cssi/SciSciGPT-SciSciNet huggingface dataset:
Testing the inference mediation hypothesis in a post-secondary context

Abstract The inference mediation hypothesis (IMH) assumes that individual difference factors that affect reading proficiency have direct and indirect effects on comprehension outcomes, with the indirect effects involving inference processes. The present study tested the IMH in a diverse sample of two and four-year college students in a task that emphasizes comprehension of the passage (traditional assessment) and a task that emphasizes complex problem solving (SBA). Participants were administered assessments of foundational skills that support reading, inference generation, a traditional assessment of comprehension proficiency, and a scenario-based reading assessment. The results support the IMH. However, the strength of the indirect relationships depended on the type of reading performance assessment. Coherence building inferences partially mediated the relationship for the traditional assessment. Elaborative inferences partially mediated the relationship for the scenario-based assessment. The results are discussed in terms of theories of purposeful reading and implications for understanding college readiness.
A single set improvement to the 3k − 4 theorem

The $3k-4$ Theorem is a classical result which asserts that if $A,\,B\subseteq \mathbb Z$ are finite, nonempty subsets with \begin{equation}\label{hyp}|A+B|=|A|+|B|+r\leq |A|+|B|+\min\{|A|,\,|B|\}-3-\delta,\end{equation} where $\delta=1$ if $A$ and $B$ are translates of each other, and otherwise $\delta=0$, then there are arithmetic progressions $P_A$ and $P_B$ of common difference such that $A\subseteq P_A$, $B\subseteq P_B$, $|B|\leq |P_B|+r+1$ and $|P_A|\leq |A|+r+1$. It is one of the few cases in Freiman's Theorem for which exact bounds on the sizes of the progressions are known. The hypothesis above is best possible in the sense that there are examples of sumsets $A+B$ having cardinality just one more, yet $A$ and $B$ cannot both be contained in short length arithmetic progressions. In this paper, we show that the hypothesis above can be significantly weakened and still yield the same conclusion for one of the sets $A$ and $B$. Specifically, if $|B|\geq 3$, $s\geq 1$ is the unique integer with $$(s-1)s\left(\frac{|B|}{2}-1\right)+s-1<|A|\leq s(s+1)\left(\frac{|B|}{2}-1\right)+s,$$ and \begin{equation}\label{hyp2} |A+B|=|A|+|B|+r< (\frac{|A|}{s}+\frac{|B|}{2}-1)(s+1),\end{equation} then we show there is an arithmetic progression $P_B\subseteq \mathbb Z$ with $B\subseteq P_B$ and $|P_B|\leq |B|+r+1$. The above hypothesis is best possible (without additional assumptions on $A$) for obtaining such a conclusion.
Options and strategies for fiscal consolidation in India

Abstract This paper uses a multi-region dynamic stochastic general equilibrium (DSGE) model to study options and strategies for fiscal consolidation in India. The challenge for India is how to put public finances on a more sustainable footing while preserving the potential for high growth and attenuating the adverse consequences on the needy. We analyze the macroeconomic implications of three fiscal consolidation scenarios. A “benchmark” case based on authorities' intentions that reduces government consumption, general transfers, and strengthens consumption tax collection; a “growth-friendly” that reallocates the savings to more public investment; and a “social-friendly” scenario that equally reallocates the savings between more public investment and more transfers to households excluded from the financial system. The simulations indicate that fiscal consolidation yields considerable long-term benefits and also entails output costs in the near term. Growth outcomes are better under the growth-friendly and social-friendly scenarios. These consolidation scenarios alone are not enough to maximize net gains for India. Other factors, such as the pace of consolidation, the combination with structural reforms and external economic conditions, play a critical role in the success of fiscal consolidation.
More cops, fewer prisoners?

A large literature establishes that hiring police officers leads to reductions in crime and that investments in police are a relatively efficient means of crime control compared to investments in prisons. One concern, however, is that because police officers make arrests in the course of their duties, police hiring, while relatively efficient, is an inevitable driver of "mass incarceration." This research considers the dynamics through which police hiring affects downstream incarceration rates. Using state-level panel data as well county-level data from California, we uncover novel evidence in favor of a potentially unexpected and yet entirely intuitive result — that investments in law enforcement are unlikely to markedly increase state prison populations and may even lead to a modest decrease in the number of state prisoners. As such, investments in police may, in fact, yield a "double dividend" to society, by reducing incarceration rates as well as crime rates.
Flexible genetic engineering using RecA protein.

With the explosion in genetic information and almost complete sequencing of the human genome, a shift in the experimental goals of molecular biologists is occurring. Instead of focusing on single genes, current attempts seek to divine the interactions of several genes and sequences. This requires increasingly complex genetic constructs and manipulations, often of very large DNA constructs, and these can be made with RecA protein-based techniques. When RecA protein combined with an oligonucleotide acts as a sequence-specific “masking tape” to block DNA from the action of DNA modifying enzymes, and can be used to direct the cleavage of DNA at single predetermined restriction endonuclease sites. This reaction is called RecA-Assisted Restriction Endonuclease (RARE) Cleavage. The reverse reaction, known as RecA-Assisted Ligation, can be used to join any two desired fragments. When one of those fragments is a vector, a desired fragment can be cloned directly without constructing a genomic library. The reagents and equipment needed are relatively inexpensive, and almost any desired genetic construct up to about 300 kb in size can be made in a straightforward manner.
The Cost of Privacy: Welfare Effects of the Disclosure of Covid-19 Cases

South Korea publicly disclosed detailed location information of individuals that tested positive for COVID-19 We quantify the effect of public disclosure on th
Independent associations of apathy and depressive symptoms with perceived social support in healthy older adults

Objective: Apathy symptoms are common in healthy older adults and may manifest independently of depressive symptoms. Perceived social support is considered a protective factor against adverse healt...
Factors Influencing Perceived Fairness in Algorithmic Decision-Making: Algorithm Outcomes, Development Procedures, and Individual Differences

Algorithmic decision-making systems are increasingly used throughout the public and private sectors to make important decisions or assist humans in making these decisions with real social consequences. While there has been substantial research in recent years to build fair decision-making algorithms, there has been less research seeking to understand the factors that affect people's perceptions of fairness in these systems, which we argue is also important for their broader acceptance. In this research, we conduct an online experiment to better understand perceptions of fairness, focusing on three sets of factors: algorithm outcomes, algorithm development and deployment procedures, and individual differences. We find that people rate the algorithm as more fair when the algorithm predicts in their favor, even surpassing the negative effects of describing algorithms that are very biased against particular demographic groups. We find that this effect is moderated by several variables, including participants' education level, gender, and several aspects of the development procedure. Our findings suggest that systems that evaluate algorithmic fairness through users' feedback must consider the possibility of "outcome favorability" bias.
Social Equity and COVID-19: The Case of African Americans.

Emerging statistics demonstrate that COVID-19 disproportionately affects African Americans. The effects of COVID-19 for this population are inextricably linked to areas of systemic oppression and disenfranchisement, which are further exacerbated by COVID-19: (1) healthcare inequality; (2) segregation, overall health, and food insecurity; (3) underrepresentation in government and the medical profession; and (4) inequalities in participatory democracy and public engagement. Following a discussion of these issues, this article shares early and preliminary lessons and strategies on how public administration scholars and practitioners can lead in crafting equitable responses to this global pandemic to uplift the African American community. This article is protected by copyright. All rights reserved.
Learning From Strategic Agents: Accuracy, Improvement, and Causality

In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' propensity to "game" the decision rule by changing their features so as to receive better decisions. Whereas the strategic classification literature has previously assumed that agents' outcomes are not causally affected by their features (and thus that strategic agents' goal is deceiving the decision-maker), we join concurrent work in modeling agents' outcomes as a function of their changeable attributes. As our main contribution, we provide efficient algorithms for learning decision rules that optimize three distinct decision-maker objectives in a realizable linear setting: accurately predicting agents' post-gaming outcomes (prediction risk minimization), incentivizing agents to improve these outcomes (agent outcome maximization), and estimating the coefficients of the true underlying model (parameter estimation). Our algorithms circumvent a hardness result of Miller et al. (2020) by allowing the decision maker to test a sequence of decision rules and observe agents' responses, in effect performing causal interventions through the decision rules.
How the deprecation of Java applets affected online visualization frameworks -- a case study

The JavaView visualization framework was designed at the end of the 1990s as a software that provides - among other services - easy, interactive geometry visualizations on web pages. We discuss how this and other design goals were met and present several applications to highlight the contemporary use-cases of the framework. However, as JavaView's easy web exports was based on Java Applets, the deprecation of this technology disabled one main functionality of the software. The remainder of the article uses JavaView as an example to highlight the effects of changes in the underlying programming language on a visualization toolkit. We discuss possible reactions of software to such challenges, where the JavaView framework serves as an example to illustrate development decisions. These discussions are guided by the broader, underlying question as to how long it is sensible to maintain a software.
Context-Dependent Models for Predicting and Characterizing Facial Expressiveness.

In recent years, extensive research has emerged in affective computing on topics like automatic emotion recognition and determining the signals that characterize individual emotions. Much less studied, however, is expressiveness, or the extent to which someone shows any feeling or emotion. Expressiveness is related to personality and mental health and plays a crucial role in social interaction. As such, the ability to automatically detect or predict expressiveness can facilitate significant advancements in areas ranging from psychiatric care to artificial social intelligence. Motivated by these potential applications, we present an extension of the BP4D+ dataset with human ratings of expressiveness and develop methods for (1) automatically predicting expressiveness from visual data and (2) defining relationships between interpretable visual signals and expressiveness. In addition, we study the emotional context in which expressiveness occurs and hypothesize that different sets of signals are indicative of expressiveness in different contexts (e.g., in response to surprise or in response to pain). Analysis of our statistical models confirms our hypothesis. Consequently, by looking at expressiveness separately in distinct emotional contexts, our predictive models show significant improvements over baselines and achieve comparable results to human performance in terms of correlation with the ground truth.
Bayesian experimental design using regularized determinantal point processes.

In experimental design, we are given $n$ vectors in $d$ dimensions, and our goal is to select $k\ll n$ of them to perform expensive measurements, e.g., to obtain labels/responses, for a linear regression task. Many statistical criteria have been proposed for choosing the optimal design, with popular choices including A- and D-optimality. If prior knowledge is given, typically in the form of a $d\times d$ precision matrix $\mathbf A$, then all of the criteria can be extended to incorporate that information via a Bayesian framework. In this paper, we demonstrate a new fundamental connection between Bayesian experimental design and determinantal point processes, the latter being widely used for sampling diverse subsets of data. We use this connection to develop new efficient algorithms for finding $(1+\epsilon)$-approximations of optimal designs under four optimality criteria: A, C, D and V. Our algorithms can achieve this when the desired subset size $k$ is $\Omega(\frac{d_{\mathbf A}}{\epsilon} + \frac{\log 1/\epsilon}{\epsilon^2})$, where $d_{\mathbf A}\leq d$ is the $\mathbf A$-effective dimension, which can often be much smaller than $d$. Our results offer direct improvements over a number of prior works, for both Bayesian and classical experimental design, in terms of algorithm efficiency, approximation quality, and range of applicable criteria.
Warm Start of Mixed-Integer Programs for Model Predictive Control of Hybrid Systems

In hybrid model predictive control (MPC), a mixed-integer quadratic program (MIQP) is solved at each sampling time to compute the optimal control action. Although these optimizations are generally very demanding, in MPC, we expect consecutive problem instances to be nearly identical. This article addresses the question of how computations performed at one time step can be reused to accelerate ( warm start ) the solution of subsequent MIQPs. Reoptimization is not a rare practice in integer programming: for small variations of certain problem data, the branch-and-bound algorithm allows an efficient reuse of its search tree and the dual bounds of its leaf nodes. In this article, we extend these ideas to the receding-horizon settings of MPC. The warm-start algorithm we propose copes naturally with arbitrary model errors, has a negligible computational cost, and frequently enables an a priori pruning of most of the search space. Theoretical considerations and experimental evidence show that the proposed method tends to reduce the combinatorial complexity of the hybrid MPC problem to that of a one-step look-ahead optimization, greatly easing the online computation burden.
Recombinase-mediated integration of a multigene cassette in rice leads to stable expression and inheritance of the stacked locus.

Efficient methods for multigene transformation are important for developing novel crop varieties. Methods based on random integrations of multiple genes have been successfully used for metabolic engineering in plants. However, efficiency of co-integration and co-expression of the genes could present a bottleneck. Recombinase-mediated integration into the engineered target sites is arguably a more efficient method of targeted integration that leads to the generation of stable transgenic lines at a high rate. This method has the potential to streamline multigene transformation for metabolic engineering and trait stacking in plants. Therefore, empirical testing of transgene(s) stability from the multigene site-specific integration locus is needed. Here, the recombinase technology based on Cre-lox recombination was evaluated for developing multigenic lines harboring constitutively-expressed and inducible genes. Targeted integration of a five genes cassette in the rice genome generated a precise full-length integration of the cassette at a high rate, and the resulting multigenic lines expressed each gene reliably as defined by their promoter activity. The stable constitutive or inducible expression was faithfully transmitted to the progeny, indicating inheritance-stability of the multigene locus. Co-localization of two distinctly inducible genes by heat or cold with the strongly constitutive genes did not appear to interfere with each other's expression pattern. In summary, high rate of co-integration and co-expression of the multigene cassette installed by the recombinase technology in rice shows that this approach is appropriate for multigene transformation and introduction of co-segregating traits. Significance Statement Recombinase-mediated site-specific integration approach was found to be highly efficacious in multigene transformation of rice showing proper regulation of each gene driven by constitutive or inducible promoter. This approach holds promise for streamlining gene stacking in crops and expressing complex multigenic traits.
On the Asymptotic Capacity of X -Secure T -Private Information Retrieval With Graph-Based Replicated Storage

The problem of private information retrieval with graph-based replicated storage was recently introduced by Raviv, Tamo and Yaakobi. Its capacity remains open in almost all cases. In this work the asymptotic (large number of messages) capacity of this problem is studied along with its generalizations to include arbitrary $T$ -privacy and $X$ -security constraints, where the privacy of the user must be protected against any set of up to $T$ colluding servers and the security of the stored data must be protected against any set of up to $X$ colluding servers. A general achievable scheme for arbitrary storage patterns is presented that achieves the rate $(\rho _{\min }-X-T)/N$ , where $N$ is the total number of servers, and each message is replicated at least $\rho _{\min }$ times. Notably, the scheme makes use of a special structure inspired by dual Generalized Reed Solomon (GRS) codes. A general converse is also presented. The two bounds are shown to match for many settings, including symmetric storage patterns. Finally, the asymptotic capacity is fully characterized for the case without security constraints $(X=0)$ for arbitrary storage patterns provided that each message is replicated no more than $T+2$ times. As an example of this result, consider PIR with arbitrary graph based storage ( $T=1, X=0$ ) where every message is replicated at exactly 3 servers. For this 3-replicated storage setting, the asymptotic capacity is equal to $2/\nu _{2}(G)$ where $\nu _{2}(G)$ is the maximum size of a 2-matching in a storage graph $G[V,E]$ . In this undirected graph, the vertices $V$ correspond to the set of servers, and there is an edge $uv\in E$ between vertices $u,v$ only if a subset of messages is replicated at both servers $u$ and $v$ .
Undiagnosed diabetes among immigrant and racial/ethnic minority adults in the United States: National Health and Nutrition Examination Survey 2011-2018.

Abstract Purpose Undiagnosed diabetes disproportionately affects medically underserved groups. It is unknown whether being an immigrant confers additional risk for undiagnosed diabetes. The purpose of this study was to examine independent associations of immigrant status and race/ethnicity with the prevalence of diagnosed and undiagnosed diabetes in a U.S.-based population sample. Methods Respondents were 21,306 adults from the 2011–2018 National Health and Nutrition Examination Survey. Immigrant status was coded as foreign-born or U.S.-born. Six racial/ethnic categories were white, Black, Mexican American, other Hispanic, Asian, and other/multiracial. Self-report and laboratory data yielded a three-level diabetes status outcome: no diabetes (88%), diagnosed diabetes (10%), and undiagnosed diabetes (2%). Results Adjusted multinomial logistic regression models evaluating immigrant status and race/ethnicity as simultaneous predictors revealed that foreign-born (vs. U.S.-born) adults had a similar prevalence of diagnosed diabetes (OR = 0.98, 95% CI: 0.79–1.22, P = .84) but a higher prevalence of undiagnosed diabetes (OR = 1.54, 95% CI: 1.21–1.97, P = .004). Models showed that all racial/ethnic minority groups except the other/multiracial group (vs. whites) had a higher prevalence of diagnosed and undiagnosed diabetes (Ps Conclusions Immigrants and racial/ethnic minority adults have increased odds of undiagnosed diabetes, even after accounting for health insurance. These groups are likely at increased risk for diabetes complications because of prolonged periods of undetected diabetes.
Impact of the MyProstateScore (MPS) Test on the Clinical Decision to Undergo Prostate Biopsy: Results From a Contemporary Academic Practice.

Abstract Objective To evaluate the association of the MyProstateScore (MPS) urine test on the decision to undergo biopsy in men referred for prostate biopsy in urology practice. Methods MPS testing was offered as an alternative to immediate biopsy in men referred to the University of Michigan for prostate biopsy from October 2013 through October 2016. The primary endpoint was the decision to perform biopsy. The proportion of patients undergoing biopsy was compared to predicted risk scores from the Prostate Cancer Prevention Trial risk calculator (PCPTrc). Analyses were stratified by the use of multiparametric magnetic resonance imaging (mpMRI). The associations of PCPTrc, MPS, and mpMRI with the decision to undergo biopsy were explored in a multivariable logistic regression model. Results Of 248 patients, 134 (54%) proceeded to prostate biopsy. MPS was significantly higher in biopsied patients (median 29 vs14, P P P Conclusion Many patients (46%) undergoing clinical MPS testing as an alternative to immediate prostate biopsy were able to avoid biopsy. Increasing MPS was strongly associated with biopsy rates. These findings were robust to use of mpMRI.
3D Microwell Platforms for Control of Single Cell 3D Geometry and Intracellular Organization.

Cell structure and migration is impacted by the mechanical properties and geometry of the cell adhesive environment. Most studies to date investigating the effects of 3D environments on cells have not controlled geometry at the single-cell level, making it difficult to understand the influence of 3D environmental cues on single cells. Here, we developed microwell platforms to investigate the effects of 2D vs. 3D geometries on single-cell F-actin and nuclear organization. We used microfabrication techniques to fabricate three polyacrylamide platforms: 3D microwells with a 3D adhesive environment (3D/3D), 3D microwells with 2D adhesive areas at the bottom only (3D/2D), and flat 2D gels with 2D patterned adhesive areas (2D/2D). We measured geometric swelling and Young’s modulus of the platforms. We then cultured C2C12 myoblasts on each platform and evaluated the effects of the engineered microenvironments on F-actin structure and nuclear shape. We tuned the mechanical characteristics of the microfabricated platforms by manipulating the gel formulation. Crosslinker ratio strongly influenced geometric swelling whereas total polymer content primarily affected Young’s modulus. When comparing cells in these platforms, we found significant effects on F-actin and nuclear structures. Our analysis showed that a 3D/3D environment was necessary to increase actin and nuclear height. A 3D/2D environment was sufficient to increase actin alignment and nuclear aspect ratio compared to a 2D/2D environment. Using our novel polyacrylamide platforms, we were able to decouple the effects of 3D confinement and adhesive environment, finding that both influenced actin and nuclear structure.
When does the Tukey Median work

We analyze the performance of the Tukey median estimator under total variation (TV) distance corruptions. Previous results show that under Huber’s additive corruption model, the breakdown point is 1/3 for high-dimensional halfspace- symmetric distributions. We show that under TV corruptions, the breakdown point reduces to 1/4 for the same set of distributions. We also show that a certain projection algorithm can attain the optimal breakdown point of 1/2. Both the Tukey median estimator and the projection algorithm achieve sample complexity linear in dimension.
The limits of distribution-free conditional predictive inference

We consider the problem of distribution-free predictive inference, with the goal of producing predictive coverage guarantees that hold conditionally rather than marginally. Existing methods such as conformal prediction offer marginal coverage guarantees, where predictive coverage holds on average over all possible test points, but this is not sufficient for many practical applications where we would like to know that our predictions are valid for a given individual, not merely on average over a population. On the other hand, exact conditional inference guarantees are known to be impossible without imposing assumptions on the underlying distribution. In this work we aim to explore the space in between these two, and examine what types of relaxations of the conditional coverage property would alleviate some of the practical concerns with marginal coverage guarantees while still being possible to achieve in a distribution-free setting.
Analysis of female song provides insight into the evolution of sex differences in a widely studied songbird

Understanding the patterns and processes related to sexual dimorphism and sex differences in diverse animal taxa is a foundational research topic in ecology and evolution. Within the realm of animal communication, studies have traditionally focused on male signals, assuming that female choice and male–male competition have promoted sex differences via elaboration of male traits, but selection on females also has the potential to drive divergence. Here, we describe female song in barn swallows, Hirundo rustica erythrogaster, for the first time, report rates of female song production and couple song data with plumage data to explore the relative degree to which sex differences in phenotypic traits are consistent with contemporary selection on males versus females. During previous intensive study of male song over 2 years, we opportunistically recorded songs for 15 females, with matched phenotypic and fitness data. We randomly selected 15 high-quality samples from our larger male data set to test whether sex differences in song and plumage are more strongly associated with fledgling success for females or genetic paternity for males. Analyses included 35 potential sexual signals including 22 song parameters and 13 plumage traits. Outcomes indicate that female songs were used in multiple contexts, restricted primarily to the beginning of the breeding season; song traits showed greater sexual differentiation than visual plumage traits; and trait correlations with reproductive success in females, rather than males, predicted sex-based differences in song and plumage. These results are consistent with phylogenetic studies showing that sex-based phenotypic differences are driven by changes in females, highlighting the potential role of female trait evolution in explaining patterns of sexual differentiation. To achieve a better understanding of sex differences and dimorphism, we require comprehensive studies that measure the same traits in males and females and their fitness consequences.
Leishmania infection induces a limited differential gene expression in the sand fly midgut

Sand flies are the vectors of Leishmania parasites. To develop in the sand fly midgut, Leishmania multiplies and undergoes various stage differentiations giving rise to the infective form, the metacyclic promastigotes. To determine the changes in sand fly midgut gene expression caused by the presence of Leishmania, we performed RNA-Seq of uninfected and Leishmania infantum-infected Lutzomyia longipalpis midguts from seven different libraries corresponding to time points which cover the various Leishmania developmental stages. The combined transcriptomes resulted in the de novo assembly of 13,841 sand fly midgut transcripts. Importantly, only 113 sand fly transcripts, about 1%, were differentially expressed in the presence of Leishmania parasites. Further, we observed distinct differentially expressed sand fly midgut transcripts corresponding to the presence of each of the various Leishmania stages suggesting that each parasite stage influences midgut gene expression in a specific manner. Two main patterns of sand fly gene expression modulation were noted. At early time points (days 1–4), more transcripts were down-regulated by Leishmania infection at large fold changes (> 32 fold). Among the down-regulated genes, the transcription factor Forkhead/HNF-3 and hormone degradation enzymes were differentially regulated on day 2 and appear to be the upstream regulators of nutrient transport, digestive enzymes, and peritrophic matrix proteins. Conversely, at later time points (days 6 onwards), most of the differentially expressed transcripts were up-regulated by Leishmania infection with small fold changes (< 32 fold). The molecular functions of these genes have been associated with the metabolism of lipids and detoxification of xenobiotics. Overall, our data suggest that the presence of Leishmania produces a limited change in the midgut transcript expression profile in sand flies. Further, Leishmania modulates sand fly gene expression early on in the developmental cycle in order to overcome the barriers imposed by the midgut, yet it behaves like a commensal at later time points where a massive number of parasites in the anterior midgut results only in modest changes in midgut gene expression.
Structure and overstability of resistive modes with runaway electrons

We investigate the effects of runaway electron current on the dispersion relation of resistive magnetohydrodynamic modes in tokamaks. We present a new theoretical model to derive the dispersion relation, which is based on the asymptotic analysis of the resistive layer structure of the modes. It is found that in addition to the conventional resistive layer, a new runaway current layer can emerge whose properties depend on the ratio of the Alfven velocity to the runaway electron convection speed. Due to the contribution from this layer, both the tearing mode and kink mode will have a real frequency in addition to a growth rate. The derived dispersion relation has been compared with numerical results using both a simplified eigenvalue calculation and a M3D-C1 linear simulation, and good agreement is found in both cases.
Learning-Based Trading Strategies in the Face of Market Manipulation

We study learning-based trading strategies in markets where prices can be manipulated through spoofing: the practice of submitting spurious orders to mislead traders who use market information. To reduce the vulnerability of learning traders to such manipulation, we propose two variations based on the standard heuristic belief learning (HBL) trading strategy, which learns transaction probabilities from market activities observed in an order book. The first variation selectively ignores orders at certain price levels, particularly where spoof orders are likely to be placed. The second considers the full order book, but adjusts its limit order price to correct for bias in decisions based on the learned heuristic beliefs. We employ agent-based simulation to evaluate these variations on two criteria: effectiveness in non-manipulated markets and robustness against manipulation. Background traders can adopt the (non-learning) zero intelligence strategies or HBL, in its basic form or the two variations. We conduct empirical game-theoretic analysis upon simulated payoffs to derive approximate strategic equilibria, and compare equilibrium outcomes across a variety of trading environments. Results show that agents can strategically make use of the option to block orders to improve robustness against spoofing, while retaining a comparable competitiveness in non-manipulated markets. Our second HBL variation exhibits a general improvement over standard HBL, in markets with and without manipulation. Further explorations suggest that traders can enjoy both improved profitability and robustness by combining the two proposed variations.
SCNIFFER: Low-Cost, Automated, Efficient Electromagnetic Side-Channel Sniffing

Electromagnetic (EM) side-channel analysis (SCA) is a prominent tool to break mathematically-secure cryptographic engines, especially on resource-constrained devices. Presently, to perform EM SCA on an embedded device, the entire chip is manually scanned and the MTD (Minimum Traces to Disclosure) analysis is performed at each point on the chip to reveal the secret key of the encryption algorithm. However, an automated end-to-end framework for EM leakage localization, trace acquisition, and attack has been missing. This work proposes SCNIFFER : a low-cost, automated EM Side Channel leakage SNIFFing platform to perform efficient end-to-end Side-Channel attacks. Using a leakage measure such as Test Vector Leakage Assessment (TVLA), or the signal to noise ratio (SNR), we propose a greedy gradient-search heuristic that converges to one of the points of highest EM leakage on the chip (dimension: $N \times N$ ) within $O(N)$ iterations, and then perform Correlational EM Analysis (CEMA) at that point. This reduces the CEMA attack time by $\sim N$ times compared to an exhaustive MTD analysis, and by $> 20\times $ compared to choosing an attack location at random. We demonstrate SCNIFFER using a low-cost custom-built 3-D scanner with an H-field probe ( $50, 000 commercial EM scanners, and a variety of microcontrollers as the devices under attack. The SCNIFFER framework is evaluated for several cryptographic algorithms (AES-128, DES, RSA) running on both an 8-bit Atmega microcontroller and a 32-bit ARM microcontroller to find a point of high leakage and then perform a CEMA at that point.
Model-Based Warp Overlapped Tiling for Image Processing Programs on GPUs

Domain-specific languages that execute image processing pipelines on GPUs, such as Halide and Forma, operate by 1)~dividing the image into overlapped tiles, and 2)~fusing loops to improve memory locality. However, current approaches have limitations: 1)~they require intra thread block synchronization, which has a nontrivial cost, 2)~they must choose between small tiles that require more overlapped computations or large tiles that increase shared memory access (and lowers occupancy), and 3) their autoscheduling algorithms use simplified GPU models that can result in inefficient global memory accesses. We present a new approach for executing image processing pipelines on GPUs that addresses these limitations as follows. 1) We fuse loops to form overlapped tiles that fit in a single warp, which allows us to use lightweight warp synchronization. 2) We introduce hybrid tiling, which stores overlapped regions in a combination of thread-local registers and shared memory. Thus hybrid tiling either increases occupancy by decreasing shared memory usage or decreases overlapping computations using larger tiles. 3) We present an automatic loop fusion algorithm that considers several factors that affect the performance of GPU kernels. We implement these techniques in PolyMage-GPU, which is a new GPU backend for PolyMage. Our approach produces code that is faster than Halide's manual schedules: 1.65x faster on an NVIDIA GTX 1080Ti and 1.33x faster on an NVIDIA Tesla V100.
Adaptive Task Allocation for Heterogeneous Multi-Robot Teams with Evolving and Unknown Robot Capabilities

For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot might be unknown prior to deployment, or might vary due to changing environmental conditions. This paper presents an adaptive task allocation and task execution framework which allows individual robots to prioritize among tasks while explicitly taking into account their efficacy at performing the tasks—the parameters of which might be unknown before deployment and/or might vary over time. Such a specialization parameter—encoding the effectiveness of a given robot towards a task—is updated on-the-fly, allowing our algorithm to reassign tasks among robots with the aim of executing them. The developed framework requires no explicit model of the changing environment or of the unknown robot capabilities—it only takes into account the progress made by the robots at completing the tasks. Simulations and experiments demonstrate the efficacy of the proposed approach during variations in environmental conditions and when robot capabilities are unknown before deployment.
Design and Autonomous Stabilization of a Ballistically-Launched Multirotor

Aircraft that can launch ballistically and convert to autonomous, free-flying drones have applications in many areas such as emergency response, defense, and space exploration, where they can gather critical situational data using onboard sensors. This paper presents a ballistically-launched, autonomously-stabilizing multirotor prototype (SQUID - Streamlined Quick Unfolding Investigation Drone) with an onboard sensor suite, autonomy pipeline, and passive aerodynamic stability. We demonstrate autonomous transition from passive to vision-based, active stabilization, confirming the multirotor’s ability to autonomously stabilize after a ballistic launch in a GPS-denied environment.
Thermodynamics of Solutions of Poly(Dimethylsiloxane) in Benzene, Cyclohexane, and Chlorobenzene

Abstract Excess volumes have been measured at 25° for mixtures of poly(dimethylsilo xane) (PDMS) of high molecular weight with each of three solvents: benzene, cyclohexane, and chlorobenzene. Chemical potentials have been determined for solutions in the first two of these solvents by high-pressure osmometry and by vapor sorption. These results, in conjunction with osmotic measurements by Kuwahara and coworkers in chlorobenzene at 20 and 60°, and the heats of mixing determined by Delmas and coworkers for each of these systems permit a comprehensive analysis of the thermodynamic properties of the PDMS mixtures. They are characterized by entropies of dilution and excess volumes that are abnormally low compared with theoretical calculations.
Computer Vision to Automatically Assess Infant Neuromotor Risk

An infant’s risk of developing neuromotor impairment is primarily assessed through visual examination by specialized clinicians. Therefore, many infants at risk for impairment go undetected, particularly in under-resourced environments. There is thus a need to develop automated, clinical assessments based on quantitative measures from widely-available sources, such as videos recorded on a mobile device. Here, we automatically extract body poses and movement kinematics from the videos of at-risk infants (N = 19). For each infant, we calculate how much they deviate from a group of healthy infants (N = 85 online videos) using a Naive Gaussian Bayesian Surprise metric. After pre-registering our Bayesian Surprise calculations, we find that infants who are at high risk for impairments deviate considerably from the healthy group. Our simple method, provided as an open-source toolkit, thus shows promise as the basis for an automated and low-cost assessment of risk based on video recordings.
Bicontinuous nanoporous design induced homogenization of strain localization in metallic glasses

Abstract Bicontinuous nanoporous metallic glasses (MG) synergize the outstanding properties of MGs and open-cell nanoporous materials. The low-density and high-specific-surface-area of bicontinuous nanoporous structures have the potential to enhance the applicability of MGs in catalysis, sensors, and lightweight structural designs. Here, we report molecular dynamics simulations of tensile loading deformation and failure of bicontinuous nanoporous Cu64Zr36 MG with 55% porosity and 4.4 nm ligament size. Results indicate an anomalous mechanical behavior featuring delocalized plastic deformation preceding ductile failure. The deformation follows two mechanisms: i) Necking of ligaments aligned with the loading direction and ii) progressive alignment of randomly oriented ligaments. Failure occurs at 0.16 strain, following massive rupture of ligaments. This work indicates that a bicontinuous nanoporous design is able to effectively delocalize strain localization in a MG due to a combination of size effect on the ductility of MGs resulting in nano ligaments necking and progressive asynchronous alignment of ligaments.
Inactivation of the mitochondrial protease Afg3l2 results in severely diminished respiratory chain activity and widespread defects in mitochondrial gene expression

The m-AAA proteases play a critical role in the proteostasis of inner mitochondrial membrane proteins, and mutations in the genes encoding these proteases cause severe incurable neurological diseases. To further explore the biological role of the m-AAA proteases and the pathological consequences of their deficiency, we used a genetic approach in the fruit fly Drosophila melanogaster to inactivate the ATPase family gene 3-like 2 (AFG3L2) gene, which encodes a critical component of the m-AAA proteases. We found that null alleles of Drosophila AFG3L2 die early in development, but partial inactivation of AFG3L2 using RNAi allowed survival to the late pupal and adult stages of development. Flies with partial inactivation of AFG3L2 exhibited behavioral defects, neurodegeneration, accumulation of unfolded mitochondrial proteins, and diminished respiratory chain (RC) activity. Further work revealed that the reduced RC activity was primarily a consequence of severely diminished mitochondrial transcription and translation. These defects were accompanied by activation of the mitochondrial unfolded protein response (mito-UPR) and autophagy. Overexpression of mito-UPR components partially rescued the AFG3L2-deficient phenotypes, indicating that protein aggregation partly accounts for the defects of AFG3L2-deficient animals. Our work suggests that strategies designed to activate mitochondrial stress pathways and mitochondrial gene expression could be therapeutic in the diseases caused by mutations in AFG3L2.
Algorithms for Reconstruction Over Single and Multiple Deletion Channels

Recent advances in DNA sequencing technology and DNA storage systems have rekindled the interest in deletion channels. Multiple recent works have looked at variants of sequence reconstruction over a single and over multiple deletion channels, a notoriously difficult problem due to its highly combinatorial nature. Although works in theoretical computer science have provided algorithms which guarantee perfect reconstruction with multiple independent observations from the deletion channel, they are only applicable in the large blocklength regime and more restrictively, when the number of observations is also large. Indeed, with only a few observations, perfect reconstruction of the input sequence may not even be possible in most cases. In such situations, maximum likelihood (ML) and maximum aposteriori (MAP) estimates for the deletion channels are natural questions that arise and these have remained open to the best of our knowledge. In this work, we take steps to answer the two aforementioned questions. Specifically: 1. We show that solving for the ML estimate over the single deletion channel (which can be cast as a discrete optimization problem) is equivalent to solving its relaxation, a continuous optimization problem; 2. We exactly compute the symbolwise posterior distributions (under some assumptions on the priors) for both the single as well as multiple deletion channels. As part of our contributions, we also introduce tools to visualize and analyze error events, which we believe could be useful in other related problems concerning deletion channels.
Computing Schur complexes

We describe a Macaulay2 package for computing Schur complexes. This package expands on the ChainComplexOperations package by David Eisenbud.
A Gluing Construction for Periodic Monopoles

In [4] and [6] Cherkis and Kapustin introduced the study of periodic monopoles (with singularities), that is, monopoles on R^{2}×S^{1} possibly singular at a finite collection of points. Four-dimensional moduli spaces of periodic monopoles with singularities are expected to provide examples of gravitational instantons, that is, complete hyperkahler four-manifolds with finite energy. In a previous paper [9] we proved that the moduli space of charge k periodic monopoles with n singularities is either empty or generically a smooth hyperkahler manifold of dimension 4(k−1)⁠. In this paper we settle the existence question, constructing periodic monopoles (with singularities) by gluing methods.
Refinement types for logical frameworks and their interpretation as proof irrelevance

Refinement types sharpen systems of simple and dependent types by offering expressive means to more precisely classify well-typed terms. We present a system of refinement types for LF in the style of recent formulations where only canonical forms are well-typed. Both the usual LF rules and the rules for type refinements are bidirectional, leading to a straightforward proof of decidability of typechecking even in the presence of intersection types. Because we insist on canonical forms, structural rules for subtyping can now be derived rather than being assumed as primitive. We illustrate the expressive power of our system with examples and validate its design by demonstrating a precise correspondence with traditional presentations of subtyping. Proof irrelevance provides a mechanism for selectively hiding the identities of terms in type theories. We show that LF refinement types can be interpreted as predicates using proof irrelevance, establishing a uniform relationship between two previously studied concepts in type theory. The interpretation and its correctness proof are surprisingly complex, lending support to the claim that refinement types are a fundamental construct rather than just a convenient surface syntax for certain uses of proof irrelevance.
X-RAY VARIATIONS AT THE ORBITAL PERIOD FROM CYGNUS X-1 IN THE HIGH/SOFT STATE

Orbital variability has been found in the X-ray hardness of the black hole candidate Cygnus X-1 during the soft/high X-ray state using light curves provided by the Rossi X-ray Timing Explorer's All-Sky Monitor. We are able to set broad limits on how the mass-loss rate and X-ray luminosity vary between the hard and soft states. The folded light curve shows diminished flux in the soft X-ray band at = 0 (defined as the time of the superior conjunction of the X-ray source). Models of the orbital variability provide slightly superior fits when the absorbing gas is concentrated in neutral clumps and better explain the strong variability in hardness. In combination with the previously established hard/low state dips, our observations give a lower limit to the mass-loss rate in the soft state ( yr–1) than the limit in the hard state ( yr–1). Without a change in the wind structure between X-ray states, the greater mass-loss rate during the low/hard state would be inconsistent with the increased flaring seen during the high-soft state.