Miracle-dz/UniFAR-FTUs huggingface dataset:
Control of seizure-like dynamics in neuronal populations with excitability adaptation related to ketogenic diet

We consider a heterogeneous, globally coupled population of excitatory quadratic integrate-and-fire neurons with excitability adaptation due to a metabolic feedback associated with ketogenic diet, a form of therapy for epilepsy. Bifurcation analysis of a three-dimensional mean-field system derived in the framework of next-generation neural mass models allows us to explain the scenarios and suggest control strategies for the transitions between the neurophysiologically desired asynchronous states and the synchronous, seizure-like states featuring collective oscillations. We reveal two qualitatively different scenarios for the onset of synchrony. For weaker couplings, a bistability region between the lower- and the higher-activity asynchronous states unfolds from the cusp point, and the collective oscillations emerge via a supercritical Hopf bifurcation. For stronger couplings, one finds seven co-dimension two bifurcation points, including pairs of Bogdanov-Takens and generalized Hopf points, such that both lower- and higher-activity asynchronous states undergo transitions to collective oscillations, with hysteresis and jump-like behavior observed in vicinity of subcritical Hopf bifurcations. We demonstrate three control mechanisms for switching between asynchronous and synchronous states, involving parametric perturbation of the adenosine triphosphate (ATP) production rate, external stimulation currents, or pulse-like ATP shocks, and indicate a potential therapeutic advantage of hysteretic scenarios.
Structured d-DNNF Is Not Closed Under Negation

Both structured d-DNNF and SDD can be exponentially more succinct than OBDD. Moreover, SDD is essentially as tractable as OBDD. But this has left two important open questions. Firstly, does OBDD support more tractable transformations than structured d-DNNF? And secondly, is structured d-DNNF more succinct than SDD? In this paper, we answer both questions in the affirmative. For the first question we show that, unlike OBDD, structured d-DNNF does not support polytime negation, disjunction, or existential quantification operations. As a corollary, we deduce that there are functions with an equivalent polynomial-sized structured d-DNNF but with no such representation as an SDD, thus answering the second question. We also lift this second result to arithmetic circuits (AC) to show a succinctness gap between PSDD and the monotone AC analogue to structured d-DNNF.
Quantitative Metrics for Benchmarking Medical Image Harmonization

Image harmonization is an important preprocessing strategy to address domain shifts arising from data acquired using different machines and scanning protocols in medical imaging. However, benchmarking the effectiveness of harmonization techniques has been a challenge due to the lack of widely available standardized datasets with ground truths. In this context, we propose three metrics: two intensity harmonization metrics and one anatomy preservation metric for medical images during harmonization, where no ground truths are required. Through extensive studies on a dataset with available harmonization ground truth, we demonstrate that our metrics are correlated with established image quality assessment metrics. We show how these novel metrics may be applied to real-world scenarios where no harmonization ground truth exists. Additionally, we provide insights into different interpretations of the metric values, shedding light on their significance in the context of the harmonization process. As a result of our findings, we advocate for the adoption of these quantitative harmonization metrics as a standard for benchmarking the performance of image harmonization techniques.
Collaborative Computing in Non-Terrestrial Networks: A Multi-Time-Scale Deep Reinforcement Learning Approach

Constructing earth-fixed cells with low-earth orbit (LEO) satellites in non-terrestrial networks (NTNs) has been the most promising paradigm to enable global coverage. The limited computing capabilities on LEO satellites however render tackling resource optimization within a short duration a critical challenge. Although the sufficient computing capabilities of the ground infrastructures can be utilized to assist the LEO satellite, different time-scale control cycles and coupling decisions between the space- and ground-segments still obstruct the joint optimization design for computing agents at different segments. To address the above challenges, in this paper, a multi-time-scale deep reinforcement learning (DRL) scheme is developed for achieving the radio resource optimization in NTNs, in which the LEO satellite and user equipment (UE) collaborate with each other to perform individual decision-making tasks with different control cycles. Specifically, the UE updates its policy toward improving value functions of both the satellite and UE, while the LEO satellite only performs finite-step rollout for decision-makings based on the reference decision trajectory provided by the UE. Most importantly, rigorous analysis to guarantee the performance convergence of the proposed scheme is provided. Comprehensive simulations are conducted to justify the effectiveness of the proposed scheme in balancing the transmission performance and computational complexity.
An ill wind that blows some good: groups and teams helping in Vanuatu after cyclone Pam

[Introduction]: Natural disasters of the scope of tropical cyclone Pam, a category five event and reportedly the strongest storm ever recorded in the southern hemisphere, bring devastation and human suffering on a vast scale. Such was the case in Vanuatu and other South Pacific nations in March 2015. The relief effort that followed, while focusing on reducing the impacts of lost lives, infrastructure, and homes by providing emergency shelter, medical supplies, food and water also created the opportunity from different organisations to combine their collective efforts for the benefit of the people of Vanuatu. An unanticipated consequence of the working relationships which developed of necessity, was the chance to compare organisational cultures and modus operandi in a working environment and circumstances unfamiliar to most participants. This paper records some initial responses from a participant observer’s perspective, as a member of the Australian Defence Force assisting with Civil/Military Cooperation (CIMIC) in the National Disaster Management Office, (NDMO), in Port Vila. It will contrast the approaches taken to group formation between actors in the Humanitarian Community, (HC), composed of a number of International Organizations, (IO), Non-government Organizations, (NGO); Government of Vanuatu, (GOVU), agencies with military members of various nations assisting with the relief efforts.
Explicit and implicit locomotor learning in individuals with chronic hemiparetic stroke

Background Motor learning involves both explicit and implicit learning processes that are fundamental to post-stroke rehabilitation as they are often utilized in concert. However, stroke may damage the neural substrates underlying explicit or implicit learning, leading to deficits in overall motor performance. Objective Determine if individuals with chronic stroke have impaired explicit and/or implicit learning, when assessed during a locomotor task that elicits dissociable contributions from both. Methods We compared explicit and implicit locomotor learning in individuals with chronic stroke to age- and sex-matched neurologically intact controls. We assessed implicit learning using split-belt adaptation (where two treadmill belts move at different speeds). We assessed explicit learning by providing visual feedback during split-belt walking to help individuals explicitly correct for step length errors created by the split-belts. The removal of visual feedback after the first 40 strides of split-belt walking, combined with task instructions, minimized contributions from explicit learning for the remainder of the task. This manipulation, combined with computational modeling, allowed us to determine the individual contributions of explicit and implicit motor learning to overall performance. Results The behavioral and computational analyses revealed that, compared to controls, individuals with chronic stroke demonstrated deficits in both explicit and implicit contributions to locomotor learning. Conclusions Post-stroke locomotor rehabilitation involves interventions that rely on explicit and implicit motor learning. Our results demonstrate that both forms of learning are impaired when examined in a single task. Future work should determine how locomotor rehabilitation interventions can be structured to optimize overall motor learning.
Effects of excess phosphate on a coastal plankton community

Eutrophication in the Baltic Sea has caused an imbalance in the inorganic nitrogen (N) to phosphorus (P) ratio, leaving excess phosphate (PO4) remaining after the phytoplankton spring bloom that terminates after N depletion. Here we investigated the fate of such excess phosphate in a two-week mesocosm (1.2 m3) experiment. The starting concentration of PO4 was 0.66 µM, and treatments included a non-treated control (control), nitrate addition (N-add; 3.6 µM), glucose addition (C-add; 25 µM) and combined nitrate and glucose addition (N+C-add); all treatments done in triplicates. Addition of N (N-add and N+C-add) stimulated nano- and microphytoplankton, with the picophytoplankton abundance increasing after N depletion. Also the Copepod biomass was positively affected by the N-addition. N-fixing cyanobacteria were present but in low abundance. Carbon addition did not enhance heterotrophic bacterial uptake of PO4, nor affecting the phyto-or zooplankton community composition. The PO4 concentration was reduced to ∼0.4 µM in the control and C-add treatments and to 0.16 µM in the two N-amended treatments, with an inorganic NP uptake ratio of 7.2. The results underscore the role of picophytoplankton in reducing the excess phosphate pool after the spring bloom, a function traditionally ascribed to bloom-forming diazotrophic cyanobacteria in the Baltic Sea.
Genomic insights into photosymbiotic evolution in Tridacna squamosa

Photosymbiosis is fundamental driving force for ecological success of benthic coral reef ecosystems, and contributing to their biodiversity and resilience. As a benchmark organism indicative of reef health, the fluted giant clam (Tridacna squamosa) forms an exemplary photosymbiotic relationship with the symbiont Symbiodiniaceae dinoflagellates, whose initiation and maturation require finely coordinated interactions. However, much of the origin and dynamics of this reciprocal interplay remains unclarified. Here, we report the first complete whole genome of T. squamosa, in conjunction with integrated multi-omics data, to illuminate the key evolutionary innovations and molecular events supporting the establishment and maintenance of photosymbiotic lifestyle in the giant clam. Programmed regulation of symbiont recognition, host immune system and GPCRs signaling activation co-contributed to dinoflagellates acquisition in T. squamosa larvae. Adaptive metabolic remodeling in the host siphonal mantle, a photosymbiotic niche, is critical to maintain the robustness of phtosymbiosis. T. squamosa has expanded light sensing gene family and evolved sophisticated signaling pathways to protect against UV photo-damage. Evidence also supports significant contribution of positive selection to host DNA-repair. Overall, our study here offers fresh mechanistic insights into the parallel evolution and molecular machinery of photosymbiosis in the giant clam-dinoflagellates duet, with implications for devising solutions to sustainable conservation.
Cascaded Multiport Converter for SRM-Based Hybrid Electrical Vehicle Applications

This paper proposes a cascaded multiport switched reluctance motor (SRM) drive for hybrid electric vehicle applications, which not only allows flexible energy conversion among the generator/ac grid, the battery bank, and the motor, but also achieves battery management (BM) function for state-of-charge balance control and bus voltage regulation. By integrating the battery packs into the asymmetrical half-bridge converter, the cascaded BM modules are designed to configure multilevel bus voltage and current capacity for SRM drive, which can accelerate the excitation and demagnetization processes during the commutation region, extend the speed range, reduce the voltage stress on the switches, and improve the torque capability and system efficiency. According to the different operation requirements, the multiple driving modes, regenerative braking modes, and charging modes are equipped in the proposed converter. Moreover, with the proposed BM strategy, each battery pack can be separately connected or disconnected from the power supply, which will greatly enhance the fault-tolerance ability and easily avoid the overcharge and overdischarge issues during the motor operation. The feasibility and effectiveness of the proposed cascaded multiport SRM drive are verified by the experiments on a three-phase 12/8 SRM.
Brain-controlled augmented hearing for spatially moving conversations in multi-talker environments

Focusing on a specific conversation amidst multiple interfering talkers presents a significant challenge, especially for the hearing-impaired. Brain-controlled assistive hearing devices aim to alleviate this problem by separating complex auditory scenes into distinct speech streams and enhancing the attended speech based on the listener’s neural signals using auditory attention decoding (AAD). Departing from conventional AAD studies that relied on oversimplified scenarios with stationary talkers, we present a realistic AAD task that mirrors the dynamic nature of acoustic settings. This task involves focusing on one of two concurrent conversations, with multiple talkers taking turns and moving continuously in space with background noise. Invasive electroencephalography (iEEG) data were collected from three neurosurgical patients as they focused on one of the two moving conversations. We propose an enhanced brain-controlled assistive hearing system that combines AAD and a binaural speaker-independent speech separation model. The separation model unmixes talkers while preserving their spatial location and provides talker trajectories to the neural decoder to improve auditory attention decoding accuracy. Our subjective and objective evaluations show that the proposed system enhances speech intelligibility and facilitates conversation tracking while maintaining spatial cues and voice quality in challenging acoustic environments. This research demonstrates the potential of our approach in real-world scenarios and marks a significant step towards developing assistive hearing technologies that adapt to the intricate dynamics of everyday auditory experiences. TAKEAWAYS - Brain-controlled hearing device for scenarios with moving conversations in multi-talker settings, closely mimicking real-world listening environments - Developed a binaural speech separation model that separates speech of moving talkers while retaining their spatial locations, enhancing auditory perception and auditory attention decoding - Proposed system enhances speech intelligibility and reduces listening effort in realistic acoustic scenes
Building Blocks of Understanding: Constructing a Reverse Genetics Platform for studying determinants of SARS-CoV-2 replication

To better understand viral pathogenesis, host-virus interactions, and potential therapeutic interventions, the development of robust reverse genetics systems for SARS-CoV-2 is crucial. Here, we present a reverse genetics platform that enables the efficient manipulation, assembly, and rescue of recombinant SARS-CoV-2. The versatility of our reverse genetics system was demonstrated by generating recombinant SARS-CoV-2 viruses. We used this system to generate N501Y and Y453F spike protein mutants. Characterization studies revealed distinct phenotypic effects, impact on viral fitness, cell binding, and replication kinetics. We also investigated a recently discovered priming site for NSP9, which is postulated to produce a short RNA antisense leader sequence. By introducing the U76G mutation into the 5’UTR, we show that this priming site is necessary for the correct production of genomic and subgenomic RNAs, and also for efficient viral replication. In conclusion, our developed reverse genetics system provides a robust and adaptable platform for the efficient generation of recombinant SARS-CoV-2 viruses for their comprehensive characterization. Significance statement In this study, we present a versatile reverse genetics platform facilitating the efficient manipulation, assembly, and rescue of recombinant SARS-CoV-2. Demonstrating its adaptability, we successfully engineered N501Y and Y453F spike protein mutants, each exhibiting distinct phenotypic effects on viral fitness, cell binding, and replication kinetics. We also investigated a novel negative sense priming site for NSP9, demonstrating a role in RNA production and viral replication. This straightforward reverse genetic system is therefore a powerful tool to generate recombinant viruses for advancing our understanding of SARS-CoV-2 biology.
Detecting Logic Bugs in Graph Database Management Systems via Injective and Surjective Graph Query Transformation

Graph Database Management Systems (GDBMSs) store graphs as data. They are used naturally in applications such as social networks, recommendation systems and program analysis. However, they can be affected by logic bugs, which cause the GDBMSs to compute incorrect results and subsequently affect the applications relying on them. In this work, we propose injective and surjective Graph Query Transformation (GQT) to detect logic bugs in GDBMSs. Given a query 𝑄 , we derive a mutated query 𝑄 ′ , so that either their result sets are: (i) semantically equivalent; or (ii) variant based on the mutation to be either a subset or superset of each other. When the expected relationship between the results does not hold, a logic bug in the GDBMS is detected. The key insight to mutate 𝑄 is that the graph pattern in graph queries enables systematic query transformations derived from injective and surjective mappings of the directed edge sets between 𝑄 and 𝑄 ′ . We implemented injective and surjective Graph Query Transformation (GQT) as a tool called GraphGenie and evaluated it on 6 popular and mature GDBMSs. GraphGenie has found 25 unknown bugs, comprising 16 logic bugs, 3 internal errors, and 6 performance issues. Our results demonstrate the practicality and effectiveness of GraphGenie in detecting logic bugs in GDBMSs which has the potential for improving the reliability of applications relying on these
Interface VISSIM Control TCP Socket QualNet TCP Socket External Interface Applications TCP connection Transportation Simulation Windows Network Simulation Linux

This paper aims at enabling accurate and efficient evaluation of emerging vehicular network applications such as Intelligent Transportation Systems (ITS). A distributed simulation platform that integrates transportation simulation and wireless network simulation is proposed and implemented, providing a user level simulation environment to evaluate the feasibility and performance limitations of VANETs in supporting ITS. The proposed simulation platform facilitates the dynamic interaction between the two simulation domains, allowing runtime control of vehicles’ behavior in the transportation simulation as they react in real time to information exchange in the simulated communication network. Case studies are conducted within the proposed simulation platform to evaluate the performance of Dynamic Route Planning when deployed in VANETs, using metrics collected at the transportation system level such as travel and delay time. The effectiveness of three representative VANET dynamic adaptation protocols in enhancing the application performance in scenarios with high vehicle density are compared in the case studies. The experiment results show that Dynamic Route Planning can be effectively supported by a VANET system with up to 118% increase on the number of vehicles reaching the destination, 36.2% reduction on travel time and 56.1% reduction on delay time.
Suppression of Conducted Emissions in Three-Phase Adjustable Drive Systems

The fundamental issue with high switching power converters is electromagnetic interference. The converter design must be electromagnetically compliant for an operation to be secure. Filters play a crucial role in mitigating electromagnetic interference (EMI) within three-phase systems. This paper presents examining the electromagnetic interference associated with a three-phase inverter interconnected to the grid. The reported measurements were taken after the filter and applied with three-phase adjustable drive system for 9-150kHz frequency ranges. Therefore, experimental evidence supports the EMI filter's noise attenuation. In conclusion, the presented study fills in the gaps measuring uncertainties for three-phase emissions, assisting engineers in the design of three-phase converters.
Classification of CXR of COVID-19 Based on ICA Dimensionality Reduction

The medical image data like chest x-ray (CXR) image tends to be more complex than traditional of other image data and needs dimensionality reduction. Whereas reducing the dimensions of the CXRs images should not affect the main characteristics, and maintain the same analytical results, therefore, reducing the dimensions is important in data pre-processing, to obtain faster time during the training process of the neural network and high accuracy results. In this paper, a CXRs images dimensional reduction method based on independent component analysis (ICA) is presented to Improve convolution neural network (CNN) Training Times, where the shorter training times give to allow for experimentation more with new datasets. Training and validation were performed on the datasets after Dimensionality Reduction through deep learning (DL) algorithms. The results demonstrate that the pre-processed dataset obtained after dimension reduction demonstrates the highest training rate and best accuracy in comparison with the original datasets. According to experimental findings, using ICA with the CNN method, which provides a solution for network training times. The results demonstrate that the proposed CNN method achieves up to 97% classification accuracy and out-performs other dimensionality reduction methods.
The Prediction of Hand, Foot and Mouth Disease (HFMD) with Multi-type Historical Data Based on LSTM

Hand-foot-mouth disease (HFMD) has gradually become prevalent in mainland China since the first official report in 2008. The incidence of HFMD is increasing among children mainly aged zero to five. Thus how to monitor and prevent HFMD is becoming an important public health challenge. This study aims to build a forecasting framework of HFMD for two Chinese cities, Chengdu in Sichuan Province and Guangdong in Guangzhou Province based on LSTM, by utilizing clinical data of HFMD, meteorological factors, and Baidu index data. Our method achieves significant results