cssi/SciSciGPT-SciSciNet huggingface dataset:
Representative Diatom and Coccolithophore Species Exhibit Divergent Responses throughout Simulated Upwelling Cycles

Wind-driven upwelling followed by relaxation results in cycles of cold nutrient-rich water fueling intense phytoplankton blooms followed by nutrient depletion, bloom decline, and sinking of cells. Surviving cells at depth can then be vertically transported back to the surface with upwelled waters to seed another bloom. As a result of these cycles, phytoplankton communities in upwelling regions are transported through a wide range of light and nutrient conditions. Diatoms appear to be well suited for these cycles, but their responses to them remain understudied. To investigate the bases for diatoms' ecological success in upwelling environments, we employed laboratory simulations of a complete upwelling cycle with a common diatom, Chaetoceros decipiens, and coccolithophore, Emiliania huxleyi We show that while both organisms exhibited physiological and transcriptomic plasticity, the diatom displayed a distinct response enabling it to rapidly shift-up growth rates and nitrate assimilation when returned to light and available nutrients following dark nutrient-deplete conditions. As observed in natural diatom communities, C. decipiens highly expresses before upwelling, or frontloads, key transcriptional and nitrate assimilation genes, coordinating its rapid response to upwelling conditions. Low-iron simulations showed that C. decipiens is capable of maintaining this response when iron is limiting to growth, whereas E. huxleyi is not. Differential expression between iron treatments further revealed specific genes used by each organism under low iron availability. Overall, these results highlight the responses of two dominant phytoplankton groups to upwelling cycles, providing insight into the mechanisms fueling diatom blooms during upwelling events.IMPORTANCE Coastal upwelling regions are among the most biologically productive ecosystems. During upwelling events, nutrient-rich water is delivered from depth resulting in intense phytoplankton blooms typically dominated by diatoms. Along with nutrients, phytoplankton may also be transported from depth to seed these blooms then return to depth as upwelling subsides creating a cycle with varied conditions. To investigate diatoms' success in upwelling regions, we compare the responses of a common diatom and coccolithophore throughout simulated upwelling cycles under iron-replete and iron-limiting conditions. The diatom exhibited a distinct rapid response to upwelling irrespective of iron status, whereas the coccolithophore's response was either delayed or suppressed depending on iron availability. Concurrently, the diatom highly expresses, or frontloads, nitrate assimilation genes prior to upwelling, potentially enabling this rapid response. These results provide insight into the molecular mechanisms underlying diatom blooms and ecological success in upwelling regions.
Differences between adult and childhood asthma

During the last 2 decades, we have gained new insights into the pathogenesis of asthma; consequently, new therapeutic agents and approaches to therapy have emerged. Nevertheless, significant gaps remain in our understanding of this disease. Important new treatment issues affect childhood (the usual time of asthma onset), and researchers have recently described increases in asthma incidence in children. Yet, most clinical studies have been performed with adults, and our knowledge about major determinants of childhood asthma remains incomplete. Major challenges in pediatric asthma include methods of easily assessing lung function and noninvasive methods of assessing asthma's inflammatory nature. Research that addresses the mechanisms responsible for disease onset is also critical to decrease the prevalence of asthma. What we know about adult asthma cannot be used in the treatment of children without further study, but it is now clear that effective treatment should begin during childhood. (J Allergy Clin Immunol 2000;106:S153-7.)
Confined subgroups of Thompson's group $F$ and its embeddings into wobbling groups

We obtain a characterisation of confined subgroups of Thompson's group $F$. As a result, we deduce that orbital graph of a point under action of $F$ has uniformly subexponential growth if and only if this point is fixed by the commutator subgroup. This allows us to prove non-embeddability of $F$ into wobbling groups of graphs with uniformly subexponential growth.
DevOps: A Historical Review and Future Works

DevOps is an emerging practice to be followed in the Software Development life cycle. The name DevOps indicates that it’s an integration of the Development and Operations team. It is followed to integrate the various stages of the development lifecycle. DevOps is an extended version of the existing Agile method. DevOps aims at Continuous Integration, Continuous Delivery, Continuous Improvement, faster Feedback and Security. This paper reviews the building blocks of DevOps, challenges in adopting DevOps, Models to improve DevOps practices and Future works on DevOps.
Nonparametric Variable Screening with Optimal Decision Stumps

Decision trees and their ensembles are endowed with a rich set of diagnostic tools for ranking and screening variables in a predictive model. Despite the widespread use of tree based variable importance measures, pinning down their theoretical properties has been challenging and therefore largely unexplored. To address this gap between theory and practice, we derive finite sample performance guarantees for variable selection in nonparametric models using a single-level CART decision tree (a decision stump). Under standard operating assumptions in variable screening literature, we find that the marginal signal strength of each variable and ambient dimensionality can be considerably weaker and higher, respectively, than state-of-the-art nonparametric variable selection methods. Furthermore, unlike previous marginal screening methods that attempt to directly estimate each marginal projection via a truncated basis expansion, the fitted model used here is a simple, parsimonious decision stump, thereby eliminating the need for tuning the number of basis terms. Thus, surprisingly, even though decision stumps are highly inaccurate for estimation purposes, they can still be used to perform consistent model selection.
Local well-posedness for the Hall-MHD system in optimal Sobolev spaces

Abstract We show that the viscous resistive magnetohydrodynamics system with Hall effect is locally well-posed in H s ( R 3 ) × H s + 1 − e ( R 3 ) with s > 1 2 and any small enough e > 0 such that s − e > 1 2 . This space is to date the largest local well-posedness space in the class of Sobolev spaces for the system. It is also optimal according to the predominant scalings of the two equations in the system.
Towards Efficient Models for Real-Time Deep Noise Suppression

With recent research advancements, deep learning models are be-coming attractive and powerful choices for speech enhancement in real-time applications. While state-of-the-art models can achieve outstanding results in terms of speech quality and background noise reduction, the main challenge is to obtain compact enough models, which are resource efficient during inference time. An important but often neglected aspect for data-driven methods is that results can be only convincing when tested on real-world data and evaluated with useful metrics. In this work, we investigate reasonably small recurrent and convolutional-recurrent network architectures for speech enhancement, trained on a large dataset considering also reverberation. We show interesting tradeoffs between computational complexity and the achievable speech quality, measured on real recordings using a highly accurate MOS estimator. It is shown that the achievable speech quality is a function of network complexity, and show which models have better tradeoffs.
Short-Term Effects of Vagus Nerve Stimulation on Learning and Evoked Activity in Auditory Cortex.

Chronic vagus nerve stimulation (VNS) has been shown to facilitate learning, but effects of acute VNS on neural coding and behavior remain less well understood. Ferrets implanted with cuff electrodes on the vagus nerve were trained by classical conditioning on an auditory tone frequency-reward association. One tone was associated with reward while another tone was not. Tone frequencies and reward associations were changed every 2 days, requiring learning of a new relationship. When tones were paired with VNS, animals consistently learned the new association within 2 days. When VNS occurred randomly between trials, learning within 2 days was unreliable. In passively listening animals, neural activity in primary auditory cortex and pupil size were recorded before and after acute VNS-tone pairing. After pairing with a neuron’s best-frequency (BF) tone, responses by a subpopulation of neurons were reduced. VNS paired with an off-BF tone or during inter-trial intervals had no effect. The BF-specific reduction in neural responses after VNS remained, even after regressing out changes explained by pupil-indexed arousal. VNS induced brief dilation in the pupil, and the size of this change predicted the magnitude of persistent changes in the neural response. This interaction suggests that fluctuations in neuromodulation associated with arousal gate the long-term VNS effects on neural activity. Significance statement Vagus nerve stimulation (VNS) has been demonstrated to facilitate learning of sensory and motor behaviors. It is believed to trigger neuromodulator release that mediates cortical plasticity associated with learning. This study explores short-term VNS effects that can support long-term plasticity in the auditory cortex (A1). Just two days of VNS were adequate to support enhanced learning of an auditory discrimination task. Neural recordings from A1 revealed briefly pairing VNS with a neuron’s best-frequency tone reduced responses in a subpopulation of neurons. This reduction persisted even after regressing out responses explained by pupil size, a measure of global arousal. These results support a role for VNS in auditory learning and help establish VNS as a tool to facilitate neural plasticity.
Bridging preference-based instrumental variable studies and cluster-randomized encouragement experiments: Study design, noncompliance, and average cluster effect ratio

Instrumental variable (IV) methods are widely used in medical research to draw causal conclusions when the treatment and outcome are confounded by unmeasured confounding variables. One important feature of such studies is that the IV is often applied at the cluster level, for example, hospitals' or physicians' preference for a certain treatment where each hospital or physician naturally defines a cluster. This paper proposes to embed such observational IV data into a cluster-randomized encouragement experiment using nonbipartite matching. Potential outcomes and causal assumptions underpinning the design are formalized and examined. Testing procedures for two commonly used estimands, Fisher's sharp null hypothesis and the pooled effect ratio (PER), are extended to the current setting. We then introduce a novel cluster-heterogeneous proportional treatment effect model and the relevant estimand: the average cluster effect ratio. This new estimand is advantageous over the structural parameter in a constant proportional treatment effect model in that it allows treatment heterogeneity, and is advantageous over the PER estimand in that it does not suffer from Simpson's paradox. We develop an asymptotically valid randomization-based testing procedure for this new estimand based on solving a mixed-integer quadratically constrained optimization problem. The proposed design and inferential methods are applied to a study of the effect of using transesophageal echocardiography during coronary artery bypass graft surgery on patients' 30-day mortality rate. R package ivdesign implements the proposed method.
On the Quantum Correction For Thermodynamic Equilibrium

The probability of a configuration is given in classical theory by the Boltzmann formula $\mathrm{exp}[\ensuremath{-}\frac{V}{\mathrm{hT}}]$ where $V$ is the potential energy of this configuration. For high temperatures this of course also holds in quantum theory. For lower temperatures, however, a correction term has to be introduced, which can be developed into a power series of $h$. The formula is developed for this correction by means of a probability function and the result discussed.
Preliminary Study on Acoustic Detection of Faults Experienced by a High-Bypass Turbofan Engine

The vehicle integrated propulsion research (VIPR) effort conducted by NASA and several partners provided an unparalleled opportunity to test a relatively low TRL concept regarding the use of far field acoustics to identify faults occurring in a high bypass turbofan engine. Though VIPR Phase II ground based aircraft installed engine testing wherein a multitude of research sensors and methods were evaluated, an array of acoustic microphones was used to determine the viability of such an array to detect failures occurring in a commercially representative high bypass turbofan engine. The failures introduced during VIPR testing included commanding the engine's low pressure compressor (LPC) exit and high pressure compressor (HPC) 14th stage bleed values abruptly to their failsafe positions during steady state
Are Bartik Regressions Always Robust to Heterogeneous Treatment Effects

Bartik regressions use locations' differential exposure to nationwide sector-level shocks as an instrument to estimate the effect of a location-level treatment on an outcome. In the canonical Bartik design, locations' differential exposure to industry-level employment shocks are used as an instrument to measure the effect of their employment evolution on their wage evolution. Some recent papers studying Bartik designs have assumed that the sector-level shocks are exogenous and all have the same expectation. This second assumption may sometimes be implausible. For instance, there could be industries whose employment is more likely to grow than that of other industries. We replace that second assumption by parallel trends assumptions. Under our assumptions, Bartik regressions identify weighted sums of location-specific effects, with weights that may be negative. Accordingly, such regressions may be misleading in the presence of heterogeneous effects, an issue that was not present under the assumptions maintained in previous papers. Estimating the weights attached to Bartik regressions is a way to assess their robustness to heterogeneous effects. We also propose an alternative estimator that is robust to location-specific effects. Finally, we revisit two applications. In both cases, Bartik regressions have fairly large negative weights attached to them. Our alternative estimator is substantially different from the Bartik regression coefficient in one application.
Non-asymptotic moment bounds for random variables rounded to non-uniformly spaced sets

We study the effects of rounding on the moments of random variables. Specifically, given a random variable $X$ and its rounded counterpart $\operatorname{rd}(X)$, we study $|\mathbb{E}[X^k] - \mathbb{E}[\operatorname{rd}(X)^{k}]|$ for non-negative integer $k$. We consider the case that the rounding function $\operatorname{rd} : \mathbb{R}\to\mathbb{F}$ corresponds either to (i) rounding to the nearest point in some discrete set $\mathbb{F}$ or (ii) rounding randomly to either the nearest larger or smaller point in this same set with probabilities proportional to the distances to these points. In both cases, we show, under reasonable assumptions on the density function of $X$, how to compute a constant $C$ such that $|\mathbb{E}[X^k] - \mathbb{E}[\operatorname{rd}(X)^{k}]| < C\epsilon^2$, provided $|\operatorname{rd}(x) - x| \leq \epsilon \: E(x)$, where $E : \mathbb{R} \to \mathbb{R}_{\geq 0}$ is some fixed positive piecewise linear function. Refined bounds for the absolute moments $\mathbb{E}[ |X^k-\operatorname{rd}(X)^{k}|]$ are also given.
Complex associations of environmental factors may explain Blanchard’s Cricket Frog, Acris blanchardi declines and drive population recovery

Blanchard’s Cricket Frog, Acris blanchardi, is a small hylid frog that was once among the most common amphibians in any part of its range. Today, it remains abundant in much of the southern portion of its range, but is now disappearing elsewhere. Our analysis of habitat characters observed across several states revealed interesting relationships of these factors with the abundance or presence of Blanchard’s Cricket Frog. Further, we later established two ½ acre ponds based on these relationships that led to immediate colonization of the ponds by cricket frogs followed by explosive production of juveniles less than a year later. Our findings suggest that habitat management for this species should specifically manage the shoreline grade and especially the aquatic floating vegetation to maximize population growth and sustenance.
Identification and Function of Leash Forming Residues in the FoF1 ATP Synthase Molecular Motor Using Single Molecule Measurements

In single molecule rotation measurements, the transient dwells in the proteolipid ring rotation of E. coli FoF1-ATP Synthase were eliminated by either mutation of aE196L or cR50L to subunit a and subunit c, respectively, of the Fo motor. These data indicate that these residues are involved in the formation of the Fo motor leash that allows rotary motion of the c-ring to a limit of ∼36° while engaged. These mutations decreased the rate of oxidative phosphorylation dependent growth to one half of that observed with wild type. The ability of ATPase dependent or NADH-dependent proton gradient formation across inverted membrane vesicles from E. coli was measured by ACMA quenching. Mutations to either residue aE196 or cR50 that altered the ability to form a salt bridge between these subunits were observed to increase the rate and extent of proton gradient formation induced by either ATP or NADH. However, none of these mutations decreased the rate of ATP hydrolysis activity of the FoF1 in the inverted membrane vesicles significantly. These results suggest that the ability to form the leash between subunits a and c biases rotation of the c-ring in the ATP synthesis direction.
Revisiting Consistent Hashing with Bounded Loads

Dynamic load balancing lies at the heart of distributed caching. Here, the goal is to assign objects (load) to servers (computing nodes) in a way that provides load balancing while at the same time dynamically adjusts to the addition or removal of servers. Load balancing is a critical topic in many areas including cloud systems, distributed databases, and distributed and data-parallel machine learning. A popular and widely adopted solution to dynamic load balancing is the two-decade-old Consistent Hashing (CH). Recently, an elegant extension was provided to account for server bounds. In this paper, we identify that existing methodologies for CH and its variants suffer from cascaded overflow, leading to poor load balancing. This cascading effect leads to decreasing performance of the hashing procedure with increasing load. To overcome the cascading effect, we propose a simple solution to CH based on recent advances in fast minwise hashing. We show, both theoretically and empirically, that our proposed solution is significantly superior for load balancing and is optimal in many senses. On the AOL search dataset and Indiana University Clicks dataset with real user activity, our proposed solution reduces cache misses by several magnitudes.
TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora

Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence can be found at https://textessence.github.io.
Entangled-photon interferometry for plasmas

Subpicosecond coincidence timing from nonlocal intensity interference of entangled photons allows quantum interferometry for plasmas. Using a warm plasma dispersion relation, we correlate phase measurement sensitivity with different plasma properties or physics mechanisms over six orders of magnitude. Due to N α ( α ≤ − 1 / 2) scaling with the photon number N, quantum interferometry using entangled light can probe small signals in plasmas not previously accessible. As an example, it is predicted that plasmas will induce shifts to a Gaussian dip, a well-known quantum optics phenomenon that is yet to be demonstrated for plasmas.
Automated Mechanism Design for Classification with Partial Verification.

We study the problem of automated mechanism design with partial verification, where each type can (mis)report only a restricted set of types (rather than any other type), induced by the principal's limited verification power. We prove hardness results when the revelation principle does not necessarily hold, as well as when types have even minimally different preferences. In light of these hardness results, we focus on truthful mechanisms in the setting where all types share the same preference over outcomes, which is motivated by applications in, e.g., strategic classification. We present a number of algorithmic and structural results, including an efficient algorithm for finding optimal deterministic truthful mechanisms, which also implies a faster algorithm for finding optimal randomized truthful mechanisms via a characterization based on the notion of convexity. We then consider a more general setting, where the principal's cost is a function of the combination of outcomes assigned to each type. In particular, we focus on the case where the cost function is submodular, and give generalizations of essentially all our results in the classical setting where the cost function is additive. Our results provide a relatively complete picture for automated mechanisms design with partial verification.
Quantifying Legal Entropy

Many scholars have employed the term “entropy” in the context of law and legal systems to roughly refer to the amount of “uncertainty” present in a given law, doctrine, or legal system. Just a few of these scholars have attempted to formulate a quantitative definition of legal entropy, and none have provided a precise formula usable across a variety of legal contexts. Here, relying upon Claude Shannon’s definition of entropy in the context of information theory, I provide a quantitative formalization of entropy in delineating, interpreting, and applying the law. In addition to offering a precise quantification of uncertainty and the information content of the law, the approach offered here provides other benefits. For example, it offers a more comprehensive account of the uses and limits of “modularity” in the law—namely, using the terminology of Henry Smith, the use of legal “boundaries” (be they spatial or intangible) that “economize on information costs” by “hiding” classes of information “behind” those boundaries. In general, much of the “work” performed by the legal system is to reduce legal entropy by delineating, interpreting, and applying the law, a process that can in principle be quantified.
Variable-Length Constrained Coding and Kraft Conditions: The Parity-Preserving Case

Previous work by the authors on parity-preserving fixed-length constrained encoders is extended to the variable-length case. Parity-preserving variable-length encoders are formally defined, and, to this end, Kraft conditions are developed for the parity-preserving variable-length setting. Then, a necessary and sufficient condition is presented for the existence of deterministic parity-preserving variable-length encoders for a given constraint. Examples are provided that show that there are coding ratios where parity-preserving variable-length encoders exist, while fixed-length encoders do not.
Airway-Associated Macrophages in Homeostasis and Repair

There is an increasing appreciation for the heterogeneity of myeloid lineages in the respiratory system, but whether distinct populations associate with the conducting airways remains unknown. We use single cell RNA sequencing, flow cytometry and immunofluorescence to characterize myeloid cells of the mouse trachea during homeostasis and epithelial injury/repair. We identify submucosal macrophages that are similar to lung interstitial macrophages and intraepithelial macrophages, and find that repair of the tracheal epithelium is impaired in Ccr2-deficient mice. Following injury there are early increases in neutrophils and submucosal macrophages, including M2-like macrophages. Unexpectedly, intraepithelial macrophages are initially lost but later replaced from CCR2+ monocytes. Mast cells and group 2 innate lymphoid cells are sources of IL13 that polarizes macrophages and directly influences basal cell behaviors. Their proximity to the airway epithelium establishes these myeloid populations as potential therapeutic targets for airway disease.
Prioritizing Primary Care Can Save the U.S. Health Care System.

Payers are being asked to commit to a change in primary care financing in the next two years.
Neuronal activity-induced BRG1 phosphorylation regulates enhancer activation

Neuronal activity-induced enhancers drive gene activation. We demonstrate that BRG1, the core subunit of SWI/SNF-like BAF ATP-dependent chromatin remodeling complexes, regulates neuronal activity-induced enhancers. Upon stimulation, BRG1 is recruited to enhancers in an H3K27Ac-dependent manner. BRG1 regulates enhancer basal activities and inducibility by affecting cohesin binding, enhancer-promoter looping, RNA polymerase II recruitment, and enhancer RNA expression. We identify a serine phosphorylation site in BRG1 that is induced by neuronal stimulations and is sensitive to CaMKII inhibition. BRG1 phosphorylation affects its interaction with several transcription co-factors, including the NuRD repressor complex and cohesin, possibly modulating BRG1-mediated transcription outcomes. Using mice with knockin mutations, we show that non-phosphorylatable BRG1 fails to efficiently induce activity-dependent genes, whereas phosphomimic BRG1 increases enhancer activity and inducibility. These mutant mice display anxiety-like phenotypes and altered responses to stress. Therefore, we reveal a mechanism connecting neuronal signaling to enhancer activities through BRG1 phosphorylation.
Benchmarking Quantum Computers and the Impact of Quantum Noise

Benchmarking is how the performance of a computing system is determined. Surprisingly, even for classical computers this is not a straightforward process. One must choose the appropriate benchmark and metrics to extract meaningful results. Different benchmarks test the system in different ways, and each individual metric may or may not be of interest. Choosing the appropriate approach is tricky. The situation is even more open ended for quantum computers, where there is a wider range of hardware, fewer established guidelines, and additional complicating factors. Notably, quantum noise significantly impacts performance and is difficult to model accurately. Here, we discuss benchmarking of quantum computers from a computer architecture perspective and provide numerical simulations highlighting challenges that suggest caution.
IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data

Increasing use of sensor data in intelligent transportation systems calls for accurate imputation algorithms that can enable reliable traffic management in the occasional absence of data. As one of the effective imputation approaches, generative adversarial networks (GANs) are implicit generative models that can be used for data imputation, which is formulated as an unsupervised learning problem. This work introduces a novel iterative GAN architecture, called Iterative Generative Adversarial Networks for Imputation (IGANI), for data imputation. IGANI imputes data in two steps and maintains the invertibility of the generative imputer, which will be shown to be a sufficient condition for the convergence of the proposed GAN-based imputation. The performance of our proposed method is evaluated on (1) the imputation of traffic speed data collected in the city of Guangzhou in China, and the training of short-term traffic prediction models using imputed data, and (2) the imputation of multi-variable traffic data of highways in Portland-Vancouver metropolitan region which includes volume, occupancy, and speed with different missing rates for each of them. It is shown that our proposed algorithm mostly produces more accurate results compared to those of previous GAN-based imputation architectures.
Expanding the potential genes of inborn errors of immunity through protein interactions.

Inborn errors of immunity (IEI) are a group of genetic disorders that impair the immune system, with over 400 genes described so far, and hundreds more to be discovered. To facilitate the search for new genes, we need a way to prioritize among all the genes in the genome those most likely to play an important role in immunity. Here we identify a new list of genes by linking known IEI genes to new ones by using open-source databases of protein-protein interactions, post-translational modifications, and transcriptional regulation. We analyze this new set of 2,530 IEI-related genes for their tolerance of genetic variation and by their expression levels in various immune cell types. By merging genes derived from protein interactions of known IEI genes with transcriptional data, we offer a new list of candidate genes that may play a role in as-yet undiscovered IEIs.
Valuation of Noise Pollution and Abatement Policy: Evidence from the Minneapolis-St. Paul International Airport

Aircraft noise pollution has adverse physical and mental health effects that are capitalized in the affected home values. We contribute to the literature estimating these noise discounts by our novel identification strategy that analyzes the “treatment effect” of two local government subsidized soundproofing initiatives near the Minneapolis-Saint Paul International Airport. Combining a repeat-sales sample with data on aircraft noise pollution (1990–2014), we find a causal noise discount of around $25,000 per sale of noise-affected, but abatement-ineligible, properties, whereas abatement-eligible homes experience a negligible effect post soundproofing indicating a return on abatement investments as a high as 40% in Minneapolis.
What Information is Leaked under Concurrent Composition

Achieving security under concurrent composition is notoriously hard. Indeed, in the plain model, far reaching impossibility results for concurrently secure computation are known. On the other hand, some positive results have also been obtained according to various weaker notions of security (such as by using a super-polynomial time simulator). This suggest that somehow, “not all is lost in the concurrent setting.” In this work, we ask what and exactly how much private information can the adversary learn by launching a concurrent attack? “Can he learn all the private inputs in all the sessions? Or, can we preserve the security of some (or even most) of the sessions fully while compromising (a small fraction of) other sessions? Or is it the case that the security of all (or most) sessions is (at least partially) compromised? If so, can we restrict him to learn an arbitrarily small fraction of input in each session?” We believe the above questions to be fundamental to the understanding of concurrent composition. Indeed, despite a large body of work on the study of concurrent composition, in our opinion, the understanding of what exactly is it that goes wrong in the concurrent setting and to what extent is currently quite unsatisfactory. Towards that end, we adopt the knowledge-complexity based approach of Goldreich and Petrank [STOC’91] to quantify information leakage in concurrently secure computation. We consider a model where the ideal world adversary (a.k.a simulator) is allowed to query the trusted party for some “leakage” on the honest party inputs. We obtain both positive and negative results, depending upon the nature of the leakage queries available to the simulator. Informally speaking, our results imply the following: in the concurrent setting, “significant loss” of security (translating to high leakage in the ideal world) in some of the sessions is unavoidable if one wishes to obtain a general result. However on the brighter side, one can make the fraction of such sessions to be an arbitrarily small polynomial (while fully preserving the security in all other sessions). Our results also have an implication on secure computation in the bounded concurrent setting [Barak-FOCS’01]: we show there exist protocols which are secure as per the standard ideal/real world notion in the bounded concurrent setting. However if the actual number of sessions happen to exceed the bound, there is a graceful degradation of security as the number of sessions increase. (In contrast, prior results do not provide any security once the bound is exceeded.) In order to obtain our positive result, we model concurrent extraction as the classical setcovering problem and develop, as our main technical contribution, a new sparse rewinding strategy. Specifically, unlike previous rewinding strategies which are very “dense”, we rewind “small ∗This paper is the full version of [GGJ13] that appeared at Crypto 2013. †Work done in part while visiting Microsoft Research India. ‡Work done in part while visiting Microsoft Research India.
Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation

In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a Gaussian multiple access channel, subject to central and local differential privacy (DP/LDP) constraints. It has been shown that the superposition nature of the wireless channel provides a dual benefit of bandwidth efficient gradient aggregation, in conjunction with strong DP guarantees for the users. Specifically, the central DP privacy leakage has been shown to scale as $\mathcal{O}(1/\sqrt{K})$ , where $K$ is the number of users. It has also been shown that user sampling coupled with orthogonal transmission can enhance the central DP privacy leakage with the same scaling behavior. In this work, we show that, by jointly incorporating both wireless aggregation and user sampling, one can obtain even stronger central DP guarantees. We propose a private wireless gradient aggregation scheme, which relies on independently randomized participation decisions by each user. The central DP leakage of our proposed scheme scales as $\mathcal{O}(1/K^{3/4})$ . In addition, we show that LDP is also boosted by user sampling.
Graph Coding for Model Selection and Anomaly Detection in Gaussian Graphical Models

A classic application of description length is for model selection with the minimum description length (MDL) principle. The focus of this paper is to extend description length for data analysis beyond simple model selection and sequences of scalars. More specifically, we extend the description length for data analysis in Gaussian graphical models. These are powerful tools to model interactions among variables in a sequence of i.i.d Gaussian data in the form of a graph. Our method uses universal graph coding methods to accurately account for model complexity, and therefore provide a more rigorous approach for graph model selection. The developed method is tested with synthetic and electrocardiogram (ECG) data to find the graph model and anomaly in Gaussian graphical models. The experiments show that our method gives better performance compared to commonly used methods.
Intermittency effects in single- and multi-phase flow and anomalous transport in heterogeneous porous media

Multi-phase flow and transport in porous media is prevalent in a wide range of challenging fluid mechanics problems related to sustainability, energy, and the environment. Accurate prediction of the displacement and interaction of such flows is vital in addressing these problems. In particular it is critical to understand the small- or pore-scale flow and its spatial and temporal evolution, which can impact behaviors at system scales in a nontrivial manner. Intermittency is a phenomenon currently observed in numerical and experimental studies of single-phase flow (Anna et al., n.d.; Morales et al., n.d.), but the case of multi-phase flow has yet to receive much study due to challenges faced in both simulations and experiments. The underlying physics of spreading, mixing, and interfacial processes must be understood for accurate predictions of transport in multi-phase flow systems. Therefore, a comprehensive understanding of multi-phase flow at these very small scales is necessary in the development of accurate system-scale prediction models. We present results from a coordinated numerical and experimental study of intermittency effects over a range of viscous and inertial flow regimes in single- and multi-phase flows in 2D heterogeneous micromodels to quantify Lagrangian flow statistics to better inform pore-scale models. The applicability of different modeling frameworks such as the correlated-continuous time random walk is tested by studying statistics of particle trajectories obtained by particle tracking velocimetry (PTV) measurements and Lattice Boltzmann simulations from single- and multi-phase flows. The results make particular note of the influence of the pore Reynolds number (Re) and inertial effects on intermittency, and compare these effects in the two flow regimes.
Memory-Efficient Learning for High-Dimensional MRI Reconstruction

Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI). Similar to compressed sensing, DL can leverage high-dimensional data (e.g. 3D, 2D+time, 3D+time) to further improve performance. However, network size and depth are currently limited by the GPU memory required for backpropagation. Here we use a memory-efficient learning (MEL) framework which favorably trades off storage with a manageable increase in computation during training. Using MEL with multi-dimensional data, we demonstrate improved image reconstruction performance for in-vivo 3D MRI and 2D+time cardiac cine MRI. MEL uses far less GPU memory while marginally increasing the training time, which enables new applications of DL to high-dimensional MRI. Our code is available at https://github.com/mikgroup/MEL_MRI.
Active gels, heavy tails, and the cytoskeleton

The eukaryotic cell's cytoskeleton is a prototypical example of an active material: objects embedded within it are driven by molecular motors acting on the cytoskeleton, leading to anomalous diffusive behavior. Experiments tracking the behavior of cell-attached objects have observed anomalous diffusion with a distribution of displacements that is non-Gaussian, with heavy tails. This has been attributed to “cytoquakes” or other spatially extended collective effects. We show, using simulations and analytical theory, that a simple continuum active gel model driven by fluctuating force dipoles naturally creates heavy power-law tails in cytoskeletal displacements. We predict that this power law exponent should depend on the geometry and dimensionality of where force dipoles are distributed through the cell; we find qualitatively different results for force dipoles in a 3D cytoskeleton and a quasi-two-dimensional cortex. We then discuss potential applications of this model both in cells and in synthetic active gels.
OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation

In this paper, we explore whether a robot can learn to hang arbitrary objects onto a diverse set of supporting items such as racks or hooks. Endowing robots with such an ability has applications in many domains such as domestic services, logistics, or manufacturing. Yet, it is a challenging manipulation task due to the large diversity of geometry and topology of everyday objects. In this paper, we propose a system that takes partial point clouds of an object and a supporting item as input and learns to decide where and how to hang the object stably. Our system learns to estimate the contact point correspondences between the object and supporting item to get an estimated stable pose. We then run a deep reinforcement learning algorithm to refine the predicted stable pose. Then, the robot needs to find a collision-free path to move the object from its initial pose to stable hanging pose. To this end, we train a neural network based collision estimator that takes as input partial point clouds of the object and supporting item. We generate a new and challenging, large-scale, synthetic dataset annotated with stable poses of objects hung on various supporting items and their contact point correspondences. In this dataset, we show that our system is able to achieve a 68.3% success rate of predicting stable object poses and has a 52.1% F1 score in terms of finding feasible paths. Supplemental material and videos are available on our project webpage https://sites.google.com/view/hangingobject.
Machine Learning: Challenges, Limitations, and Compatibility for Audio Restoration Processes

In this paper, machines learning networks are explored for their use in restoring degraded and compressed speech audio. The project intent is to build a new trained model from voice data to learn features of compression artifacting (distortion introduced by data loss from lossy compression) and resolution loss with an existing algorithm presented in ‘SEGAN: Speech Enhancement Generative Adversarial Network’. The resulting generator from the model was then to be used to restore degraded speech audio. This paper details an examination of the subsequent compatibility and operational issues presented by working with deprecated code, which obstructed the trained model from successfully being developed. This paper further serves as an examination of the challenges, limitations, and compatibility in the current state of machine learning.
Real earnings management and the properties of analysts' forecasts

Abstract Prior literature generally finds analysts are able to identify and process complex financial information. However, research suggests that in certain settings, analysts struggle to fully incorporate into their forecasts all available information. We examine analysts' forecast properties in the face of a specific type of complex financial information: real earnings management (REM). First, we investigate the relation between measures of REM and analysts' forecast properties. We find REM measures are associated with greater forecast error and dispersion in the following year. However, REM measures, by definition, capture abnormal operating results, and thus include both firms engaging in manipulative REM as well as firms experiencing firm-specific economic shocks. Thus, we conduct cross-sectional tests of analysts' forecasts for firms with and without incentives to manipulate earnings. We find that firms with low incentives to engage in earnings management (i.e., firms most likely experiencing firm-specific economic shocks) generate the strongest positive relation between REM measures and the following year's analysts' forecast properties, suggesting analysts more fully incorporate the earnings implications of firms with high incentives (i.e., firms most likely engaging in manipulative REM). Our results are consistent across numerous REM proxies and indicators of earnings management incentives.
Delayed effects of climate on vital rates lead to demographic divergence in Amazonian forest fragments.

Deforestation often results in landscapes where remaining forest habitat is highly fragmented, with remnants of different sizes embedded in an often highly contrasting matrix. Local extinction of species from individual fragments is common, but the demographic mechanisms underlying these extinctions are poorly understood. It is often hypothesized that altered environmental conditions in fragments drive declines in reproduction, recruitment, or survivorship. The Amazon basin, in addition to experiencing continuing fragmentation, is experiencing climate change-related increases in the frequency and intensity of droughts and unusually wet periods. Whether plant populations in tropical forest fragments are particularly susceptible to extremes in precipitation remains unclear. Most studies of plants in fragments are relatively short (1-6 years), focus on a single life-history stage, and often do not compare to populations in continuous forest. Even fewer studies consider delayed effects of climate on demographic vital rates despite the importance of delayed effects in studies that consider them. Using a decade of demographic and climate data from an experimentally fragmented landscape in the Central Amazon, we assess the effects of climate on populations of an understory herb (Heliconia acuminata, Heliconiaceae). We used distributed lag nonlinear models to understand the delayed effects of climate (measured as standardized precipitation evapotranspiration index, SPEI) on survival, growth, and flowering. We detected delayed effects of climate up to 36 months. Extremes in SPEI in the previous year reduced survival, drought in the wet season 8-11 months prior to the February census increased growth, and drought two dry seasons prior increased flowering probability. Effects of extremes in precipitation on survival and growth were more pronounced in forest fragments compared to continuous forest. The complex delayed effects of climate and habitat fragmentation in our study point to the importance of long-term demography experiments in understanding the effects of anthropogenic change on plant populations.
Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism.

Physics-Informed Neural Networks (PINNs) have emerged recently as a promising application of deep neural networks to the numerical solution of nonlinear partial differential equations (PDEs). However, the original PINN algorithm is known to suffer from stability and accuracy problems in cases where the solution has sharp spatio-temporal transitions. These stiff PDEs require an unreasonably large number of collocation points to be solved accurately. It has been recognized that adaptive procedures are needed to force the neural network to fit accurately the stubborn spots in the solution of stiff PDEs. To accomplish this, previous approaches have used fixed weights hard-coded over regions of the solution deemed to be important. In this paper, we propose a fundamentally new method to train PINNs adaptively, where the adaptation weights are fully trainable, so the neural network learns by itself which regions of the solution are difficult and is forced to focus on them, which is reminiscent of soft multiplicative-mask attention mechanism used in computer vision. The basic idea behind these Self-Adaptive PINNs is to make the weights increase where the corresponding loss is higher, which is accomplished by training the network to simultaneously minimize the losses and maximize the weights, i.e., to find a saddle point in the cost surface. We show that this is formally equivalent to solving a PDE-constrained optimization problem using a penalty-based method, though in a way where the monotonically-nondecreasing penalty coefficients are trainable. Numerical experiments with an Allen-Cahn stiff PDE, the Self-Adaptive PINN outperformed other state-of-the-art PINN algorithms in L2 error by a wide margin, while using a smaller number of training epochs. An Appendix contains additional results with Burger's and Helmholtz PDEs, which confirmed the trends observed in the Allen-Cahn experiments.