Miracle-dz/UniFAR-FTUs huggingface dataset:
AlgoNet: C∞ Smooth Algorithmic Neural Networks

Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems. On the other hand, for many problems, classic algorithms exist, which typically exceed the accuracy and stability of neural networks. To combine these two concepts, we present a new kind of neural networks—algorithmic neural networks (AlgoNets). These networks integrate smooth versions of classic algorithms into the topology of neural networks. A forward AlgoNet includes algorithmic layers into existing architectures while a backward AlgoNet can solve inverse problems without or with only weak supervision. In addition, we present the algonet package, a PyTorch based library that includes, inter alia, a smoothly evaluated programming language, a smooth 3D mesh renderer, and smooth sorting algorithms.
Advances in Network Controllability

The last decade has seen an explosion of research in network controllability. The present article reviews some basic concepts, significant progress, important results and recent advances in the studies of the controllability of networked linear dynamical systems, regarding the relationship of the network topology, node-system dynamics, external control inputs and inner dynamical interactions with the controllability of such complex networked dynamical systems. Different approaches to analyzing the network controllability are evaluated. Some advanced topics on the selection of driver nodes, optimization of network controllability and control energy are discussed. Potential applications to real-world networked systems are also described. Finally, a near-future research outlook is highlighted.
Phase Retrieval From Quantized Measurements via Approximate Message Passing

In this letter, the problem of sparse signal reconstruction from quantized noisy magnitudes <inline-formula><tex-math notation="LaTeX">${\mathbf y}=Q\left(|{\mathbf z}+{\mathbf w}|+{\mathbf n}\right)$</tex-math></inline-formula> is studied, where <inline-formula><tex-math notation="LaTeX">${\mathbf z}={\mathbf A}{\mathbf x}$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$Q(\cdot)$</tex-math></inline-formula> denotes a quantizer, and <inline-formula><tex-math notation="LaTeX">$\mathbf x$</tex-math></inline-formula> is the sparse signal. According to expectation propagation, the above-mentioned problem can be solved by exchanging extrinsic information between the standard linear model (SLM) module and the minimum mean square error (MMSE) module. For the MMSE module, exploiting the fact that the likelihood is only a function of the absolute value of <inline-formula><tex-math notation="LaTeX">$\mathbf z$</tex-math></inline-formula>, the magnitude and phase information of <inline-formula><tex-math notation="LaTeX">$\mathbf z$</tex-math></inline-formula> are decoupled, and only the posterior means and variances of the magnitude of <inline-formula><tex-math notation="LaTeX">$\mathbf z$</tex-math></inline-formula> are calculated. While for the SLM module, the approximate message passing (AMP) is used. To obtain the closed-form expression during the iteration, a novel approximation is adopted. We refer to the resulting algorithm as generalized AMP (Gr-AMP) based quantized phase retrieval algorithm. Finally, several numerical simulations are conducted to demonstrate the performances of the proposed approach.
Dynamic Multiple Access Configuration in Intelligent Lifi Attocellular Access Points

The exponential growth in the global demand for wireless connectivity calls for efficient and reliable management of the available wireless resources. Light fidelity (LiFi) harnesses the vast untapped wireless transmission resources in the infrared spectrum and visible light spectrum to create ultra-dense wireless networks which support user mobility, multiuser access, and handover. Various multiuser access (MA) protocols have been developed to meet the varying system requirements, including orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes. While NOMA, on the one hand, allows for significant enhancement in the achievable data rates, its performance may be severely degraded under particular conditions such as a large number of connected users or users existing in highly symmetrical locations. OMA, on the other hand, provides better link reliability in such scenarios but at the expense of decreased spectral efficiency. Therefore, there is a need to enable a degree of intelligence in the LiFi access point (AP) to facilitate real-time configuration of the MA protocol. To this end, this paper develops a novel cross-layer design framework for dynamic multiple access selections (DMAS) in intelligent LiFi APs. The developed framework runs at LiFi attocell system level and can be configured to cater for various system requirements in terms of sum data rate, average outage probability, and fairness. The obtained results show that DMAS introduces an effective solution for multiuser resource allocation by achieving better satisfaction of the system requirements compared to the static configuration of a single MA scheme.
An Improved Practical State-Space FDAF With Fast Recovery of Abrupt Echo-Path Changes

A variable step-size echo cancellation algorithm in the frequency domain based on soft-decision and status information of the filter is established for enhancing tracking capability. The first-order Markov model is initially used to represent the time-variant acoustic path. In the meantime, the step size of the filter is replaced by the Kalman gain. Afterward, the magnitude squared coherence function of the reference signal and error signal are calculated as the filter status. The status of the filter is then mapped to a soft-decision value and is used to weigh the channel transmission parameter. Finally, the acoustic path is tracked by the weighted channel transmission parameter, which facilitates the reconvergence of the adaptive filter. The theoretical and experimental analysis is demonstrated on the validity of the algorithm. Compared to similar algorithms, the proposed algorithm has less misalignment but can also converge faster during abrupt echo-path changes. Compared to the state-space frequency domain adaptive filters (SS-FDAF) algorithm, the convergence time is 44% shorter. In addition, the amount of misalignment decreases by 8 dB. Moreover, the filter update time only increases by 15.38%.
Dissection on Java Organs in GitHub Repositories

Organ transplantation has brought convenience for software reuse and evolution since it was proposed. However, studies about mature, high-quality organs are still insufficient. It is still unclear about the detailed characteristics of organs in the open-source environment. In this paper, we look deep into organs obtained from software evolution processes of the ten large-scale Java repositories hosted on GitHub, aiming at providing practical information for utilizing organs in the open-source environment. We found that: 1) commits use add as a keyword in their comments possess the most organs, occupying 38% of the total amount, but commits with the keyword fix possess the highest locating accuracy (about 57%); 2) developers prefer to add new classes when they bring new functionalities to the projects in that the proportion of class level organs is 40%, more than statement level organs’ and function level organs’ (35% and 25%, respectively); 3) nearly 70% of the total amount are cross-file organs with the median of the number of files each organ spans reaching three and the average of this value being around four; 4) a small number (0.55%) of organs are multi-commit; 5) more than 40% of code reuse in the open-source software can be finished by organ transplantation. Based on our findings, we highlight implications for future studies and design the mode of using organs to conduct code reuse.
Hardware Acceleration Implementation of Sparse Coding Algorithm With Spintronic Devices

Sparse coding provides a powerful means to perform feature extraction on high-dimensional data and recently has attracted broad interest for applications. However, it still suffers from a big challenge to realize real-time energy-efficient feature extraction with conventional CPUs/GPUs owing to its highly intensive computation. Benefitting from the non-volatility, low power, high speed, great scalability, and good compatibility with CMOS technology, spintronic devices, such as magnetic tunnel junction and domain wall motion (DWM) device, have been explored for applications, ranging from memory, logic, to neuromorphic computing. In this paper, we explore the possibility of hardware acceleration implementation of the sparse coding algorithm with spintronic devices by a series of design optimizations across the algorithm, architecture, circuit, and device. First, one sparse coding algorithm is selected well suited for parallelization and hardware acceleration. Then, a DWM-based compound spintronic device (CSD) is engineered and modeled, which is envisioned to achieve multiple conductance states. Sequentially, a parallel architecture is presented based on a dense cross-point array of the proposed DWM-based CSD, which can be envisioned to accelerate the selected sparse coding algorithm with a designed dedicated periphery read and write circuit owing to its massively parallel read and write operation. Experimental results show that the selected sparse coding algorithm can be accelerated by $\text{1400}\times$ with the proposed parallel architecture in comparison with software implementation. Moreover, its energy dissipation is eight orders of magnitude smaller than that with software implementation. Additionally, an artificial neural circuit (ANC) with the proposed DWM-based CSD is also presented, which can achieve a multi-step transfer function. By using the proposed DWM-based CSD as a single synapse and the proposed ANC as one neuron, a fully forward-connected artificial neural network is constructed to classify the learned feature vectors, and the recognition error rate can reach 7.25%.
Advance in Long-Length REBCO Coated Conductors Prepared by Reel-to-Reel Metalorganic Solution and Ion-Beam-Assisted Deposition

In the present work, we report our recent efforts to improve the homogeneity of MgO buffer layers and superconducting YBa2Cu3O7–δ coated tapes prepared by ion-beam-assisted deposition and metalorganic solution deposition (MOD), respectively. Based on previous research results involving fluorine, a reduced atmospheric pressure is used for the MOD technique to improve the in-plane texture and suppress microcracks in the film, and then more than 500 m of MOD-coated superconducting tape is manufactured, exhibiting an excellent biaxial texture and superconducting performance. The full width at half maximum values for both in-plane and out-of-plane textured films are as low as 3° based on X-ray diffraction (XRD) measurements. The superconducting current-carrying capacities for 12-mm tapes are approximately 450–520 and 450–500 A for the 100-m level and 500-m level (77 K, self-field), respectively. The current-carrying capacities were measured by both reel-to-reel transport and an inductive system, and the results demonstrated that the cost-effective MOD technique is very promising for long-length high-quality coated conductors.
DnaA protein is required for replication of the minimal replicon of the broad-host-range plasmid RK2 in Escherichia coli

The minimal origin of replication of the broad-host-range plasmid RK2 has two potential recognition sequences for the DnaA protein of Escherichia coli. DNA transfer by transformation into a dnaA-null mutant of E. coli showed that DnaA protein is needed for replication or maintenance of mini-RK2. We isolated and purified DnaA protein as a chimeric protein, covalently attached to a piece of collagen and beta-galactosidase. The hybrid protein specifically bound to restriction fragments from the oriV region of RK2, which contained the two dnaA boxes. Deletion of the second dnaA box inactivated the origin and abolished the binding of the hybrid protein to the DNA fragment that had suffered the deletion. When the second dnaA box was replaced with an EcoRI linker of identical length, origin activity was restored. Binding experiments showed that the linker provided a weak dnaA box. An alternative explanation was that the linker restored proper spacing between sequences on either side of the deleted box, thus restoring origin activity.
Multi-Agent Reinforcement Learning Based Cooperative Content Caching for Mobile Edge Networks

To address the drastic growth of data traffic dominated by streaming of video-on-demand files, mobile edge caching/computing (MEC) can be exploited to develop intelligent content caching at mobile network edges to alleviate redundant traffic and improve content delivery efficiency. Under the MEC architecture, content providers (CPs) can deploy popular video files at MEC servers to improve users’ quality of experience (QoE). Designing an efficient content caching policy is crucial for CPs due to the content dynamics, unknown spatial-temporal traffic demands, and limited service capacity. The knowledge of users’ preference is very useful and important for efficient content caching, yet often unavailable in advance. Under this circumstance, machine learning can be used to learn the users’ preference based on historical demand information and decide the video files to be cached at the MEC servers. In this paper, we propose a multi-agent reinforcement learning (MARL)-based cooperative content caching policy for the MEC architecture when the users’ preference is unknown and only the historical content demands can be observed. We formulate the cooperative content caching problem as a multi-agent multi-armed bandit problem and propose a MARL-based algorithm to solve the problem. The simulation experiments are conducted based on a real dataset from MovieLens and the numerical results show that the proposed MARL-based cooperative content caching scheme can significantly reduce content downloading latency and improve content cache hit rate when compared with other popular caching schemes.
A 0.4-ps-Jitter −52-dBc-Spur Synthesizable Injection-Locked PLL With Self-Clocked Nonoverlap Update and Slope-Balanced Subsampling BBPD

In this letter, a fully synthesizable injection-locked phase-locked loop (IL-PLL) is presented. The proposed PLL employed a nonmodified digital standard cell library, and enable fast design migration to other processes. To minimize the reference spur, a self-clocked nonoverlap update scheme is proposed to reduce the reference spur caused by digital logic clocking. Besides, a slope-balanced fully symmetrical multiplexer (MUX) and subsampling bang–bang phase detector (SS-BBPD) is proposed. With constraint-directed automatic layout synthesis, the delay offset is greatly reduced. Implemented in a 65-nm CMOS process, the PLL achieved a 0.4-ps integrated jitter at 1-GHz output frequency with −52-dBc reference spur. The power consumptions are 1.2 mW, corresponding to figures of merit of −247.2 dB.
Exploiting Hardware Unobservability for Low-Power Design and Safety Analysis in Formal Verification-Driven Design Flows

Formal techniques for the functional verification of System-on-Chip (SoC) hardware have matured significantly over the last years. They can penetrate deeply into a design to exhibit complex functional dependencies between various design components in terms of detailed logical and temporal relationships. They can also provide a well-defined formal relationship between an abstract system model of a design and its concrete implementation at the register-transfer level (RTL). This paper shows how such knowledge available from formal verification can be “condensed” into a database that stores all registers and flip-flops, at which time points they are actually relevant for the correct behavior of the design and when they are not. We show that the comprehensive information on temporary unobservabilities in the design can be of great value to reach two nonfunctional design goals that play a dominant role in many design flows: safety and low power consumption. This paper presents techniques to assess the effects of soft errors by single-event upsets (SEUs) with formal precision and to relate the results of the proposed analysis to an abstract system model. For example, our analysis can determine which soft errors may lead to a system “crash” and which are guaranteed not to cause any harm. For the application of the proposed approach in power optimization, this paper presents techniques for clock gating and power gating. For the examined designs, we observe a reduction of power consumption between 10% and 50% on top of the state-of-the-art commercial power synthesis.
Employer recruitment preferences and discrimination: a stated preference experiment

This paper presents a novel approach of applying stated preference methods in the field of labour economics. Differences in behaviour and labour market disadvantage are connected to the presence, and ages of children, the so-called „family gap‟. There are major difficulties in collecting accurate information about the recruiting practices of employers and identifying their preferences towards different characteristics of new recruits. Employer answers to direct questions may not illicit reliable answers due to them having unconscious biases, confounding various potential employee characteristics, social or legal pressures on not appearing to be biased against certain types of potential employees or them practicing discrimination. This paper applies stated preference methods to identifying employer preferences to three sets of characteristics of potential recruits: age, gender and presence and age of their youngest child. This method is tested using face-to-face interviews with 52 firms. The results indicate that there are strong employer preferences against those: having childcare responsibilities for children aged under 5; and being over 50 years old. Employer preferences favour: those between the ages of 25 and 39; those with no childcare responsibilities; and women. This suggests that the influences of age, gender and children are crucial factor when discussing gender and labour demand.
Wnt5A promotes an adaptive, senescent‐like stress response, while continuing to drive invasion in melanoma cells

We have previously shown that Wnt5A drives invasion in melanoma. We have also shown that Wnt5A promotes resistance to therapy designed to target the BRAFV600E mutation in melanoma. Here, we show that melanomas characterized by high levels of Wnt5A respond to therapeutic stress by increasing p21 and expressing classical markers of senescence, including positivity for senescence‐associated β‐galactosidase (SA‐β‐gal), senescence‐associated heterochromatic foci (SAHF), H3K9Me chromatin marks, and PML bodies. We find that despite this, these cells retain their ability to migrate and invade. Further, despite the expression of classic markers of senescence such as SA‐β‐gal and SAHF, these Wnt5A‐high cells are able to colonize the lungs in in vivo tail vein colony‐forming assays. This clearly underscores the fact that these markers do not indicate true senescence in these cells, but instead an adaptive stress response that allows the cells to evade therapy and invade. Notably, silencing Wnt5A reduces expression of these markers and decreases invasiveness. The combined data point to Wnt5A as a master regulator of an adaptive stress response in melanoma, which may contribute to therapy resistance.
Kernel LDP Based Discriminant Analysis for Face Recognition

Locally discriminating projection (LDP) is a new subspace feature extraction method which takes special consideration of both the local information and the class information. As the LDP model is linear, it may fail to extract the nonlinear features. This paper proposes to address this problem using an alternative formulation, kernel locally preserving projection (KLDP). The proposed method consists of two steps: kernel principal component analysis (KPCA) plus LDP. An outline for implementing KLDP is provided. Experiments on the AR face database and Yale face database demonstrate the effectiveness of the proposed method.
Flow-Level Capacity of Fractionally Loaded OFDMA Networks with Proportional Fair Scheduling

We define and evaluate the downlink flow-level capacity of a fractionally loaded OFDMA network utilizing round robin (RR) or proportional fair (PF) scheduling. The flow-level capacity is the maximum traffic intensity that can be supported by a sector while satisfying a minimum flow throughput requirement for bursty, elastic traffic. We obtain a simple, insightful capacity expression for RR scheduling; for PF scheduling, we exploit a fast convergence property of the multi-user diversity gain to implement a computationally efficient, iterative algorithm to solve for the capacity. A hybrid simulation/analysis approach is applied, where a detailed rate distribution obtained via simulation is used as input to a generalized processor sharing queue model. The analytical expressions and numerical results indicate that enhancement of edge throughputs can significantly improve the flow-level capacity.
A program of continuing education for medical educators.

"Medicine, in company with most of the other scienices, has experienced through the centuries an irregtlar alterniation between periods of raw empiricism and periods of extreme r-ationalism. But the secular tendenicv, particularly in recent generations, has been towvard repudiating these misleading alternatives. There has been a steady move toward reducing what Conant has called the 'degree of empiricism' in medicine and in the sciences upoIn which it draws. In this commitmenit to the scientific poinit of view, there is an insistenit pressure to transform the empiricist 'let's try it and see' kind of experimentation, little informed by prior theoretical formulations, into the kind of controlled observation and experiment which is guided by ain explicit concepttual scheme. Like the sciences generally, medicinle is engaged in a continuing effort to reduce the degree of empiricism in its body of knowledge by increasing the systematic empirical verificatio'4 of logicallv connected sets of ideas. "For a long time, it was tacitly assumed that this ouitlook is appropriate for medicine but not for the studv of medical education. More recently, the same scienitific outlook is beinig transferred to this latter sphere. Committed to a belief in the eventual superio(rity of systematic inquiry over casual empiricism, some medical educators, together with associates in psychology and sociology, are turning to methodical sttldv of the educational process, rather than relying uiponl casual impressions. "
Building a Baccalaureate Community Nursing Curriculum Using a Participatory Evaluation Approach

The University of North Florida School of Nursing used a participatory approach to develop a community-based and community-focused curriculum that engaged students, faculty, and community stakeholders. This Home-base Model evolved over 6 years, building inductively using a continuous quality improvement process. We describe the formative evaluation process, the strategies used to engage the key stakeholders, and identify the benefits leading to the sustainability of the Home-base Model. A plan for external evaluation is in progress, building on existing stakeholder involvement and focusing on outcomes. The development of the Home-base Model is offered as an exemplar of how a participatory approach can build community nursing curricula that are consistent with community-driven agendas.