Miracle-dz/UniFAR-FTUs huggingface dataset:
A User-experience Driven SSIM-Aware Adaptation Approach for DASH Video Streaming

Dynamic Adaptive Streaming over HTTP (DASH) is a video streaming technique largely used. One key point is the adaptation mechanism which resides at the client's side. This mechanism impacts greatly on the overall Quality of Experience (QoE) of the video streaming. In this paper, we propose a new adaptation algorithm for DASH, namely SSIM Based Adaptation (SBA). This mechanism is user-experience driven: it uses the Structural Similarity Index Measurement (SSIM) as main video perceptual quality indicator; moreover, the adaptation is based on a joint consideration of SSIM indicator and the physical resources (buffer occupancy, bandwidth) in order to minimize the buffer starvation (rebuffering) and video quality instability, as well as to maximize the overall video quality (through SSIM). To evaluate the performance of our proposal, we carried out trace-driven emulation with real traffic traces (captured in real mobile network). Comparisons with some representative algorithms (BBA, FESTIVE, OSMF) through major QoE metrics show that our adaptation algorithm SBA achieves an efficient adaptation minimizing both the rebuffering and instability, whereas the displayed video is maintained at a high level of bitrate.
HpGAN: Sequence Search With Generative Adversarial Networks

Sequences play an important role in many engineering applications. Searching sequences with desired properties has long been an intriguing but also challenging research topic. This article proposes a novel method, called HpGAN, to search desired sequences algorithmically using generative adversarial networks (GANs). HpGAN is based on the idea of zero-sum game to train a generative model, which can generate sequences with characteristics similar to the training sequences. In HpGAN, we design the Hopfield network as an encoder to avoid the limitations of GAN in generating discrete data. Compared with traditional sequence construction by algebraic tools, HpGAN is particularly suitable for complex problems which are intractable by mathematical analysis. We demonstrate the search capabilities of HpGAN in two applications: 1) HpGAN successfully found many different mutually orthogonal complementary sequence sets (MOCSSs) and optimal odd-length binary Z-complementary pairs (OB-ZCPs) which are not part of the training set. In the literature, both MOCSSs and OB-ZCPs have found wide applications in wireless communications and 2) HpGAN found new sequences which achieve a four-times increase of signal-to-interference ratio—benchmarked against the well-known Legendre sequences—of a mismatched filter (MMF) estimator in pulse compression radar systems. These sequences outperform those found by AlphaSeq.
Fatigue in idiopathic pulmonary fibrosis measured by the Fatigue Assessment Scale during antifibrotic treatment

ABSTRACT Background: Fatigue is a common complaint in patients with idiopathic pulmonary fibrosis (IPF) and has been reported in a considerable percentage of patients. Fatigue is also a registered side effect of pirfenidone, one of two approved antifibrotic drugs. The Fatigue Assessment Scale (FAS) was developed for assessment of fatigue in sarcoidosis and validated in patients with sarcoidosis. FAS has been used in a few IPF studies but has not been validated. Aims: To study the change in FAS after initiation of pirfenidone or nintedanib in the treatment of patients with IPF during a six-month period. Methods: Between April 2017 and January 2018, all incident patients with IPF starting antifibrotic treatment were invited to complete FAS before, four weeks, three, and six months after initiation of antifibrotic treatment. Baseline characteristics including lung function were registered. Results: Fifty-two patients were included, mean FVC% 84.8, mean DLCO% 51.4. Nintedanib was started in 25 patients; 27 patients started pirfenidone. Sixty-four percent of patients had a FAS score >22 indicating substantial fatigue at baseline. There was no statistically significant difference in FAS score for patients treated with nintedanib or pirfenidone at any time point. FAS score increased statistically significantly during the six-month follow-up. This change was driven by patients without substantial fatigue at baseline with an increase in FAS score of 8.4 points; patients with substantial fatigue at baseline experienced no statistically significant change. Conclusion: A majority of patients with IPF suffered from substantial fatigue at the time of diagnosis. Fatigue progressed over time and increasing fatigue was associated with younger age, nintedanib treatment and low degree of fatigue at baseline. There was no significant difference in FAS score between the two antifibrotic treatments at any time point, even though fatigue is not a registered side effect in nintedanib.
Image recovery with the solar gravitational lens

We report on the initial results obtained with an image convolution/deconvolution computer code that we developed and used to study the image formation capabilities of the solar gravitational lens (SGL). Although the SGL of a spherical Sun creates a greatly blurred image, knowledge of the SGL's point-spread function (PSF) makes it possible to reconstruct the original image and remove the blur by way of deconvolution. We discuss the deconvolution process, which can be implemented either with direct matrix inversion or with the Fourier quotient method. We observe that the process introduces a "penalty" in the form of a reduction in the signal-to-noise ratio (SNR) of a recovered image, compared to the SNR at which the blurred image data is collected. We estimate the magnitude of this penalty using an analytical approach and confirm the results with a series of numerical simulations. We find that the penalty is substantially reduced when the spacing between image samples is large compared to the telescope aperture. The penalty can be further reduced with suitable noise filtering, which can yield ${\cal O}(10)$ or better improvement for low-quality imaging data. Our results confirm that it is possible to use the SGL for imaging purposes. We offer insights on the data collection and image processing strategies that could yield a detailed image of an exoplanet within image data collection times that are consistent with the duration of a realistic space mission.
DONE: Distributed Newton-type Method for Federated Edge Learning

There is growing interest in applying distributed machine learning to edge computing, forming \emph{federated edge learning}. Compared with conventional distributed machine learning in a datacenter, federated edge learning faces non-independent and identically distributed (non-i.i.d.) and heterogeneous data, and the communications between edge workers, possibly through distant locations with unstable wireless networks, are more costly than their local computational overhead. In this work, we propose a distributed Newton-type algorithm (DONE) with fast convergence rate for communication-efficient federated edge learning. First, with strongly convex and smooth loss functions, we show that DONE can produce the Newton direction approximately in a distributed manner by using the classical Richardson iteration on each edge worker. Second, we prove that DONE has linear-quadratic convergence and analyze its computation and communication complexities. Finally, the experimental results with non-i.i. d. and heterogeneous data show that DONE attains the same performance as the Newton's method. Notably, DONE requires considerably fewer communication iterations compared to the distributed gradient descent algorithm and outperforms DANE, a state-of-the-art, in the case of non-quadratic loss functions.
Weakly Supervised Arrhythmia Detection Based on Deep Convolutional Neural Network

Supervised deep learning has been widely used in the studies of automatic ECG classification, which largely benefits from sufficient annotation of large datasets. However, most of the existing large ECG datasets are roughly annotated, so the classification model trained on them can only detect the existence of abnormalities in a whole recording, but cannot determine their exact occurrence time. In addition, it may take huge time and economic cost to construct a fine-annotated ECG dataset. Therefore, this study proposes weakly supervised deep learning models for detecting abnormal ECG events and their occurrence time. The available supervision information for the models is limited to the event types in an ECG record, excluding the specific occurring time of each event. By leverage of feature locality of deep convolution neural network, the models first make predictions based on the local features, and then aggregate the local predictions to infer the existence of each event during the whole record. Through training, the local predictions are expected to reflect the specific occurring time of each event. To test their potentials, we apply the models for detecting cardiac rhythmic and morphological arrhythmias by using the AFDB and MITDB datasets, respectively. The results show that the models achieve beat-level accuracies of 99.09% in detecting atrial fibrillation, and 99.13% in detecting morphological arrhythmias, which are comparable to that of fully supervised learning models, demonstrating their effectiveness. The local prediction maps revealed by this method are also helpful to analyze and diagnose the decision logic of record-level classification models.
Understanding Learned Reward Functions

In many real-world tasks, it is not possible to procedurally specify an RL agent's reward function. In such cases, a reward function must instead be learned from interacting with and observing humans. However, current techniques for reward learning may fail to produce reward functions which accurately reflect user preferences. Absent significant advances in reward learning, it is thus important to be able to audit learned reward functions to verify whether they truly capture user preferences. In this paper, we investigate techniques for interpreting learned reward functions. In particular, we apply saliency methods to identify failure modes and predict the robustness of reward functions. We find that learned reward functions often implement surprising algorithms that rely on contingent aspects of the environment. We also discover that existing interpretability techniques often attend to irrelevant changes in reward output, suggesting that reward interpretability may need significantly different methods from policy interpretability.
Transfer Learning for Efficient Iterative Safety Validation

Safety validation is important during the development of safety-critical autonomous systems but can require significant computational effort. Existing algorithms often start from scratch each time the system under test changes. We apply transfer learning to improve the efficiency of reinforcement learning based safety validation algorithms when applied to related systems. Knowledge from previous safety validation tasks is encoded through the action value function and transferred to future tasks with a learned set of attention weights. Including a learned state and action value transformation for each source task can improve performance even when systems have substantially different failure modes. We conduct experiments on safety validation tasks in gridworld and autonomous driving scenarios. We show that transfer learning can improve the initial and final performance of validation algorithms and reduce the number of training steps.
Evidence of nontrivial Berry phase and Kondo physics in SmBi

Realization of semimetals with non-trivial topologies such as Dirac and Weyl semimetals, have provided a boost in the study of these quantum materials. Presence of electron correlation makes the system even more exotic due to enhanced scattering of charge carriers, Kondo screening etc. Here, we studied the electronic properties of single crystalline, SmBi employing varied state of the art bulk measurements. Magnetization data reveals two magnetic transitions; an antiferromagnetic order with a Neel temperature of ~ 9 K and a second magnetic transition at a lower temperature (= 7 K). The electrical resistivity data shows an upturn typical of a Kondo system and the estimated Kondo temperature is found to be close to the Neel temperature. High quality of the crystal enabled us to discover signature of quantum oscillation in the magnetization data even at low magnetic field. Using a Landau level fan diagram analysis, a non-trivial Berry phase is identified for a Fermi pocket revealing the topological character in this material. These results demonstrate an unique example of the Fermiology in the antiferromagnetic state and opens up a new paradigm to explore the Dirac fermion physics in correlated topological metal via interplay of Kondo interaction, topological order and magnetism.
Social Media Insights Into US Mental Health Amid the COVID-19 Pandemic. A Longitudinal Twitter Analysis (JANUARY-APRIL 2020)

Background: The COVID-19 pandemic led to unprecedented mitigation efforts that disrupted the daily lives of millions. Beyond the general health repercussions of the pandemic itself, these measures also present a significant challenge to the world's mental health and healthcare systems. Considering traditional survey methods are time-consuming and expensive, we need timely and proactive data sources to respond to the rapidly evolving effects of health policy on our population's mental health. Significant pluralities of the US population now use social media platforms, such as Twitter, to express the most minute details of their daily lives and social relations. This behavior is expected to increase during the COVID-19 pandemic, rendering social media data a rich field from which to understand personal wellbeing. Purpose: Broadly, this study answers three research questions: RQ1: What themes emerge from a corpus of US tweets about COVID19? ; RQ2: To what extent does social media use increase during the onset of the COVID-19 pandemic? ; and RQ3: Does sentiment change in response to the COVID-19 pandemic? Methods: We analyzed 86,581,237 public domain English-language US tweets collected from an open-access public repository in three steps. First, we characterized the evolution of hashtags over time using Latent Dirichlet Allocation (LDA) topic modeling. Second, we increased the granularity of this analysis by downloading Twitter timelines of a large cohort of individuals (n = 354,738) in 20 major US cities to assess changes in social media use. Finally, using this timeline data, we examined collective shifts in public mood in relation to evolving pandemic news cycles by analyzing the average daily sentiment of all timeline tweets with the Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment tool. Results: LDA topics generated in the early months of the dataset corresponded to major COVID-19 specific events. However, as state and municipal governments began issuing stay-at-home orders, latent themes shifted towards US-related lifestyle changes rather than global pandemic-related events. Social media volume also increased significantly, peaking during stay-at-home mandates. Finally, VADER sentiment analysis sentiment scores of user timelines were initially high and stable, but decreased significantly, and continuously, by late March. Discussion & Conclusion: Our findings underscore the negative effects of the pandemic on overall population sentiment. Increased usage rates suggest that, for some, social media may be a coping mechanism to combat feelings of isolation related to long-term social distancing. However, in light of the documented negative effect of heavy social media usage on mental health, for many social media may further exacerbate negative feelings in the long-term. Thus, considering the overburdened US mental healthcare structure, these findings have important implications for ongoing mitigation efforts.
Responding to COVID-19 Stress: Disseminating Mind-Body Resiliency Approaches

The COVID-19 pandemic has resulted in unprecedented stress and uncertainty, particularly among vulnerable populations such as healthcare workers who are facing a multitude of current and looming economic and psychosocial stressors. As clinician-scientists delivering mind-body interventions in our hospital, we suggest applying evidence-based mind-body techniques that promote resiliency and adaptive coping during these difficult times. Interventions that package a variety of mind-body skills into one cohesive program, such as the Stress Management and Resiliency Training – Relaxation Response Resiliency Program (SMART-3RP), offer promise for meeting the variety of stress management needs (e.g., health concerns, isolation) present during the COVID-19 pandemic. From our work with frontline healthcare clinicians and other caregiver populations, we offer recommendations for adapting the delivery, modality, and content of mind-body practices during the COVID-19 pandemic and suggest key skills for promoting resiliency and buffering against the future stressors that lie ahead for everyone.
Learning Tubule-Sensitive CNNs for Pulmonary Airway and Artery-Vein Segmentation in CT

Training convolutional neural networks (CNNs) for segmentation of pulmonary airway, artery, and vein is challenging due to sparse supervisory signals caused by the severe class imbalance between tubular targets and background. We present a CNNs-based method for accurate airway and artery-vein segmentation in non-contrast computed tomography. It enjoys superior sensitivity to tenuous peripheral bronchioles, arterioles, and venules. The method first uses a feature recalibration module to make the best use of features learned from the neural networks. Spatial information of features is properly integrated to retain relative priority of activated regions, which benefits the subsequent channel-wise recalibration. Then, attention distillation module is introduced to reinforce representation learning of tubular objects. Fine-grained details in high-resolution attention maps are passing down from one layer to its previous layer recursively to enrich context. Anatomy prior of lung context map and distance transform map is designed and incorporated for better artery-vein differentiation capacity. Extensive experiments demonstrated considerable performance gains brought by these components. Compared with state-of-the-art methods, our method extracted much more branches while maintaining competitive overall segmentation performance. Codes and models are available at http://www.pami. sjtu.edu. cn/News/56.
Serverless Computing: Behind the Scenes of Major Platforms

Serverless computing offers an event driven pay-as-you-go framework for application development. A key selling point is the concept of no back-end server management, allowing developers to focus on application functionality. This is achieved through severe abstraction of the underlying architecture the functions run on. We examine the underlying architecture and report on the performance of serverless functions and how they are effected by certain factors such as memory allocation and interference caused by load induced by other users on the platform. Specifically, we focus on the serverless offerings of the four largest platforms; AWS Lambda, Google Cloud Functions, Microsoft Azure Functions and IBM Cloud Functions. In this paper, we observe and contrast between these platforms in their approach to the common issue of “cold starts”, we devise a means to unveil the underlying architecture serverless functions execute on and we investigate the effects of interference from load on the platform over the time span of one month.
The evolution of the low-density H i intergalactic medium from z = 3.6 to 0: data, transmitted flux, and H i column density★,†,‡

We present a new, uniform analysis of the H i transmitted flux (F) and H i column density ($N_{\mathrm{H\,{\small I}}}$) distribution in the low-density IGM as a function of redshift z for 0 < z < 3.6 using 55 HST/COS FUV (Δz = 7.2 at z < 0.5), five HST/STIS + COS NUV (Δz = 1.3 at z ∼ 1) and 24 VLT/UVES, and Keck/HIRES (Δz = 11.6 at 1.7 < z < 3.6) AGN spectra. We performed a consistent, uniform Voigt profile analysis to combine spectra taken with different instruments, to reduce systematics and to remove metal-line contamination. We confirm previously known conclusions on firmer quantitative grounds in particular by improving the measurements at z ∼ 1. Two flux statistics at 0 < F < 1, the mean H i flux and the flux probability distribution function (PDF), show that considerable evolution occurs from z = 3.6 to z = 1.5, after which it slows down to become effectively stable for z < 0.5. However, there are large sightline variations. For the H i column density distribution function (CDDF, f ∝ $N_{\rm H\,{\small I}}^{-\beta }$) at $\log (N_{\mathrm{H\,{\small I}}}/1\, {\mathrm{cm}^{-2}})$ ∈ [13.5, 16.0], β increases as z decreases from β = 1.60 at z ∼ 3.4 to β = 1.82 at z ∼ 0.1. The CDDF shape at lower redshifts can be reproduced by a small amount of clockwise rotation of a higher-z CDDF with a slightly larger CDDF normalization. The absorption line number per z (dn/dz) shows a similar evolutionary break at z ∼ 1.5 as seen in the flux statistics. High-$N_{\mathrm{H\,{\small I}}}$ absorbers evolve more rapidly than low-$N_{\mathrm{H\,{\small I}}}$ absorbers to decrease in number or cross-section with time. The individual dn/dz shows a large scatter at a given z. The scatter increases towards lower z, possibly caused by a stronger clustering at lower z.
Distributed Computing Model: Classical vs. Quantum vs. Post-Quantum.

Computation refers to an algorithmic process of producing outputs from a given set of inputs. We consider a particular model of computation where the computing device is comprised of several components/ports placed at different spacetime locations -- the inputs are distributed among different ports, whereas the output is to be assessed by some other port. Importantly, communications among the different ports are limited as the physical systems allowed to be transmitted among the different ports are constrained with information theoretic restrictions. Interestingly, this limited communication distributed computing prototype provides an efficient modus operandi to compare the computational strength of different mathematical models used to describe those physical systems. We come up with computing tasks where quantum theory outperforms its classical counterpart. More surprisingly, a broader class of operational theories with state or/and effect space structures more exotic than quantum theory turns out to be suboptimal in performing some distributed computing tasks. We characterize the computations that can be perfectly accomplished in quantum theory but not in other theories. The proposed computing model thus provides a new approach to single out quantum theory in the theory space and promises a novel perspective towards the axiomatic derivation of Hilbert space quantum mechanics.
Acute Blood Loss Anemia in the Setting of Abdominal Aortic Aneurysm Rupture in a Jehovah's Witness

Acute blood loss anemia occurs due to many variants. The standard of care in managing acute blood loss anemia is challenged in this case. Jehovah's Witnesses's (JW) management of blood loss anemia continues to remain a controversy in medicine since they do not allow the use of blood products. This case highlights the management of acute blood loss anemia, utilizing a multidisciplinary bloodless approach in a JW who underwent an endovascular aneurysmal repair (EVAR) of an impending rupture of abdominal aortic aneurysm (AAA). The severity of anemia with hemoglobin of 2.7 g/dL and survival outcome is unique; however, the minimal hemoglobin level required to sustain life is still unclear.