cssi/SciSciGPT-SciSciNet huggingface dataset:
When is Enough Enough? "Just Enough" Decision Making with Recurrent Neural Networks for Radio Frequency Machine Learning

Prior work has demonstrated that recurrent neural network architectures show promising improvements over other machine learning architectures when processing temporally correlated inputs, such as wireless communication signals. Additionally, recurrent neural networks typically process data on a sequential basis, enabling the potential for near real-time results. In this work, we investigate the novel usage of "just enough" decision making metrics for making decisions during inference based on a variable number of input received symbols. Since some signals are more complex than others, due to channel conditions, transmitter/receiver effects, etc., being able to dynamically utilize just enough of the received symbols to make a reliable decision allows for more efficient decision making in applications such as electronic warfare and dynamic spectrum sharing. To demonstrate the validity of this concept, four approaches to making "just enough" decisions are considered in this work and each are analyzed for their applicability to wireless communication machine learning applications.
Energy, Efficiency Gains and Economic Development: When Will Global Energy Demand Saturate?

Not anytime soon. Using a novel dataset covering 127 countries and spanning two centuries, we find evidence for an energy Kuznets curve, with an initial decline of energy demand at low levels of per capita income followed by stages of acceleration and then saturation at high-income levels. Historical trends in energy efficiency have reduced energy demand, globally, by about 1.2 percent per year and have, thus, helped bring forward a plateau in energy demand for high income countries. At middle incomes energy and income move in lockstep. The decline in the manufacturing share of value added, globally, accounted for about 0.2 percentage points of the energy efficiency gains. At the country level, the decline (rise) of the manufacturing sector has reduced (increased) US (China) energy demand by 4.1 (10.7) percent between 1990 and 2017.
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow.

We consider the maximum mean discrepancy ($\mathrm{MMD}$) GAN problem and propose a parametric kernelized gradient flow that mimics the min-max game in gradient regularized $\mathrm{MMD}$ GAN. We show that this flow provides a descent direction minimizing the $\mathrm{MMD}$ on a statistical manifold of probability distributions. We then derive an explicit condition which ensures that gradient descent on the parameter space of the generator in gradient regularized $\mathrm{MMD}$ GAN is globally convergent to the target distribution. Under this condition, we give non asymptotic convergence results of gradient descent in MMD GAN. Another contribution of this paper is the introduction of a dynamic formulation of a regularization of $\mathrm{MMD}$ and demonstrating that the parametric kernelized descent for $\mathrm{MMD}$ is the gradient flow of this functional with respect to the new Riemannian structure. Our obtained theoretical result allows ones to treat gradient flows for quite general functionals and thus has potential applications to other types of variational inferences on a statistical manifold beyond GANs. Finally, numerical experiments suggest that our parametric kernelized gradient flow stabilizes GAN training and guarantees convergence.
Mandatory CSR and sustainability reporting: economic analysis and literature review

This study collates potential economic effects of mandated disclosure and reporting standards for corporate social responsibility (CSR) and sustainability topics. We first outline key features of CSR reporting. Next, we draw on relevant academic literatures in accounting, finance, economics, and management to discuss and evaluate the potential economic consequences of a requirement for CSR and sustainability reporting for U.S. firms, including effects in capital markets, on stakeholders other than investors, and on firm behavior. We also discuss issues related to the implementation and enforcement of CSR and sustainability reporting standards as well as two approaches to sustainability reporting that differ in their overarching goals and materiality standards. Our analysis yields a number of insights that are relevant for the current debate on mandatory CSR and sustainability reporting. It also points scholars to avenues for future research.
Expanding Explainability: Towards Social Transparency in AI systems

As AI-powered systems increasingly mediate consequential decision-making, their explainability is critical for end-users to take informed and accountable actions. Explanations in human-human interactions are socially-situated. AI systems are often socio-organizationally embedded. However, Explainable AI (XAI) approaches have been predominantly algorithm-centered. We take a developmental step towards socially-situated XAI by introducing and exploring Social Transparency (ST), a sociotechnically informed perspective that incorporates the socio-organizational context into explaining AI-mediated decision-making. To explore ST conceptually, we conducted interviews with 29 AI users and practitioners grounded in a speculative design scenario. We suggested constitutive design elements of ST and developed a conceptual framework to unpack ST’s effect and implications at the technical, decision-making, and organizational level. The framework showcases how ST can potentially calibrate trust in AI, improve decision-making, facilitate organizational collective actions, and cultivate holistic explainability. Our work contributes to the discourse of Human-Centered XAI by expanding the design space of XAI.
Not so sweet and simple: impacts of SARS-CoV-2 on the β cell

The link between COVID-19 infection and diabetes has been explored in several studies since the start of the pandemic, with associations between comorbid diabetes and poorer prognosis in patients infected with the virus and reports of diabetic ketoacidosis occurring with COVID-19 infection. As such, significant interest has been generated surrounding mechanisms by which the virus may exert effects on the pancreatic s cells. In this review, we consider possible routes by which SARS-CoV-2 may impact s cells. Specifically, we outline data that either support or argue against the idea of direct infection and injury of s cells by SARS-CoV-2. We also discuss s cell damage due to a "bystander" effect in which infection with the virus leads to damage to surrounding tissues that are essential for s cell survival and function, such as the pancreatic microvasculature and exocrine tissue. Studies elucidating the provocation of a cytokine storm following COVID-19 infection and potential impacts of systemic inflammation and increases in insulin resistance on s cells are also reviewed. Finally, we summarize the existing clinical data surrounding diabetes incidence since the start of the COVID-19 pandemic.
“When it rains, it pours”: Fiscal policy, credit constraints and business cycles in emerging and developed economies

Abstract Fiscal procyclicality, meaning co-movement between government expenditure and macroeconomic fundamentals, is an important feature of business cycle dynamics for emerging and poor economies. I estimate a panel SVAR to investigate the reasons for fiscal procyclicality. The analysis sheds light on the role of external financial constraints in shaping fiscal policy. My findings suggest that the response of emerging governments to output fluctuations is similar to that of developed governments. However, emerging governments curtail spending in response to increases in the sovereign borrowing rate, which forces their consumption expenditure to act more procyclically. Using counterfactual analysis, I show that the key forces behind fiscal procyclicality are the sensitivity of government spending to international borrowing costs and the procyclical nature of these costs for emerging economies.
Infinite-dimensional linear algebra and solvability of partial differential equations

We discuss linear algebra of infinite-dimensional vector spaces in terms of algebraic (Hamel) bases. As an application we prove the surjectivity of a large class of linear partial differential operators with smooth ($\mathcal C^\infty$-coefficients) coefficients, called in the article \emph{regular}, acting on the algebraic dual $\mathcal D^*(\Omega)$ of the space of test-functions $\mathcal D(\Omega)$. The surjectivity of the partial differential operators guarantees solvability of the corresponding partial differential equations within $\mathcal D^*(\Omega)$. We discuss our result in contrast to and comparison with similar results about the restrictions of the regular operators on the space of Schwartz distribution $\mathcal D^\prime(\Omega)$, where these operators are often non-surjective.
Theory of radar detection of solitons during ionospheric heating

RF modifiers at some existing ionospheric-heating facilities are found to be sufficiently intense for the production of (collapsing) solitons. The detection of these solitons using Thomson radar is considered, and the problem of an electromagnetic wave scattering off a collection of collapsing solitons is treated. The cross section for the process is found to be relatively large, and an intense plasma-line backscatter is predicted. An explanation of the phenomenon of 'plasma-line overshoot' is suggested.
Protective population behavior change in outbreaks of emerging infectious disease.

BACKGROUND: During outbreaks of emerging and re-emerging infections, the lack of effective drugs and vaccines increases reliance on non-pharmacologic public health interventions and behavior change to limit human-to-human transmission. Interventions that increase the speed with which infected individuals remove themselves from the susceptible population are paramount, particularly isolation and hospitalization. Ebola virus disease (EVD), Severe Acute Respiratory Syndrome (SARS), and Middle East Respiratory Syndrome (MERS) are zoonotic viruses that have caused significant recent outbreaks with sustained human-to-human transmission. METHODS: This investigation quantified changing mean removal rates (MRR) and days from symptom onset to hospitalization (DSOH) of infected individuals from the population in seven different outbreaks of EVD, SARS, and MERS, to test for statistically significant differences in these metrics between outbreaks. RESULTS: We found that epidemic week and viral serial interval were correlated with the speed with which populations developed and maintained health behaviors in each outbreak. CONCLUSIONS: These findings highlight intrinsic population-level changes in isolation rates in multiple epidemics of three zoonotic infections with established human-to-human transmission and significant morbidity and mortality. These data are particularly useful for disease modelers seeking to forecast the spread of emerging pathogens.
Economic Crises and Returns to University Education in Middle-Income Countries: Stylized Facts and COVID-19 Projections

In this study, we develop a conceptual framework that explains the reasons behind a widening of the gaps in private rates of return to university education during an economic crisis such as COVID-19. Next, we report stylized facts on the private rates of return to university education before and after economic crises in Indonesia, Pakistan, and South Africa. We further conduct panel regression analysis to assess the statistical significance of the relationship between private returns and crises in the three countries. We conclude by speculating on COVID-19 implications and future research.
Approximate Gomory–Hu tree is faster than n – 1 max-flows

The Gomory-Hu tree or cut tree (Gomory and Hu, 1961) is a classic data structure for reporting s−t mincuts (and by duality, the values of s−t maxflows) for all pairs of vertices s and t in an undirected graph. Gomory and Hu showed that it can be computed using n−1 exact maxflow computations. Surprisingly, this remains the best algorithm for Gomory-Hu trees more than 50 years later, even for approximate mincuts. In this paper, we break this longstanding barrier and give an algorithm for computing a (1+є)-approximate Gomory-Hu tree using log(n) maxflow computations. Specifically, we obtain the runtime bounds we describe below. We obtain a randomized (Monte Carlo) algorithm for undirected, weighted graphs that runs in O(m + n3/2) time and returns a (1+є)-approximate Gomory-Hu tree algorithm whp. Previously, the best running time known was O(n5/2), which is obtained by running Gomory and Hu’s original algorithm on a cut sparsifier of the graph. Next, we obtain a randomized (Monte Carlo) algorithm for undirected, unweighted graphs that runs in m4/3+o(1) time and returns a (1+є)-approximate Gomory-Hu tree algorithm whp. This improves on our first result for sparse graphs, namely m = o(n9/8). Previously, the best running time known for unweighted graphs was O(mn) for an exact Gomory-Hu tree (Bhalgat et al., STOC 2007); no better result was known if approximations are allowed. As a consequence of our Gomory-Hu tree algorithms, we also solve the (1+є)-approximate all pairs mincut and single source mincut problems in the same time bounds. (These problems are simpler in that the goal is to only return the s−t mincut values, and not the mincuts.) This improves on the recent algorithm for these problems in O(n2) time due to Abboud et al. (FOCS 2020).
Advancing Speech Synthesis using EEG

In this paper we introduce an attention-regression model to demonstrate predicting acoustic features from electroencephalography (EEG) features recorded in parallel with spoken sentences with lower Mel cepstral distortion (MCD) values during test time compared to baseline results for the majority of the experiments. First, we demonstrate predicting acoustic features directly from EEG features using our attention model, and then we demonstrate predicting acoustic features from EEG features using a two-step approach where in the first step we use our attention model to predict articulatory features from EEG features and then in the second step another attention-regression model is trained to transform the predicted articulatory features to acoustic features. We then propose a deep learning model that takes raw EEG waveform signals as input and directly produces audio waveform as output with low root mean squared error (RMSE) values during test time. We finally demonstrate predicting 16 different acoustic features from EEG features. The results presented in this paper shows how different acoustic features are related to non-invasive neural EEG signals recorded during speech perception and production.
Byzantine-Resilient High-Dimensional SGD with Local Iterations on Heterogeneous Data

We study stochastic gradient descent (SGD) with local iterations in the presence of malicious/Byzantine clients, motivated by the federated learning. The clients, instead of communicating with the central server in every iteration, maintain their local models, which they update by taking several SGD iterations based on their own datasets and then communicate the net update with the server, thereby achieving communication-efficiency. Furthermore, only a subset of clients communicate with the server, and this subset may be different at different synchronization times. The Byzantine clients may collaborate and send arbitrary vectors to the server to disrupt the learning process. To combat the adversary, we employ an efficient high-dimensional robust mean estimation algorithm at the server to filter-out corrupt vectors; and to analyze the outlier-filtering procedure, we develop a novel matrix concentration result that may be of independent interest. We provide convergence analyses for both strongly-convex and non-convex smooth objectives in the heterogeneous data setting, where different clients may have different local datasets, and we do not make any probabilistic assumptions on data generation. We believe that ours is the first Byzantine-resilient algorithm and analysis with local iterations. We derive our convergence results under minimal assumptions of bounded variance for SGD and bounded gradient dissimilarity (which captures heterogeneity among local datasets); and we provide bounds on these quantities in the statistical heterogeneous data setting. We also extend our results to the case when clients compute full-batch gradients.
Strategy and Benchmark for Converting Deep Q-Networks to Event-Driven Spiking Neural Networks.

Spiking neural networks (SNNs) have great potential for energy-efficient implementation of Deep Neural Networks (DNNs) on dedicated neuromorphic hardware. Recent studies demonstrated competitive performance of SNNs compared with DNNs on image classification tasks, including CIFAR-10 and ImageNet data. The present work focuses on using SNNs in combination with deep reinforcement learning in ATARI games, which involves additional complexity as compared to image classification. We review the theory of converting DNNs to SNNs and extending the conversion to Deep Q-Networks (DQNs). We propose a robust representation of the firing rate to reduce the error during the conversion process. In addition, we introduce a new metric to evaluate the conversion process by comparing the decisions made by the DQN and SNN, respectively. We also analyze how the simulation time and parameter normalization influence the performance of converted SNNs. We achieve competitive scores on 17 top-performing Atari games. To the best of our knowledge, our work is the first to achieve state-of-the-art performance on multiple Atari games with SNNs. Our work serves as a benchmark for the conversion of DQNs to SNNs and paves the way for further research on solving reinforcement learning tasks with SNNs.
The Common Origin of High-energy Protons in Solar Energetic Particle Events and Sustained Gamma-Ray Emission from the Sun

We report that the number of > 500 MeV protons (Ng) inferred from sustained gamma ray emission (SGRE) from the Sun is significantly correlated with that of protons propagating into space (NSEP) as solar energetic particles (SEPs). Under the shock paradigm for SGRE, shocks driven by coronal mass ejections (CMEs) accelerate high-energy protons sending them toward the Sun to produce SGRE by interacting with the atmospheric particles. Particles also escape into the space away from the Sun to be detected as SEP events. Therefore, the significant NSEP vs. Ng correlation (correlation coefficient 0.77) is consistent with the common shock origin for the two proton populations. Furthermore, the underlying CMEs have properties akin to those involved in ground level enhancement (GLE) events indicating the presence of high-energy (up to GeV) particles required for SGRE. We show that the observed gamma-ray flux is an underestimate in limb events (central meridian distance > 60 degrees) because SGRE sources are partially occulted when the emission is spatially extended. With the assumption that the SEP spectrum at the shock nose is hard and that the 100 MeV particles are accelerated throughout the shock surface (half width in the range 60 to 120 degrees) we find that the latitudinal widths of SEP distributions are energy dependent with the smallest width at the highest energies. Not using the energy-dependent width results in an underestimate of NSEP in SGRE events occurring at relatively higher latitudes. Taking these two effects into account removes the apparent lack of NSEP - Ng correlation reported in previous studies.
Delayed dosing of minocycline plus N-acetylcysteine reduces neurodegeneration in distal brain regions and restores spatial memory after experimental traumatic brain injury.

Abstract Multiple drugs to treat traumatic brain injury (TBI) have failed clinical trials. Most drugs lose efficacy as the time interval increases between injury and treatment onset. Insufficient therapeutic time window is a major reason underlying failure in clinical trials. Few drugs have been developed with therapeutic time windows sufficiently long enough to treat TBI because little is known about which brain functions can be targeted if therapy is delayed hours to days after injury. We identified multiple injury parameters that are improved by first initiating treatment with the drug combination minocycline (MINO) plus N-acetylcysteine (NAC) at 72 h after injury (MN72) in a mouse closed head injury (CHI) experimental TBI model. CHI produces spatial memory deficits resulting in impaired performance on Barnes maze, hippocampal neuronal loss, and bilateral damage to hippocampal neurons, dendrites, spines and synapses. MN72 treatment restores Barnes maze acquisition and retention, protects against hippocampal neuronal loss, limits damage to dendrites, spines and synapses, and accelerates recovery of microtubule associated protein 2 (MAP2) expression, a key protein in maintaining proper dendritic architecture and synapse density. These data show that in addition to the structural integrity of the dendritic arbor, spine and synapse density can be successfully targeted with drugs first dosed days after injury. Retention of substantial drug efficacy even when first dosed 72 h after injury makes MINO plus NAC a promising candidate to treat clinical TBI.
Nonlinear Spin-Wave Theory for the Anisotropic Heisenberg Antiferromagnet

The theory of the anisotropic Heisenberg antiferromagnet is developed in the nonlinear spin-wave approximation. It is shown that the thermodynamic quantities of interest depend on two renormalization parameters. These parameters can be calculated analytically over the entire temperature range of interest. It appears that nonlinear spin-wave theory does not give a good estimate of the transition temperature as the anisotropy increases.
Local vortex line topology and geometry in turbulence

The local streamline topology classification method of Chong et al. (Phys. Fluids A: Fluid Dyn., vol. 2, no. 5, 1990, pp. 765–777) is adapted and extended to describe the geometry of infinitesimal vortex lines. Direct numerical simulation (DNS) data of forced isotropic turbulence reveals that the joint probability density function (p.d.f.) of the second (. The same p.d.f. shape is also seen at the late stages of breakdown of a Taylor–Green vortex suggesting the universality of the bell-shaped p.d.f. form in turbulent flows. Additionally, vortex reconnection from different initial configurations is examined. The local topology and geometry of the reconnection bridge is shown to be nearly identical in all cases considered in this work. Overall, topological characterization of the vorticity field provides a useful analytical basis for examining vorticity dynamics in turbulence and other fluid flows.
Test and Evaluation Framework for Multi-Agent Systems of Autonomous Intelligent Agents

Test and evaluation is a necessary process for ensuring that engineered systems perform as intended under a variety of conditions, both expected and unexpected. In this work, we consider the unique challenges of developing a unifying test and evaluation framework for complex ensembles of cyber-physical systems with embedded artificial intelligence. We propose a framework that incorporates test and evaluation throughout not only the development life cycle, but continues into operation as the system learns and adapts in a noisy, changing, and contended environment. The framework accounts for the challenges of testing the integration of diverse systems at various hierarchical scales of composition while respecting that testing time and resources are limited. A generic use case is provided for illustrative purposes. Research directions emerging as a result of exploring the use case via the framework are suggested.
Machine Learning Practices Outside Big Tech: How Resource Constraints Challenge Responsible Development

Practitioners from diverse occupations and backgrounds are increasingly using machine learning (ML) methods. Nonetheless, studies on ML Practitioners typically draw populations from Big Tech and academia, as researchers have easier access to these communities. Through this selection bias, past research often excludes the broader, lesser-resourced ML community---for example, practitioners working at startups, at non-tech companies, and in the public sector. These practitioners share many of the same ML development difficulties and ethical conundrums as their Big Tech counterparts; however, their experiences are subject to additional under-studied challenges stemming from deploying ML with limited resources, increased existential risk, and absent access to in-house research teams. We contribute a qualitative analysis of 17 interviews with stakeholders from organizations which are less represented in prior studies. We uncover a number of tensions which are introduced or exacerbated by these organizations' resource constraints---tensions between privacy and ubiquity, resource management and performance optimization, and access and monopolization. Increased academic focus on these practitioners can facilitate a more holistic understanding of ML limitations, and so is useful for prescribing a research agenda to facilitate responsible ML development for all.
Wood stove combustion modeling and simulation: Technical review and recommendations

Abstract With the rise in wood stove use in recent years, a number of experimental research efforts have been undertaken with the objective of developing cleaner, and more efficient stove designs. However, numerical modeling of stoves is still in its nascent years, thus making it imperative to start adapting more mature computational techniques, established in other combustion-based applications (e.g., combustion engines), to wood stoves. This study focuses on a critical review of the state-of-the-art in wood combustion modeling techniques and on providing recommendations for needed enhancements. In order to develop an optimum conceptual numerical model for wood stoves, the basic processes inside the stove are broken down and isolated into generic computational processes: surface reactions, gas reactions, and the fluid flow. Then state-of-the-art numerical efforts for wood stove and similar applications are broken down in a similar manner to highlight points of strength and possible improvements. Finally, based on the comparison between the different models, an improved modeling approach is proposed as a road map for future research efforts to achieve a higher fidelity wood stove numerical model.
Memento: How to Reconstruct your Secrets from a Single Password in a Hostile Environment.

Passwords are inherently vulnerable to dictionary attacks, but are quite secure if guessing attempts can be slowed down, for example by an online server. If this server gets compromised, however, the attacker can again perform an offline attack. The obvious remedy is to distribute the password verification process over multiple servers, so that the password remains secure as long as no more than a threshold of the servers are compromised. By letting these servers additionally host shares of a strong secret that the user can recover upon entering the cor- rect password, the user can perform further cryptographic tasks using this strong secret as a key, e.g., encrypting data in the cloud. Threshold password-authenticated secret sharing (TPASS) protocols provide exactly this func- tionality, but the two only known schemes by Bagherzandi et al. (CCS 2011) and Camenisch et al. (CCS 2012) leak the password if a user mistakenly executes the protocol with malicious servers. Authenticating to the wrong servers is a common scenario when users are tricked in phishing attacks. We propose the first t-out-of-n TPASS protocol for any n > t that does not suffer from this shortcoming. We prove our protocol secure in the UC frame- work, which for the particular case of password-based protocols offers important advantages over property-based definitions, e.g., by correctly modeling typos in password attempts.
Characterizing stochastic cell cycle dynamics in exponential growth

Two powerful and complementary experimental approaches are commonly used to study the cell cycle and cell biology: One class of experiments characterizes the statistics (or demographics) of an unsynchronized exponentially-growing population, while the other captures cell cycle dynamics, either by time-lapse imaging of full cell cycles or in bulk experiments on synchronized populations. In this paper, we study the subtle relationship between observations in these two distinct experimental approaches. We begin with an existing model: a single-cell deterministic description of cell cycle dynamics where cell states (i.e. periods or phases) have precise lifetimes. We then generalize this description to a stochastic model in which the states have stochastic lifetimes, as described by arbitrary probability distribution functions. Our analyses of the demographics of an exponential culture reveal a simple and exact correspondence between the deterministic and stochastic models: The corresponding state lifetimes in the deterministic model are equal to the exponential mean of the lifetimes in the stochastic model. An important implication is therefore that the demographics of an exponential culture will be well-fit by a deterministic model even if the state timing is stochastic. Although we explore the implications of the models in the context of the Escherichia coli cell cycle, we expect both the models as well as the significance of the exponential-mean lifetimes to find many applications in the quantitative analysis of cell cycle dynamics in other biological systems.
Hydrology without dimensions

Abstract. By rigorously accounting for dimensional homogeneity in physical laws, the Pi theorem and the related self-similarity hypotheses allow us to achieve a dimensionless reformulation of scientific hypotheses in a lower dimensional context. This paper presents applications of these concepts to the partitioning of water and soil on terrestrial landscapes, for which the process complexity and lack of first principle formulation make dimensional analysis an excellent tool to formulate theories that are amenable to empirical testing and analytical developments. The resulting scaling laws help reveal the dominant environmental controls for these partitionings. In particular, we discuss how the dryness index and the storage index affect the long term rainfall partitioning, the key nonlinear control of the dryness index in global datasets of weathering rates, and the existence of new macroscopic relations among average variables in landscape evolution statistics. The scaling laws for the partitioning of sediments, the elevation profile, and the spectral scaling of self-similar topographies also unveil tantalizing analogies with turbulent fluctuations.
Dilaton-gravity, deformations of the minimal string, and matrix models

A large class of two-dimensional dilaton-gravity theories in asymptotically AdS2 spacetimes are holographically dual to a matrix integral, interpreted as an ensemble average over Hamiltonians. Viewing these theories as Jackiw-Teitelboim gravity with a gas of defects, we extend this duality to a broader class of dilaton potentials compared to previous work by including conical defects with small deficit angles. In order to do this, we show that these theories are equal to the large p limit of a natural deformation of the (2,p) minimal string theory.
Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-trained Language Models

Public security vulnerability reports (e.g., CVE reports) play an important role in the maintenance of computer and network systems. Security companies and administrators rely on information from these reports to prioritize tasks on developing and deploying patches to their customers. Since these reports are unstructured texts, automatic information extraction (IE) can help scale up the processing by converting the unstructured reports to structured forms, e.g., software names and versions [8] and vulnerability types [38]. Existing works on automated IE for security vulnerability reports often rely on a large number of labeled training samples [8, 18, 48]. However, creating massive labeled training set is both expensive and time consuming. In this work, for the first time, we propose to investigate this problem where only a small number of labeled training samples are available. In particular, we investigate the performance of fine-tuning several state-of-the-art pre-trained language models on our small training dataset. The results show that with pre-trained language models and carefully tuned hyperparameters, we have reached or slightly outperformed the state-of-the-art system [8] on this task. Consistent with previous two-step process of first fine-tuning on main category and then transfer learning to others as in [7], if otherwise following our proposed approach, the number of required labeled samples substantially decrease in both stages: 90% reduction in fine-tuning from 5758 to 576, and 88.8% reduction in transfer learning with 64 labeled samples per category. Our experiments thus demonstrate the effectiveness of few-sample learning on NER for security vulnerability report. This result opens up multiple research opportunities for few-sample learning for security vulnerability reports, which is discussed in the paper. Our implementation for few-sample vulnerability entity tagger in security reports could be found at https://github.com/guanqun-yang/FewVulnerability.
Deformations of Varieties of General Type

We prove that small deformations of a projective variety of general type are also projective varieties of general type, with the same plurigenera.
Volumetric Objectives for Multi-Robot Exploration of Three-Dimensional Environments

Volumetric objectives for exploration and perception tasks seek to capture a sense of value (or reward) for hypothetical observations at one or more camera views for robots operating in unknown environments. For example, a volumetric objective may reward robots proportionally to the expected volume of unknown space to be observed. We identify connections between existing information-theoretic and coverage objectives in terms of expected coverage, particularly that mutual information without noise is a special case of expected coverage. Likewise, we provide the first comparison, of which we are aware, between information-based approximations and coverage objectives for exploration, and we find, perhaps surprisingly, that coverage objectives can significantly outperform information-based objectives in practice. Additionally, the analysis for information and coverage objectives demonstrates that Randomized Sequential Partitions—a method for efficient distributed sensor planning—applies for both classes of objectives, and we provide simulation results in a variety of environments for as many as 32 robots.
Developers Who Vlog: Dismantling Stereotypes through Community and Identity

Developers are more than "nerds behind computers all day", they lead a normal life, and not all take the traditional path to learn programming. However, the public still sees software development as a profession for "math wizards". To learn more about this special type of knowledge worker from their first-person perspective, we conducted three studies to learn how developers describe a day in their life through vlogs on YouTube and how these vlogs were received by the broader community. We first interviewed 16 developers who vlogged to identify their motivations for creating this content and their intention behind what they chose to portray. Second, we analyzed 130 vlogs (video blogs) to understand the range of the content conveyed through videos. Third, we analyzed 1176 comments from the 130 vlogs to understand the impact the vlogs have on the audience. We found that developers were motivated to promote and build a diverse community, by sharing different aspects of life that define their identity, and by creating awareness about learning and career opportunities in computing. They used vlogs to share a variety of how software developers work and live---showcasing often unseen experiences, including intimate moments from their personal life. From our comment analysis, we found that the vlogs were valuable to the audience to find information and seek advice. Commenters sought opportunities to connect with others over shared triumphs and trials they faced that were also shown in the vlogs. As a central theme, we found that developers use vlogs to challenge the misconceptions and stereotypes around their identity, work-life, and well-being. These social stigmas are obstacles to an inclusive and accepting community and can deter people from choosing software development as a career. We also discuss the implications of using vlogs to support developers, researchers, and beyond.
A Quantitative Risk Estimation Platform for Indoor Aerosol Transmission of COVID-19

Abstract Aerosol transmission has played a significant role in the transmission of COVID-19 disease worldwide. We developed a COVID-19 aerosol transmission risk estimation model to better understand how key parameters associated with indoor spaces and infector emissions affect inhaled deposited dose of aerosol particles that convey the SARS-CoV-2 virus. The model calculates the concentration of size-resolved, virus-laden aerosol particles in well-mixed indoor air challenged by emissions from an index case(s). The model uses a mechanistic approach, accounting for particle emission dynamics, particle deposition to indoor surfaces, ventilation rate, and single-zone filtration. The novelty of this model relates to the concept of ?inhaled & deposited dose? in the respiratory system of receptors linked to a dose?response curve for human coronavirus HCoV-229E. We estimated the volume of inhaled & deposited dose of particles in the 0.5?4 ?m range expressed in picoliters (pL) in a well-documented COVID-19 outbreak in restaurant X in Guangzhou China. We anchored the attack rate with the dose?response curve of HCoV-229E which provides a preliminary estimate of the average SARS-CoV-2 dose per person, expressed in plaque forming units (PFUs). For a reasonable emission scenario, we estimate approximately three PFU per pL deposited, yielding roughly 10 PFUs deposited in the respiratory system of those infected in restaurant X. To explore the model's utility, we tested it with four COVID-19 outbreaks. The risk estimates from the model fit reasonably well with the reported number of confirmed cases given available metadata from the outbreaks and uncertainties associated with model assumptions.
Selectively-Amortized Resource Bounding.

We consider the problem of automatically proving resource bounds. That is, we study how to prove that an integer-valued resource variable is bounded by a given program expression. Automatic resource-bound analysis has recently received significant attention because of a number of important applications (e.g., detecting performance bugs, preventing algorithmic-complexity attacks, identifying side-channel vulnerabilities), where the focus has often been on developing precise amortized reasoning techniques to infer the most exact resource usage. While such innovations remain critical, we observe that fully precise amortization is not always necessary to prove a bound of interest. And in fact, by amortizing selectively, the needed supporting invariants can be simpler, making the invariant inference task more feasible and predictable. We present a framework for selectively-amortized analysis that mixes worst-case and amortized reasoning via a property decomposition and a program transformation. We show that proving bounds in any such decomposition yields a sound resource bound in the original program, and we give an algorithm for selecting a reasonable decomposition.
Planning Strategies for Lane Reversals in Transportation Networks

This paper studies strategies to optimize the lane configuration of a transportation network for a given set of Origin-Destination demands using a planning macroscopic network flow model. The lane reversal problem is, in general, NP-hard since the optimization is made over integer variables. To overcome this burden, we reformulate the problem using a piecewise affine approximation of the travel latency function which allows us to exploit the total unimodularity property of Integer Linear Programming (ILP). Consequently, we transform the ILP problem to a linear program by relaxing the integer variables. In addition, our method is capable of solving the problem for a desired number of lane reversals which serves to perform cost-benefit analysis. We perform a case study using the transportation network of Eastern Massachusetts (EMA) and we test our method against the original lane configuration and a projected lower bound solution. Our empirical results quantify the travel time savings for different levels of demand intensity. We observe reduction in travel times up to 40% for certain links in the network.
Differentiable Quality Diversity

Quality diversity (QD) is a growing branch of stochastic optimization research that studies the problem of generating an archive of solutions that maximize a given objective function but are also diverse with respect to a set of specified measure functions. However, even when these functions are differentiable, QD algorithms treat them as "black boxes", ignoring gradient information. We present the differentiable quality diversity (DQD) problem, a special case of QD, where both the objective and measure functions are first order differentiable. We then present MAP-Elites via Gradient Arborescence (MEGA), a DQD algorithm that leverages gradient information to efficiently explore the joint range of the objective and measure functions. Results in two QD benchmark domains and in searching the latent space of a StyleGAN show that MEGA significantly outperforms state-of-the-art QD algorithms, highlighting DQD's promise for efficient quality diversity optimization when gradient information is available. Source code is available at this https URL.
Bacterial response to spatial gradients of algal-derived nutrients in a porous microplate

Photosynthetic microalgae are responsible for 50% of the global atmospheric CO2 fixation into organic matter and hold potential as a renewable bioenergy source. Their metabolic interactions with the surrounding microbial community (the algal microbiome) play critical roles in carbon cycling, but due to methodological limitations, it has been challenging to examine how community development is influenced by spatial proximity to their algal host. Here we introduce a copolymer-based porous microplate to co-culture algae and bacteria, where metabolites are constantly exchanged between the microorganisms while maintaining physical separation. In the microplate, we found that the diatom Phaeodactylum tricornutum accumulated to cell abundances ~20 fold higher than under normal batch conditions due to constant replenishment of nutrients through the porous structure. We also demonstrate that algal-associated bacteria, both single isolates and complex communities, responded to inorganic nutrients away from their host as well as organic nutrients originating from the algae in a spatially predictable manner. These experimental findings coupled with a mathematical model suggest that host proximity and algal culture growth phase impact bacterial community development in a taxon-specific manner through organic and inorganic nutrient availability. Our novel system presents a useful tool to investigate universal metabolic interactions between microbes in aquatic ecosystems.
Dopamine D3 receptor ligand suppresses the expression of levodopa-induced dyskinesia in nonhuman primate model of parkinson's disease.

Abstract Parkinson's disease (PD) is a complex multisystem, chronic and so far incurable disease with significant unmet medical needs. The incidence of PD increases with aging and the expected burden will continue to escalate with our aging population. Since its discovery in the 1961 levodopa has remained the gold standard pharmacotherapy for PD. However, the progressive nature of the neurodegenerative process in and beyond the nigrostriatal system causes a multitude of side effects, including levodopa-induced dyskinesia within 5 years of therapy. Attenuating dyskinesia has been a significant challenge in the clinical management of PD. We report on a small molecule that eliminates the expression of levodopa-induced dyskinesia and significantly improves PD-like symptoms. The lead compound PD13R we discovered is a dopamine D3 receptor partial agonist with high affinity and selectivity, orally active and with desirable drug-like properties. Future studies are aimed at developing this lead compound for treating PD patients with dyskinesia.
Searching for Superconductivity in High Entropy Oxide Ruddlesden-Popper Cuprate Films

In this work, the high entropy oxide A2CuO4 Ruddlesden–Popper (La0.2Pr0.2Nd0.2Sm0.2Eu0.2)2CuO4 is explored by charge doping with Ce+4 and Sr+2 at concentrations known to induce superconductivity in the simple parent compounds, Nd2CuO4 and La2CuO4. Electron doped (La0.185Pr0.185Nd0.185Sm0.185Eu0.185Ce0.075)2CuO4 and hole doped (La0.18Pr0.18Nd0.18Sm0.18Eu0.18Sr0.1)2CuO4 are synthesized and shown to be single crystal, epitaxially strained, and highly uniform. Transport measurements demonstrate that all as-grown films are insulating regardless of doping. Annealing studies show that resistivity can be tuned by modifying oxygen stoichiometry and inducing metallicity but without superconductivity. These results, in turn, are connected to extended x-ray absorption fine structure results, indicating that the lack of superconductivity in the high entropy cuprates likely originates from a large distortion within the Cu–O plane (σ2 > 0.015 A2) due to A-site cation size variance, which drives localization of charge carriers. These findings describe new opportunities for controlling charge- and orbital-mediated functional responses in Ruddlesden–Popper crystal structures, driven by balancing of cation size and charge variances that may be exploited for functionally important behaviors such as superconductivity, antiferromagnetism, and metal-insulator transitions while opening less understood phase spaces hosting doped Mott insulators, strange metals, quantum criticality, pseudogaps, and ordered charge density waves.
Lectures on celestial amplitudes

Lecture notes prepared for the 2021 SAGEX PhD School in Amplitudes hosted by the University of Copenhagen August 10th through 13th. Topics covered include: the manifestation of asymptotic symmetries via soft theorems, their organization into currents in a celestial CFT, aspects of the holographic dictionary, a literature guide, and accompanying exercises.