Miracle-dz/UniFAR-FTUs huggingface dataset:
Endovascular and surgical obliteration rates of spinal dural arteriovenous fistulae: a single UK Centre experience

Abstract Objective Endovascular treatment (EVT) of spinal dural arteriovenous fistulae (SDAVF) has become increasingly popular given its less invasive nature. This study aims to assess radiological obliteration rates after surgery and EVT for SDAVF in a major tertiary referral centre serving a population of 2.2 million. Method A retrospective review of all patients diagnosed with SDAVF between February 2010 and February 2018 was undertaken, identifying baseline demographics, treatment modality and the final radiological outcome (i.e., persistence of the SDAVF). Patients were identified from the departmental neurovascular database, clinical notes and imaging reports. Results Twenty patients were identified with an angiographically confirmed SDAVF. Two (10%) were managed conservatively. Nine patients (45%) underwent EVT. Obliteration was achieved in one patient (11%) after a single procedure, while one patient required two sessions. Further surgery was required in five patients (56%) to achieve complete obliteration. Nine patients (45%) underwent surgical disconnection as first treatment. Obliteration was radiologically confirmed in eight patients (89%). No radiological (MRI or angiographic) follow-up data was available for two patients (one from each group) and these were excluded from analysis. In this study, the obliteration rate of SDAVF after surgery was superior compared to EVT (p <0.01). Conclusion Complete obliteration and recurrence rates after single treatment with EVT were inferior compared to surgical intervention. EVT may be better suited for specific presentations of SDAVF either in isolation or as an adjunct in multi-modality treatment. A national registry of outcomes may aid ongoing refinement of patient selection for EVT.
Economic evaluation of the Hepatitis C virus elimination program in the country of Georgia, 2015 to 2017

In 2015, the country of Georgia launched an elimination program aiming to reduce the prevalence of Hepatitis C virus (HCV) infection by 90% from 5.4% prevalence (~150 000 people). During the first 2.5 years of the program, 770 832 people were screened, 48 575 were diagnosed with active HCV infection, and 41 483 patients were treated with direct‐acting antiviral (DAA)‐based regimens, with a >95% cure rate.
Plasma miR-151-3p as a Candidate Diagnostic Biomarker for Head and Neck Cancer: A Cross-sectional Study within the INHANCE Consortium

Abstract Background: Identification of screening tests for the detection of head and neck cancer (HNC) at an early stage is an important strategy to improving prognosis. Our objective was to identify plasma circulating miRNAs for the diagnosis of HNC (oral and laryngeal subsites), within a multicenter International Head and Neck Cancer Epidemiology consortium. Methods: A high-throughput screening phase with 754 miRNAs was performed in plasma samples of 88 cases and 88 controls, followed by a validation phase of the differentially expressed miRNAs, identified in the screening, in samples of 396 cases and 396 controls. Comparison of the fold changes (FC) was carried out using the Wilcoxon rank-sum test and the Dunn multiple comparison test. Results: We identified miR-151-3p (FC = 1.73, P = 0.007) as differentially expressed miRNAs in the screening and validation phase. The miR-151-3p was the only overexpressed miRNA in validation sample of patients with HNC with early stage at diagnosis (FC = 1.81, P = 0.008) and it was confirmed upregulated both in smoker early-stage cases (FC = 3.52, P = 0.024) and in nonsmoker early-stage cases (FC = 1.60, P = 0.025) compared with controls. Conclusions: We identified miR-151-3p as an early marker of HNC. This miRNA was the only upregulated in patients at early stages of the disease, independently of the smoking status. Impact: The prognosis for HNC is still poor. The discovery of a new diagnostic biomarker could lead to an earlier tumor discovery and therefore to an improvement in patient prognosis.
Psychological maltreatment and substance use among college students: Psychological distress, belongingness, and social support.

Substance use in college students is an important public health problem associated with substantial consequences. It is a critical step to examine risk and protective factors associated with substance use to develop prevention and intervention strategies for promoting college students' healthy development and well-being. The present cross-sectional study aimed to examine the mediating role of psychological distress in the relationship between childhood psychological maltreatment and substance use and the moderating effect of social support and belongingness in this association among college students. The sample of this cross-sectional study included 382 emerging adults. Participants included 56.8% female and 43.2% male, and they ranged in age between 20 and 29 years (M = 22.05, SD = 2.17). Findings from the study first confirmed that psychological maltreatment was a significant predictor of social support, belongingness, psychological distress, and substance use. Subsequent results revealed that psychological distress mediated the association between psychological maltreatment and college students' substance use. Also, belongingness and social support had a buffering effect on this association. The findings suggest that psychological maltreatment is a significant risk factor in developing substance use behavior and that belongingness and social support services can serve as protective resources. Based on these results, prevention and intervention services focusing on improving the feeling of belonging and social support could be conducive to the decreased substance use of psychologically maltreated emerging adults.
Delay-constrained quality maximization in RSMA-based video streaming networks

Recent studies have shown that rate splitting multiple access (RSMA), which depends on multi-antenna rate splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, successfully controls interference in multi-antenna communication networks. This paper examines RSMA's applicability to video streaming applications in cloud radio access networks (C-RAN). We aim to address a practical challenge to maximize the perceived quality of end users while keeping the delay constraints remained satisfied using RSMA. We propose a learning-based framework to select appropriate video quality together with beamforming vectors according to current defined system state. The simulation figure confirms that the learning behavior of proposed learning scheme is stable.
One-vs-All Convolutional Neural Networks for Synthetic Aperture Radar Target Recognition

Abstract Convolutional Neural Networks (CNN) have been widely utilized for Automatic Target Recognition (ATR) in Synthetic Aperture Radar (SAR) images. However, a large number of parameters and a huge training data requirements limit CNN’s use in SAR ATR. While previous works have primarily focused on model compression and structural modification of CNN, this paper employs the One-Vs-All (OVA) technique on CNN to address these issues. OVA-CNN comprises several Binary classifying CNNs (BCNNs) that act as an expert in correctly recognizing a single target. The BCNN that predicts the highest probability for a given target determines the class to which the target belongs. The evaluation of the model using various metrics on the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark dataset illustrates that the OVA-CNN has fewer weight parameters and training sample requirements while exhibiting a high recognition rate.
Benchmarking energy consumption and latency for neuromorphic computing in condensed matter and particle physics

The massive use of artificial neural networks (ANNs), increasingly popular in many areas of scientific computing, rapidly increases the energy consumption of modern high-performance computing systems. An appealing and possibly more sustainable alternative is provided by novel neuromorphic paradigms, which directly implement ANNs in hardware. However, little is known about the actual benefits of running ANNs on neuromorphic hardware for use cases in scientific computing. Here, we present a methodology for measuring the energy cost and compute time for inference tasks with ANNs on conventional hardware. In addition, we have designed an architecture for these tasks and estimate the same metrics based on a state-of-the-art analog in-memory computing (AIMC) platform, one of the key paradigms in neuromorphic computing. Both methodologies are compared for a use case in quantum many-body physics in two-dimensional condensed matter systems and for anomaly detection at 40 MHz rates at the Large Hadron Collider in particle physics. We find that AIMC can achieve up to one order of magnitude shorter computation times than conventional hardware at an energy cost that is up to three orders of magnitude smaller. This suggests great potential for faster and more sustainable scientific computing with neuromorphic hardware.
Resilience based Electric Sector Optimization in Response to Extreme Weather Conditions with Distributed Generation Systems

Extreme weather events stemming from climate change can cause significant damage and disruption to power systems. Failure to mitigate and adapt to climate change and its cascading effects can lead to short and long-term issues. The profound costs of outage in power systems, integrated with the impacts on individual safety and security from loss of critical services, necessitate an urgent need to guarantee resilience in electric power systems. This article proposes a framework to optimize the electricity sector design to be more resilient to climate change and extreme weather events by using distributed generators. The proposed framework considers components’ dynamic behavior and interdependencies under an uncertain environment. The climate data and socio-economic factors are used to generate demand and supply pattern scenarios. The generated scenarios are simulated and utilized in a stochastic optimization model to find the optimal resilience-based system design.
Minimizing Convex Functions with Rational Minimizers

Given a separation oracle SO for a convex function f defined on ℝn that has an integral minimizer inside a box with radius R, we show how to find an exact minimizer of f using at most O(n (n log log (n)/log (n) + log (R))) calls to SO and poly (n, log (R)) arithmetic operations, or O(n log (nR) calls to SO and exp (O(n)) ⋅ poly (log (R)) arithmetic operations. When the set of minimizers of f has integral extreme points, our algorithm outputs an integral minimizer of f. This improves upon the previously best oracle complexity of O(n2 (n + log (R))) for polynomial time algorithms and O(n2 log (nR) for exponential time algorithms obtained by [Grötschel, Lovász and Schrijver, Prog. Comb. Opt. 1984, Springer 1988] over thirty years ago. Our improvement on Grötschel, Lovász and Schrijver’s result generalizes to the setting where the set of minimizers of f is a rational polyhedron with bounded vertex complexity. For the Submodular Function Minimization problem, our result immediately implies a strongly polynomial algorithm that makes at most O(n3 log log (n)/log (n)) calls to an evaluation oracle, and an exponential time algorithm that makes at most O(n2 log (n)) calls to an evaluation oracle. These improve upon the previously best O(n3 log2(n)) oracle complexity for strongly polynomial algorithms given in [Lee, Sidford and Wong, FOCS 2015] and [Dadush, Végh and Zambelli, SODA 2018], and an exponential time algorithm with oracle complexity O(n3 log (n)) given in the former work. Our result is achieved via a reduction to the Shortest Vector Problem in lattices. We show how an approximately shortest vector of an auxiliary lattice can be used to effectively reduce the dimension of the problem. Our analysis of the oracle complexity is based on a potential function that simultaneously captures the size of the search set and the density of the lattice, which we analyze via tools from convex geometry and lattice theory.
Revisiting Discrete Soft Actor-Critic

We study the adaption of soft actor-critic (SAC) from continuous action space to discrete action space. We revisit vanilla SAC and provide an in-depth understanding of its Q value underestimation and performance instability issues when applied to discrete settings. We thereby propose entropy-penalty and double average Q-learning with Q-clip to address these issues. Extensive experiments on typical benchmarks with discrete action space, including Atari games and a large-scale MOBA game, show the efficacy of our proposed method. Our code is at:https://github.com/coldsummerday/Revisiting-Discrete-SAC.
Fast-Image2Point: Towards Real-Time Point Cloud Reconstruction of a Single Image using 3D Supervision

A key question in the problem of 3D reconstruction is how to train a machine or a robot to model 3D objects. Many tasks like navigation in real-time systems such as autonomous vehicles directly depend on this problem. These systems usually have limited computational power. Despite considerable progress in 3D reconstruction systems in recent years, applying them to real-time systems such as navigation systems in autonomous vehicles is still challenging due to the high complexity and computational demand of the existing methods. This study addresses current problems in reconstructing objects displayed in a single-view image in a faster (real-time) fashion. To this end, a simple yet powerful deep neural framework is developed. The proposed framework consists of two components: the feature extractor module and the 3D generator module. We use point cloud representation for the output of our reconstruction module. The ShapeNet dataset is utilized to compare the method with the existing results in terms of computation time and accuracy. Simulations demonstrate the superior performance of the proposed method.
Boosting Star-GANs for Voice Conversion with Contrastive Discriminator

Nonparallel multi-domain voice conversion methods such as the StarGAN-VCs have been widely applied in many scenarios. However, the training of these models usually poses a challenge due to their complicated adversarial network architectures. To address this, in this work we leverage the state-of-the-art contrastive learning techniques and incorporate an efficient Siamese network structure into the StarGAN discriminator. Our method is called SimSiam-StarGAN-VC and it boosts the training stability and effectively prevents the discriminator overfitting issue in the training process. We conduct experiments on the Voice Conversion Challenge (VCC 2018) dataset, plus a user study to validate the performance of our framework. Our experimental results show that SimSiam-StarGAN-VC significantly outperforms existing StarGAN-VC methods in terms of both the objective and subjective metrics.
Joint PMD Tracking and Nonlinearity Compensation With Deep Neural Networks

Overcoming fiber nonlinearity is one of the core challenges limiting the capacity of optical fiber communication systems. Machine learning based solutions such as learned digital backpropagation (LDBP) and the recently proposed deep convolutional recurrent neural network (DCRNN) have been shown to be effective for fiber nonlinearity compensation (NLC). Incorporating distributed compensation of polarization mode dispersion (PMD) within the learned models can improve their performance even further but at the same time, it also couples the compensation of nonlinearity and PMD. Consequently, it is important to consider the time variation of PMD for such a joint compensation scheme. In this paper, we investigate the impact of PMD drift on the DCRNN model with distributed compensation of PMD. We propose a transfer learning based selective training scheme to adapt the learned neural network model to changes in PMD. We demonstrate that fine-tuning only a small subset of weights as per the proposed method is sufficient for adapting the model to PMD drift. Using decision directed feedback for online learning, we track continuous PMD drift resulting from a time-varying rotation of the state of polarization (SOP). We show that transferring knowledge from a pre-trained base model using the proposed scheme significantly reduces the re-training efforts for different PMD realizations. Applying the hinge model for SOP rotation, our simulation results show that the learned models maintain their performance gains while tracking the PMD.
Lamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning Toward Asynchronous Commercial Games

Despite the emerging progress of integrating evolutionary computation into reinforcement learning, the absence of a high-performance platform endowing composability and massive parallelism causes nontrivial difficulties for research and applications related to asynchronous commercial games. Here, we introduce Lamarckian1—an open-source platform featuring support for evolutionary reinforcement learning scalable to distributed computing resources. To improve the training speed and data efficiency, Lamarckian adopts optimized communication methods and an asynchronous evolutionary reinforcement learning workflow. To meet the demand for an asynchronous interface by commercial games and various methods, Lamarckian tailors an asynchronous Markov Decision Process interface and designs an object-oriented software architecture with decoupled modules. In comparison with the state-of-the-art RLlib, we empirically demonstrate the unique advantages of Lamarckian on benchmark tests with up to 6000 CPU cores: 1) both the sampling efficiency and training speed are doubled when running proximal policy optimization (PPO) on Google football game; 2) the training speed is 13 times faster when running PBT+PPO on Pong game. Moreover, we also present two use cases: 1) how Lamarckian is applied to generating behavior-diverse game AI; 2) how Lamarckian is applied to game balancing tests for an asynchronous commercial game.
Meta-Learning for Adaptive Filters with higher-order Frequency Dependencies

Adaptive filters are applicable to many signal processing tasks including acoustic echo cancellation, beamforming, and more. Adaptive filters are typically controlled using algorithms such as least-mean squares (LMS), recursive least squares (RLS), or Kalman filter updates. Such models are often applied in the frequency domain, assume frequency independent processing, and do not exploit higher-order frequency dependencies, for simplicity. Recent work on meta-adaptive filters, however, has shown that we can control filter adaptation using neural networks without manual derivation, motivating new work to exploit such information. In this work, we present higher-order meta-adaptive filters, a key improvement to meta-adaptive filters that incorporates higher-order frequency dependencies. We demonstrate our approach on acoustic echo cancellation and develop a family of filters that yield multi-dB improvements over competitive baselines, and are at least an order-of-magnitude less complex. Moreover, we show our improvements hold with or without a downstream speech enhancer.
A deep learning workflow for quantification of Micronuclei in DNA damage studies in cultured cancer cell lines: a proof of principle investigation

The cytokinesis block micronucleus assay is widely used for measuring/scoring/counting micronuclei, a marker of genome instability in cultured and primary cells. Though a gold standard method, this is a laborious and time-consuming process with person-to-person variation observed in quantification of micronuclei. We report in this study the utilisation of a new deep learning workflow for detection of micronuclei in DAPI stained nuclear images. The proposed deep learning framework achieved an average precision of >90% in detection of micronuclei. This proof of principle investigation in a DNA damage studies laboratory supports the idea of deploying AI powered tools in a cost-effective manner for repetitive and laborious tasks with relevant computational expertise. These systems will also help improving the quality of data and wellbeing of researchers. Simple Summary This study aims to test a suitable deep learning method for micronucleus detection in images acquired for cytokinesis block micronucleus assay. This study has reached a mean average precision of >90%.
Acute hyper- and hypoglycemia uncouples the metabolic cooperation between glucose and lactate to disrupt sleep

The sleep-wake cycle is a master regulator of metabolic and neuronal activity and when altered, can have profound effects on metabolic health and disease. Although consideration is given to how fluctuations in blood glucose affect peripheral physiology and metabolism, less is known about how glucose dysregulation impacts the intrinsic cooperation between brain metabolism and neuronal activity to regulate sleep. To understand the effect of peripheral hyper- and hypoglycemia on these relationships, we paired biosensors measuring hippocampal interstitial fluid (ISF) levels of glucose and lactate with cortical EEG/EMG recordings to produce simultaneous subsecond recordings of ISF glucose, lactate, and sleep-wake states. First, we describe a conserved temporal relationships between ISF glucose and lactate based on their intrinsic oscillations, diurnal rhythms, and sleep/wake cycles. ISF glucose and lactate oscillations are largely anti-correlated but the frequency of their oscillations dictate their power, coherence, and phase. While ISF glucose and lactate both have diurnal fluctuations, only ISF lactate is consistently elevated during wake. During wake, fluctuations in ISF lactate are associated with changes in the EEG power spectrum, suggesting wake-related activity is more closely associated with ISF lactate. Modulation of glucose availability via both hyper- or hypoglycemia disrupts the relationship between peripheral metabolism, brain metabolism, and sleep. Hyper- and hypo-glycemia increase ISF lactate, decrease NREM, and alter EEG spectral activity, again demonstrating ISF lactate drives wake-associated behaviors and disrupts sleep. Taken together, these studies demonstrate that peripheral glucose homeostasis is necessary for maintaining the relationships between brain metabolism, neuronal activity, and sleep-wake patterns and deviations in blood glucose levels are sufficient to disrupt the metabolic signature of sleep-wake states, putting the brain at risk in diseases like type-2-diabetes and Alzheimer’s disease. Graphical Abstract. Peripheral glucose homeostasis directly modifies sleep/wake patterns
Chronic Gq activation of ventral hippocampal neurons and astrocytes differentially affects memory and behavior

Network dysfunction is implicated in numerous diseases and psychiatric disorders, and the hippocampus serves as a common origin for these abnormalities. To test the hypothesis that chronic modulation of neurons and astrocytes induces impairments in cognition, we activated the hM3D(Gq) pathway in CaMKII+ neurons or GFAP+ astrocytes within the ventral hippocampus across 3, 6 and 9 months. CaMKII-hM3Dq activation impaired fear extinction at 3 months and acquisition at 9 months. Both CaMKII-hM3Dq manipulation and aging had differential effects on anxiety and social interaction. GFAP-hM3Dq activation impacted fear memory at 6 and 9 months. GFAP-hM3Dq activation impacted anxiety in the open field only at the earliest time point. CaMKII-hM3Dq activation modified the number of microglia, while GFAP-hM3Dq activation impacted microglial morphological characteristics, but neither affected these measures in astrocytes. Overall, our study elucidates how distinct cell types can modify behavior through network dysfunction, while adding a more direct role for glia in modulating behavior. Highlights CaMKII- and GFAP-Gq activation impacted memory, anxiety, and social behaviors. Novel environment exploration was affected by CaMKII- and GFAP-Gq activation. CaMKII-Gq modified microglial number, while GFAP-Gq affected microglial morphology. Neither cell manipulation affected astrocytic number or morphology.