Miracle-dz/UniFAR-FTUs huggingface dataset:
Maximizing Aggregation Throughput for Distributed Training with Constrained In-Network Computing

Distributed training (DT) has become an important and popular practice for collaborative training of high-quality machine learning (ML) models. The communication efficiency of gradient aggregation has been shown to be the primary performance bottleneck for distributed training today. Advanced programmable switches with in-network computing capabilities provide a promising direction for improving the communication efficiency of DT by offloading some gradient aggregations from the host to switches in the network. In this paper, we propose SPAR to optimize the performance of gradient aggregation under constrained in-network computing capabilities. To improve the aggregation throughput, SPAR jointly optimizes the deployment of in-network aggregation switches and the routing of aggregation requests from workers. We formulate this joint optimization problem as an integer nonlinear programming problem and design an efficient greedy algorithm to compute solutions quickly. The experimental results show that SPAR significantly outper-forms the other state-of-the-art solutions based on in-network aggregation, improving aggregation throughput by up to 3×.
Acceleration and loss of relativistic electrons during geomagnetic storms

We analyze the response of relativistic electrons to the 276 moderate and intense geomagnetic storms spanning the 11 years from 1989 through 2000. We find that geomagnetic storms can either increase or decrease the fluxes of relativistic electrons in the radiation belts. Surprisingly, only about half of all storms increased the fluxes of relativistic electrons, one quarter decreased the fluxes, and one quarter produced little or no change in the fluxes. We also found that the pre‐storm and post‐storm fluxes were highly uncorrelated suggesting that storms do not simply “pump up” the radiation belts. We found that these conclusions were independent of the strength of the storm (minimum Dst) and independent of L‐shell. In contrast, we found that higher solar wind velocities increase the probability of a large flux increase. However, for all solar wind velocities both increases and decreases were still observed. Our analysis suggests that the effect of geomagnetic storms on radiation belt fluxes are a delicate and complicated balance between the effects of particle acceleration and loss.
The Purposes and Pedagogies of Social Foundations in Teacher Education

Abstract The social foundations of education (SFE), an interdisciplinary educational field of study, occupies a tenuous position in university-based schools of education. In the era of teacher professionalization and practice-based teacher education, SFE has been relegated to the fringe and its value to teaching practice has been largely disregarded. Focusing specifically on conceptual essays and qualitative studies published by SFE university faculty that advocate for a closer relationship between SFE and teacher education, this literature review examines the ways that SFE teacher educators articulate the value, purpose, and contributory role of SFE in teacher education and explores the pedagogical approaches that they use in their pre-service teacher education classrooms. After analyzing the purposes of SFE in teacher education, as defined by SFE faculty, and the pedagogies that inform their teaching practice, I conclude this review by discussing the fundamental tensions and deeply embedded conflicts between teacher professionalism and the cultural perspectives and knowledges that are highly valued within the SFE discipline and by offering recommendations concerning the future of SFE.
Spatiotemporal impacts of wildfire and climate warming on permafrost across a subarctic region, Canada

Field observations show significant impacts of wildfires on active layer thickness and ground temperatures. However, the importance of fires to permafrost conditions at regional scales remains unclear, especially with climate warming. This study evaluated the regional impacts of fire on permafrost with climate change from 1942 to 2100 using a process‐based model in a large subarctic region in the Northwest Territories, Canada. Climate warming is shown to be the dominant factor for permafrost reduction. The warming trend of climate reduces permafrost extent in this region from 67% at present to 2% by 2100. For burned areas, fire increases the reduction of permafrost extent by up to 9% on average, with up to 16% for forest, 10% for tundra and bogs, and 4% for fens. Fire accelerates permafrost disappearance by 5 years on average. The effects of fire on active layer thickness and permafrost extent are much larger in forest areas than in tundra, bogs, and fens. Since active layer is thicker after a fire and cannot recover in most of the areas, the fire effects on active layer are widespread. On average, fires thickens active layer by about 0.5 m. The fire effects on active layer increased significantly after 1990 due to climate warming.
Joint Horizontal and Vertical Backup for Highly Reliable Telemedicine Services

Recently, IoT, SDN and NFV have emerged as significant technological enablers for telemedicine. Because of specific characteristics of telemedicine services, reliability is one of the critical elements to guarantee the quality of services. To maintain high availability of services, existing backup solutions focus on resources constraints where backup instances are placed at the same node (vertical backup) or distributively deployed at different nodes (horizontal backup). While they put more effort to satisfy resource requirements, routing issues are often neglected such as end-to-end latency, multi-path routing, and synchronization in a multi-path scenario. Such aspects are key requirements to deploy high reliability telemedicine services. Therefore, we investigate the dynamic backup mechanism for a telemedicine system. We aim to optimize the reliability of telemedicine service function chains (TSFCs) where a joint horizontal/vertical backup (JHVB) optimization problem is first formulated. Since JHVB is a combinatorial optimization problem, which is NP-Hard, we then solve this problem in both offline and online fashions using Block Successive Upper Bound Minimization (BSUM) and Multi-Armed Bandit (MAB) frameworks. We compare our methods to the benchmarks via intensive simulations based on the Nano Datacenter solution that is used for enabling telemedicine services. The results demonstrates an outstanding performance in terms of failure awareness and service reliability.
Aligned Collagen Sponges with Tunable Pore Size for Skeletal Muscle Tissue Regeneration

Volumetric muscle loss (VML) is a traumatic injury where at least 20% of the mass of a skeletal muscle has been destroyed and functionality is lost. The standard treatment for VML, autologous tissue transfer, is limited as approximately 1 in 10 grafts fail because of necrosis or infection. Tissue engineering strategies seek to develop scaffolds that can regenerate injured muscles and restore functionality. Many of these scaffolds, however, are limited in their ability to restore muscle functionality because of an inability to promote the alignment of regenerating myofibers. For aligned myofibers to form on a scaffold, myoblasts infiltrate the scaffold and receive topographical cues to direct targeted myofiber growth. We seek to determine the optimal pore size for myoblast infiltration and differentiation. We developed a method of tuning the pore size within collagen scaffolds while inducing longitudinal alignment of these pores. Significantly different pore sizes were generated by adjusting the freezing rate of the scaffolds. Scaffolds frozen at −20 °C contained the largest pores. These scaffolds promoted the greatest level of cell infiltration and orientation in the direction of pore alignment. Further research will be conducted to induce higher levels of myofiber formation, to ultimately create an off-the-shelf treatment for VML injuries.
Joint Resource Allocation and String-Stable CACC Design with Multi-Agent Reinforcement Learning

Resource allocation has always been a challenging task in vehicular networks due to their dynamic nature. In this paper, we study the decentralized joint subchannel allocation and power control problem for a Cooperative Adaptive Cruise Control (CACC) system to satisfy string stability in a platoon of connected and autonomous vehicles. The developed optimization problem takes the string stability of the platoon as well as the reliability of all the Vehicle to Vehicle (V2V) links into account, aiming at maximizing the total ergodic capacity. We tackle the optimization problem from two different angles. The first approach is a centralized classical algorithm governed from the Base Station (BS) perspective, where we assume that the BS only knows the large-scale fading information of the V2V links due to the rapidly changing channel conditions in vehicular environments. In the second strategy, we devise a Federated Multi-Agent Reinforcement Learning (MARL) based algorithm where each transmitter vehicle in the platoon acts as an independent agent and tries to find an optimal policy to maximize its total expected reward. Finally, to better understand the policies each agent has learned, we also compare the performance of these algorithms in terms of per-link achievable capacity.
Long-term trend of tropospheric ozone over the Yangtze Delta Region of China

Analysis of tropospheric ozone residual (TOR) data from satellite measurements indicates an increasing trend of tropospheric ozone over the Yangtze Delta region of China. The increasing trend can be derived both from the annual mean TOR and from the monthly mean TOR except for January and March. The increase rate of the decadal mean TOR was 0.82 DU during 1978-2000. The impact of this long-term trend on the climate and atmospheric oxidizing capacity over the region should be further studied. Data comparison shows a significant correlation between the TOR and surface ozone data collected at Lin'an background station in the Yangtze Delta region, suggesting an internal connection between both quantities.
Due Diligence of a Diastolic Index as a Prognostic Factor in Heart Failure with Preserved Ejection Fraction

Of the existing non-invasive diastolic indices, none consider arterial load. This article reveals points of caution for determining the diastolic prognostic index using a novel index of vascular resistance-integrated diastolic function in old, real-world patients with heart failure with preserved ejection fraction (HFpEF) in Japan. This index represents the ratio of left ventricular diastolic elastance (Ed) to arterial elastance (Ea), where Ed/Ea = (E/e′)/(0.9 × systolic blood pressure), showing a relative ratio of left atrial filling pressure to left ventricular end-systolic pressure. The role of hemodynamic prognostic factors related to diastolic function, such as Ed/Ea, may differ according to the clinical endpoint, follow-up duration, and sex. In HFpEF patients with heterogenous cardiac structure and function, an assessment using a serial echocardiographic diastolic index in clinical care can provide an accurate prognosis.
A W-Band Marchand Balun in 0.1 μm GaAs pHEMT Process

The increasing interest in fully differential W-band circuit designs poses the requirement for high-performing balun circuits. This paper proposes a Marchand balun implemented in WIN Semiconductor’s 0.1 µm GaAs pHEMT process. The circuit demonstrates excellent broadband performance across the frequency range from 82.5 GHz to 123 GHz with a measured insertion loss of 1.5 dB. The average measured magnitude and phase imbalances are less than 0.25 dB and 1.25 0 respectively. To the best of the authors’ knowledge, this is the best-reported W-band Marchand balun with minimum insertion loss and phase and magnitude imbalances.
Learning-Based Dynamic Clustering for Coordinated Multipoint Transmission in 5G and Beyond

The rapid growth of data traffic along with the diversity of services/use cases supported by the next generation network, necessitates the requirement of coordination and flexibility in the network so as to take prompt action with the changes in the network dynamics such as time varying channel conditions. To address the issue of sudden degradation in user performance due to channel variations in the network, we focus on dynamic clustering for Coordinated Multi-point (CoMP) transmission. In this paper, we consider an optimal user-centric dynamic clustering algorithm for CoMP with the aim of maximizing the throughput subject to the constraint on the cost of transmission from the CoMP cluster i.e., coordinating set of Base Stations (BSs). An on-line dynamic clustering algorithm for CoMP is proposed based on Q-learning approach. The proposed algorithm does not require explicit knowledge of the transition probability of the channel state variation for implementation. We conducted extensive simulations and simulation results demonstrate the significant performance improvement of the proposed algorithm which converges to the optimal policy obtained from Relative Value Iteration Algorithm (RVIA).
Production from natural resources with latent abundance information: The case of the fishery

Resource exploitation without appropriate property right is often inefficient. Traditional management attempts to limit over-exploitation by directly controlling input. The effectiveness of an input control hinges on individual’s response. This paper uses the fishery as a case and examines fishermen’s short-run fishing behavior. That is, given fishing capacity, how do fishermen adjust their fishing effort in response to ecological and economic variations? Since a fisherman does not earn wage, a shadow wage is constructed from fishing profit. This shadow wage depends on the fish stock, which is observable to fishermen but latent to econometricians. To deal with the latent variable problem in fishing effort models, a three-step estimation approach is proposed. First, count data models are used to describe trip frequency, in which stock is lumped into a time effect. Second, a stock index is inferred through a structural harvest-stock relationship. Third, the time effect is regressed on the stock index and other individual-invariant regressors. With three steps, the fishing effort model can be identified without knowing the biological stock. The empirical model is illustrated with an application to the reef-fish fishery in the northeastern Gulf of Mexico. The results indicate that reef-fish fishermen use utility/profit maximizing behavior and the labor supply curve is not backward bending. It is also found that stock, fish price, fuel price, weather, and unemployment rate have significant impacts on fishing effort. However, without accounting for these impacts econometrically, analysts will draw spurious inferences about the effectiveness of traditional fisheries management.
A Novel Configurable RO-Obfuscated PUF Design with Machine Learning Immunity

Strong physical unclonable function (PUF) is a low-cost hardware security primitive to protect Internet-of-Things (IoT) devices. However, it may be attacked by machine learning (ML). Various PUF models designed in complex structures, such as nonlinearity or challenge-response pair (CRP) obfuscation, have been presented to combat these risks. However, these methods mainly increase area overhead, and the prediction rate is still very high. Therefore, this paper proposes a structure obfuscated PUF named SO-PUF. Our proposal derives from the configurable ring oscillator (CRO) PUF. The CRO can be transformed into two different structures depending on the challenges by randomly deleting one of the two NOT gates in each stage. Thus, half of the CRPs generated by SO-PUF are invalid, which will confuse the attackers. We have implemented and verified the performance of SO-PUF on the Xilinx-6XC6SLX25 microboard. Experimental results show that compared with the dual-mode PUF, the SO-PUF improves uniqueness by 0.71 %, temperature reliability by 38.4 %, and voltage reliability by 1.92 %. In addition, the SO-PUF also reduces the prediction rates of LR, SVM, and ANN modeling attacks by 0.26 %, 0.38 %, and 5.62 %, respectively. The results prove that SO-PUF has better resistance to ML attacks than dual-mode PUF.
Ensemble Learning With Tournament Selected Glowworm Swarm Optimization Algorithm for Cyberbullying Detection on Social Media

Online social network (OSN) plays a crucial role to facilitate social connections; but, this social networking media increases antisocial behaviors, like trolling, cyberbullying, and hate speech. Cyberbullying has often resulted in serious physical and mental distress, especially for children and women, and even sometimes forces them to commit suicide. Conventional techniques for detecting cyberbullying, such as relying on users to report the instance of bullying, are not always effective. Deep learning (DL) and Machine learning (ML) techniques are trained to automatically recognize and flag potential cyberbullying content, along with identifying behavior patterns that are indicative of cyberbullying. Therefore, this study concentrates on the design and development of ensemble deep learning with tournament-selected glowworm swarm optimization (EDL-TSGSO) algorithm for cyberbullying detection and classification on Twitter data. The goal of the study is to examine social media data through the use of natural language processing (NLP) and ensemble learning process. This EDL-TSGSO technique preprocesses the raw tweets and then employs the Glove word embedding technique. In addition, the presented EDL-TSGSO technique utilizes ensemble long short-term memory with Adaboost (ELSTM-AB) model for effective cyberbullying detection and classification. The ensemble ELSTM-AB classifier integrates the prediction of LSTM and Adaboost models to enhance the overall classification performance. To further develop the cyberbullying detection performance of the EDL-TSGSO algorithm, the TSGSO algorithm is applied as a hyperparameter optimizer. The experimental validation of the EDL-TSGSO algorithm on the Twitter dataset demonstrates its promising performance over other state of art approaches in terms of different measures.
ESTIMATING METHOD FOR THE IN-SOIL DEFORMATION BEHAVIOR OF GEOGRID BASED ON THE RESULTS OF DIRECT BOX SHEAR TEST

This paper proposes a method to estimate in-soil deformation behavior of geogrid based on the results of direct box shear tests. A series of drained direct box shear (DBS) tests were performed, consisting of two types of normal stress loading methods and a series of pull-out tests in which the space of the pull-out opening, embedded length of geogrid and conditions of in-soil end restraint were changed. Comparisons were made between the calculated in-soil deformation behavior of the geogrid based on the results of the DBS tests and that of the results observed by pull-out tests. Based on this study, the following findings were obtained. Strength parameters corresponding to the peak or the residual states obtained from the two types of direct box shear tests almost coincide with each other. Using the method proposed in this paper with the strength parameters obtained by DBS tests, it is possible to estimate the in-soil behavior of geogrid. As the pull-out force vs. pull-out displacement relationships depend not on the conditions of in-soil end restraint of geogrid but on the normal stress exerted on the geogrid, it is important to construct a reinforced soil structure so that sufficient frictional resistance develops between soil and geogrid.
First-Pass Success of Video Laryngoscope Compared With Direct Laryngoscope in Intubations Performed by Residents in the Emergency Department

Background: The video laryngoscope (VL) has been widely used for intubation in the emergency department (ED). However, their effectiveness remains controversial, particularly among airway management performed by residents in the ED. Methods: We aimed to examine whether the use of VL, compared to a direct laryngoscope (DL), was associated with higher first-attempt intubation success among intubations performed by residents in the ED. This is a secondary analysis of the data from a prospective, observational, multicentre study of 15 Japanese EDs from April 2012 through March 2020. We included all adult patients who underwent intubation with VL or DL by residents (postgraduate years ≤5) in the ED. The outcome measures were first-pass success and intubation-related adverse events (overall, major, and minor adverse events). To determine the association of VL use with each of the outcomes, we constructed logistic regression models with generalized estimating equations to account for patients clustering within the ED, adjusting for patient demographics, primary indications, intubation difficulty, and intubation methods. Results: Of 5,261 eligible patients who underwent an initial intubation attempt by residents, 1,858 (35%) patients were attempted with VL. Intubations performed with VL had a non-significantly higher first-pass success rate than those with DL (77% vs. 64%; unadjusted odds ratio (OR)=1.20; 95% CI=0.87-1.65; P=0.27). This association was significant after adjustment for potential confounders (adjusted OR, 1.33; 95% CI, 1.06-1.67; P=0.01). As for adverse events, the use of VL was associated with a lower rate of any (adjusted OR=0.67; 95% CI=0.51-0.86; P=0.002) and minor (adjusted OR=0.69; 95% CI=0.55-0.87; P=0.002) adverse events. Conclusion: The use of VL was associated with a higher first-attempt success rate and a lower rate of any adverse events compared to that with DL among intubations performed by residents in the EDs.
Noncontact Measurements of Sound Absorption Coefficient with a Pressure-velocity Probe, a Laser Doppler Vibrometer, and a Microphone Array

Kundt's tube and reverberant chamber are common methods for determining the sound absorption coefficient or acoustic impedance of materials. These measurement methodologies are well-known and standardized, albeit not being practicable in-situ and requiring the isolation of samples of the material under test. Furthermore, Kundt’s tube results are affected by the size and the diameter of sample, and by the tube length, while reverberant chamber ones by the room dimensions and diffusiveness. In literature, noncontact techniques for sound absorption coefficient and acoustic impedance measurement are widely debated. In this paper, three different noncontact systems for measuring the sound absorption coefficient have been investigated: a pressure-velocity probe, a Laser Doppler Vibrometer, and a spherical microphone array featuring 64 capsules. The three methods have been evaluated through in-situ measurements of materials with known acoustic characteristics: Basotect G+ and Expanded Polystyrene. Furthermore, the results obtained with the standard test signal, i.e., white noise, are compared with the exponential sine sweep technique, which provides an increased signal to noise ratio, and allows for removing nonlinear high order distortions and acoustic reflections. As a main contribution of this work, it will be shown that microphone arrays are an optimal solution for measuring the sound absorption coefficient.