Miracle-dz/UniFAR-FTUs huggingface dataset:
Tumor-associated nonmyelinating Schwann cell–expressed PVT1 promotes pancreatic cancer kynurenine pathway and tumor immune exclusion

One of the major obstacles to treating pancreatic ductal adenocarcinoma (PDAC) is its immunoresistant microenvironment. The functional importance and molecular mechanisms of Schwann cells in PDAC remains largely elusive. We characterized the gene signature of tumor-associated nonmyelinating Schwann cells (TASc) in PDAC and indicated that the abundance of TASc was correlated with immune suppressive tumor microenvironment and the unfavorable outcome of patients with PDAC. Depletion of pancreatic-specific TASc promoted the tumorigenesis of PDAC tumors. TASc-expressed long noncoding RNA (lncRNA) plasmacytoma variant translocation 1 (PVT1) was triggered by the tumor cell–produced interleukin-6. Mechanistically, PVT1 modulated RAF proto-oncogene serine/threonine protein kinase–mediated phosphorylation of tryptophan 2,3-dioxygenase in TASc, facilitating its enzymatic activities in catalysis of tryptophan to kynurenine. Depletion of TASc-expressed PVT1 suppressed PDAC tumor growth. Furthermore, depletion of TASc using a small-molecule inhibitor effectively sensitized PDAC to immunotherapy, signifying the important roles of TASc in PDAC immune resistance.
A tool to assess risk of bias in studies estimating the prevalence of mental health disorders (RoB-PrevMH)

Objective: Biases affect how certain we are about the available evidence, however no standard tool for assessing the risk of bias (RoB) in prevalence studies exists. For the purposes of a living systematic review on prevalence of mental health disorders during the COVID-19 pandemic, we developed a RoB tool to evaluate prevalence studies in mental health (RoB-PrevMH) and tested interrater reliability. Methods: We reviewed existing RoB tools for prevalence studies until September 2020, to develop a tool for prevalence studies in mental health. We tested the reliability of assessments by different users of RoB-PrevMH in 83 studies stemming from two systematic reviews of prevalence studies in mental health. We assessed the interrater agreement by calculating the proportion of agreement and Kappa statistic for each item. Results: RoB-PrevMH consists of three items that address selection bias and information bias. Introductory and signaling questions guide the application of the tool to the review question. The interrater agreement for the three items was 83%, 90% and 93%. The weighted kappa was 0.63 (95% CI 0.54 to 0.73), 0.71 (95% CI 0.67 to 0.85) and 0.32 (95% CI -0.04 to -0.63), respectively. Conclusions: We developed a brief, user friendly, and adaptable tool for assessing RoB in studies on prevalence of mental health disorders. Initial results for interrater agreement were fair to substantial. The tool's validity, reliability, and applicability should be assessed in future projects.
Measurement of objects with low vibration frequencies using modified Michelson interferometry

A simple, low-cost, and non-contact optical method using modified Michelson interferometry is proposed for measuring the vibration frequencies of objects. Two beams which are split from a laser beam project onto two rough surfaces (compared with the laser-beam wavelength) of a fixed reference object and a vibrating tested object respectively, and two quasi-spherical waves scattered or diffused from the two rough surfaces individually are combined to form interference fringes on an observation plane. The vibration frequencies of the tested object can be derived by analyzing the time-dependent gray-value signal (caused by the fringe-shifting phenomena) for a specified position on the observation plane. The signal is transformed from the time domain to the frequency domain to form a spectrum by first using the fast Fourier transformation. Then, all the spectrum values corresponding to different frequencies in the spectrum are compared with a threshold value. Finally, all frequencies in the spectrum with local peaks higher than the threshold value are considered to be the vibration frequencies of the tested object. The experiment is a non-contact type with no lenses and is able to be conducted easily with high accuracy and low cost for testing objects vibrating with one, two, or three frequencies. The results and features confirm the utility of the proposed method.
Evaluation of statistical detection of change algorithm for triaging multiple sclerosis patients with new lesion activity on longitudinal brain MRI

Background and Purpose: Identification of new MS lesions on longitudinal MRI by human readers is time-consuming and prone to error. Our objective was to evaluate the improvement in a subject-level detection performance by readers when assisted by the automated statistical detection of change (SDC) algorithm. Materials and Methods: A total of 200 MS patients with mean inter-scan interval of 13.2 {+/-} 2.4 months were included. SDC was applied to the baseline and follow-up FLAIR images to detect potential new lesions for confirmation by readers (Reader+SDC method). This method was compared with readers operating in the clinical workflow (Reader method) for a subject-level detection of new lesions. Results: Reader+SDC found 30 subjects (15.0%) with at least one new lesion, while Reader detected 16 subjects (8.0%). As a subject-level triage tool, SDC achieved a perfect sensitivity of 1.00 (95% CI: [0.88, 1.00]) and a moderate specificity of 0.67 (95% CI: [0.59, 0.74]). The agreement on a subject-level was 0.91 (95% CI: [0.87, 0.95]) between Reader+SDC and Reader, and 0.72 (95% CI: [0.66, 0.78]) between Reader+SDC and SDC. Conclusion: SDC improves the detection accuracy of human readers and can serve as a time-saving patient triage tool for detecting new MS lesion activity on longitudinal FLAIR images.
Lessons Learned from Health Coverage Expansion in Massachusetts: Primary Care for Individuals with Serious Psychiatric Illness: Pre- and Post- 2006 Coverage Mandates

Purpose: Improved access to care, particularly primary care, is a key goal of healthcare reform proposals. Individuals with severe psychiatric illness have high rates of emergency room use rather than primary/preventive care. Massachusetts implemented health care reform in 2006 intended to provide more individuals with health care insurance and, thereby, access to primary care. To our knowledge no study has assessed whether this legislation impacted barriers to primary care access among individuals with serious mental illness. Methods: This study looked at effects of the 2006 Massachusetts legislation among individuals being hospitalized at a large psychiatric hospital drawing patients from throughout Eastern Massachusetts. A retrospective review of records noted whether a primary care physician was identified, along with demographic and clinical characteristics for each patient. Results: Primary care affiliation was significantly lower in 2008 than 2005. Affiliation increased 9 years after legislation, though not to the levels of the year prior to the legislation. Risk ratios for PCP non-affiliation were similar whether the model controlled for demographic characteristics only; primary and drug and alcohol related diagnoses in addition to demographic characteristics; or insurance type in addition to demographic characteristics and diagnoses. Conclusions: The risk of being unaffiliated with a PCP 2 years after legislative reform was nearly 20% higher than 1 year before. Our findings are concerning in that patients at this large regional psychiatry hospital may have been receiving less primary/preventive care in 2008 than in 2005, despite legislation aimed at improving primary care access for the general population.
Development and validation of a novel mobility test for IRDs, from reality to virtual reality

Purpose. To validate a novel mobility test (MOST, MObility Standardized Test) and performance outcomes in real (RL) and virtual (VR) environments to be used for interventional clinical studies in order to characterize vision impairment in rod-cone dystrophies, also known as retinitis pigmentosa (RP). Design. Prospective, interventional, non-invasive, longitudinal study (test-retest). Participants. 89 participants in three experimental phases: 15 non visually impaired (controls) in Phase 1 (average age, 27.4 years; 66% women), 14 participants with RP in Phase 2 (average age, 45.2 years, 36% women), and 60 participants (30 RP; average age, 47.4; 44.6% women; and 30 controls, average age, 47.6 years; 45.4% women) in Phase 3. Methods. We designed a mobility test (MOST) to be used in both VR and RL and ran three experimental studies to (1) validate the difficulty of the mobility courses, (2) determine the optimal number of light levels and training trials, and (3) validate the reproducibility (test-retest), reliability (VR/RL), sensitivity, and construct, and content validity of the test. A comprehensive ophthalmologic examination was performed in all subjects. Main outcomes measures. The primary outcome is the performance score in the mobility test. The secondary outcomes include visual acuity, contrast sensitivity, dark adaptation thresholds, static and kinetic visual field parameters, and ellipsoid zone from optical coherence tomography. Correlation between the performance score in the mobility tests and visual function were assessed. Results. Results revealed that the mobility courses developed exhibited statistically similar difficulty, and that five trials are sufficient to control for the learning effect in a session. MOST is highly reproducible (test-retest intra-class correlations > .98) and reliable (correlation VR/RL = .98). MOST achieved a discrimination between RP participants and controls (accuracy larger than 95% in all conditions) and between early and late stages of the disease (mean accuracy of 82.3%). The performance score is correlated with visual function parameter (.57 to .94). Conclusion. MOST is a tool offering validated mobility test, a controlled learning effect, which demonstrates excellent reproducibility and high agreement between real and virtual conditions, as well as sensitivity and specificity to measure disease progression and therapeutic benefit in IRD.
Augmentation Component Analysis: Modeling Similarity via the Augmentation Overlaps

Self-supervised learning aims to learn a embedding space where semantically similar samples are close. Contrastive learning methods pull views of samples together and push different samples away, which utilizes semantic invariance of augmentation but ignores the relationship between samples. To better exploit the power of augmentation, we observe that semantically similar samples are more likely to have similar augmented views. Therefore, we can take the augmented views as a special description of a sample. In this paper, we model such a description as the augmentation distribution and we call it augmentation feature. The similarity in augmentation feature reflects how much the views of two samples overlap and is related to their semantical similarity. Without computational burdens to explicitly estimate values of the augmentation feature, we propose Augmentation Component Analysis (ACA) with a contrastive-like loss to learn principal components and an on-the-fly projection loss to embed data. ACA equals an efficient dimension reduction by PCA and extracts low-dimensional embeddings, theoretically preserving the similarity of augmentation distribution between samples. Empirical results show our method can achieve competitive results against various traditional contrastive learning methods on different benchmarks.
Awake Complexity of Distributed Minimum Spanning Tree

We study the distributed minimum spanning tree (MST) problem, a fundamental problem in distributed computing. It is well-known that distributed MST can be solved in $\tilde{O}(D+\sqrt{n})$ rounds in the standard CONGEST model (where $n$ is the network size and $D$ is the network diameter) and this is essentially the best possible round complexity (up to logarithmic factors). However, in resource-constrained networks such as ad hoc wireless and sensor networks, nodes spending so much time can lead to significant spending of resources such as energy. Motivated by the above consideration, we study distributed algorithms for MST under the \emph{sleeping model} [Chatterjee et al., PODC 2020], a model for design and analysis of resource-efficient distributed algorithms. In the sleeping model, a node can be in one of two modes in any round -- \emph{sleeping} or \emph{awake} (unlike the traditional model where nodes are always awake). Only the rounds in which a node is \emph{awake} are counted, while \emph{sleeping} rounds are ignored. A node spends resources only in the awake rounds and hence the main goal is to minimize the \emph{awake complexity} of a distributed algorithm, the worst-case number of rounds any node is awake. We present deterministic and randomized distributed MST algorithms that have an \emph{optimal} awake complexity of $O(\log n)$ time with a matching lower bound. We also show that our randomized awake-optimal algorithm has essentially the best possible round complexity by presenting a lower bound of $\tilde{\Omega}(n)$ on the product of the awake and round complexity of any distributed algorithm (including randomized) that outputs an MST. To complement our trade-off lower bound, we present a parameterized family of distributed algorithms that gives an essentially optimal trade-off between the awake complexity and the round complexity.
OpenSpike: An OpenRAM SNN Accelerator

This paper presents a spiking neural network (SNN) accelerator made using fully open-source EDA tools, process design kit (PDK), and memory macros synthesized using Open-RAM. The chip is taped out in the 130 nm SkyWater process and integrates over 1 million synaptic weights, and offers a reprogrammable architecture. It operates at a clock speed of 40 MHz, a supply of 1.8 V, uses a PicoRV32 core for control, and occupies an area of 33.3 mm2. The throughput of the accelerator is 48,262 images per second with a wallclock time of 20.72 $\mu \mathbf{s}$, at 56.8 GOPS/W. The spiking neurons use hysteresis to provide an adaptive threshold (i.e., a Schmitt trigger) which can reduce state instability. This results in high performing SNNs across a range of benchmarks that remain competitive with state-of-the-art, full precision SNNs. The design is open sourced and available online: https://githuh.com/sJmth/OpenSpike
Lower Bounds for Learning in Revealing POMDPs

This paper studies the fundamental limits of reinforcement learning (RL) in the challenging \emph{partially observable} setting. While it is well-established that learning in Partially Observable Markov Decision Processes (POMDPs) requires exponentially many samples in the worst case, a surge of recent work shows that polynomial sample complexities are achievable under the \emph{revealing condition} -- A natural condition that requires the observables to reveal some information about the unobserved latent states. However, the fundamental limits for learning in revealing POMDPs are much less understood, with existing lower bounds being rather preliminary and having substantial gaps from the current best upper bounds. We establish strong PAC and regret lower bounds for learning in revealing POMDPs. Our lower bounds scale polynomially in all relevant problem parameters in a multiplicative fashion, and achieve significantly smaller gaps against the current best upper bounds, providing a solid starting point for future studies. In particular, for \emph{multi-step} revealing POMDPs, we show that (1) the latent state-space dependence is at least $\Omega(S^{1.5})$ in the PAC sample complexity, which is notably harder than the $\widetilde{\Theta}(S)$ scaling for fully-observable MDPs; (2) Any polynomial sublinear regret is at least $\Omega(T^{2/3})$, suggesting its fundamental difference from the \emph{single-step} case where $\widetilde{O}(\sqrt{T})$ regret is achievable. Technically, our hard instance construction adapts techniques in \emph{distribution testing}, which is new to the RL literature and may be of independent interest.
Near-Field Integrated Sensing and Communications

A near-field integrated sensing and communications (ISAC) framework is proposed, which introduces an additional distance dimension for both sensing and communications compared to the conventional far-field system. In particular, the Cramér-Rao bound for the near-field joint distance and angle sensing is derived, which is minimized subject to the minimum communication rate requirement of each user. Both fully digital antennas and hybrid digital and analog antennas are investigated. For fully digital antennas, a globally optimal solution of the ISAC waveform is obtained via semidefinite relaxation. For hybrid antennas, a high-quality solution is obtained through two-stage optimization. Numerical results demonstrate the performance gain introduced by the additional distance dimension of the near-field ISAC over the far-field ISAC.
MARLIN: Soft Actor-Critic based Reinforcement Learning for Congestion Control in Real Networks

Fast and efficient transport protocols are the foundation of an increasingly distributed world. The burden of continuously delivering improved communication performance to support next-generation applications and services, combined with the increasing heterogeneity of systems and network technologies, has promoted the design of Congestion Control (CC) algorithms that perform well under specific environments. The challenge of designing a generic CC algorithm that can adapt to a broad range of scenarios is still an open research question. To tackle this challenge, we propose to apply a novel Reinforcement Learning (RL) approach. Our solution, MARLIN, uses the Soft Actor-Critic algorithm to maximize both entropy and return and models the learning process as an infinite-horizon task. We trained MARLIN on a real network with varying background traffic patterns to overcome the sim-to-real mismatch that researchers have encountered when applying RL to CC. We evaluated our solution on the task of file transfer and compared it to TCP Cubic. While further research is required, results have shown that MARLIN can achieve comparable results to TCP with little hyperparameter tuning, in a task significantly different from its training setting. Therefore, we believe that our work represents a promising first step towards building CC algorithms based on the maximum entropy RL framework.
Blocking JavaScript without Breaking the Web: An Empirical Investigation

Modern websites heavily rely on JavaScript (JS) to implement legitimate functionality as well as privacy-invasive advertising and tracking. Browser extensions such as NoScript block any script not loaded by a trusted list of endpoints, thus hoping to block privacy-invasive scripts while avoiding breaking legitimate website functionality. In this paper, we investigate whether blocking JS on the web is feasible without breaking legitimate functionality. To this end, we conduct a large-scale measurement study of JS blocking on 100K websites. We evaluate the effectiveness of different JS blocking strategies in tracking prevention and functionality breakage. Our evaluation relies on quantitative analysis of network requests and resource loads as well as manual qualitative analysis of visual breakage. First, we show that while blocking all scripts is quite effective at reducing tracking, it significantly degrades functionality on approximately two-thirds of the tested websites. Second, we show that selective blocking of a subset of scripts based on a curated list achieves a better trade-off. However, there remain approximately 15% “mixed” scripts, which essentially merge tracking and legitimate functionality and thus cannot be blocked without causing website breakage. Finally, we show that fine-grained blocking of a subset of JS methods, instead of scripts, reduces major breakage by 3.8× while providing the same level of tracking prevention. Our work highlights the promise and open challenges in fine-grained JS blocking for tracking prevention without breaking the web.
Applications and resource reductions in measurement-based variational quantum eigensolvers

We discuss the procedure for obtaining measurement-based implementations of quantum algorithms given by quantum circuit diagrams and how to reduce the required resources needed for a given measurement-based computation. This forms the foundation for quantum computing on photonic systems in the near term. To demonstrate that these ideas are well grounded we present three different problems which are solved by employing a measurement-based implementation of the variational quantum eigensolver algorithm (MBVQE). We show that by utilising native measurement-based gates rather than standard gates, such as the standard controlled not gate (CNOT), measurement-based quantum computations may be obtained that are both shallow and have simple connectivity while simultaneously exhibiting a large expressibility. We conclude that MBVQE has promising prospects for resource states that are not far from what is already available today.
FAVOR#: Sharp Attention Kernel Approximations via New Classes of Positive Random Features

The problem of efficient approximation of a linear operator induced by the Gaussian or softmax kernel is often addressed using random features (RFs) which yield an unbiased approximation of the operator's result. Such operators emerge in important applications ranging from kernel methods to efficient Transformers. We propose parameterized, positive, non-trigonometric RFs which approximate Gaussian and softmax-kernels. In contrast to traditional RF approximations, parameters of these new methods can be optimized to reduce the variance of the approximation, and the optimum can be expressed in closed form. We show that our methods lead to variance reduction in practice ($e^{10}$-times smaller variance and beyond) and outperform previous methods in a kernel regression task. Using our proposed mechanism, we also present FAVOR#, a method for self-attention approximation in Transformers. We show that FAVOR# outperforms other random feature methods in speech modelling and natural language processing.
Quantitative stochastic homogenization of elliptic equations with unbounded coefficients

In this paper, we consider stochastic homogenization of elliptic equations with unbounded and non-uniformly elliptic coefficients. Extending subadditive arguments, we get an estimate for the rate of the convergence of the solution of the Dirichlet problem under the condition that coefficients in the unit cube have a certain exponential integrability. For the coefficient field $\mathbf{a}$ in this paper, we only assume a constant decrease at a constant distance of the maximal correlation as an assumption of ergodicity, and stationarity with respect to $\mathbb{Z}^d$-translations.