dynamic performance and robustness to parameter mismatch errors, both control strategies aim to minimize the circulating current harmonics, which are mainly due to deadtime effects and back-EMF harmonics in six-phase PMSM drives. Simulation results obtained with MATLAB/Simulink® are presented in this paper to compare and validate the performance of both control strategies.
Additively manufactured air-cooled lightweight rotor for an automotive electric motor

Additive manufacturing of rotors for electrical machines offers great potential in terms of cooling and lightweight construction due to the various design possibilities offered by this method. This paper explains how additive manufacturing can be used to design an electrical machine rotor for an automotive application. To take advantage of the benefits of additive manufacturing, various modifications, such as an air-cooling system and lightweight structures, were included in the rotor. The disadvantages of additive manufacturing, such as comparatively large minimum manufacturing widths of the edge bridges and easier formation of eddy currents, were addressed through limiting modifications. The completed design was built and tested on a motor test bench. The results showed that such a machine can be feasibly manufactured and can provide comparable or even better performance than a conventionally manufactured motor.
An AoI-Guaranteed Sensor Data Collection Strategy for RIS-assisted UAV Communication System

The recently proposed concept of Age of Information (AoI) measures the freshness of the sensor data sampled by remote Internet of Things (IoT) devices, which is an important indicator for the smart city. Unmanned Aerial Vehicles (UAV) communication can significantly reduce the sensor data’s AoI due to its high maneuverability. However, the UAV’s high energy consumption remains a challenging issue. In this paper, we propose a Reconfigurable Intelligent Surface (RIS) assisted UAV data collection framework to minimize UAV energy consumption. By adjusting the phase of the RIS, the UAV can collect more sensor data simultaneously. A RIS phase alignment method is introduced to reduce the complexity of RIS phase solving. The IoT devices are clustered to discretize UAV trajectory. The optimal clustering scheme to maximize the AoI suppression efficiency is found by adjusting the RIS phase alignment points continuously. Therefore, the AoI requirement can be achieved with a shorter UAV flight distance. Finally, the UAV trajectory is solved by the Deep Q network (DQN) algorithm to guarantee the long-term AoI requirement. Simulation results validate the effectiveness of the proposed method.