Miracle-dz/UniFAR-FTUs huggingface dataset:
Dramatic increase in reactive volatile organic compound (VOC) emissions from ships at berth after implementing the fuel switch policy in the Pearl River Delta Emission Control Area

Abstract. Limiting fuel sulfur content (FSC) is a widely adopted approach for reducing ship emissions of sulfur dioxide ( SO2 ) and particulate matter (PM), particularly in emission control areas (ECAs), but its impact on the emissions of volatile organic compounds (VOCs) is still not well understood. In this study, emissions from ships at berth in Guangzhou, southern China, were characterized before and after the implementation of the fuel switch policy (IFSP) with an FSC limit of 0.5 % in the Pearl River Delta ECA (ECA-PRD). After IFSP, the emission factors (EFs) of SO2 and PM 2.5 for the coastal vessels decreased by 78 % and 56 % on average, respectively; however, the EFs of the VOCs were 1807±1746 mg kg −1 , approximately 15 times that of 118±56.1 mg kg −1 before IFSP. This dramatic increase in the emissions of the VOCs might have been largely due to the replacement of high-sulfur residual fuel oil with low-sulfur diesel or heavy oils, which are typically richer in short-chain hydrocarbons. Moreover, reactive alkenes surpassed alkanes to become the dominant group among the VOCs, and low-carbon-number VOCs, such as ethylene, propene and isobutane, became the dominant species after IFSP. As a result of the largely elevated EFs of the reactive alkenes and aromatics after IFSP, the emitted VOCs per kilogram of fuel burned had nearly 29 times greater ozone formation potential (OFP) and approximately 2 times greater secondary organic aerosol formation potential (SOAFP) than those before IFSP. Unlike the coastal vessels, the river vessels in the region used diesel fuels consistently and were not affected by the fuel switch policy, but the EFs of their VOCs were 90 % greater than those of the coastal vessels after IFSP, with approximately 120 % greater fuel-based OFP and 70 %–140 % greater SOAFP. The results from this study suggest that while the fuel switch policy could effectively reduce SO2 and PM emissions, and thus help control PM 2.5 pollution, it will also lead to greater emissions of reactive VOCs, which may threaten ozone pollution control in harbor cities. This change for coastal or ocean-going vessels, in addition to the large amounts of reactive VOCs from the river vessels, raises regulatory concerns for ship emissions of reactive VOCs.
On the last zero process of a spectrally negative L\'evy process

Let $X$ be a spectrally negative Levy process and consider $g_t$ the last time $X$ is below the level zero before time $t\geq 0$. We derive an Ito formula for the three dimensional process $\{(g_t,t,X_t), t\geq 0 \}$ and its infinitesimal generator using a perturbation method for Levy processes. We also find an explicit formula for calculating functionals that include the whole path of the length of current positive excursion at time $t\geq 0$,q $U_t:=t-g_t$. These results are applied to optimal prediction problems for the last zero $g:=\lim_{t \rightarrow \infty} g_t$, when $X$ drifts to infinity. Moreover, the joint Laplace transform of $(U_{e_q},X_{e_q})$ where $e_q$ is an independent exponential time is found and a formula for a density of the $q$-potential measure of the process $\{(U_t,X_t,),t\geq 0 \}$ is derived.
A Rate Splitting Strategy for Mitigating Intra-Cell Pilot Contamination in Massive MIMO

The spectral efficiency (SE) of Massive MIMO (MaMIMO) systems is affected by low quality channel estimates. Rate-Splitting (RS) has recently gained some interest in multiuser multiple antenna systems as an effective means to mitigate the multi-user interference due to imperfect channel state information. This paper investigates the benefits of RS in the downlink of a single-cell MaMIMO system when all the users use the same pilot sequence for channel estimation. Novel expressions for the SE achieved in the downlink by a single-layer RS strategy (that relies on a single successive interference cancellation at each user side) are derived and used to design precoding schemes and power allocation strategies for common and private messages. Numerical results are used to show that the proposed RS solution achieves higher spectral efficiency that conventional MaMIMO with maximum ratio precoding.
Covid-19 spread: Reproduction of data and prediction using a SIR model on Euclidean network.

We study the datafor the cumulative as well as daily number of cases in the Covid-19 outbreak in China. The cumulative data can be fit to an empirical form obtained from a Susceptible-Infected-Removed (SIR) model studied on an Euclidean network previously. Plotting the number of cases against the distance from the epicenter for both China and Italy, we find an approximate power law variation with an exponent $\sim 1.85$ showing strongly that the spatial dependence plays a key role, a factor included in the model. We report here that the SIR model on the Eucledean network can reproduce with a high accuracy the data for China for given parameter values, and can also predict when the epidemic, at least locally, can be expected to be over.
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction

Deep reinforcement learning can learn effective policies for a wide range of tasks, but is notoriously difficult to use due to instability and sensitivity to hyperparameters. The reasons for this remain unclear. When using standard supervised methods (e.g., for bandits), on-policy data collection provides "hard negatives" that correct the model in precisely those states and actions that the policy is likely to visit. We call this phenomenon "corrective feedback. " We show that bootstrapping-based Q-learning algorithms do not necessarily benefit from this corrective feedback, and training on the experience collected by the algorithm is not sufficient to correct errors in the Q-function. In fact, Q-learning and related methods can exhibit pathological interactions between the distribution of experience collected by the agent and the policy induced by training on that experience, leading to potential instability, sub-optimal convergence, and poor results when learning from noisy, sparse or delayed rewards. We demonstrate the existence of this problem, both theoretically and empirically. We then show that a specific correction to the data distribution can mitigate this issue. Based on these observations, we propose a new algorithm, DisCor, which computes an approximation to this optimal distribution and uses it to re-weight the transitions used for training, resulting in substantial improvements in a range of challenging RL settings, such as multi-task learning and learning from noisy reward signals. Blog post presenting a summary of this work is available at: this https URL.
Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning

This paper studies the problem of post-hoc calibration of machine learning classifiers. We introduce the following desiderata for uncertainty calibration: (a) accuracy-preserving, (b) data-efficient, and (c) high expressive power. We show that none of the existing methods satisfy all three requirements, and demonstrate how Mix-n-Match calibration strategies (i.e., ensemble and composition) can help achieve remarkably better data-efficiency and expressive power while provably maintaining the classification accuracy of the original classifier. Mix-n-Match strategies are generic in the sense that they can be used to improve the performance of any off-the-shelf calibrator. We also reveal potential issues in standard evaluation practices. Popular approaches (e.g., histogram-based expected calibration error (ECE)) may provide misleading results especially in small-data regime. Therefore, we propose an alternative data-efficient kernel density-based estimator for a reliable evaluation of the calibration performance and prove its asymptotically unbiasedness and consistency. Our approaches outperform state-of-the-art solutions on both the calibration as well as the evaluation tasks in most of the experimental settings. Our codes are available at this https URL.
Scrutinizer

Organizations spend significant amounts of time and money to manually fact check text documents summarizing data. The goal of the Scrutinizer system is to reduce verification overheads by supporting human fact checkers in translating text claims into SQL queries on an database. Scrutinizer coordinates teams of human fact checkers. It reduces verification time by proposing queries or query fragments to the users. Those proposals are based on claim text classifiers, that gradually improve during the verification of a large document. In addition, Scrutinizer uses tentative execution of query candidates to narrow down the set of alternatives. The verification process is controlled by a cost-based optimizer. It optimizes the interaction with users and prioritizes claim verifications. For the latter, it considers expected verification overheads as well as the expected claim utility as training samples for the classifiers. We evaluate the Scrutinizer system using simulations and a user study with professional fact checkers, based on actual claims and data. Our experiments consistently demonstrate significant savings in verification time, without reducing result accuracy.
Universal and optimal coin sequences for high entanglement generation in 1D discrete time quantum walks

Entanglement is a key resource in many quantum information applications and achieving high values independently of the initial conditions is an important task. Here we address the problem of generating highly entangled states in a discrete time quantum walk irrespective of the initial state using two different approaches. First, we present and analyze a deterministic sequence of coin operators which produces high values of entanglement in a universal manner for a class of localized initial states. In a second approach, we optimize the discrete sequence of coin operators using a reinforcement learning algorithm. While the amount of entanglement produced by the deterministic sequence is fully independent of the initial states considered, the optimized sequences achieve in general higher average values of entanglement that do however depend on the initial state parameters. Our proposed sequence and optimization algorithm are especially useful in cases where the initial state is not fully known or entanglement has to be generated in a universal manner for a range of initial states.
Learning Reinforced Agents with Counterfactual Simulation for Medical Automatic Diagnosis

Medical automatic diagnosis (MAD) aims to learn an agent that mimics the behavior of human doctors, i.e. inquiring symptoms and informing diseases. Due to medical ethics concerns, it is impractical to directly apply reinforcement learning techniques to MAD, e.g., training a reinforced agent with human patients. Developing a patient simulator by using the collected patient-doctor dialogue records has been proposed as a promising workaround to MAD. However, most of these existing works overlook the causal relationship between patient symptoms and diseases. For example, these simulators simply generate the "not-sure" response to the symptom inquiry if the symptom was not observed in the dialogue record. Consequently, the MAD agent is usually trained without exploiting the counterfactual reasoning beyond the factual observations. To address this problem, this paper presents a propensity-based patient simulator (PBPS), which is capable of facilitating the training of MAD agents by generating informative counterfactual answers along with the disease diagnosis. Specifically, our PBPS estimates the propensity score of each record with the patient-doctor dialogue reasoning, and can thus generate the counterfactual answers by searching across records. That is, the unrecorded symptom for one patient can be found in the records of other patients according to the propensity score matching. The informative and causal-aware responses from PBPS are beneficial for modeling diagnostic confidence. To this end, we also propose a progressive assurance agent~(P2A) trained with PBPS, which includes two separate yet cooperative branches accounting for the execution of symptom-inquiry and disease-diagnosis actions, respectively.
Guaranteed convergence for a class of coupled-cluster methods based on Arponen's extended theory

A wide class of coupled-cluster methods is introduced, based on Arponen's extended coupled-cluster theory. This class of methods is formulated in terms of a coordinate transformation of the cluster operators. The mathematical framework for the error analysis of coupled-cluster methods based on Arponen's bivariational principle is presented, in which the concept of local strong monotonicity of the flipped gradient of the energy is central. A general mathematical result is presented, describing sufficient conditions for coordinate transformations to preserve the local strong monotonicity. The result is applied to the presented class of methods, which include the standard and quadratic coupled-cluster methods, and also Arponen's canonical version of extended coupled-cluster theory. Some numerical experiments are presented, and the use of canonical coordinates for diagnostics is discussed. GRAPHICAL ABSTRACT
OmniTact: A Multi-Directional High-Resolution Touch Sensor

Incorporating touch as a sensing modality for robots can enable finer and more robust manipulation skills. Existing tactile sensors are either flat, have small sensitive fields or only provide low-resolution signals. In this paper, we introduce OmniTact, a multi-directional high-resolution tactile sensor. OmniTact is designed to be used as a fingertip for robotic manipulation with robotic hands, and uses multiple micro-cameras to detect multi-directional deformations of a gel-based skin. This provides a rich signal from which a variety of different contact state variables can be inferred using modern image processing and computer vision methods. We evaluate the capabilities of OmniTact on a challenging robotic control task that requires inserting an electrical connector into an outlet, as well as a state estimation problem that is representative of those typically encountered in dexterous robotic manipulation, where the goal is to infer the angle of contact of a curved finger pressing against an object. Both tasks are performed using only touch sensing and deep convolutional neural networks to process images from the sensor’s cameras. We compare with a state-of-the-art tactile sensor that is only sensitive on one side, as well as a state-of-the-art multi-directional tactile sensor, and find that OmniTact’s combination of high-resolution and multi-directional sensing is crucial for reliably inserting the electrical connector and allows for higher accuracy in the state estimation task. Videos and supplementary material can be found here.4
Resolution Adaptive Networks for Efficient Inference

Adaptive inference is an effective mechanism to achieve a dynamic tradeoff between accuracy and computational cost in deep networks. Existing works mainly exploit architecture redundancy in network depth or width. In this paper, we focus on spatial redundancy of input samples and propose a novel Resolution Adaptive Network (RANet), which is inspired by the intuition that low-resolution representations are sufficient for classifying “easy” inputs containing large objects with prototypical features, while only some “hard” samples need spatially detailed information. In RANet, the input images are first routed to a lightweight sub-network that efficiently extracts low-resolution representations, and those samples with high prediction confidence will exit early from the network without being further processed. Meanwhile, high-resolution paths in the network maintain the capability to recognize the “hard” samples. Therefore, RANet can effectively reduce the spatial redundancy involved in inferring high-resolution inputs. Empirically, we demonstrate the effectiveness of the proposed RANet on the CIFAR-10, CIFAR-100 and ImageNet datasets in both the anytime prediction setting and the budgeted batch classification setting.
Toward Adversarial Robustness via Semi-supervised Robust Training

Adversarial examples have been shown to be the severe threat to deep neural networks (DNNs). One of the most effective adversarial defense methods is adversarial training (AT) through minimizing the adversarial risk $R_{adv}$, which encourages both the benign example $x$ and its adversarially perturbed neighborhoods within the $\ell_{p}$-ball to be predicted as the ground-truth label. In this work, we propose a novel defense method, the robust training (RT), by jointly minimizing two separated risks ($R_{stand}$ and $R_{rob}$), which is with respect to the benign example and its neighborhoods respectively. The motivation is to explicitly and jointly enhance the accuracy and the adversarial robustness. We prove that $R_{adv}$ is upper-bounded by $R_{stand} + R_{rob}$, which implies that RT has similar effect as AT. Intuitively, minimizing the standard risk enforces the benign example to be correctly predicted, and the robust risk minimization encourages the predictions of the neighbor examples to be consistent with the prediction of the benign example. Besides, since $R_{rob}$ is independent of the ground-truth label, RT is naturally extended to the semi-supervised mode ($i.e.$, SRT), to further enhance the adversarial robustness. Moreover, we extend the $\ell_{p}$-bounded neighborhood to a general case, which covers different types of perturbations, such as the pixel-wise ($i.e.$, $x + \delta$) or the spatial perturbation ($i.e.$, $ AX + b$). Extensive experiments on benchmark datasets not only verify the superiority of the proposed SRT method to state-of-the-art methods for defensing pixel-wise or spatial perturbations separately, but also demonstrate its robustness to both perturbations simultaneously. The code for reproducing main results is available at \url{this https URL}.
Debiasing the crowd: selectively exchanging social information improves collective decision making

Collective decision making is ubiquitous across biological systems. However, biases at the individual level can impair the quality of collective decisions. One prime bias is the human tendency to underestimate quantities. We performed estimation experiments in human groups, in which we re-wired the structure of information exchange, favouring the exchange of estimates closest to an overestimation of the median, expected to approximate the truth. We show that this re-wiring of social information exchange counteracts the underestimation bias and boosts collective decisions compared to random exchange. Underlying this result are a human tendency to herd, to trust large numbers more than small numbers, and to follow disparate social information less. We introduce a model that reproduces all the main empirical results, and predicts conditions for optimising collective decisions. Our results show that leveraging existing knowledge on biases can boost collective decision making, paving the way for combating other cognitive biases threatening collective systems.
RSVQA: Visual Question Answering for Remote Sensing Data

This article introduces the task of visual question answering for remote sensing data (RSVQA). Remote sensing images contain a wealth of information, which can be useful for a wide range of tasks, including land cover classification, object counting, or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high-level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method introduced in this article, we built two data sets (using low- and high-resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The data sets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task. We report the results obtained by applying a model based on convolutional neural networks (CNNs) for the visual part and a recurrent neural network (RNN) for the natural language part of this task. The model is trained on the two data sets, yielding promising results in both cases.
Benefits and Harms of the Mediterranean Diet Compared to Other Diets

s from MEDLINE (n = 7,672) were reviewed in duplicate independently by investigators and research associates. Abstracts from the CINAHL database (n = 131) and Cochrane Library (n = 1,799) were reviewed by a co-investigator or research associate. Trained researchers identified articles published in peer-reviewed journals related to one of the Key Questions (KQs). For studies addressing KQs 1 and 2 in diseases other than cancer, RA, or cognitive impairment we included RCTs or controlled clinical trials (CCTs) with at least 100 subjects followed for at least one year. For studies of RA and cognitive impairment we included RCTs, CCTs, and cohort studies with any number of participants and no limit on follow-up time. For studies of cancer and for KQ3 we included RCTs and cohort studies with at least 100 participants followed for at least one year. In addition, in order to be included, studies addressing Benefits and Harms of the Mediterranean Diet Evidence-based Synthesis Program Compared to Other Diets 16 KQ3 must have taken place in the United States or Canada. For all questions and outcomes, we excluded case series, case reports, qualitative reports, narrative reviews, editorials, or letters. Studies must have also reported one of our outcomes of interest as defined in the Analytic Framework (Figure 1) and PICOTS (above). All included studies met our definition of a Mediterranean diet. In the randomized trials, the composition of the intervention diets were defined a priori. In the cohort studies, food frequency surveys (either self-administered or administered by research staff) were typically used to categorize subjects’ diets based on validated dietary indices. Commonly used indices include the Mediterranean-diet score, the alternate Mediterranean-diet score (aMED), the Healthy Eating Index (HEI), and the Alternate Healthy Eating Index (aHEI). For these indices we generally compared the highest quantile of conformity to a Mediterranean diet to the lowest. We excluded the following: ∑ Studies that did not involve outpatient adults; ∑ Studies with diets that were not labelled Mediterranean and did not test or measure a diet that met our criteria for a Mediterranean diet (stated above); ∑ Studies in women who were pregnant or lactating; and ∑ For KQ3, studies that were conducted in countries other than the US and Canada. Full-text reports of studies identified as potentially eligible were obtained for further review using the inclusion and exclusion criteria described above. Each article was independently reviewed by 2 trained researchers. Reasons for excluding a study at full-text review were noted.
Mirror symmetry for double cover Calabi--Yau varieties

The presented paper is a continuation of the series of papers arXiv:1810.00606 and arXiv:1903.09373. In this paper, utilizing Batyrev and Borisov's duality construction on nef-partitions, we generalize the recipe in arXiv:1810.00606 and arXiv:1903.09373 to construct a pair of singular double cover Calabi--Yau varieties $(Y,Y^{\vee})$ over toric manifolds and compute their topological Euler characteristics and Hodge numbers. In the $3$-dimensional cases, we show that $(Y,Y^{\vee})$ forms a topological mirror pair, i.e., $h^{p,q}(Y)=h^{3-p,q}(Y^{\vee})$ for all $p,q$.