Miracle-dz/UniFAR-FTUs huggingface dataset:
Studies on the Monimiaceae. I. Floral morphology and gametophyte development of Hedycarya arborea J.R. et.G. Forst. (subfamily Monimioideae)

Inflorescences, flowers, and floral vascularization of the New Zealand endemic species Hedycarya arborea are described. Varying carpel vasculature suggests derivation of the uniovulate condition in Hedycarya from ancestors having multiovulate carpels with ovules in two rows, Floral ontogeny is described and it is noted that the terminal stigmatic region of the carpel develops from a solid terminal meristem, in contrast to many woody Ranales in which the stigma consists of crests surrounding the carpel cleft. The stigmatic surface is a mass of globose projections, apparently serving as pollen traps. No comparable type of stigma has previously been reported in the woody Ranales. The microsporangium has a typically thickened endothecium and a tapetum of the secretory type with tapetal cells becoming binucleate during the first meiotic division of pollen mother cells. Pollen mother cell division is of the successive type with cytokinesis by centrifugally extending cell plates. The generative cell is cut off towards the distal face of the microspore. The pollen, in permanent tetrads, is shed in the two-celled condition. Ovules are bitegmic, crassinucellate, and anatropous with a Polygonum type of embryo sac development. Some comparisons are made with the Australian species Hedycarya angustifolia.
Twitter discussions and concerns about COVID-19 pandemic: Twitter data analysis using a machine learning approach

The objective of the study is to examine coronavirus disease (COVID-19) related discussions, concerns, and sentiments that emerged from tweets posted by Twitter users. We collected 22 million Twitter messages related to the COVID-19 pandemic using a list of 25 hashtags such as "coronavirus," "COVID-19," "quarantine" from March 1 to April 21 in 2020. We used a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigram, bigrams, salient topics and themes, and sentiments in the collected Tweets. Popular unigrams included "virus," "lockdown," and "quarantine. " Popular bigrams included "COVID-19," "stay home," "corona virus," "social distancing," and "new cases. " We identified 13 discussion topics and categorized them into different themes, such as "Measures to slow the spread of COVID-19," "Quarantine and shelter-in-place order in the U.S.," "COVID-19 in New York," "Virus misinformation and fake news," "A need for a vaccine to stop the spread," "Protest against the lockdown," and "Coronavirus new cases and deaths. " The dominant sentiments for the spread of coronavirus were anticipation that measures that can be taken, followed by a mixed feeling of trust, anger, and fear for different topics. The public revealed a significant feeling of fear when they discussed the coronavirus new cases and deaths. The study concludes that Twitter continues to be an essential source for infodemiology study by tracking rapidly evolving public sentiment and measuring public interests and concerns. Already emerged pandemic fear, stigma, and mental health concerns may continue to influence public trust when there occurs a second wave of COVID-19 or a new surge of the imminent pandemic. Hearing and reacting to real concerns from the public can enhance trust between the healthcare systems and the public as well as prepare for a future public health emergency.
Finite-size effects in wave transmission through plasmonic crystals: A tale of two scales

We study optical coefficients that characterize wave propagation through layered structures called plasmonic crystals. These consist of a finite number of stacked metallic sheets embedded in dielectric hosts with a subwavelength spacing. By adjustment of the frequency, spacing, number as well as geometry of the layers, these structures may exhibit appealing transmission properties in a range of frequencies from the terahertz to the mid-infrared regime. Our approach uses a blend of analytical and numerical methods for the distinct geometries with infinite, translation-invariant, flat sheets and nanoribbons. We describe the transmission of plane waves through a plasmonic crystal in comparison to an effective dielectric slab of equal total thickness that emerges from homogenization, in the limit of zero interlayer spacing. We demonstrate numerically that the replacement of the discrete plasmonic crystal by its homogenized counterpart can accurately capture a transmission coefficient akin to the extinction spectrum, even for a relatively small number of layers. We point out the role of a geometry-dependent corrector field, which expresses the effect of subwavelength surface plasmons. In particular, by use of the corrector we describe lateral resonances inherent to the nanoribbon geometry.
Group-In: Group Inference from Wireless Traces of Mobile Devices

This paper proposes Group-In, a wireless scanning system to detect static or mobile people groups in indoor or outdoor environments. Group-In collects only wireless traces from the Bluetooth-enabled mobile devices for group inference. The key problem addressed in this work is to detect not only static groups but also moving groups with a multi-phased approach based only noisy wireless Received Signal Strength Indicator (RSSIs) observed by multiple wireless scanners without localization support. We propose new centralized and decentralized schemes to process the sparse and noisy wireless data, and leverage graph-based clustering techniques for group detection from short-term and long-term aspects. Group-In provides two outcomes: 1) group detection in short time intervals such as two minutes and 2) long-term linkages such as a month. To verify the performance, we conduct two experimental studies. One consists of 27 controlled scenarios in the lab environments. The other is a real-world scenario where we place Bluetooth scanners in an office environment, and employees carry beacons for more than one month. Both the controlled and real-world experiments result in high accuracy group detection in short time intervals and sampling liberties in terms of the Jaccard index and pairwise similarity coefficient.
Leaflets of Cycadaceae

1. A key based on topographical characters of leaflets is workable for genera. 2. The leaflets of the different species vary in color from bright green to dark green or dull gray-green; the shape and texture of the leaflets show an equal amount of variation. 3. A thick-walled, cutinized epidermis is characteristic of all the species examined; and a hypodermis is differentiated to some extent in all the species studied except Bowenia. 4. A well differentiated upper palisade is present in Cycas, Dioon, Encephalartos, Ceratozamia, Macrozamia, and Zamia furfuracea. Other species of Zamia show a more or less palisade region, but the cells are scarcely elongated or regular enough to constitute a true palisade. 5. The spongy mesophyll in most of the species examined is composed of transversely elongated cells. In nine of the species the cells are yoke-shaped. 6. In all of the species studied, thick-walled cells are found around the vascular bundles or scattered through the mesophyll. 7. Mucilage canals are present in Macrozamia, Dioon, Stangeria, and some species of Encephalartos. 8. In all of the species considered, except Bowenia serrulata, crystals of calcium oxalate are found. 9. Stomata were found on the upper as well as on the lower surfaces of the leaflets of Macrozamia Moorei and Bowenia serrulata. In the other species examined they were found on the lower surface only. 10. In studying the character of the leaflets two factors, heredity and environment, must be taken into consideration. The favorable environmental factors in greenhouse cultivation have perhaps modified the xerophytic structure of the leaflets studied.
Sound Context Classification Basing on Join Learning Model and Multi-Spectrogram Features

In this paper, we present a deep learning framework applied for Acoustic Scene Classification (ASC), the task of classifying scene contexts from environmental input sounds. An ASC system generally comprises of two main steps, referred to as front-end feature extraction and back-end classification. In the first step, an extractor is used to extract low-level features from raw audio signals. Next, the discriminative features extracted are fed into and classified by a classifier, reporting accuracy results. Aim to develop a robust framework applied for ASC, we address exited issues of both the front-end and back-end components in an ASC system, thus present three main contributions: Firstly, we carry out a comprehensive analysis of spectrogram representation extracted from sound scene input, thus propose the best multi-spectrogram combinations. In terms of back-end classification, we propose a novel join learning architecture using parallel convolutional recurrent networks, which is effective to learn spatial features and temporal sequences of spectrogram input. Finally, good experimental results obtained over benchmark datasets of IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) 2016 Task 1, 2017 Task 1, 2018 Task 1A & 1B, LITIS Rouen prove our proposed framework general and robust for ASC task.
CalliGAN: Style and Structure-aware Chinese Calligraphy Character Generator

Chinese calligraphy is the writing of Chinese characters as an art form performed with brushes so Chinese characters are rich of shapes and details. Recent studies show that Chinese characters can be generated through image-to-image translation for multiple styles using a single model. We propose a novel method of this approach by incorporating Chinese characters' component information into its model. We also propose an improved network to convert characters to their embedding space. Experiments show that the proposed method generates high-quality Chinese calligraphy characters over state-of-the-art methods measured through numerical evaluations and human subject studies.
CLR-DRAM: A Low-Cost DRAM Architecture Enabling Dynamic Capacity-Latency Trade-Off

DRAM is the prevalent main memory technology, but its long access latency can limit the performance of many workloads. Although prior works provide DRAM designs that reduce DRAM access latency, their reduced storage capacities hinder the performance of workloads that need large memory capacity. Because the capacity-latency trade-off is fixed at design time, previous works cannot achieve maximum performance under very different and dynamic workload demands.This paper proposes Capacity-Latency-Reconfigurable DRAM (CLR-DRAM), a new DRAM architecture that enables dynamic capacity-latency trade-off at low cost. CLR-DRAM allows dynamic reconfiguration of any DRAM row to switch between two operating modes: 1) max-capacity mode, where every DRAM cell operates individually to achieve approximately the same storage density as a density-optimized commodity DRAM chip and 2) high-performance mode, where two adjacent DRAM cells in a DRAM row and their sense amplifiers are coupled to operate as a single low-latency logical cell driven by a single logical sense amplifier.We implement CLR-DRAM by adding isolation transistors in each DRAM subarray. Our evaluations show that CLR-DRAM can improve system performance and DRAM energy consumption by 18.6% and 29.7% on average with four-core multiprogrammed workloads. We believe that CLR-DRAM opens new research directions for a system to adapt to the diverse and dynamically changing memory capacity and access latency demands of workloads.
CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction

Pedestrian path prediction is an essential topic in computer vision and video understanding. Having insight into the movement of pedestrians is crucial for ensuring safe operation in a variety of applications including autonomous vehicles, social robots, and environmental monitoring. Current works in this area utilize complex generative or recurrent methods to capture many possible futures. However, despite the inherent real-time nature of predicting future paths, little work has been done to explore accurate and computationally efficient approaches for this task. To this end, we propose a convolutional approach for real-time pedestrian path prediction, CARPe. It utilizes a variation of Graph Isomorphism Networks in combination with an agile convolutional neural network design to form a fast and accurate path prediction approach. Notable results in both inference speed and prediction accuracy are achieved, improving FPS considerably in comparison to current state-of-the-art methods while delivering competitive accuracy on well-known path prediction datasets.
Post-Quantum Multi-Party Computation in Constant Rounds

We obtain the first constant-round post-quantum multi-party computation protocol for general classical functionalities in the plain model, with security against malicious corruptions. We assume mildly super-polynomial quantum hardness of learning with errors (LWE), and quantum polynomial hardness of an LWE-based circular security assumption. Along the way, we also construct the following protocols that may be of independent interest. (1) Constant-round zero-knowledge against parallel quantum verifiers from quantum polynomial assumptions. Here, we develop a novel parallel no-cloning non-black-box simulation technique. This uses as a starting point the recently introduced no-cloning technique of Bitansky and Shmueli (STOC 2020) and Ananth and La Placa (ePrint 2019), which in turns builds on the classical non-black-box technique of Bitansky, Khurana and Paneth (STOC 2019). Our approach relies on a new technical tool, spooky encryption for relations computable by quantum circuits, that we also construct. (2) Constant-round post-quantum non-malleable commitments from mildly super-polynomial quantum hardness of LWE. This is the first construction of post-quantum non-malleable commitments in the plain model, and is obtained by transforming the construction of Khurana and Sahai (FOCS 2017) to obtain post-quantum security. We achieve quantum security by building a new straight-line non-black-box simulator against parallel verifiers that does not clone the adversary's state. This technique may also be relevant to the classical setting.
Active Measure Reinforcement Learning for Observation Cost Minimization

Standard reinforcement learning (RL) algorithms assume that the observation of the next state comes instantaneously and at no cost. In a wide variety of sequential decision making tasks ranging from medical treatment to scientific discovery, however, multiple classes of state observations are possible, each of which has an associated cost. We propose the active measure RL framework (Amrl) as an initial solution to this problem where the agent learns to maximize the costed return, which we define as the discounted sum of rewards minus the sum of observation costs. Our empirical evaluation demonstrates that Amrl-Q agents are able to learn a policy and state estimator in parallel during online training. During training the agent naturally shifts from its reliance on costly measurements of the environment to its state estimator in order to increase its reward. It does this without harm to the learned policy. Our results show that the Amrl-Q agent learns at a rate similar to standard Q-learning and Dyna-Q. Critically, by utilizing an active strategy, Amrl-Q achieves a higher costed return.
Circuit quantum electrodynamics

Quantum mechanical effects at the macroscopic level were first explored in Josephson junction-based superconducting circuits in the 1980's. In the last twenty years, the emergence of quantum information science has intensified research toward using these circuits as qubits in quantum information processors. The realization that superconducting qubits can be made to strongly and controllably interact with microwave photons, the quantized electromagnetic fields stored in superconducting circuits, led to the creation of the field of circuit quantum electrodynamics (QED), the topic of this review. While atomic cavity QED inspired many of the early developments of circuit QED, the latter has now become an independent and thriving field of research in its own right. Circuit QED allows the study and control of light-matter interaction at the quantum level in unprecedented detail. It also plays an essential role in all current approaches to quantum information processing with superconducting circuits. In addition, circuit QED enables the study of hybrid quantum systems interacting with microwave photons. Here, we review the coherent coupling of superconducting qubits to microwave photons in high-quality oscillators focussing on the physics of the Jaynes-Cummings model, its dispersive limit, and the different regimes of light-matter interaction in this system. We discuss coupling of superconducting circuits to their environment, which is necessary for coherent control and measurements in circuit QED, but which also invariably leads to decoherence. Dispersive qubit readout, a central ingredient in almost all circuit QED experiments, is also described. Following an introduction to these fundamental concepts that are at the heart of circuit QED, we discuss important use cases of these ideas in quantum information processing and in quantum optics.
Unsupervised Domain Expansion from Multiple Sources

Given an existing system learned from previous source domains, it is desirable to adapt the system to new domains without accessing and forgetting all the previous domains in some applications. This problem is known as domain expansion. Unlike traditional domain adaptation in which the target domain is the domain defined by new data, in domain expansion the target domain is formed jointly by the source domains and the new domain (hence, domain expansion) and the label function to be learned must work for the expanded domain. Specifically, this paper presents a method for unsupervised multi-source domain expansion (UMSDE) where only the pre-learned models of the source domains and unlabelled new domain data are available. We propose to use the predicted class probability of the unlabelled data in the new domain produced by different source models to jointly mitigate the biases among domains, exploit the discriminative information in the new domain, and preserve the performance in the source domains. Experimental results on the VLCS, ImageCLEF_DA and PACS datasets have verified the effectiveness of the proposed method.
The short-term seasonal analyses between atmospheric environment and COVID-19 in epidemic areas of Cities in Australia, South Korea, and Italy

The impact of the outbreak of COVID-19 on health has been widely concerned. Disease risk assessment, prediction, and early warning have become a significant research field. Previous research suggests that there is a relationship between air quality and the disease. This paper investigated the impact of the atmospheric environment on the basic reproduction number (R$_0$) in Australia, South Korea, and Italy by using atmospheric environment data, confirmed case data, and the distributed lag non-linear model (DLNM) model based on Quasi-Poisson regression. The results show that the air temperature and humidity have lag and persistence on short-term R$_0$, and seasonal factors have an apparent decorating effect on R$_0$. PM$_{10}$ is the primary pollutant that affects the excess morbidity rate. Moreover, O$_3$, PM$_{2.5}$, and SO$_2$ as perturbation factors have an apparent cumulative effect. These results present beneficial knowledge for correlation between environment and COVID-19, which guiding prospective analyses of disease data.
COVID-19 and the Social Distancing Paradox: dangers and solutions

Background: Without proven effect treatments and vaccines, Social Distancing is the key protection factor against COVID-19. Social distancing alone should have been enough to protect again the virus, yet things have gone very differently, with a big mismatch between theory and practice. What are the reasons? A big problem is that there is no actual social distancing data, and the corresponding people behavior in a pandemic is unknown. We collect the world-first dataset on social distancing during the COVID-19 outbreak, so to see for the first time how people really implement social distancing, identify dangers of the current situation, and find solutions against this and future pandemics. Methods: Using a sensor-based social distancing belt we collected social distance data from people in Italy for over two months during the most critical COVID-19 outbreak. Additionally, we investigated if and how wearing various Personal Protection Equipment, like masks, influences social distancing. Results: Without masks, people adopt a counter-intuitively dangerous strategy, a paradox that could explain the relative lack of effectiveness of social distancing. Using masks radically changes the situation, breaking the paradoxical behavior and leading to a safe social distance behavior. In shortage of masks, DIY (Do It Yourself) masks can also be used: even without filtering protection, they provide social distancing protection. Goggles should be recommended for general use, as they give an extra powerful safety boost. Generic Public Health policies and media campaigns do not work well on social distancing: explicit focus on the behavioral problems of necessary mobility are needed.
MTFuzz: fuzzing with a multi-task neural network

Fuzzing is a widely used technique for detecting software bugs and vulnerabilities. Most popular fuzzers generate new inputs using an evolutionary search to maximize code coverage. Essentially, these fuzzers start with a set of seed inputs, mutate them to generate new inputs, and identify the promising inputs using an evolutionary fitness function for further mutation.Despite their success, evolutionary fuzzers tend to get stuck in long sequences of unproductive mutations. In recent years, machine learning (ML) based mutation strategies have reported promising results. However, the existing ML-based fuzzers are limited by the lack of quality and diversity of the training data. As the input space of the target programs is high dimensional and sparse, it is prohibitively expensive to collect many diverse samples demonstrating successful and unsuccessful mutations to train the model.In this paper, we address these issues by using a Multi-Task Neural Network that can learn a compact embedding of the input space based on diverse training samples for multiple related tasks (i.e.,predicting for different types of coverage). The compact embedding can guide the mutation process by focusing most of the mutations on the parts of the embedding where the gradient is high. MTFuzz uncovers 11 previously unseen bugs and achieves an average of 2× more edge coverage compared with 5 state-of-the-art fuzzer on 10 real-world programs