cssi/SciSciGPT-SciSciNet huggingface dataset:
Generalized SAT-Attack-Resistant Logic Locking

Logic locking is used to protect integrated circuits (ICs) from piracy and counterfeiting. An encrypted IC implements the correct function only when the right key is input. Many existing logic-locking methods are subject to the powerful satisfiability (SAT)-based attack. Recently, an Anti-SAT scheme has been developed. By adopting two complementary logic blocks that consist of AND/NAND trees, it makes the number of iterations needed by the SAT attack exponential to the number of input bits. Nevertheless, the Anti-SAT scheme is vulnerable to the later AppSAT and removal attacks. This article proposes a generalized (G-)Anti-SAT scheme. Different from the Anti-SAT scheme, a variety of complementary or non-complementary functions can be adopted for the two blocks in our G-Anti-SAT scheme. The Anti-SAT scheme is just a special case of our proposed design. Our design can achieve higher output corruptibility, which is also tunable, so that better resistance to the AppSAT and removal attacks is achieved. Meanwhile, unlike existing AppSAT-resilient designs, our design does not sacrifice the resistance to the SAT attack.
Integrable and Chaotic Systems Associated with Fractal Groups

Fractal groups (also called self-similar groups) is the class of groups discovered by the first author in the 1980s with the purpose of solving some famous problems in mathematics, including the question of raising to von Neumann about non-elementary amenability (in the association with studies around the Banach-Tarski Paradox) and John Milnor’s question on the existence of groups of intermediate growth between polynomial and exponential. Fractal groups arise in various fields of mathematics, including the theory of random walks, holomorphic dynamics, automata theory, operator algebras, etc. They have relations to the theory of chaos, quasi-crystals, fractals, and random Schrodinger operators. One important development is the relation of fractal groups to multi-dimensional dynamics, the theory of joint spectrum of pencil of operators, and the spectral theory of Laplace operator on graphs. This paper gives a quick access to these topics, provides calculation and analysis of multi-dimensional rational maps arising via the Schur complement in some important examples, including the first group of intermediate growth and its overgroup, contains a discussion of the dichotomy “integrable-chaotic” in the considered model, and suggests a possible probabilistic approach to studying the discussed problems.
Fair Clustering via Equitable Group Representations

What does it mean for a clustering to be fair? One popular approach seeks to ensure that each cluster contains groups in (roughly) the same proportion in which they exist in the population. The normative principle at play is balance: any cluster might act as a representative of the data, and thus should reflect its diversity. But clustering also captures a different form of representativeness. A core principle in most clustering problems is that a cluster center should be representative of the cluster it represents, by being "close" to the points associated with it. This is so that we can effectively replace the points by their cluster centers without significant loss in fidelity, and indeed is a common "use case" for clustering. For such a clustering to be fair, the centers should "represent" different groups equally well. We call such a clustering a group-representative clustering. In this paper, we study the structure and computation of group-representative clusterings. We show that this notion naturally parallels the development of fairness notions in classification, with direct analogs of ideas like demographic parity and equal opportunity. We demonstrate how these notions are distinct from and cannot be captured by balance-based notions of fairness. We present approximation algorithms for group representative k-median clustering and couple this with an empirical evaluation on various real-world data sets. We also extend this idea to facility location, motivated by the current problem of assigning polling locations for voting
Hurwitz Theory of Elliptic Orbifolds, I

An elliptic orbifold is the quotient of an elliptic curve by a finite group. In 2001, Eskin and Okounkov proved that generating functions for the number of branched covers of an elliptic curve with specified ramification are quasimodular forms for SL2(ℤ). In 2006, they generalized this theorem to branched covers of the quotient of an elliptic curve by ±1, proving quasimodularity for Γ0(2). We generalize their work to the quotient of an elliptic curve by ⟨ζN⟩ for N=3, 4, 6, proving quasimodularity for Γ(N), and extend their work in the case N=2. It follows that certain generating functions of hexagon, square and triangle tilings of compact surfaces are quasimodular forms. These tilings enumerate lattice points in moduli spaces of flat surfaces. We analyze the asymptotics as the number of tiles goes to infinity, providing an algorithm to compute the Masur–Veech volumes of strata of cubic, quartic, and sextic differentials. We conclude a generalization of the Kontsevich–Zorich conjecture: these volumes are polynomial in π.
The Informational Role of Investor Relations: Evidence from the Debt Market

We explore the role of investor relations (IR) in debt markets. Using earnings announcements as a laboratory, we examine whether, when, and to what extent IR departments help credit investors assimilate information. We find that the presence of IR decreases (increases) the negative (positive) impact on CDS spreads stemming from bad (good) earnings news, suggesting that IR efforts improve information precision and reduce transparency risk. Cross-sectional analyses suggest that IR matters more when uncertainty is high and creditors are concerned about the credit-risk implications of firm performance. We also find that IR firms receive higher credit ratings and fewer covenants when issuing bonds, and CDS markets react less negatively when IR firms’ bond ratings are downgraded below investment grade. Because firms choose their IR activities, we show that our inferences are robust to multiple methods of addressing endogeneity. Overall, we find that IR departments are influential in debt markets.
The role of social network support in treatment outcomes for medication for opioid use disorder: A systematic review

Abstract Background Social connections can lead to contagion of healthy behaviors. Successful treatment of patients with opioid use disorder may lay in rebuilding social networks. Strong social networks of support can reinforce the benefits of medication treatments that are the current standard of care and the most effective tool physicians have to fight the opioid epidemic. Methods The research team conducted a systematic review of electronic research databases, specialist journals and grey literature up to August 2020 to identify randomized controlled trials of social network support in patient populations receiving medication for opioid use disorder (MOUD). The research team placed the studies into a framework of dynamic social networks, examining the role of networks before MOUD treatment is initiated, during the treatment, and in the long-term following the treatment. The research team analyzed the results across three sources of social network support: partner relationships, family, and peer networks. Results Of 5193 articles screened, eight studies were identified as meeting inclusion criteria. Five studies indicated that social network support had a statistically significant effect on improved MOUD treatment outcomes. We find the strongest support for the positive impact of family social network support. Conclusions Social networks significantly shape effectiveness of opioid use disorder treatments. While negative social ties reinforce addiction, positive social support networks can amplify the benefits of medication treatments. Targeted interventions to improve treatment outcomes can be designed and added to MOUD treatment with their effects evaluated in improving patients' odds of recovery from opioid use disorder and reversing the rising trend in opioid deaths.
When Is a Puiseux Monoid Atomic

A Puiseux monoid is an additive submonoid of the nonnegative rational numbers. If M is a Puiseux monoid, then the question of whether each nonunit element of M can be written as a sum of irreducibl...
Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations

Abstract When researchers develop new econometric methods it is common practice to compare the performance of the new methods to those of existing methods in Monte Carlo studies. The credibility of such Monte Carlo studies is often limited because of the discretion the researcher has in choosing the Monte Carlo designs reported. To improve the credibility we propose using a class of generative models that has recently been developed in the machine learning literature, termed Generative Adversarial Networks (GANs) which can be used to systematically generate artificial data that closely mimics existing datasets. Thus, in combination with existing real data sets, GANs can be used to limit the degrees of freedom in Monte Carlo study designs for the researcher, making any comparisons more convincing. In addition, if an applied researcher is concerned with the performance of a particular statistical method on a specific data set (beyond its theoretical properties in large samples), she can use such GANs to assess the performance of the proposed method, e.g. the coverage rate of confidence intervals or the bias of the estimator, using simulated data which closely resembles the exact setting of interest. To illustrate these methods we apply Wasserstein GANs (WGANs) to the estimation of average treatment effects. In this example, we find that ( i ) there is not a single estimator that outperforms the others in all three settings, so researchers should tailor their analytic approach to a given setting, ( i i ) systematic simulation studies can be helpful for selecting among competing methods in this situation, and ( i i i ) the generated data closely resemble the actual data.
Impact of size and thermal gradient on supercooling of phase change materials for thermal energy storage

Abstract Phase change material based thermal energy storage has many current and potential applications in the heating and cooling of buildings, battery and electronics thermal management, thermal textiles, and dry cooling of power plants. However, connecting lab scale thermal data obtained with differential scanning calorimetry (DSC) to the performance of large-scale practical systems has been a major challenge primarily due to the dependence of supercooling on the size and temperature gradient of the system. In this work we show how a phase change material’s supercooling behavior can be characterized experimentally using common lab scale thermal analysis techniques. We then develop a statistics based theoretical model that uses the lab-scale data on small samples to quantitatively predict the supercooling performance for a general thermal energy storage application of any size, including also allowing for the possibility of temperature gradients. Finally, we validate the modeling methodology by comparing to experimental results for solid-solid phase change in neopentyl glycol, which shows how the model successfully predicts the changes in supercooling temperature across a large range of cooling rates (2 orders of magnitude) and volumes (3 orders of magnitude). By accounting for thermal gradients, the model avoids ~2x error incurred by lumped approximations.
Retribution and Reform

The last twenty or so years have seen a rise in sophisticated and complex theories of retributive justice: many have called it a “retributivist revival.” But has this theoretical progress had any relevance to the state of America’s punishment practice? American prisons, it is almost universally agreed, are in a shameful state, and criminal sentences are too long. So here we have an apparent paradox: at a time when punishment theory is flourishing, the practice of punishment in America is at a low point. Is there any relationship between punishment theory and practice? Should there be? In a series of essays, book reviews, testimony, and in an enormously influential book, legal historian and Yale Law School professor James Q. Whitman has faulted contemporary retributive theory as at best irrelevant to practice and at worst positively harmful. My essay responds to Whitman’s criticisms, while also paying some promissory notes made in earlier essays. I develop a novel theory of retribution that shows retribution -contra Whitman -as rooted in a concern with the “harshness” of our emotional reactions to wrongdoing. I also defend retributivism’s claim to be the only theory that can justify proportionality in punishment, responding to a series of arguments to the contrary made recently by inter alia Alice Ristroph, Edward Rubin, and Ian Farrell. Finally, I address Whitman’s particular claims against retributive theory, contending that he either exaggerates retributivism’s irrelevance to practice, or unfairly holds retribution to a higher standard than other theories of punishment. * J.D. 2007, Yale; Ph.D. 2004, Chicago (philosophy). Assistant Professor of Law, Saint Louis University School of Law. Please address all comments to cflande2@slu.edu. This essay I hope pays some promissory notes made by Dan Markel and Chad Flanders, Bentham on Stilts? The Irrelevance of Subjectivity to Retributive Justice CAL. L. REV. (forthcoming 2010). I am grateful for Dan’s guidance in that project; he should not be taken to agree with everything in this essay. I thank Julia Kleinheider, Matt Hall, Danny Priel, Will Baude, Christopher Bradley, Hanah Volokh, Sam Bray, Rob Wiygul and Terrence Burek for helpful comments and conversations on earlier drafts.
Unifying Clustered and Non-stationary Bandits

Non-stationary bandits and clustered bandits lift the restrictive assumptions in contextual bandits and provide solutions to many important real-world scenarios. Though they have been studied independently so far, we point out the essence in solving these two problems overlaps considerably. In this work, we connect these two strands of bandit research under the notion of test of homogeneity, which seamlessly addresses change detection for non-stationary bandit and cluster identification for clustered bandit in a unified solution framework. Rigorous regret analysis and extensive empirical evaluations demonstrate the value of our proposed solution, especially its flexibility in handling various environment assumptions, e.g., a clustered non-stationary environment.
Robustness of Uncertain Switching Nonlinear Feedback Systems against Large Time-Variation

For a MIMO nonlinear feedback system, the robustness against uncertain time-variation (slow or infrequently large) in the feedback loop is investigated in an input-output framework. A couple of sufficient conditions in terms of bounds on average rates of time-variation for the system to be stable are derived. The conditions provide an useful tool for designing adaptive controllers for systems against uncertain large time-variation.
Mycobacterium tuberculosis stimulates IL-1β production by macrophages in an ESAT-6 dependent manner with the involvement of serum amyloid A3

Despite its critical roles in immune responses against tuberculosis infection and immune pathology, the molecular details of interleukin (IL)-1β production in tuberculosis infection remain elusive. To explore IL-1β production in tuberculosis infection, we infected mouse bone marrow-derived macrophages (BMDM) with Mycobacterium tuberculosis (Mtb) H37Rv, its early secreted antigenic target protein of 6 kDa (ESAT-6) gene deletion (H37Rv:Δ3875) or complemented strain (H37Rv:Δ3875C) and evaluated IL-1β production. H37Rv induced significantly increased IL-1β production by BMDMs compared to non-infected BMDMs. In contrast, H37Rv:Δ3875 induced significantly less mature IL-1β production despite eliciting comparable levels of pro-IL-1β and IL-8 from BMDMs compared to H37Rv and H37Rv:Δ3875C. Blocking either NLRP3 or K+ efflux diminished H37Rv-induced IL-1β production by BMDMs. Infection of mice intranasally with H37Rv:Δ3875 induced less IL-1β production in the lungs compared with H37Rv. Intranasal delivery of ESAT-6 but not CFP10 induced production of IL-1β in mouse lungs and RNA-Seq analysis identified serum amyloid A (SAA) 3 as one of the highly expressed genes in mouse lungs. Infection of mice with H37Rv but not H37Rv:Δ3875 induced expression of lung SAA3 mRNA and protein, consistent with the effect of intranasal delivery of ESAT-6. Silencing SAA3 reduced Mtb-induced IL-1β production by BMDMs. We conclude that SAA3 plays critical role in ESAT-6 dependent IL-1β production by macrophages in tuberculosis infection.
Data-Efficient Framework for Real-World Multiple Sound Source 2d Localization

Deep neural networks have recently led to promising results for the task of multiple sound source localization. Yet, they require a lot of training data to cover a variety of acoustic conditions and micro-phone array layouts. One can leverage acoustic simulators to inexpensively generate labeled training data. However, models trained on synthetic data tend to perform poorly with real-world recordings due to the domain mismatch. Moreover, learning for different microphone array layouts makes the task more complicated due to the infinite number of possible layouts. We propose to use adversarial learning methods to close the gap between synthetic and real do-mains. Our novel ensemble-discrimination method significantly improves the localization performance without requiring any label from the real data. Furthermore, we propose a novel explicit transformation layer to be embedded in the localization architecture. It enables the model to be trained with data from specific microphone array layouts while generalizing well to unseen layouts during inference.
The statistical mechanics of near-extremal black holes

An important open question in black hole thermodynamics is about the existence of a “mass gap” between an extremal black hole and the lightest near-extremal state within a sector of fixed charge. In this paper, we reliably compute the partition function of Reissner-Nordstrom near-extremal black holes at temperature scales comparable to the conjectured gap. We find that the density of states at fixed charge does not exhibit a gap; rather, at the expected gap energy scale, we see a continuum of states. We compute the partition function in the canonical and grand canonical ensembles, keeping track of all the fields appearing through a dimensional reduction on S2 in the near-horizon region. Our calculation shows that the relevant degrees of freedom at low temperatures are those of 2d Jackiw-Teitelboim gravity coupled to the electromagnetic U(1) gauge field and to an SO(3) gauge field generated by the dimensional reduction.
A Context-based Automated Approach for Method Name Consistency Checking and Suggestion

Misleading method names in software projects can confuse developers, which may lead to software defects and affect code understandability. In this paper, we present DeepName, a context-based, deep learning approach to detect method name inconsistencies and suggest a proper name for a method. The key departure point is the philosophy of "Show Me Your Friends, I'll Tell You Who You Are". Unlike the state-of-the-art approaches, in addition to the method's body, we also consider the interactions of the current method under study with the other ones including the caller and callee methods, and the sibling methods in the same enclosing class. The sequences of sub-tokens in the program entities' names in the contexts are extracted and used as the input for an RNN-based encoder-decoder to produce the representations for the current method. We modify that RNN model to integrate the copy mechanism and our newly developed component, called the non-copy mechanism, to emphasize on the possibility of a certain sub-token not to be copied to follow the current sub-token in the currently generated method name. We conducted several experiments to evaluate DeepName on large datasets with +14M methods. For consistency checking, DeepName improves the state-of-the-art approach by 2.1%, 19.6%, and 11.9% relatively in recall, precision, and F-score, respectively. For name suggestion, DeepName improves relatively over the state-of-the-art approaches in precision (1.8%–30.5%), recall (8.8%–46.1%), and F-score (5.2%–38.2%). To assess DeepName's usefulness, we detected inconsistent methods and suggested new method names in active projects. Among 50 pull requests, 12 were merged into the main branch. In total, in 30/50 cases, the team members agree that our suggested method names are more meaningful than the current names.
Oxytocin receptor activation does not mediate associative fear deficits in a Williams Syndrome model.

Williams Syndrome results in distinct behavioral phenotypes, which include learning deficits, anxiety, increased phobias, and hypersociability. While the underlying mechanisms driving this subset of phenotypes is unknown, oxytocin dysregulation is hypothesized to be involved as some studies have shown elevated blood oxytocin and altered oxytocin receptor expression in patients. A 'Complete Deletion' mouse, modeling the hemizygous deletion in Williams Syndrome, recapitulates many of the phenotypes present in humans. These Complete Deletion mice also exhibit impaired fear responses in the conditioned fear task. Here, we address whether oxytocin dysregulation is responsible for this impaired associative fear memory response. We show direct delivery of an oxytocin receptor antagonist to the central nervous system did not rescue the attenuated contextual or cued fear memory responses in Complete Deletion mice. Thus, increased oxytocin signaling is not acutely responsible for this phenotype. We also evaluated oxytocin receptor and serotonin transporter availability in regions related to fear learning, memory, and sociability using autoradiography in wild type and Complete Deletion mice. While no differences withstood correction, we identified regions that may warrant further investigation. There was a nonsignificant decrease in oxytocin receptor expression in the lateral septal nucleus, and nonsignificant lowered serotonin transporter availability in the striatum and orbitofrontal cortex. Together, these data suggest the fear conditioning anomalies in the Williams Syndrome mouse model are independent of any alterations in the oxytocinergic system caused by deletion of the Williams locus.
Adaptive Contention Window Design Using Deep Q-Learning

We study the problem of adaptive contention window (CW) design for random-access wireless networks. More precisely, our goal is to design an intelligent node that can dynamically adapt its minimum CW (MCW) parameter to maximize a network-level utility knowing neither the MCWs of other nodes nor how these change over time. To achieve this goal, we adopt a reinforcement learning (RL) framework where we circumvent the lack of system knowledge with local channel observations and we reward actions that lead to high utilities. To efficiently learn these preferred actions, we follow a deep Q-learning approach, where the Q-value function is parametrized using a multi-layer perceptron. In particular, we implement a rainbow agent, which incorporates several empirical improvements over the basic deep Q-network. Numerical experiments based on the NS3 simulator reveal that the proposed RL agent performs close to optimal and markedly improves upon existing learning and non-learning based alternatives.
Contractions of subcurves of families of log curves

Let C be a nodal curve, and let E be a union of semistable subcurves of C. We consider the problem of contracting the connected components of E to singularities in a way that preserves the genus of...
Deformations of Totally Geodesic Foliations and Minimal Surfaces in Negatively Curved 3-Manifolds

Let $$g_t$$ be a smooth 1-parameter family of negatively curved metrics on a closed hyperbolic 3-manifold M starting at the hyperbolic metric. We construct foliations of the Grassmann bundle $$Gr_2(M)$$ of tangent 2-planes whose leaves are (lifts of) minimal surfaces in $$(M,g_t)$$ . These foliations are deformations of the foliation of $$Gr_2(M)$$ by (lifts of) totally geodesic planes projected down from the universal cover $${\mathbb {H}}^3$$ . Our construction continues to work as long as the sum of the squares of the principal curvatures of the (projections to M) of the leaves remains pointwise smaller in magnitude than the ambient Ricci curvature in the normal direction. In the second part of the paper we give some applications and construct negatively curved metrics for which $$Gr_2(M)$$ cannot admit a foliation as above.
Co-Located vs Distributed vs Semi-Distributed MIMO: Measurement-Based Evaluation

With the growing interest in cell-free massive multiple-input multiple-output (MIMO) systems, the benefits of single-antenna access points (APs) versus multi-antenna APs must be analyzed in order to optimize deployment. In this paper, we compare various antenna system topologies based on achievable downlink spectral efficiency, using both measured and synthetic channel data in an indoor environment. We assume multi-user scenarios, analyzing both conjugate beamforming (or maximum-ratio transmission (MRT)) and zero-forcing (ZF) precoding methods. The results show that the semi-distributed multi-antenna APs can reduce the number of APs, and still achieve the comparable achievable rates as the fully-distributed single-antenna APs with the same total number of antennas.
Measuring the repertoire of age-related behavioral changes in Drosophila melanogaster

Aging affects almost all aspects of an organism -- its morphology, its physiology, its behavior. Isolating which biological mechanisms are regulating these changes, however, has proven difficult, potentially due to our inability to characterize the full repertoire of an animal9s behavior across the lifespan. Using data from fruit flies (D. melanogaster) we measure the full repertoire of behaviors as a function of age. We observe a sexually dimorphic pattern of changes in the behavioral repertoire during aging. Although the stereotypy of the behaviors and the complexity of the repertoire overall remains relatively unchanged, we find evidence that the observed alterations in behavior can be explained by changing the fly9s overall energy budget, suggesting potential connections between metabolism, aging, and behavior.
Predictors of incident viral symptoms ascertained in the era of COVID-19.

Author(s): Marcus, Gregory M; Olgin, Jeffrey E; Peyser, Noah D; Vittinghoff, Eric; Yang, Vivian; Joyce, Sean; Avram, Robert; Tison, Geoffrey H; Wen, David; Butcher, Xochitl; Eitel, Helena; Pletcher, Mark J | Abstract: BackgroundIn the absence of universal testing, effective therapies, or vaccines, identifying risk factors for viral infection, particularly readily modifiable exposures and behaviors, is required to identify effective strategies against viral infection and transmission.MethodsWe conducted a world-wide mobile application-based prospective cohort study available to English speaking adults with a smartphone. We collected self-reported characteristics, exposures, and behaviors, as well as smartphone-based geolocation data. Our main outcome was incident symptoms of viral infection, defined as fevers and chills plus one other symptom previously shown to occur with SARS-CoV-2 infection, determined by daily surveys.FindingsAmong 14, 335 participants residing in all 50 US states and 93 different countries followed for a median 21 days (IQR 10-26 days), 424 (3%) developed incident viral symptoms. In pooled multivariable logistic regression models, female biological sex (odds ratio [OR] 1.75, 95% CI 1.39-2.20, pl0.001), anemia (OR 1.45, 95% CI 1.16-1.81, p = 0.001), hypertension (OR 1.35, 95% CI 1.08-1.68, p = 0.007), cigarette smoking in the last 30 days (OR 1.86, 95% CI 1.35-2.55, pl0.001), any viral symptoms among household members 6-12 days prior (OR 2.06, 95% CI 1.67-2.55, pl0.001), and the maximum number of individuals the participant interacted with within 6 feet in the past 6-12 days (OR 1.15, 95% CI 1.06-1.25, pl0.001) were each associated with a higher risk of developing viral symptoms. Conversely, a higher subjective social status (OR 0.87, 95% CI 0.83-0.93, pl0.001), at least weekly exercise (OR 0.57, 95% CI 0.47-0.70, pl0.001), and sanitizing one's phone (OR 0.79, 95% CI 0.63-0.99, p = 0.037) were each associated with a lower risk of developing viral symptoms.InterpretationWhile several immutable characteristics were associated with the risk of developing viral symptoms, multiple immediately modifiable exposures and habits that influence risk were also observed, potentially identifying readily accessible strategies to mitigate risk in the COVID-19 era.
Exponential Weights Algorithms for Selective Learning

We study the selective learning problem introduced by Qiao and Valiant (2019), in which the learner observes $n$ labeled data points one at a time. At a time of its choosing, the learner selects a window length $w$ and a model $\hat\ell$ from the model class $\mathcal{L}$, and then labels the next $w$ data points using $\hat\ell$. The \emph{excess risk} incurred by the learner is defined as the difference between the average loss of $\hat\ell$ over those $w$ data points and the smallest possible average loss among all models in $\mathcal{L}$ over those $w$ data points. We give an improved algorithm, termed the \emph{hybrid exponential weights} algorithm, that achieves an expected excess risk of $O((\log\log|\mathcal{L}| + \log\log n)/\log n)$. This result gives a doubly exponential improvement in the dependence on $|\mathcal{L}|$ over the best known bound of $O(\sqrt{|\mathcal{L}|/\log n})$. We complement the positive result with an almost matching lower bound, which suggests the worst-case optimality of the algorithm. We also study a more restrictive family of learning algorithms that are \emph{bounded-recall} in the sense that when a prediction window of length $w$ is chosen, the learner's decision only depends on the most recent $w$ data points. We analyze an exponential weights variant of the ERM algorithm in Qiao and Valiant (2019). This new algorithm achieves an expected excess risk of $O(\sqrt{\log |\mathcal{L}|/\log n})$, which is shown to be nearly optimal among all bounded-recall learners. Our analysis builds on a generalized version of the selective mean prediction problem in Drucker (2013); Qiao and Valiant (2019), which may be of independent interest.
Nonlinear Transient Growth and Boundary Layer Transition

Parabolized stability equations (PSE) are used in a variational approach to study the optimal, non-modal disturbance growth in a Mach 3 at plate boundary layer and a Mach 6 circular cone boundary layer. As noted in previous works, the optimal initial disturbances correspond to steady counter-rotating streamwise vortices, which subsequently lead to the formation of streamwise-elongated structures, i.e., streaks, via a lift-up effect. The nonlinear evolution of the linearly optimal stationary perturbations is computed using the nonlinear plane-marching PSE for stationary perturbations. A fully implicit marching technique is used to facilitate the computation of nonlinear streaks with large amplitudes. To assess the effect of the finite-amplitude streaks on transition, the linear form of plane- marching PSE is used to investigate the instability of the boundary layer flow modified by spanwise periodic streaks. The onset of bypass transition is estimated by using an N- factor criterion based on the amplification of the streak instabilities. Results show that, for both flow configurations of interest, streaks of sufficiently large amplitude can lead to significantly earlier onset of transition than that in an unperturbed boundary layer without any streaks.
The h-index is no longer an effective correlate of scientific reputation

The impact of individual scientists is commonly quantified using citation-based measures. The most common such measure is the h-index. A scientist’s h-index affects hiring, promotion, and funding decisions, and thus shapes the progress of science. Here we report a large-scale study of scientometric measures, analyzing millions of articles and hundreds of millions of citations across four scientific fields and two data platforms. We find that the correlation of the h-index with awards that indicate recognition by the scientific community has substantially declined. These trends are associated with changing authorship patterns. We show that these declines can be mitigated by fractional allocation of citations among authors, which has been discussed in the literature but not implemented at scale. We find that a fractional analogue of the h-index outperforms other measures as a correlate and predictor of scientific awards. Our results suggest that the use of the h-index in ranking scientists should be reconsidered, and that fractional allocation measures such as h-frac provide more robust alternatives.
Exposure duration modulates the response of Caribbean corals to global change stressors

Global change is threatening coral reefs, with rising temperatures leading to repeat bleaching events (dysbiosis of coral hosts and their symbiotic algae) and ocean acidification reducing net coral calcification. Although global-scale mass bleaching events are revealing fine-scale patterns of coral resistance and resilience, traits that lead to persistence under environmental stress remain elusive. Here, we conducted a 95-day controlled-laboratory experiment to investigate how duration of exposure to ocean warming (28, 31{degrees}C), acidification (pCO2 = 400-2800 atm), and their interaction influence the physiological responses of two Caribbean reef-building coral species (Siderastrea siderea, Pseudodiploria strigosa) from two reef zones of the Belize Mesoamerican Barrier Reef System. Every 30 days, calcification rate, total host protein and carbohydrate, chlorophyll a pigment concentration, and symbiont cell density were quantified for the same coral colony to characterize acclimatory responses of each genotype. Physiologies of the two species were differentially affected by these stressors, with exposure duration modulating responses. Siderastrea siderea was most affected by extreme pCO2 (~2800 atm), which resulted in reduced calcification rate, symbiont density, and chlorophyll a concentration. Siderastrea siderea calcification rate initially declined under extreme pCO2 but recovered by the final time point, and overall demonstrated resistance to next-century pCO2 and temperature stress. In contrast, P. strigosa was more negatively impacted by elevated temperature (31{degrees}C). Reductions in P. strigosa calcification rate and total carbohydrates were consistently observed over time regardless of pCO2 treatment, with the greatest reductions observed under elevated temperature. However, nearshore colonies of P. strigosa maintained calcification rates under elevated temperature throughout all exposure durations, suggesting individuals from this environment may be locally adapted to the warmer temperatures characterizing their natal reef zone. This experiment highlights how tracking individual coral colony physiology across broad exposure durations can capture acclimatory responses of corals to global change stressors.
Effects of Ingested Atmospheric Turbulence on Measured Tail Rotor Acoustics

Results from an outdoor hover test of a full-scale Lynx tail rotor are presented. The investigation was designed to further the understanding of the acoustics of an isolated tail rotor hovering out-of-ground effect in atmospheric turbulence, without the effects of the main rotor wake or other helicopter components. Measurements include simultaneous rotor performance, noise, inflow, and far-field atmospheric turbulence. Results with grid-generated inflow turbulence are also presented. The effects of turbulence ingestion on rotor noise are quantified. Turbulence ingestion noise is found to be the dominant noise mechanism at locations near the rotor axis. At these locations, the sound radiated by the hovering rotor increases with both increasing atmospheric wind speed and ingested rms turbulent velocity.
Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets.

Direct-sequencing technologies, such as Oxford Nanopore’s, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data. Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization. Sequoia’s interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at https://github.com/dnonatar/Sequoia .
The Landscape of Auto-bidding Auctions: Value versus Utility Maximization

Internet advertisers are increasingly adopting automated bidders to buy advertising opportunities. Automated bidders simplify the procurement process by allowing advertisers to specify their goals and then bidding on their behalf in the auctions that are used to sell advertising slots. One popular goal adopted by advertisers is to maximize their clicks (or conversions) subject to a return on spend (RoS) constraint, which imposes that the ratio of total value to total spend is greater than a target ratio specified by the advertisers. The emergence of automated bidders brings into question whether the standard mechanisms used to sell ads are still effective in this new landscape. Thus motivated, in this paper, we study the problem of characterizing optimal mechanisms for selling an item to one of multiple agents with return on spend constraints when either the values or target ratios are private. We consider two objectives for the agents: value maximization, which is becoming the prevalent objective in advertising markets, and utility maximization, which is the de facto paradigm in economic theory. Our goal is to understand the impact of the agents' private information and their objectives on the seller's revenue, and determine whether the first-best revenue, which is the optimal revenue when all the private information is public, is achievable. We show that first-best revenue is achievable for value-maximizing buyers when either the target ratio or the values are private, but not when both are private. In the case of utility-maximizing buyers, first-best is never achievable and we characterize revenue-maximizing mechanisms.
Constructing tensor network influence functionals for general quantum dynamics

We describe an iterative formalism to compute influence functionals that describe the general quantum dynamics of a subsystem beyond the assumption of linear coupling to a quadratic bath. We use a space-time tensor network representation of the influence functional and investigate its approximability in terms of its bond dimension and time-like entanglement in the tensor network description. We study two numerical models, the spin-boson model and a model of interacting hard-core bosons in a 1D harmonic trap. We find that the influence functional and the intermediates involved in its construction can be efficiently approximated by low bond dimension tensor networks in certain dynamical regimes, which allows the quantum dynamics to be accurately computed for longer times than with direct time evolution methods. However, as one iteratively integrates out the bath, the correlations in the influence functional can first increase before decreasing, indicating that the final compressibility of the influence functional is achieved via non-trivial cancellation.
Quality interaction between mission assurance and project team members

Mission Assurance's independent assessments started during the SPITZER development cycle and continued through post-launch operations. During the operations phase, the health and safety of the observatory is of utmost importance. Therefore, Mission Assurance must ensure requirements compliance and focus on the process improvements required across the operational systems, including new/modified products, tools, and procedures. To avoid problem reoccurrences, an interactive model involving three areas was deployed: Team Member Interaction, Root Cause Analysis Practices, and Risk Assessment. In applying this model, a metric-based measurement process was found to have the most significant benefit. Considering a combination of root cause analysis and risk approaches allows project engineers to the ability to prioritize and quantify their corrective actions based on a well-defined set of root cause definitions (i.e., closure criteria for problem reports), success criteria, and risk rating definitions.
Ensuring Fairness under Prior Probability Shifts

Prior probability shift is a phenomenon where the training and test datasets differ structurally within population subgroups. This phenomenon can be observed in the yearly records of several real-world datasets, for example, recidivism records and medical expenditure surveys. If unaccounted for, such shifts can cause the predictions of a classifier to become unfair towards specific population subgroups. While the fairness notion called Proportional Equality (PE) accounts for such shifts, a procedure to ensure PE-fairness was unknown. In this work, we design an algorithm, called CAPE, that ensures fair classification under such shifts. We introduce a metric, called prevalence difference, which CAPE attempts to minimize in order to achieve fairness under prior probability shifts. We theoretically establish that this metric exhibits several properties that are desirable for a fair classifier. We evaluate the efficacy of CAPE via a thorough empirical evaluation on synthetic datasets. We also compare the performance of CAPE with several state-of-the-art fair classifiers on real-world datasets like COMPAS (criminal risk assessment) and MEPS (medical expenditure panel survey). The results indicate that CAPE ensures a high degree of PE-fairness in its predictions, while performing well on other important metrics.
Metathesis Cascade-Triggered Depolymerization of Enyne Self-Immolative Polymers**

A novel class of enyne self-immolative polymers (SIPs) capable of metathesis cascade-triggered depolymerization is reported. Studies on model compounds established 1,6-enyne structures for efficient metathesis cascade reactions. SIPs incorporating the optimized 1,6-enyne motif were prepared via both polycondensation and iterative exponential growth approaches. These SIPs demonstrated excellent stability in strong acid, base, nucleophiles, or at elevated temperatures, and can undergo efficient and complete depolymerization once triggered by a metathesis catalyst. Further studies revealed that introducing a terminal alkene to the chain end of the enyne SIPs improved the depolymerization efficiency, and established their potential as stimuli-responsive materials.
Impacts of COVID-19 on the self-employed

This study estimates random effects and difference-in-difference-in-differences models to examine the initial impacts of COVID-19 on the employment and hours of unincorporated self-employed workers using monthly panel data from the Current Population Survey. For these workers, effects were visible in March as voluntary social distancing began, largest in April as complete shutdowns occurred, and slightly smaller in May as some restrictions were eased. We find differential effects by gender that favor men, by marital status and gender that favor married men over married women, and by gender, marital, and parental status that favor married fathers over married mothers. The evidence suggests that self-employed married mothers were forced out of the labor force to care for children presumably due to prescribed gender norms and the division and specialization of labor within households. Remote work and working in an essential industry mitigated some of the negative effects on employment and hours. Among the unincorporated self-employed, married mothers were less likely to be employed and worked fewer hours during the COVID-19 pandemic than married fathers. Effects were visible in March as voluntary social distancing began, largest in April as complete shutdowns occurred, and slightly smaller in May as some restrictions were eased. Our results suggest that COVID-19 forced self-employed women back into the home due to gender norms about who cares for children. However, having a plausibly remote job or being in an essential industry helped mitigate some of the negative effects on employment and hours worked. Besides providing evidence that married mothers’ presence among the self-employed has been diminished by COVID-19, we find that the pandemic hurt the unincorporated self-employed more than other types of workers. This finding provides further evidence that it is important for researchers to distinguish between the incorporated and unincorporated self-employed when analyzing variation in self-employment at different points in the business cycle.
Low-Energy and CPA-Resistant Adiabatic CMOS/MTJ Logic for IoT Devices

The tremendous growth in the number of Internet of Things (IoT) devices has increased focus on the energy efficiency and security of an IoT device. In this paper, we will present a design level, non-volatile adiabatic architecture for low-energy and Correlation Power Analysis (CPA) resistant IoT devices. IoT devices constructed with CMOS integrated circuits suffer from high dynamic energy and leakage power. To solve this, we look at both adiabatic logic and STT-MTJs (Spin Transfer Torque Magnetic Tunnel Junctions) to reduce both dynamic energy and leakage power. Furthermore, CMOS integrated circuits suffer from side-channel leakage making them insecure against power analysis attacks. We again look to adiabatic logic to design secure circuits with uniform power consumption, thus, defending against power analysis attacks. We have developed a hybrid adiabatic-MTJ architecture using two-phase adiabatic logic. We show that hybrid adiabatic-MTJ circuits are both low energy and secure when compared with CMOS circuits. As a case study, we have constructed one round of PRESENT and have shown energy savings of 64.29% at a frequency of 25 MHz. Furthermore, we have performed a correlation power analysis attack on our proposed design and determined that the key was kept hidden.