Miracle-dz/UniFAR-FTUs huggingface dataset:
Tame Cuspidal Representations in Non-Defining Characteristics

Let k be a non-archimedean local field of odd residual characteristic p. Let G be a (connected) reductive group that splits over a tamely ramified field extension of k. We revisit Yu's construction of smooth complex representations of G(k) from a slightly different perspective and provide a proof that the resulting representations are supercuspidal. Moreover, we show that an analogous construction yields smooth, irreducible, cuspidal representations over an arbitrary algebraically closed field R of characteristic different from p and that this construction provides all smooth, irreducible, cuspidal R-representations if p does not divide the order of the Weyl group of G.
Inductive Guided Filter: Real-time Deep Image Matting with Weakly Annotated Masks on Mobile Devices

Recently, significant progress has been achieved in deep image matting. Most of the classical image matting methods are time-consuming and require an ideal trimap which is difficult to attain in practice. A high efficient image matting method based on a weakly annotated mask is in demand for mobile applications. In this paper, we propose a novel method based on Deep Learning and Guided Filter, called Inductive Guided Filter, which can tackle the real-time general image matting task on mobile devices. We design a lightweight hourglass network to parameterize the original Guided Filter method that takes an image and a weakly annotated mask as input. Further, the use of Gabor loss is proposed for training networks for complicated textures in image matting. Moreover, we create an image matting dataset MAT-2793 with a variety of foreground objects. Experimental results demonstrate that our proposed method massively reduces running time with robust accuracy.
Optical photometric variable stars towards Cygnus OB7

We present optical I-band light curves of the stars towards a star-forming region Cygnus OB7 from 17-night photometric observations. The light curves are generated from a total of 381 image frames with very good photometric precision. From the light curves of 1900 stars and their periodogram analyses, we detect 31 candidate variables including five previously identified. 14 out of 31 objects are periodic and exhibit the rotation rates in the range of 0.15–11.60 d. We characterize those candidate variables using optical/infrared colour–colour diagram and colour–magnitude diagram (CMD). From spectral indices of the candidate variables, it turns out that four are probably Classical T-Tauri stars (CTTSs), rest remain unclassified from present data, they are possibly field stars or discless pre-main-sequence stars towards the region. Based on their location on the various CMDs, the ages of two T Tauri Stars were estimated to be ∼5 Myr. The light curves indicate at least five of the periodic variables are eclipsing systems. The spatial distribution of young variable candidates on Planck 857 GHz (350 $\mu$m) and 2MASS (Two Micron All Sky Survey) Ks images suggest that at least two of the CTTSs are part of the active star-forming cloud Lynds 1003.
Associations between Virologic and Immunologic Dynamics in Blood and in the Male Genital Tract

ABSTRACT To determine the influence of asymptomatic genital viral infections on the cellular components of semen and blood, we evaluated the associations between the numbers and activation statuses of CD4+ and CD8+ T lymphocytes in both compartments and the seminal levels of cytomegalovirus (CMV), herpes simplex virus (HSV), and human immunodeficiency virus 1 (HIV). Paired blood and semen samples were collected from 36 HIV-infected antiretroviral-naïve individuals and from 40 HIV-uninfected participants. We performed multiparameter flow cytometry analysis (CD45, CD45RA, CD3, CD4, CD8, and CD38) of seminal and blood cellular components and measured HIV RNA and CMV and HSV DNA levels in seminal and blood plasma by real-time PCR. Compared to HIV-uninfected participants, in the seminal compartment HIV-infected participants had higher levels of CMV (P < 0.05), higher numbers of total CD3+ (P < 0.01) and CD8+ subset (P < 0.01) T lymphocytes, and higher CD4+ and CD8+ T lymphocyte activation (RA-CD38+) (P < 0.01). Seminal CMV levels positively correlated with absolute numbers of CD4+ and CD8+ T cells in semen (P < 0.05) and with the activation status of CD4+ T cells in semen and in blood (P < 0.01). HIV levels in semen (P < 0.05) and blood (P < 0.01) were positively associated with T-cell activation in blood. Activation of CD8+ T cells in blood remained an independent predictor of HIV levels in semen in multivariate analysis. The virologic milieu in the male genital tract strongly influences the recruitment and activation of immune cells in semen and may also modulate T-cell immune activation in blood. These factors likely influence replication dynamics, sexual transmission risk, and disease outcomes for all three viruses.
Data Poisoning Attacks on Stochastic Bandits

Stochastic multi-armed bandits form a class of online learning problems that have important applications in online recommendation systems, adaptive medical treatment, and many others. Even though potential attacks against these learning algorithms may hijack their behavior, causing catastrophic loss in real-world applications, little is known about adversarial attacks on bandit algorithms. In this paper, we propose a framework of offline attacks on bandit algorithms and study convex optimization based attacks on several popular bandit algorithms. We show that the attacker can force the bandit algorithm to pull a target arm with high probability by a slight manipulation of the rewards in the data. Then we study a form of online attacks on bandit algorithms and propose an adaptive attack strategy against any bandit algorithm without the knowledge of the bandit algorithm. Our adaptive attack strategy can hijack the behavior of the bandit algorithm to suffer a linear regret with only a logarithmic cost to the attacker. Our results demonstrate a significant security threat to stochastic bandits.
YerbaBuena: Securing Deep Learning Inference Data via Enclave-based Ternary Model Partitioning

Deploying and serving deep learning (DL) models in the public cloud facilitates the process to bootstrap artificial intelligence (AI) services. Yet, preserving the confidentiality of sensitive input data remains a concern to most service users. Accidental disclosures of user input data may breach increasingly stringent data protection regulations and inflict reputation damage. In this paper, we systematically investigate the life cycles of input data in deep learning image classification pipelines and further identify the potential places for information disclosures. Based on the discovered insights, we build YerbaBuena, an enclave-based model serving system to protect the confidentiality and integrity of user input data. To accommodate the performance and capacity limitations of today's enclave technology, we employ a Ternary Model Partitioning strategy that allows service users to securely partition their proprietary DL models on local machines. Therefore, we can (I) enclose sensitive computation in a secure enclave to mitigate input information disclosures and (II) delegate non-sensitive workloads to run out of enclave with hardware-assisted DL acceleration. Our comprehensive partitioning analysis and workload measurement demonstrate how users can automatically determine the optimal partitioning for their models, thus to maximize confidentiality guarantees with low performance costs.
Deep Compressed Sensing

Compressed sensing (CS) provides an elegant framework for recovering sparse signals from compressed measurements. For example, CS can exploit the structure of natural images and recover an image from only a few random measurements. CS is flexible and data efficient, but its application has been restricted by the strong assumption of sparsity and costly reconstruction process. A recent approach that combines CS with neural network generators has removed the constraint of sparsity, but reconstruction remains slow. Here we propose a novel framework that significantly improves both the performance and speed of signal recovery by jointly training a generator and the optimisation process for reconstruction via meta-learning. We explore training the measurements with different objectives, and derive a family of models based on minimising measurement errors. We show that Generative Adversarial Nets (GANs) can be viewed as a special case in this family of models. Borrowing insights from the CS perspective, we develop a novel way of improving GANs using gradient information from the discriminator.
Lipopolysaccharide‐induced reductions in cellular coupling correlate with tyrosine phosphorylation of connexin 43

We have previously shown in cultured rat microvascular endothelial cells (RMEC) that lipopolysaccharide (LPS) stimulates a protein tyrosine kinase (PTK)‐dependent reduction in cellular coupling. We hypothesized that connexin 43 (Cx43) becomes phosphorylated following exposure to LPS. Cx43 was immunoprecipitated from control and LPS‐treated RMEC monolayers. Tyrosine phosphorylation of Cx43, detected by immunoblot, was found only in the LPS treatment. To verify these results, Cx43 was radiolabeled with [32P]‐orthophosphate. Radiolabeled Cx43 exhibited a slight increase in phosphorylation in response to LPS; phosphoamino acid analysis displayed equivalent amounts of phosphoserine in control and LPS treatments, but detected phosphotyrosine only in the LPS treatment. The PTK inhibitors PP‐2 (10 nM) and geldanamycin (200 nM) were found to block the response to LPS in terms of Cx43 tyrosine phosphorylation and cellular coupling. The phosphatase inhibitor BpV (1 μM) accentuated the effect of LPS, while the putative phosphatase activator C6‐ceramide prevented it. When measuring cell communication, phosphatase inhibition also blocked the reversal of the LPS response following LPS washout. We conclude that Cx43 is tyrosine phosphorylated following exposure to LPS and suggest that the LPS‐induced increase in intercellular resistance may be mediated by tyrosine phosphorylation of this connexin. Altering tyrosine kinase and phosphatase activities can modulate the LPS‐induced tyrosine phosphorylation of Cx43 and reductions in cellular coupling. J. Cell. Physiol. 193: 373–379, 2002. © 2002 Wiley‐Liss, Inc.
Cosmic shear covariance matrix in wCDM: Cosmology matters

We present here the cosmo-SLICS, a new suite of simulations specially designed for the analysis of current and upcoming weak lensing data beyond the standard two-point cosmic shear. We sampled the [Ωm, σ8, h, w0] parameter space at 25 points organised in a Latin hyper-cube, spanning a range that contains most of the 2σ posterior distribution from ongoing lensing surveys. At each of these nodes we evolved a pair of N-body simulations in which the sampling variance is highly suppressed, and ray-traced the volumes 800 times to further increase the effective sky coverage. We extracted a lensing covariance matrix from these pseudo-independent light-cones and show that it closely matches a brute-force construction based on an ensemble of 800 truly independent N-body runs. More precisely, a Fisher analysis reveals that both methods yield marginalized two-dimensional constraints that vary by less than 6% in area, a result that holds under different survey specifications and that matches to within 15% the area obtained from an analytical covariance calculation. Extending this comparison with our 25 wCDM models, we probed the cosmology dependence of the lensing covariance directly from numerical simulations, reproducing remarkably well the Fisher results from the analytical models at most cosmologies. We demonstrate that varying the cosmology at which the covariance matrix is evaluated in the first place might have an order of magnitude greater impact on the parameter constraints than varying the choice of covariance estimation technique. We present a test case in which we generate fast predictions for both the lensing signal and its associated variance with a flexible Gaussian process regression emulator, achieving an accuracy of a few percent on the former and 10% on the latter.
Autonomous Membership Service for Enclave Applications

Trusted Execution Environment, or enclave, promises to protect data confidentiality and execution integrity of an outsourced computation on an untrusted host. Extending the protection to distributed applications that run on physically separated hosts, however, remains non-trivial. For instance, the current enclave provisioning model hinders elasticity of cloud applications. Furthermore, it remains unclear how an enclave process could verify if there exists another concurrently running enclave process instantiated using the same codebase, or count a number of such processes. In this paper, we seek an autonomous membership service for enclave applications. The application owner only needs to partake in instantiating the very first process of the application, whereas all subsequent process commission and decommission will be administered by existing and active processes of that very application. To achieve both safety and liveness, our protocol design admits unjust excommunication of a non-faulty process from the membership group. We implement the proposed membership service in a system called AMES. Our experimental study shows that AMES incurs an overhead of 5% - 16% compared to vanilla enclave execution.
Ricci reheating

We present a model for viable gravitational reheating involving a scalar field directly coupled to the Ricci curvature scalar. Crucial to the model is a period of kination after inflation, which causes the Ricci scalar to change sign thus inducing a tachyonic effective mass m2 ∝ −H2 for the scalar field. The resulting tachyonic growth of the scalar field provides the energy for reheating, allowing for temperatures high enough for thermal leptogenesis. Additionally, the required period of kination necessarily leads to a blue-tilted primordial gravitational wave spectrum with the potential to be detected by future experiments. We find that for reheating temperatures TRH ≲ 1 GeV, the possibility exists for the Higgs field to play the role of the scalar field.
An Empirical Analysis of Deep Learning for Cardinality Estimation

We implement and evaluate deep learning for cardinality estimation by studying the accuracy, space and time trade-offs across several architectures. We find that simple deep learning models can learn cardinality estimations across a variety of datasets (reducing the error by 72% - 98% on average compared to PostgreSQL). In addition, we empirically evaluate the impact of injecting cardinality estimates produced by deep learning models into the PostgreSQL optimizer. In many cases, the estimates from these models lead to better query plans across all datasets, reducing the runtimes by up to 49% on select-project-join workloads. As promising as these models are, we also discuss and address some of the challenges of using them in practice.
Rolling Optimization of Mobile Energy Storage Fleets for Resilient Service Restoration

Mobile energy storage systems (MESSs) provide promising solutions to enhance distribution system resilience in terms of mobility and flexibility. This paper proposes a rolling integrated service restoration strategy to minimize the total system cost by coordinating the scheduling of MESS fleets, resource dispatching of microgrids, and network reconfiguration of distribution systems. The integrated strategy takes into account damage and repair to both the roads in transportation networks and the branches in distribution systems. The uncertainties in load consumption and the status of roads and branches are modeled as scenario trees using Monte Carlo simulation method. The operation strategy of MESSs is modeled by a stochastic multi-layer time-space network technique. A rolling optimization framework is adopted to dynamically update system damage, and the coordinated scheduling at each time interval over the prediction horizon is formulated as a two-stage stochastic mixed-integer linear program with temporal–spatial and operation constraints. The proposed model is verified on two integrated test systems, one is with Sioux Falls transportation network and four 33-bus distribution systems, and the other is the Singapore transportation network-based test system connecting six 33-bus distribution systems. The results demonstrate the effectiveness of MESS mobility to enhance distribution system resilience due to the coordination of mobile and stationary resources.
Towards Interlingua Neural Machine Translation

Common intermediate language representation in neural machine translation can be used to extend bilingual to multilingual systems by incremental training. In this paper, we propose a new architecture based on introducing an interlingual loss as an additional training objective. By adding and forcing this interlingual loss, we are able to train multiple encoders and decoders for each language, sharing a common intermediate representation. Translation results on the low-resourced tasks (Turkish-English and Kazakh-English tasks, from the popular Workshop on Machine Translation benchmark) show the following BLEU improvements up to 2.8. However, results on a larger dataset (Russian-English and Kazakh-English, from the same baselines) show BLEU loses if the same amount. While our system is only providing improvements for the low-resourced tasks in terms of translation quality, our system is capable of quickly deploying new language pairs without retraining the rest of the system, which may be a game-changer in some situations (i.e. in a disaster crisis where international help is required towards a small region or to develop some translation system for a client). Precisely, what is most relevant from our architecture is that it is capable of: (1) reducing the number of production systems, with respect to the number of languages, from quadratic to linear (2) incrementally adding a new language in the system without retraining languages previously there and (3) allowing for translations from the new language to all the others present in the system
Deep Reference Generation With Multi-Domain Hierarchical Constraints for Inter Prediction

Inter prediction is an important module in video coding for temporal redundancy removal, where similar reference blocks are searched from previously coded frames and employed to predict the block to be coded. Although existing video codecs can estimate and compensate for block-level motions, their inter prediction performance is still heavily affected by the remaining inconsistent pixel-wise displacement caused by irregular rotation and deformation. In this paper, we address the problem by proposing a deep frame interpolation network to generate additional reference frames in coding scenarios. First, we summarize the previous adaptive convolutions used for frame interpolation and propose a factorized kernel convolutional network to improve the modeling capacity and simultaneously keep its compact form. Second, to better train this network, multi-domain hierarchical constraints are introduced to regularize the training of our factorized kernel convolutional network. For spatial domain, we use a gradually down-sampled and up-sampled auto-encoder to generate the factorized kernels for frame interpolation at different scales. For quality domain, considering the inconsistent quality of the input frames, the factorized kernel convolution is modulated with quality-related features to learn to exploit more information from high quality frames. For frequency domain, a sum of absolute transformed difference loss that performs frequency transformation is utilized to facilitate network optimization from the view of coding performance. With the well-designed frame interpolation network regularized by multi-domain hierarchical constraints, our method surpasses HEVC on average $\text{3.8}\%$ BD-rate saving for the luma component under the random access configuration and also obtains on average $\text{0.83}\%$ BD-rate saving over the upcoming VVC.
Non-asymptotic inference in a class of optimization problems

This paper describes a method for carrying out non-asymptotic inference on partially identified parameters that are solutions to a class of optimization problems. The optimization problems arise in applications in which grouped data are used for estimation of a model's structural parameters. The parameters are characterized by restrictions that involve the population means of observed random variables in addition to the structural parameters of interest. Inference consists of finding confidence intervals for the structural parameters. Our method is non-asymptotic in the sense that it provides a finite-sample bound on the difference between the true and nominal probabilities with which a confidence interval contains the true but unknown value of a parameter. We contrast our method with an alternative non-asymptotic method based on the median-of-means estimator of Minsker (2015). The results of Monte Carlo experiments and an empirical example illustrate the usefulness of our method.
Low risk of type‐specific carcinogenic HPV re‐appearance with subsequent cervical intraepithelial neoplasia grade 2/3

Carcinogenic human papillomavirus (HPV) infections are very common after sexual debut and nearly all become undetectable (“clear”) within a few years. Following clearance, the long‐term risks of type‐specific HPV re‐appearance and subsequent risk of cervical intraepithelial neoplasia grade 2 or worse (CIN2+) are not well defined. In the 7‐year, population‐based cohort study in Guanacaste, Costa Rica, we studied how often type‐specific carcinogenic HPV infections re‐appeared after clearance and how often re‐appearance led to CIN2+. We considered 1,740 carcinogenic HPV infections detected by MY09/11 PCR among 2,805 women (18–91 years old, median 34) who were actively followed at 6‐ or 12‐month intervals. We identified women with one or more type‐specific HPV infections that cleared and re‐appeared and further defined a subgroup of “definite clearance and re‐appearance” (≥2 intervening negative results over a period of ≥1 year). We determined the absolute risk of CIN2+ among the different groups. p values are two‐sided. Only 7.7% (81/1,052) of HPV‐infected women had intervening negative results. Very few (3.7%, 39/1,052) had “definite clearance and re‐appearance”, of which 5.1% (2/39) subsequently persisted to a diagnosis of CIN2. There were zero CIN3+ lesions. Extremely few women (2/2,805 of women in our cohort) had a type‐specific carcinogenic HPV infection clear, re‐appear and lead to CIN2+. If confirmed, this argues against vaccination to avoid re‐appearance that leads to precursor lesions and against the need of frequent HPV screening after initial negative results.
Exploration-Exploitation Trade-off in Reinforcement Learning on Online Markov Decision Processes with Global Concave Rewards

We consider an agent who is involved in a Markov decision process and receives a vector of outcomes every round. Her objective is to maximize a global concave reward function on the average vectorial outcome. The problem models applications such as multi-objective optimization, maximum entropy exploration, and constrained optimization in Markovian environments. In our general setting where a stationary policy could have multiple recurrent classes, the agent faces a subtle yet consequential trade-off in alternating among different actions for balancing the vectorial outcomes. In particular, stationary policies are in general sub-optimal. We propose a no-regret algorithm based on online convex optimization (OCO) tools (Agrawal and Devanur 2014) and UCRL2 (Jaksch et al. 2010). Importantly, we introduce a novel gradient threshold procedure, which carefully controls the switches among actions to handle the subtle trade-off. By delaying the gradient updates, our procedure produces a non-stationary policy that diversifies the outcomes for optimizing the objective. The procedure is compatible with a variety of OCO tools.