Miracle-dz/UniFAR-FTUs huggingface dataset:
Tracking Public Opinion in China through Various Stages of the COVID-19 Pandemic

In recent months, COVID-19 has become a global pandemic and had a huge impact on the world. People under different conditions have very different attitudes toward the epidemic. Due to the real-time and large-scale nature of social media, we can continuously obtain a massive amount of public opinion information related to the epidemic from social media. In particular, researchers may ask questions such as "how is the public reacting to COVID-19 in China during different stages of the pandemic? ", "what factors affect the public opinion orientation in China? ", and so on. To answer such questions, we analyze the pandemic related public opinion information on Weibo, China's largest social media platform. Specifically, we have first collected a large amount of COVID-19-related public opinion microblogs. We then use a sentiment classifier to recognize and analyze different groups of users' opinions. In the collected sentiment orientated microblogs, we try to track the public opinion through different stages of the COVID-19 pandemic. Furthermore, we analyze more key factors that might have an impact on the public opinion of COVID-19 (e.g., users in different provinces or users with different education levels). Empirical results show that the public opinions vary along with the key factors of COVID-19. Furthermore, we analyze the public attitudes on different public-concerning topics, such as staying at home and quarantine.
Motion2Vec: Semi-Supervised Representation Learning from Surgical Videos

Learning meaningful visual representations in an embedding space can facilitate generalization in downstream tasks such as action segmentation and imitation. In this paper, we learn a motion-centric representation of surgical video demonstrations by grouping them into action segments/subgoals/options in a semi-supervised manner. We present Motion2Vec, an algorithm that learns a deep embedding feature space from video observations by minimizing a metric learning loss in a Siamese network: images from the same action segment are pulled together while pushed away from randomly sampled images of other segments, while respecting the temporal ordering of the images. The embeddings are iteratively segmented with a recurrent neural network for a given parametrization of the embedding space after pre-training the Siamese network. We only use a small set of labeled video segments to semantically align the embedding space and assign pseudo-labels to the remaining unlabeled data by inference on the learned model parameters. We demonstrate the use of this representation to imitate surgical suturing kinematic motions from publicly available videos of the JIGSAWS dataset. Results give 85.5% segmentation accuracy on average suggesting performance improvement over several state-of-the-art baselines, while kinematic pose imitation gives 0.94 centimeter error in position per observation on the test set. Videos, code and data are available at: https://sites.google. com/view/motion2vec
Explicit solutions for a class of nonlinear BSDEs and their nodal sets

In this paper, we investigate a class of nonlinear backward stochastic differential equations (BSDEs) arising from financial economics, and give specific information about the nodal sets of the related solutions. As applications, we are able to obtain the explicit solutions to an interesting class of nonlinear BSDEs including the k-ignorance BSDE arising from the modeling of ambiguity of asset pricing.
Consistent feature selection for neural networks via Adaptive Group Lasso

One main obstacle for the wide use of deep learning in medical and engineering sciences is its interpretability. While neural network models are strong tools for making predictions, they often provide little information about which features play significant roles in influencing the prediction accuracy. To overcome this issue, many regularization procedures for learning with neural networks have been proposed for dropping non-significant features. Unfortunately, the lack of theoretical results casts doubt on the applicability of such pipelines. In this work, we propose and establish a theoretical guarantee for the use of the adaptive group lasso for selecting important features of neural networks. Specifically, we show that our feature selection method is consistent for single-output feed-forward neural networks with one hidden layer and hyperbolic tangent activation function. We demonstrate its applicability using both simulation and data analysis.
Superconductors: The Standing Wave Model and Super-fluid Density

The standing wave model describes the well-known phenomenon of superconductivity in a new way [1]. Starting from a new definition of superconductivity, a microscopic London relation is derived from first principles. The relation between the macroscopic electric current and the field is derived from this microscopic relation. The coherence length as function of the superfluid density is derived from the microscopic theory. The critical temperature as function of the superfluid density is thus obtained. These results are compared to the experimental work of Bozovic et. al. [2].
Modeling Personalized Item Frequency Information for Next-basket Recommendation

Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential modeling and recommendation based on a sequence of baskets. NBR is in general more complex than the widely studied sequential (session-based) recommendation which recommends the next item based on a sequence of items. Recurrent neural network (RNN) has proved to be very effective for sequential modeling, and thus been adapted for NBR. However, we argue that existing RNNs cannot directly capture item frequency information in the recommendation scenario. Through careful analysis of real-world datasets, we find that personalized item frequency (PIF) information (which records the number of times that each item is purchased by a user) provides two critical signals for NBR. But, this has been largely ignored by existing methods. Even though existing methods such as RNN based methods have strong representation ability, our empirical results show that they fail to learn and capture PIF. As a result, existing methods cannot fully exploit the critical signals contained in PIF. Given this inherent limitation of RNNs, we propose a simple item frequency based k-nearest neighbors (kNN) method to directly utilize these critical signals. We evaluate our method on four public real-world datasets. Despite its relative simplicity, our method frequently outperforms the state-of-the-art NBR methods - including deep learning based methods using RNNs - when patterns associated with PIF play an important role in the data.
Spatial Distribution of the Mean Peak Age of Information in Wireless Networks

This paper considers a large-scale wireless network consisting of source-destination (SD) pairs, where the sources send time-sensitive information, termed status updates, to their corresponding destinations in a time-slotted fashion. We employ age of information (AoI) for quantifying the freshness of the status updates measured at the destination nodes under the preemptive and non-preemptive queueing disciplines with no storage facility. The non-preemptive queue drops the newly arriving updates until the update in service is successfully delivered, whereas the preemptive queue replaces the current update in service with the newly arriving update, if any. As the update delivery rate for a given link is a function of the interference field seen from the receiver, the temporal mean AoI can be treated as a random variable over space. Our goal in this paper is to characterize the spatial distribution of the mean AoI observed by the SD pairs by modeling them as a bipolar Poisson point process (PPP). Towards this objective, we first derive accurate bounds on the moments of success probability while efficiently capturing the interference-induced coupling in the activities of the SD pairs. Using this result, we then derive tight bounds on the moments as well as the spatial distribution of peak AoI (PAoI). Our numerical results verify our analytical findings and demonstrate the impact of various system design parameters on the mean PAoI.
Infectious Disease Forecasting for Public Health

Forecasting transmission of infectious diseases, especially for vector-borne diseases, poses unique challenges for researchers. Behaviors of and interactions between viruses, vectors, hosts, and the environment each play a part in determining the transmission of a disease. Public health surveillance systems and other sources provide valuable data that can be used to accurately forecast disease incidence. However, many aspects of common infectious disease surveillance data are imperfect: cases may be reported with a delay or in some cases not at all, data on vectors may not be available, and case data may not be available at high geographical or temporal resolution. In the face of these challenges, researchers must make assumptions to either account for these underlying processes in a mechanistic model or to justify their exclusion altogether in a statistical model.
Multiscale Cloud Detection in Remote Sensing Images Using a Dual Convolutional Neural Network

Semantic segmentation by convolutional neural networks (CNN) has advanced the state of the art in pixel-level classification of remote sensing images. However, processing large images typically requires analyzing the image in small patches, and hence, features that have a large spatial extent still cause challenges in tasks, such as cloud masking. To support a wider scale of spatial features while simultaneously reducing computational requirements for large satellite images, we propose an architecture of two cascaded CNN model components successively processing undersampled and full-resolution images. The first component distinguishes between patches in the inner cloud area from patches at the cloud’s boundary region. For the cloud-ambiguous edge patches requiring further segmentation, the framework then delegates computation to a fine-grained model component. We apply the architecture to a cloud detection data set of complete Sentinel-2 multispectral images, approximately annotated for minimal false negatives in a land-use application. On this specific task and data, we achieve a 16% relative improvement in pixel accuracy over a CNN baseline based on patching.
A Combined Approach To Detect Key Variables In Thick Data Analytics

In machine learning one of the strategic tasks is the selection of only significant variables as predictors for the response(s). In this paper an approach is proposed which consists in the application of permutation tests on the candidate predictor variables in the aim of identifying only the most informative ones. Several industrial problems may benefit from such an approach, and an application in the field of chemical analysis is presented. A comparison is carried out between the approach proposed and Lasso, that is one of the most common alternatives for feature selection available in the literature.
A frame semantics based approach to comparative study of digitized corpus

in this paper, we present a corpus linguistics based approach applied to analyzing digitized classical multilingual novels and narrative texts, from a semantic point of view. Digitized novels such as "the hobbit (Tolkien J. R. R., 1937)" and "the hound of the Baskervilles (Doyle A. C. 1901-1902)", which were widely translated to dozens of languages, provide rich materials for analyzing languages differences from several perspectives and within a number of disciplines like linguistics, philosophy and cognitive science. Taking motion events conceptualization as a case study, this paper, focus on the morphologic, syntactic, and semantic annotation process of English-Arabic aligned corpus created from a digitized novels, in order to re-examine the linguistic encodings of motion events in English and Arabic in terms of Frame Semantics. The present study argues that differences in motion events conceptualization across languages can be described with frame structure and frame-to-frame relations.
Hadamard Wirtinger Flow for Sparse Phase Retrieval

We consider the problem of reconstructing an $n$-dimensional $k$-sparse signal from a set of magnitude-only measurements. Formulating the problem as an unregularized empirical risk minimization task, we study the sample complexity performance of gradient descent with Hadamard parametrization, which we call Hadamard Wirtinger flow (HWF). Provided knowledge of the signal sparsity $k$, we prove that a single step of HWF is able to recover the support from $\mathcal{O}(k(x^*_{max})^{-2} \log n)$ samples, where $x^*_{max}$ is the largest component of the signal in magnitude. This support recovery procedure can be used to initialize existing reconstruction methods and yields algorithms with total runtime proportional to the cost of reading the data and improved sample complexity, which is linear in $k$ when the signal contains at least one large component. We numerically investigate the performance of HWF at convergence and show that, while not requiring any explicit form of regularization nor knowledge of $k$, HWF adapts to the signal sparsity and reconstructs sparse signals with fewer measurements than existing state-of-the-art methods.
Cyber LOPA: A New Approach for CPS Safety Design in the Presence of Cyber Attacks

Safety risk assessment is an essential process to ensure a dependable Cyber-Physical Systems (CPS) design. Traditional risk assessment considers only physical failures. For modern CPS, failures caused by cyberattacks are on the rise. The focus of latest research effort is on safety-security lifecycle integration and the expansion of modeling formalism for risk assessment to incorporate security failures. The interaction between safety and security and its impact on the overall system design, as well as the reliability loss resulting from ignoring security failures are some of the overlooked research questions. This paper addresses these research questions by presenting a new safety design method named Cyber Layer Of Protection Analysis (CLOPA) that extends existing LOPA framework to include failures caused by cyberattacks. The proposed method provides a rigorous mathematical formulation that expresses quantitatively the tradeoff between designing a highly-reliable versus a highly-secure CPS. We further propose a co-design lifecycle process that integrates the safety and security risk assessment processes. We evaluate the proposed CLOPA approach and the integrated lifecycle on a practical case study of a process reactor controlled by an industrial control testbed, and provide a comparison between the proposed CLOPA and current LOPA risk assessment practice.
Probabilistic Forecasting of Patient Waiting Times in an Emergency Department

Problem definition: We study the estimation of the probability distribution of individual patient waiting times in an emergency department (ED). Whereas it is known that waiting-time estimates can help improve patients’ overall satisfaction and prevent abandonment, existing methods focus on point forecasts, thereby completely ignoring the underlying uncertainty. Communicating only a point forecast to patients can be uninformative and potentially misleading. Methodology/results: We use the machine learning approach of quantile regression forest to produce probabilistic forecasts. Using a large patient-level data set, we extract the following categories of predictor variables: (1) calendar effects, (2) demographics, (3) staff count, (4) ED workload resulting from patient volumes, and (5) the severity of the patient condition. Our feature-rich modeling allows for dynamic updating and refinement of waiting-time estimates as patient- and ED-specific information (e.g., patient condition, ED congestion levels) is revealed during the waiting process. The proposed approach generates more accurate probabilistic and point forecasts when compared with methods proposed in the literature for modeling waiting times and rolling average benchmarks typically used in practice. Managerial implications: By providing personalized probabilistic forecasts, our approach gives low-acuity patients and first responders a more comprehensive picture of the possible waiting trajectory and provides more reliable inputs to inform prescriptive modeling of ED operations. We demonstrate that publishing probabilistic waiting-time estimates can inform patients and ambulance staff in selecting an ED from a network of EDs, which can lead to a more uniform spread of patient load across the network. Aspects relating to communicating forecast uncertainty to patients and implementing this methodology in practice are also discussed. For emergency healthcare service providers, probabilistic waiting-time estimates could assist in ambulance routing, staff allocation, and managing patient flow, which could facilitate efficient operations and cost savings and aid in better patient care and outcomes. Supplemental Material: The online supplement is available at https://doi.org/10.1287/msom.2023. 1210 .
C : N ratios and carbon distribution profile across rooting zones in oilseed and pulse crops

Knowledge on the C : N ratio of crop residues is of great importance for modelling carbon (C) and nitrogen (N) dynamics of agricultural systems. This study determined (i) the C : N ratios in the seed, straw, and roots of selected broadleaf crops and (ii) the root C and N distribution in the 0–100 cm rooting zone. Three oilseed (Brassica napus canola, Brassica juncea mustard, Linum usitatissimum flax), three pulse crops (Cicer arietinum chickpea, Pisum sativum dry pea, Lens culinaris lentil), and spring wheat (Triticum aestivum L.) were grown under field conditions with low- (rainfall only) and high-water (rainfall plus irrigation) availability. Root C mass decreased substantially with rooting depth, with ~58% of root C mass in the top 20 cm of the soil, 78% in the top 40 cm, and 94% in the top 60 cm. Significant differences in root C mass between crop species occurred in the top 20 cm with canola, mustard, and wheat allocating 66% of their root C total, compared with 55% for dry pea, lentil, and flax, and 41% for chickpea. Root N mass followed a similar response to root C. Seed C : N ratios ranged between 6 and 17, whereas straw C : N ranged between 14 and 55, and root C : N between 17 and 75. Under low-water conditions, canola and mustard had a straw C : N of 33, lower than that of flax (38) and wheat (41). Under higher-water availability, however, mustard and wheat had straw C : N ratios at 55, greater than canola, mustard and flax (47). Three pulses had an average straw C : N ratio of 17, significantly lower than 41 for the oilseeds and 32 for wheat. On average, canola, mustard and wheat had greater root C : N ratios (44) than chickpea (33) and lentil (29), with dry pea having a smallest root C : N ratio (18). Root C : N ratios did not change with soil depth. These detailed measurements on the vertical distribution of root C and N as well as C : N ratios for various crops will assist in improving estimates of inputs for C and N cycling studies.
Land‐use Change Dynamics, Soil Type and Species Forming Mono‐dominant Patches: the Case of Pteridium aquilinum in a Neotropical Rain Forest Region

Deforestation and agricultural land degradation in tropical regions can create conditions for growth of perennial plant species forming mono‐dominated patches (MDP). Such species might limit forest regeneration, and their proliferation forces the abandonment of fields and subsequent deforestation to establish new fields. Therefore, identifying factors fostering MDP species is critical for biodiversity conservation in human‐modified landscapes. Here, we propose a conceptual framework to identify such factors and apply it to the case of Pteridium aquilinum (bracken fern), a light‐demanding species, tolerant of low soil fertility and fire. We hypothesize that bracken proliferation is promoted by land‐use changes that increase light availability, especially in sites with low soil fertility and land uses involving fire. We assessed this idea using agricultural fields in southeastern Mexico with different land‐use change histories and quantifying prevalence and cover of bracken. Five different land‐use change histories resulted from transitions among forest, crop, pasture, and fallow field stages. Of the 133 fields sampled, 71 percent had P. aquilinum; regression tree analysis indicated that 65 percent of inter‐field variation in prevalence and 90 percent in cover was explained by land‐use change history and soil type. Maximum prevalence, cover, and rates of increase in bracken were found on fields with low fertility sandy/clay soils, which had been used for crops and pasture, were frequently burned, and had high levels of light. Fields on fertile alluvial soil never used for pasture were bracken‐free. Agriculture promoting high light environments on less fertile soils is a major cause of bracken proliferation and likely that of other MDP species.
Mussel reefs on soft sediments: a severely reduced but important habitat for macroinvertebrates and fishes in New Zealand

Green-lipped mussels (Perna canaliculus) formed extensive reefs on soft sediments in sheltered embayments around northern New Zealand until overfishing and/or increased sediment input caused their virtual disappearance by 1980. To determine the role of mussel reefs as habitat for other animals, we located remnant soft-sediment reefs in five locations and compared the density, biomass, productivity and composition of mobile macroinvertebrate communities, and the density of small fishes associated with mussels, with fauna in the surrounding soft sediments. The mussel reefs had a distinct assemblage of macroinvertebrates, which had 3.5 times the density, 3.4 times the biomass and 3.5 times the productivity of surrounding areas. The density of small fishes was 13.7 times higher than in surrounding areas. These results show that soft-sediment mussel reefs support an abundant and productive fauna, highlighting the probable large loss of productivity associated with the historical decline in mussel habitat and the consequent desirability of restoration efforts.
Description and Classification of Lindera melissifolia Habitat in the Southeastern Coastal Plain

ABSTRACT Lindera melissifolia is a federally endangered wetland shrub endemic to the Southeastern Coastal Plain and the Lower Mississippi Alluvial Valley. Lindera melissifolia is known to occupy seasonally flooded habitats, but little work has been done to describe habitat characteristics in the Southeastern Coastal Plain region. This research uses vegetation and environmental data collected from 50 plots in North Carolina, South Carolina, and Georgia to develop a habitat classification and description for L. melissifolia in the Southeastern Coastal Plain. Cluster analysis, nonmetric multidimensional scaling, indicator species analysis, and analysis of variance were used to assign plots to four distinct isolated wetland habitat types. Swamp Tupelo Depression Pond and Successional Swamp Forest communities supported the densest and healthiest L. melissifolia populations, followed by Pond-Cypress Pond and Pocosin, and Limestone Sink Forest habitats. Habitat degradation and other forms of disturbance were found to impact L. melissifolia populations in this region. This classification and description can be used to advance overall understanding of L. melissifolia ecology, and to provide specific habitat requirement information to land managers in the Southeastern Coastal Plain.