Miracle-dz/UniFAR-FTUs huggingface dataset:
KPC: Learning-Based Model Predictive Control with Deterministic Guarantees

We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown system dynamics are used to learn several models through the formalism of non-parametric kernel regression. By treating each prediction step individually, we dispense with the need of propagating sets through highly non-linear maps, a procedure that often involves multiple conservative approximation steps. Finite-sample error bounds are then used to enforce state-feasibility by employing an efficient robust formulation. We then present a relaxation strategy that exploits on-line data to weaken the optimization problem constraints while preserving safety. Two numerical examples are provided to illustrate the applicability of the proposed control method.
Adverse Effects of Immune-Checkpoint Inhibitors in Hepatocellular Carcinoma

Abstract Immune-modulatory therapy, especially with immune-checkpoint inhibitors (ICIs), has reshaped cancer therapeutics. Immunotherapy is relatively a novel approach that can effectively delay the progression of aggressive tumors and inhibit tumor recurrence and metastasis in many different tumor types. In the past years, ICIs have shown a sustained response and promising long-term survival in patients with advanced hepatocellular carcinoma (HCC). Nevertheless, ICI therapy can unbalance the immune system and result in a wide range of immune-related adverse events (irAEs), which are generally manageable but occasionally lead to a fatal outcome. HCC generally develops in the context of liver cirrhosis which is typically caused by viral hepatitis and non-alcoholic steatohepatitis. These underlying diseases may cause symptoms that overlap with irAEs and lead to consequences such as late recognition, inadequate work-up, and inappropriate treatment. Owing to the growing use of immunotherapy in HCC, it is necessary for clinicians to strengthen their understanding of the frequency, clinical features, and management of irAEs. This review focuses on the common toxicities associated with ICI therapy in patients with HCC and summarizes therapeutic strategies that can be used to monitor and manage such toxicities.
Classical Shadows with Noise

The classical shadows protocol, recently introduced by Huang, Kueng, and Preskill [Nat. Phys. 16, 1050 (2020)], is a quantum-classical protocol to estimate properties of an unknown quantum state. Unlike full quantum state tomography, the protocol can be implemented on near-term quantum hardware and requires few quantum measurements to make many predictions with a high success probability. In this paper, we study the effects of noise on the classical shadows protocol. In particular, we consider the scenario in which the quantum circuits involved in the protocol are subject to various known noise channels and derive an analytical upper bound for the sample complexity in terms of a shadow seminorm for both local and global noise. Additionally, by modifying the classical post-processing step of the noiseless protocol, we define a new estimator that remains unbiased in the presence of noise. As applications, we show that our results can be used to prove rigorous sample complexity upper bounds in the cases of depolarizing noise and amplitude damping.
What have we learned so far about COVID-19 volunteering in the UK? A rapid review of the literature

Background: Community engagement and volunteering are essential for the public response to COVID-19. Since March 2020 a large number of people in the UK have been regularly doing unpaid activities to benefit others besides their close relatives. Although most mutual aid groups emerged from local neighbourhoods and communities, official public institutions also fostered community volunteering, namely through the community champions scheme. By considering a broad definition of COVID-19 volunteering, this article describes a systematic review of the literature focused on two broad questions: What have we learned so far from COVID-19 volunteering both at the UK national level and the more local community level? What have we learned from engagement with local communities and community champions during the COVID-19 period? Methods: A rapid review of the literature in peer-reviewed databases and grey literature was applied in our search, following the PRISMA principles. The search was conducted from 10 to 16 of October 2020, and sources were included on the basis of having been published between January and October 2020, focusing on COVID-19 and addressing community groups, volunteering groups, volunteers, or community champions in the UK. Results: After initial screening, a total of 40 relevant sources were identified. From these, 28 were considered eligible. Findings suggest that food shopping and emotional support were the most common activities, but there were diverse models of organisation and coordination in COVID-19 volunteering. Additionally, community support groups seem to be adjusting their activities and scope of action to current needs and challenges. Volunteers were mostly women, middle-class, highly educated, and working-age people. Social networks and connections, local knowledge, and social trust were key dimensions associated with community organising and volunteering. Furthermore, despite the efforts of a few official public institutions and councils, there has been limited community engagement and collaboration with volunteering groups and other community-based organisations. Conclusions: We identified important factors for fostering community engagement and COVID volunteering as well as gaps in the current literature. We suggest that future research should be directed towards deepening knowledge on sustaining community engagement, collaboration and community participation over time, during and beyond this pandemic.
A theoretical framework for BL Her stars – I. Effect of metallicity and convection parameters on period–luminosity and period–radius relations

We present a new grid of convective BL Herculis models using the state-of-the-art 1D non-linear radial stellar pulsation tool mesa-rsp. We investigate the impact of metallicity and four sets of different convection parameters on multiwavelength properties. Non-linear models were computed for periods typical for BL Her stars, i.e. 1 ≤ P(d) ≤ 4 covering a wide range of input parameters – metallicity (−2.0 dex ≤ [Fe/H] ≤ 0.0 dex), stellar mass (0.5–0.8 M⊙), luminosity (50–300 L⊙), and effective temperature (full extent of the instability strip; in steps of 50 K). The total number of BL Her models with full-amplitude stable pulsations used in this study is 10 280 across the four sets of convection parameters. We obtain their multiband (UBVRIJHKLL′M) light curves and derive new theoretical period–luminosity (PL), period–Wesenheit (PW), and period–radius (PR) relations at mean light. We find that the models computed with radiative cooling show statistically similar slopes for PL, PW, and PR relations. Most empirical relations match well with the theoretical PL, PW, and PR relations from the BL Her models computed using the four sets of convection parameters. However, PL slopes of the models with radiative cooling provide a better match to empirical relations for BL Her stars in the Large Magellanic Cloud in the HKS bands. For each set of convection parameters, the effect of metallicity is significant in U and B bands and negligible in infrared bands, which is consistent with empirical results. No significant metallicity effects are seen in the PR relations.
Antibody response patterns in COVID‐19 patients with different levels of disease severity in Japan

We analyzed antibody response patterns according to the level of disease severity in patients with novel coronavirus disease 2019 (COVID‐19) in Japan. We analyzed 611 serum specimens from 231 patients with COVID‐19 (mild, 170; severe, 31; critical, 30). Immunoglobulin M (IgM) and IgG antibodies against nucleocapsid protein (N) and spike 1 protein (S1) were detected by enzyme‐linked immunosorbent assays. The peaks of fitting curves for the optical density (OD) values of IgM and IgG antibodies against N appeared simultaneously, while those against S1 were delayed compared with N. The OD values of IgM against N and IgG against both N and S1 were significantly higher in the severe and critical cases than in the mild cases at 11 days after symptom onset. The seroconversion rates of IgG were higher than those of IgM against both N and S1 during the clinical course based on the optimal cut‐off values defined in this study. The seroconversion rates of IgG and IgM against N and S1 were higher in the severe and critical cases than in the mild cases. Our findings show that a stronger antibody response occurred in COVID‐19 patients with greater disease severity and there were low seroconversion rates of antibodies against N and S1 in the mild cases.
Data-driven Holistic Framework for Automated Laparoscope Optimal View Control with Learning-based Depth Perception

Laparoscopic Field of View (FOV) control is one of the most fundamental and important components in Minimally Invasive Surgery (MIS), nevertheless the traditional manual holding paradigm may easily bring fatigue to surgical assistants, and misunderstanding between surgeons also hinders assistants to provide a high-quality FOV. Targeting this problem, we here present a data-driven framework to realize an automated laparoscopic optimal FOV control. To achieve this goal, we offline learn a motion strategy of laparoscope relative to the surgeon’s hand-held surgical tool from our in-house surgical videos, developing our control domain knowledge and an optimal view generator. To adjust the laparoscope online, we first adopt a learning-based method to segment the two-dimensional (2D) position of the surgical tool, and further leverage this outcome to obtain its scale-aware depth from dense depth estimation results calculated by our novel unsupervised RoboDepth model only with the monocular camera feedback, hence in return fusing the above real-time 3D position into our control loop. To eliminate the misorientation of FOV caused by Remote Center of Motion (RCM) constraints when moving the laparoscope, we propose a novel rotation constraint using an affine map to minimize the visual warping problem, and a null-space controller is also embedded into the framework to optimize all types of errors in a unified and decoupled manner. Experiments are conducted using Universal Robot (UR) and Karl Storz Laparoscope/Instruments, which prove the feasibility of our domain knowledge and learning enabled framework for automated camera control.
Lifestyle Modifies the Diabetes-Related Metabolic Risk, Conditional on Individual Genetic Differences

Metabolic syndrome is a group of heritable metabolic traits that are highly associated with type 2 diabetes (T2DM). Classical interventions to T2DM include individual self-management of environmental risk factors, such as improving diet quality, increasing physical activity, and reducing smoking and alcohol consumption, which decreases the risk of developing metabolic syndrome. However, it is poorly understood how the phenotypes of diabetes-related metabolic traits change with respect to lifestyle modifications at the individual level. In the analysis, we used 12 diabetes-related metabolic traits and eight lifestyle covariates from the UK Biobank comprising 288,837 white British participants genotyped for 1,133,273 genome-wide single nucleotide polymorphisms. We found 16 GxE interactions. Modulation of genetic effects by physical activity was seen for four traits (glucose, HbA1c, C-reactive protein, systolic blood pressure) and by alcohol and smoking for three (BMI, glucose, waist–hip ratio and BMI and diastolic and systolic blood pressure, respectively). We also found a number of significant phenotypic modulations by the lifestyle covariates, which were not attributed to the genetic effects in the model. Overall, modulation in the metabolic risk in response to the level of lifestyle covariates was clearly observed, and its direction and magnitude were varied depending on individual differences. We also showed that the metabolic risk inferred by our model was notably higher in T2DM prospective cases than controls. Our findings highlight the importance of individual genetic differences in the prevention and management of diabetes and suggest that the one-size-fits-all approach may not benefit all.
Sparse linear regression - CLuP achieves the ideal \emph{exact} ML

In this paper we revisit one of the classical statistical problems, the so-called sparse maximum-likelihood (ML) linear regression. As a way of attacking this type of regression, we present a novel CLuP mechanism that to a degree relies on the \bl{\textbf{Random Duality Theory (RDT)}} based algorithmic machinery that we recently introduced in \cite{Stojnicclupint19,Stojnicclupcmpl19,Stojnicclupplt19,Stojniccluplargesc20,Stojniccluprephased20}. After the initial success that the CLuP exhibited in achieving the exact ML performance while maintaining excellent computational complexity related properties in MIMO ML detection in \cite{Stojnicclupint19,Stojnicclupcmpl19,Stojnicclupplt19}, one would naturally expect that a similar type of success can be achieved in other ML considerations. The results that we present here confirm that such an expectation is indeed reasonable. In particular, within the sparse regression context, the introduced CLuP mechanism indeed turns out to be able to \bl{\textbf{\emph{achieve the ideal ML performance}}}. Moreover, it can substantially outperform some of the most prominent earlier state of the art algorithmic concepts, among them even the variants of the famous LASSO and SOCP from \cite{StojnicPrDepSocp10,StojnicGenLasso10,StojnicGenSocp10}. Also, our recent results presented in \cite{Stojniccluplargesc20,Stojniccluprephased20} showed that the CLuP has excellent \bl{\textbf{\emph{large-scale}}} and the so-called \bl{\textbf{\emph{rephasing}}} abilities. Since such large-scale algorithmic features are possibly even more desirable within the sparse regression context we here also demonstrate that the basic CLuP ideas can be reformulated to enable solving with a relative ease the regression problems with \bl{\textbf{\emph{several thousands}}} of unknowns.
On Random Matrices Arising in Deep Neural Networks: General I.I.D. Case

We study the distribution of singular values of product of random matrices pertinent to the analysis of deep neural networks. The matrices resemble the product of the sample covariance matrices, however, an important difference is that the population covariance matrices assumed to be non-random or random but independent of the random data matrix in statistics and random matrix theory are now certain functions of random data matrices (synaptic weight matrices in the deep neural network terminology). The problem has been treated in recent work [25, 13] by using the techniques of free probability theory. Since, however, free probability theory deals with population covariance matrices which are independent of the data matrices, its applicability has to be justified. The justification has been given in [22] for Gaussian data matrices with independent entries, a standard analytical model of free probability, by using a version of the techniques of random matrix theory. In this paper we use another, more streamlined, version of the techniques of random matrix theory to generalize the results of [22] to the case where the entries of the synaptic weight matrices are just independent identically distributed random variables with zero mean and finite fourth moment. This, in particular, extends the property of the so-called macroscopic universality on the considered random matrices.
Survival and relative dose intensity of 5-fluorouracil, oxaliplatin and irinotecan in real-life treatment of metastatic colorectal cancer

Introduction Combinations of 5-fluorouracil/leucovorin (5-FU/LV) with oxaliplatin (FOLFOX) or irinotecan (FOLFIRI) are part of standard treatments for metastatic colorectal cancer (mCRC). For these molecules, the impact of a low relative dose intensity (RDI) on survival is not sufficiently known in real-life. Material and methods Data were collected retrospectively from patients treated in our center for an unresectable mCRC with FOLFOX or FOLFIRI as a first-line treatment. To study the impact on progression-free survival (PFS) and overall survival (OS), patients were divided into high and low RDI according to the median RDI of 5-FU on one end, and the median RDI of oxaliplatin or irinotecan (OXA-IRI) on the other. Results In our population of 75 patients, the median age was 67.1 years and 77% of patients were treated with FOLFIRI. Patients with high RDI for OXA-IRI had better PFS compared to patients with low RDI (hazard ratio [HR], 0.58; p = 0.03). There was no statistically significant difference in PFS for patients with high RDI for 5-FU (HR, 0.66; p = 0.09). No difference was found in overall survival according to the RDI of OXA-IRI (HR, 0.72; p = 0.18) or 5-FU (HR, 0.77; p = 0.29). RDI had no significant impact on toxicities. Conclusions Our analysis suggests that a low RDI of oxaliplatin and irinotecan has a negative effect on PFS. RDI had no significant effect on OS in our cohort. The clinical benefit of maintaining high RDI in these patients appears low.
Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images

We present a hierarchical VAE that, for the first time, outperforms the PixelCNN in log-likelihood on all natural image benchmarks. We begin by observing that VAEs can actually implement autoregressive models, and other, more efficient generative models, if made sufficiently deep. Despite this, autoregressive models have traditionally outperformed VAEs. We test if insufficient depth explains the performance gap by by scaling a VAE to greater stochastic depth than previously explored and evaluating it on CIFAR-10, ImageNet, and FFHQ. We find that, in comparison to the PixelCNN, these very deep VAEs achieve higher likelihoods, use fewer parameters, generate samples thousands of times faster, and are more easily applied to high-resolution images. We visualize the generative process and show the VAEs learn efficient hierarchical visual representations. We release our source code and models at https://github.com/openai/vdvae.
A PDE Characterization of Anisotropic Hardy Spaces

We obtain a differential characterization for the anisotropic Hardy space $H_A^p$ by identifying it with a parabolic Hardy space associated with a general continuous group. This allows $H_A^p$ to be defined using a parabolic differential equation of Calderon and Torchinsky. We also provide a classification of dilations corresponding to equivalent anisotropic Hardy spaces with respect to linear transformations.
Language-Guided Navigation via Cross-Modal Grounding and Alternate Adversarial Learning

The emerging vision-and-language navigation (VLN) problem aims at learning to navigate an agent to the target location in unseen photo-realistic environments according to the given language instruction. The main challenges of VLN arise mainly from two aspects: first, the agent needs to attend to the meaningful paragraphs of the language instruction corresponding to the dynamically-varying visual environments; second, during the training process, the agent usually imitate the expert demonstrations, i.e., the shortest-path to the target location specified by associated language instructions. Due to the discrepancy of action selection between training and inference, the agent solely on the basis of imitation learning does not perform well. Existing VLN approaches address this issue by sampling the next action from its predicted probability distribution during the training process. This allows the agent to explore diverse routes from the environments, yielding higher success rates. Nevertheless, without being presented with the golden shortest navigation paths during the training process, the agent may arrive at the target location through an unexpected longer route. To overcome these challenges, we design a cross-modal grounding module, which is composed of two complementary attention mechanisms, to equip the agent with a better ability to track the correspondence between the textual and visual modalities. We then propose to recursively alternate the learning schemes of imitation and exploration to narrow the discrepancy between training and inference. We further exploit the advantages of both these two learning schemes via adversarial learning. Extensive experimental results on the Room-to-Room (R2R) benchmark dataset demonstrate that the proposed learning scheme is generalized and complementary to prior arts. Our method performs well against state-of-the-art approaches in terms of effectiveness and efficiency.
Turnpike in Lipschitz—nonlinear optimal control

We present a new proof of the turnpike property for nonlinear optimal control problems, when the running target is a steady control-state pair of the underlying system. Our strategy combines the construction of quasi-turnpike controls via controllability, and a bootstrap argument, and does not rely on analyzing the optimality system or linearization techniques. This in turn allows us to address several optimal control problems for finite-dimensional, control-affine systems with globally Lipschitz (possibly nonsmooth) nonlinearities, without any smallness conditions on the initial data or the running target. These results are motivated by applications in machine learning through deep residual neural networks, which may be fit within our setting. We show that our methodology is applicable to controlled PDEs as well, such as the semilinear wave and heat equation with a globally Lipschitz nonlinearity, once again without any smallness assumptions.
A minimal power-spectrum-based moment expansion for CMB B-mode searches

The characterization and modeling of polarized foregrounds has become a critical issue in the quest for primordial B-modes. A typical method to proceed is to factorize and parametrize the spectral properties of foregrounds and their scale dependence (i.e. assuming that foreground spectra are well described everywhere by their sky average). Since in reality foreground properties vary across the Galaxy, this assumption leads to inaccuracies in the model that manifest themselves as biases in the final cosmological parameters (in this case the tensor-to-scalar ratio r). This is particularly relevant for surveys over large fractions of the sky, such as the Simons Observatory (SO), where the spectra should be modeled over a distribution of parameter values. Here we propose a method based on the existing “moment expansion” approach to address this issue in a power-spectrum-based analysis that is directly applicable in ground-based multi-frequency data. Additionally, the method uses only a small set of parameters with simple physical interpretation, minimizing the impact of foreground uncertainties on the final B-mode constraints. We validate the method using SO-like simulated observations, recovering an unbiased estimate of the tensor-to-scalar ratio r with standard deviation σ(r) ≃ 0.003, compatible with official forecasts. When applying the method to the public BICEP2/Keck data, we find an upper bound r < 0.06 (95% C.L.), compatible with the result found by BICEP2/Keck when parametrizing spectral index variations through a scale-independent frequency decorrelation parameter. We also discuss the formal similarities between the power spectrum-based moment expansion and methods used in the analysis of CMB lensing.