cssi/SciSciGPT-SciSciNet huggingface dataset:
Conversational Implicatures and Legal Texts

Legal texts are often given interpretations that deviate from their literal meanings. While legal concerns often motivate these interpretations, others can be traced to linguistic phenomena. This paper argues that systematicities of language usage, captured by certain theories of conversational implicature, can sometimes explain why the meanings given to legal texts by judges differ from the literal meanings of the texts. Paul Grice's account of conversational implicature is controversial, and scholars have offered a variety of ways to conceptualize implicatures and Grice's maxims of conversation. Approaches that emphasize the systematic nature of implicatures can provide explanatory accounts of the gap between literal meaning and the meaning communicated in the text. For example, a theory of scalar implicature, a type of generalized conversational implicature, can account for the application of the interpretive principle known as ejusdem generis, which narrows the scope of “catch-all” clauses located at the end of lists of items. Despite the availability of such theories, some scholars have argued that conversational implicatures are not applicable to legislation. The arguments, based primarily on the uniqueness of the legislative context and its noncooperative nature, though, do not establish the inapplicability of conversational implicatures to legislation.
Reexamining dual-class stock

Abstract Snapchat’s initial public offering, which provided shares with no voting rights, is a culmination of the growing trend of dual-class shares. It contradicts the precept of one-share, one-vote that is essential for corporate democracy. Snapchat’s action caused an uproar among influential investors. In January 2017, a coalition of the world’s biggest money managers, which together control more than $17 trillion in assets, demanded a total ban on dual-class shares. We reason that the increasing prominence of dual-class stock is explained by the confluence of three economic trends: the growing importance of intangible investments, the rise of activist investors, and the decline of staggered boards and poison pills. A dual-class structure offers immunity against proxy contests initiated by short-term investors. It enables managers to ignore capital market pressures and to avoid myopic actions such as cutting research and development, which hurt companies in the long term. Thus, a dual-class structure is optimal in certain scenarios. We put forth alternatives to dual-class structure that enable managers to maintain control while retaining focus on sustainable value creation.
Soft-NeuroAdapt: A 3-DOF neuro-adaptive patient pose correction system for frameless and maskless cancer radiotherapy

Precise patient positioning is fundamental to successful removal of malignant tumors during treatment of head and neck cancers. Errors in patient positioning have been known to damage critical organs and cause complications. To better address issues of patient positioning and motion, we introduce a 3-DOF neuro-adaptive soft-robot, called Soft-NeuroAdapt to correct deviations along 3 axes. The robot consists of inflatable air bladders that adaptively control head deviations from target while ensuring patient safety and comfort. The adaptive-neuro controller combines a state feedback component, a feedforward regulator, and a neural network that ensures correct adaptation. States are measured by a 3D vision system. We validate Soft-NeuroAdapt on a 3D printed head-and-neck dummy, and demonstrate that the controller provides adaptive actuation that compensates for intrafractional deviations in patient positioning.
The Diversity of Cloud Responses to Twentieth Century Sea Surface Temperatures

Low-level clouds are shown to be the conduit between the observed sea surface temperatures (SST) and large decadal fluctuations of the top of the atmosphere radiative imbalance. The influence of low-level clouds on the climate feedback is shown for global mean time series as well as particular geographic regions. The changes of clouds are found to be important for a midcentury period of high sensitivity and a late century period of low sensitivity. These conclusions are drawn from analysis of amip-piForcing simulations using three atmospheric general circulation models (AM2.1, AM3, and AM4.0). All three models confirm the importance of the relationship between the global climate sensitivity and the eastern Pacific trends of SST and low-level clouds. However, this work argues that the variability of the climate feedback parameter is not driven by stratocumulus-dominated regions in the eastern ocean basins, but rather by the cloudy response in the rest of the tropics.
Revenue maximization with an uncertainty-averse buyer

Most work in mechanism design assumes that buyers are risk neutral; some considers risk aversion arising due to a non-linear utility for money. Yet behavioral studies have established that real agents exhibit risk attitudes which cannot be captured by any expected utility model. We initiate the study of revenue-optimal mechanisms under behavioral models beyond expected utility theory. We adopt a model from prospect theory which arose to explain these discrepancies and incorporates agents under-weighting uncertain outcomes. In our model, an event occurring with probability x x times the value of the event when it occurs with certainty. We present three main results. First, we characterize optimal mechanisms as menus of two-outcome lotteries. Second, we show that under a reasonable bounded-risk-aversion assumption, posted pricing obtains a constant approximation to the optimal revenue. Notably, this result is "risk-robust" in that it does not depend on the details of the buyer's risk attitude. Third, we consider dynamic settings in which the buyer's uncertainty about his future value may allow the seller to extract more revenue. In contrast to the positive result above, here we show it is not possible to achieve any constant-factor approximation to revenue using deterministic mechanisms in a risk-robust manner.
Emergence of Fourier's Law of Heat Transport in Quantum Electron Systems.

The microscopic origins of Fourier’s venerable law of thermal transport in quantum electron systems has remained somewhat of a mystery, given that previous derivations were forced to invoke intrinsic scattering rates far exceeding those occurring in real systems. We propose an alternative hypothesis, namely, that Fourier’s law emerges naturally if many quantum states participate in the transport of heat across the system. We test this hypothesis systematically in a graphene flake junction and show that the temperature distribution becomes nearly classical when the broadening of the individual quantum states of the flake exceeds their energetic separation. We develop a thermal resistor network model to investigate the scaling of the sample and contact thermal resistances and show that the latter is consistent with classical thermal transport theory in the limit of large level broadening.
Leveraging Polygenic Functional Enrichment to Improve GWAS Power

Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory, and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, functionally informed novel discovery of risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9%–38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N = 130K). Averaged across traits, we attained a 13% increase in genome-wide significant loci detected (including a 20% increase for disease traits) compared to unweighted raw p values that do not use functional data. We replicated the additional loci in independent UK Biobank and non-UK Biobank data, yielding a highly statistically significant replication slope (0.66–0.69) in each case. Finally, we applied FINDOR to the full UK Biobank release (average N = 416K), attaining smaller relative improvements (consistent with simulations) but larger absolute improvements, detecting an additional 583 GWAS loci. In conclusion, leveraging functional enrichment using our method robustly increases GWAS power.
Communication Complexity of Statistical Distance

We prove nearly matching upper and lower bounds on the randomized communication complexity of the following problem: Alice and Bob are each given a probability distribution over n elements, and they wish to estimate within ±e the statistical (total variation) distance between their distributions. For some range of parameters, there is up to a log n factor gap between the upper and lower bounds, and we identify a barrier to using information complexity techniques to improve the lower bound in this case. We also prove a side result that we discovered along the way: the randomized communication complexity of n-bit Majority composed with n-bit Greater Than is Θ (n log n).
Optimal dispatch of electrified autonomous mobility on demand vehicles during power outages

The era of fully autonomous, electrified taxi fleets is rapidly approaching, and with it the opportunity to innovate myriad on-demand services that extend beyond the realm of human mobility. This project envisions a future where autonomous plug-in electric vehicle (PEV) fleets can be dispatched as both a taxi service and a source of on-demand power serving customers during power outages. We develop a PDE-based scheme to manage the optimal dispatch of an autonomous fleet to serve passengers and electric power demand during outages as an additional stream of revenue. We use real world power outage and taxi data from San Francisco for our case study, modeling the optimal dispatch of several fleet sizes over the course of one day; we examine both moderate and extreme outage scenarios. In the moderate scenario, the revenue earned serving power demand is negligible compared with revenue earned serving passenger trips. In the extreme scenario, supplying power accounts for between $1 and $2 million, amounting to between 32% and 40% more revenue than is earned serving mobility only, depending on fleet size. While the overall value of providing on-demand power depends on the frequency and severity of power outages, our results show that serving power demand during large-scale outages can provide a substantial value stream, comparable to the value to be earned providing grid services.
Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

This work presents a method for adapting a single, fixed deep neural network to multiple tasks without affecting performance on already learned tasks. By building upon ideas from network quantization and pruning, we learn binary masks that “piggyback” on an existing network, or are applied to unmodified weights of that network to provide good performance on a new task. These masks are learned in an end-to-end differentiable fashion, and incur a low overhead of 1 bit per network parameter, per task. Even though the underlying network is fixed, the ability to mask individual weights allows for the learning of a large number of filters. We show performance comparable to dedicated fine-tuned networks for a variety of classification tasks, including those with large domain shifts from the initial task (ImageNet), and a variety of network architectures. Our performance is agnostic to task ordering and we do not suffer from catastrophic forgetting or competition between tasks.
The Law of Confusion: An Examination of Misunderstanding, Mistake, and Ignorance in Contract Law

ABSTRACT Courts have distinguished between several forms of confusion on the part of parties to a contract. This article examines the leading cases defining these related states of mind and their differing legal effects. It offers a fresh look at old chestnuts familiar to generations of law students: cases involving two sailing ships, a pregnant cow, and an uncut diamond. And it shares revealing backstories rarely mentioned in law school.
The Costs of Corporate Tax Complexity

Does tax code complexity alter corporate behavior? This paper investigates this question by focusing on the decision to claim refunds for tax losses. In a sample of 1.2M observations from the population of corporate tax returns, only 37% of eligible firms claim their refund. A simple cost-benefit analysis of the tax loss choice cannot explain low take-up, which motivates an investigation of how tax complexity alters this calculation. A research design exploiting tax preparer switches, deaths, and relocations shows that sophisticated preparers increase the claiming behavior of small and mid-market firms. Tax complexity decreases take-up among large firms through interactions of refund claims with other tax code provisions and with the audit process.
A Survey of Semantics-Aware Performance Optimization for Data-Intensive Computing

We are living in the era of Big Data and witnessing the explosion of data. Given that the limitation of CPU and I/O in a single computer, the mainstream approach to scalability is to distribute computations among a large number of processing nodes in a cluster or cloud. This paradigm gives rise to the term of data-intensive computing, which denotes a data parallel approach to process massive volume of data. Through the efforts of different disciplines, several promising programming models and a few platforms have been proposed for data-intensive computing, such as MapReduce, Hadoop, Apache Spark and Dyrad. Even though a large body of research work has being proposed to improve overall performance of these platforms, there is still a gap between the actual performance demand and the capability of current commodity systems. This paper is aimed to provide a comprehensive understanding about current semantics-aware approaches to improve the performance of data-intensive computing. We first introduce common characteristics and paradigm shifts in the evolution of data-intensive computing, as well as contemporary programming models and technologies. We then propose four kinds of performance defects and survey the state-of-the-art semantics-aware techniques. Finally, we discuss the research challenges and opportunities in the field of semantics-aware performance optimization for data-intensive computing.
Analysis of International ETF Tracking Error in Country-Specific Funds

The goal of this study is to determine which fund or country-specific characteristics predict accurate performance in terms of tracking country-specific stock market indices. Ninety-three country-specific exchange-traded funds from 47 different countries are included in this study. In accordance with market integration theory, the Heritage Foundation Economic Freedom Index is a significant explanatory variable for tracking error. In agreement with the momentum effect, the exchange-traded fund return relative to the total U.S. equity market return is a significant explanatory variable for tracking error. Finally, the exchange-traded fund expense ratio is a significant explanatory variable for tracking error. Investors seeking returns from international investments should carefully examine their country of interest Economic Freedom Index and fund-specific expense ratio in order to anticipate any divergence from their exchange-traded fund return and the benchmark index return.
Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning

The focus in machine learning has branched beyond training classifiers on a single task to investigating how previously acquired knowledge in a source domain can be leveraged to facilitate learning in a related target domain, known as inductive transfer learning. Three active lines of research have independently explored transfer learning using neural networks. In weight transfer, a model trained on the source domain is used as an initialization point for a network to be trained on the target domain. In deep metric learning, the source domain is used to construct an embedding that captures class structure in both the source and target domains. In few-shot learning, the focus is on generalizing well in the target domain based on a limited number of labeled examples. We compare state-of-the-art methods from these three paradigms and also explore hybrid adapted-embedding methods that use limited target-domain data to fine tune embeddings constructed from source-domain data. We conduct a systematic comparison of methods in a variety of domains, varying the number of labeled instances available in the target domain (k), as well as the number of target-domain classes. We reach three principal conclusions: (1) Deep embeddings are far superior, compared to weight transfer, as a starting point for inter-domain transfer or model re-use (2) Our hybrid methods robustly outperform every few-shot learning and every deep metric learning method previously proposed, with a mean error reduction of 34% over state-of-the-art. (3) Among loss functions for discovering embeddings, the histogram loss (Ustinova & Lempitsky, 2016) is most robust. We hope our results will motivate a unification of research in weight transfer, deep metric learning, and few-shot learning.
How to Make Causal Inferences with Time-Series Cross-Sectional Data under Selection on Observables

Repeated measurements of the same countries, people, or groups over time are vital to many fields of political science. These measurements, sometimes called time-series cross-sectional (TSCS) data, allow researchers to estimate a broad set of causal quantities, including contemporaneous effects and direct effects of lagged treatments. Unfortunately, popular methods for TSCS data can only produce valid inferences for lagged effects under some strong assumptions. In this paper, we use potential outcomes to define causal quantities of interest in these settings and clarify how standard models like the autoregressive distributed lag model can produce biased estimates of these quantities due to post-treatment conditioning. We then describe two estimation strategies that avoid these post-treatment biases—inverse probability weighting and structural nested mean models—and show via simulations that they can outperform standard approaches in small sample settings. We illustrate these methods in a study of how welfare spending affects terrorism.
The role of “Prominent Numbers” in open numerical judgment: Strained decision makers choose from a limited set of accessible numbers

Abstract Numerate adults can represent an infinite array of integers. When a judgment requires them to “pick a number,” how do they select one to represent the abstract signal in mind? Drawing from research on the cognitive psychology of number representation, we conjecture that judges who operate primarily in decimal systems simplify by initially selecting from a set of chronically accessible “Prominent Numbers” defined as the powers of ten, their doubles, and their halves [… 5, 10, 20, 50, 100, 200…]; then, when willing and able, refining from there. A sample of 3 billion stock trades reveals that traders’ decisions reflect Prominent-Number clustering (Study 1) and a “natural experiment” reveals more clustering in rushed trading conditions (Study 2). Three sets of subsequent studies provide evidence consistent with an accessibility-based account of Prominent-Number usage: Experiments show that judges rely more on Prominent Numbers when they are induced to rush rather than take their time (Studies 3a and 3b), and when they are under high versus low cognitive load (Studies 4a, 4b, and 4c); and a final correlational study shows that Prominent-Number clustering is more apparent for judgments that require judges to scan a wider range of plausible values (Study 5). This work underscores the need to differentiate between Round Numbers and Prominent Numbers, and between representational properties of graininess and accessibility, in numerical judgment.
Spatial variation of selenium in Appalachian coal seams

Selenium (Se) is a trace element that can cause human physical deformations due to selenosis, and mutagenic changes on a range of sensitive macro-organisms. Previous studies indicated that Se enrichment is significantly greater in coal than in other rock units, suggesting a correlation between Se and coal bed proximity. Since coal extraction can cause a release of Se, some regulatory authorities have responded by requiring sampling of coal seams and adjacent rock for Se prior to issuing coal mining permits. This sampling is done under the assumption that Se in a small number of samples will be correlated to the Se concentrations throughout the strata they represent. For example, in one case a single continuous deep rock core was sampled for Se to estimate the concentrations of Se across a 2.52 km2 proposed surface coal mine. This project examined the adequacy of such an approach using univariate statistics and variograms to determine the spatial variability and correlation of Se concentrations collected from six Appalachian coal seams. The results from all analyses showed no significant spatial correlation of Se within any coal seam examined. Given this lack of measured Se spatial correlation in Appalachian coal seams for scales as small as 0.10 km, it is unlikely that a Se spatial correlation exists in adjacent rock units at similar scales either, which is currently the assumption being made for sampling to minimize Se pollution from surface coal mining.
BROCKMAN: deciphering variance in epigenomic regulators by k-mer factorization.

Variation in chromatin organization across single cells can help shed important light on the mechanisms controlling gene expression, but scale, noise, and sparsity pose significant challenges for interpretation of single cell chromatin data. Here, we develop BROCKMAN (Brockman Representation Of Chromatin by K-mers in Mark-Associated Nucleotides), an approach to infer variation in transcription factor (TF) activity across samples through unsupervised analysis of the variation in DNA sequences associated with an epigenomic mark. BROCKMAN represents each sample as a vector of epigenomic-mark-associated DNA word frequencies, and decomposes the resulting matrix to find hidden structure in the data, followed by unsupervised grouping of samples and identification of the TFs that distinguish groups. Applied to single cell ATAC-seq, BROCKMAN readily distinguished cell types, treatments, batch effects, experimental artifacts, and cycling cells. We show that each variable component in the k-mer landscape reflects a set of co-varying TFs, which are often known to physically interact. For example, in K562 cells, AP-1 TFs were central determinant of variability in chromatin accessibility through their variable expression levels and diverse interactions with other TFs. We provide a theoretical basis for why cooperative TF binding – and any associated epigenomic mark – is inherently more variable than non-cooperative binding. BROCKMAN and related approaches will help gain a mechanistic understanding of the trans determinants of chromatin variability between cells, treatments, and individuals.
From Laconic Zero-Knowledge to Public-Key Cryptography

Since its inception, public-key encryption (\(\mathsf {PKE}\)) has been one of the main cornerstones of cryptography. A central goal in cryptographic research is to understand the foundations of public-key encryption and in particular, base its existence on a natural and generic complexity-theoretic assumption. An intriguing candidate for such an assumption is the existence of a cryptographically hard language Open image in new window .
A geo-aware server assignment problem for mobile edge computing

As mobile devices have become the preferred tool for communication, work, and entertainment, traffic at the edge of the network is growing more rapidly than ever. To improve user experience, commod...
Quis custodiet ipsos custodes? Despite evidence to the contrary, the American Economic Review concluded that all was well with its archive

In 2011, the Annual Report of the Editor of the American Economic Review reported that the journal's data-code archive was functioning well, and no changes were made to the archive rules. This was based on an audit of the archive that the Editor had commissioned. In point of fact, all was not well with the archive: the archive did not support the publication of reproducible research. The rules for the archive should have been changed and were not; thus the American Economic Review continued to publish articles that were not reproducible. The cause of reproducible research was set back many years. Currently it appears that the AER intends to reproduce articles prior to publication; this would be a horrible mistake.
Conforming nanoparticle sheets to surfaces with Gaussian curvature

Nanoparticle monolayer sheets are ultrathin inorganic–organic hybrid materials that combine highly controllable optical and electrical properties with mechanical flexibility and remarkable strength. Like other thin sheets, their low bending rigidity allows them to easily roll into or conform to cylindrical geometries. Nanoparticle monolayers not only can bend, but also cope with strain through local particle rearrangement and plastic deformation. This means that, unlike thin sheets such as paper or graphene, nanoparticle sheets can much more easily conform to surfaces with complex topography characterized by non-zero Gaussian curvature, like spherical caps or saddles. Here, we investigate the limits of nanoparticle monolayers’ ability to conform to substrates with Gaussian curvature by stamping nanoparticle sheets onto lattices of larger polystyrene spheres. Tuning the local Gaussian curvature by increasing the size of the substrate spheres, we find that the stamped sheet morphology evolves through three characteristic stages: from full substrate coverage, where the sheet extends over the interstices in the lattice, to coverage in the form of caps that conform tightly to the top portion of each sphere and fracture at larger polar angles, to caps that exhibit radial folds. Through analysis of the nanoparticle positions, obtained from scanning electron micrographs, we extract the local strain tensor and track the onset of strain-induced dislocations in the particle arrangement. By considering the interplay of energies for elastic and plastic deformations and adhesion, we construct arguments that capture the observed changes in sheet morphology as Gaussian curvature is tuned over two orders of magnitude.
Shaking Hands with Hitler: The Politics‐Administration Dichotomy and Engagement with Fascism

Researchers have examined the impact of the politics-administration dichotomy on the practice and theory of public administration within the United States. But the dichotomy also influenced patterns of international engagement by American experts in the 1920s and 1930s. Americans believed that they could set politics aside and collaborate on administrative questions with regimes that did not respect democracy and human rights. This belief was tested after the rise of Hitler. American experts in public administration engaged with the Nazi regime for three years, ignoring controversy over Nazi policies. The breaking point came in 1936. American experts finally recognized that it was impossible to ignore political questions and became forthright proponents of "democratic administration." This struggle to define the boundaries of international engagement is relevant today, as specialists in public administration find themselves in a world where a shared commitment to democracy and human rights cannot be taken for granted.
Pain interventions for organ transplant patients undergoing incisional hernia repair: Is epidural or transversus abdominus plane block a better option?

BACKGROUND Postoperative pain management in transplant recipients undergoing incisional herniorraphy is challenging. Historically limited to intravenous or oral opioids, alternatives including transversus abdominus plane (TAP) block catheters and thoracic epidural catheters have been introduced. The aim of this study was to determine whether TAP catheters and thoracic epidural analgesia significantly impacted on postoperative pain and opioid usage in transplant recipients undergoing incisional hernia repair. METHODS This single-center retrospective study included 154 patients undergoing incisional hernia repair from January 2011 to June 2015. Of these, 56 received epidurals, 51 received TAP catheters, and 47 received no intervention. RESULTS Demographic profiles were comparable among the three groups including type of previous transplant and type of hernia surgery. Thoracic epidural analgesia was associated with lower median, mean, and maximum pain scores (P < 0.001) and less opioid requirement (P < 0.001). There was no difference in pain scores and opioid usage among the TAP catheter and no intervention groups. There was no difference in time to first flatus or first bowel movement, length of hospital stay, individual opioid-related side effects, and adverse reactions among the three groups. CONCLUSION This study supports the use of thoracic epidural analgesia in patients undergoing hernia repair after transplant surgery.
Reducing Network Agnostophobia

Agnostophobia, the fear of the unknown, can be experienced by deep learning engineers while applying their networks to real-world applications. Unfortunately, network behavior is not well defined for inputs far from a networks training set. In an uncontrolled environment, networks face many instances that are not of interest to them and have to be rejected in order to avoid a false positive. This problem has previously been tackled by researchers by either a) thresholding softmax, which by construction cannot return "none of the known classes", or b) using an additional background or garbage class. In this paper, we show that both of these approaches help, but are generally insufficient when previously unseen classes are encountered. We also introduce a new evaluation metric that focuses on comparing the performance of multiple approaches in scenarios where such unseen classes or unknowns are encountered. Our major contributions are simple yet effective Entropic Open-Set and Objectosphere losses that train networks using negative samples from some classes. These novel losses are designed to maximize entropy for unknown inputs while increasing separation in deep feature space by modifying magnitudes of known and unknown samples. Experiments on networks trained to classify classes from MNIST and CIFAR-10 show that our novel loss functions are significantly better at dealing with unknown inputs from datasets such as Devanagari, NotMNIST, CIFAR-100 and SVHN.
Detecting the major charge-carrier scattering mechanism in graphene antidot lattices

Abstract Charge carrier scattering is critical to the electrical properties of two-dimensional materials such as graphene, black phosphorene, and tellurene. Beyond pristine two-dimensional materials, further tailored properties can be achieved by nanoporous patterns such as nano- or atomic-scale pores (antidots) across the material. As one example, structure-dependent electrical/optical properties for graphene antidot lattices (GALs) have been studied in recent years. However, detailed charge carrier scattering mechanism is still not fully understood. In this paper, the energy sensitivity of charge-carrier scattering and thus the dominant scattering mechanisms are revealed for GALs by analyzing the maximum Seebeck coefficient with a tuned gate voltage and thus shifted Fermi levels. It shows that the scattering from pore-edge-trapped charges is dominant. For thermoelectric interests, the gate-voltage-dependent power factor for a GAL with the square pattern can reach as high as 554 μW / cm ⋅ K 2 at 400 K. With their high thermal conductivities and power factors, these GALs can be well suitable for “active coolers” within electronic devices, where heat generated at the hot spot can be removed with both passive heat conduction and active Peltier cooling.
Depressive Symptoms Following Recent Sexual Assault: The Role of Drug and Alcohol Use, Acute Stress, and Assault Characteristics:

Sexual assault is a common traumatic experience that can have a wide-ranging impact on psychological functioning, including experience of depressive symptoms. Although many studies have examined lifetime rates of depression among those with sexual assault history, less is known regarding risk factors for depressive symptoms following recent sexual assault. The study examined whether drug use history is uniquely related to depressive symptoms following recent assault. N = 65 individuals, 95.4% female; 73.8% White; M(SD)age = 28.89 (10.29), who had recently experienced sexual assault (less than 60 days) and completed a SAMFE (sexual assault medical forensic examination) were interviewed via phone and completed questionnaires regarding depressive and acute/post-traumatic stress symptoms and substance use history. Demographic information as well as information related to the assault was also collected. 68.7% of the sample reported clinically significant levels of depressive symptoms (PHQ-9, Patient Health Que...
Targeted expression of cyclin D2 ameliorates late stage anthracycline cardiotoxicity.

Aims Doxorubicin (DOX) is a widely used and effective anti-cancer therapeutic. DOX treatment is associated with both acute and late onset cardiotoxicity, limiting its overall efficacy. Here, the impact of cardiomyocyte cell cycle activation was examined in a juvenile model featuring aspects of acute and late onset DOX cardiotoxicity. Methods and results Two-week old MHC-cycD2 transgenic mice (which express cyclin D2 in postnatal cardiomyocytes and exhibit sustained cardiomyocyte cell cycle activity; D2 mice) and their wild type (WT) littermates received weekly DOX injections for 5 weeks (25 mg/kg cumulative dose). One week after the last DOX treatment (acute stage), cardiac function was suppressed in both groups. Acute DOX cardiotoxicity in D2 and WT mice was associated with similar increases in the levels of cardiomyocyte apoptosis and Ku70/Ku80 expression (markers of DNA damage and oxidative stress), as well as similar reductions in hypertrophic cardiomyocyte growth. Cardiac dysfunction persisted in WT mice for 13 weeks following the last DOX treatment (late stage) and was accompanied by increased levels of cardiomyocyte apoptosis, Ku expression, and myocardial fibrosis. In contrast, D2 mice exhibited a progressive recovery in cardiac function, which was indistinguishable from saline-treated animals by 9 weeks following the last DOX treatment. Improved cardiac function was accompanied by reductions in the levels of late stage cardiomyocyte apoptosis, Ku expression, and myocardial fibrosis. Conclusion These data suggest that cardiomyocyte cell cycle activity can promote recovery of cardiac function and preserve cardiac structure following DOX treatment.
Zinc: A Necessary Ion for Mammalian Sperm Fertilization Competency

The importance of zinc for male fertility only emerged recently, being propelled in part by consumer interest in nutritional supplements containing ionic trace minerals. Here, we review the properties, biological roles and cellular mechanisms that are relevant to zinc function in the male reproductive system, survey available peer-reviewed data on nutritional zinc supplementation for fertility improvement in livestock animals and infertility therapy in men, and discuss the recently discovered signaling pathways involving zinc in sperm maturation and fertilization. Emphasis is on the zinc-interacting sperm proteome and its involvement in the regulation of sperm structure and function, from spermatogenesis and epididymal sperm maturation to sperm interactions with the female reproductive tract, capacitation, fertilization, and embryo development. Merits of dietary zinc supplementation and zinc inclusion into semen processing media are considered with livestock artificial insemination (AI) and human assisted reproductive therapy (ART) in mind. Collectively, the currently available data underline the importance of zinc ions for male fertility, which could be harnessed to improve human reproductive health and reproductive efficiency in agriculturally important livestock species. Further research will advance the field of sperm and fertilization biology, provide new research tools, and ultimately optimize semen processing procedures for human infertility therapy and livestock AI.
Effects of Selective Substitution of Cysteine Residues on the Conformational Properties of Chlorotoxin Explored by Molecular Dynamics Simulations.

Chlorotoxin (CTX) is a 36–amino acid peptide with eight Cys residues that forms four disulfide bonds. It has high affinity for the glioma-specific chloride channel and matrix metalloprotease-2. Structural and binding properties of CTX analogs with various Cys residue substitutions with l-α-aminobutyric acid (Abu) have been previously reported. Using 4.2 µs molecular dynamics, we compared the conformational and essential space sampling of CTX and analogs with selective substitution of the Cys residues and associated disulfide bonds with either Abu or Ser. The native and substituted peptides maintained a high degree of α-helix propensity from residues 8 through 21, with the exception of substitution of the Cys5–Cys28 residues with Ser and the Cys16–Cys33 residues with Abu. In agreement with previous circular dichroism spectropolarimetry results, the C-terminal β-sheet content varied less from residues 25 through 29 and 32 through 36 and was well conserved in most analogs. The Cys16–Cys33 and Cys20–Cys35 disulfide-bonded residues appear to be required to maintain the αβ motif of CTX. Selective substitution with the hydrophilic Ser, may mitigate the destabilizing effect of Cys16–Cys33 substitution through the formation of an inter residue H-bond from Ser16:OγH to Ser33:OγH bridged by a water molecule. All peptides shared considerable sampled conformational space, which explains the retained receptor binding of the non-native analogs.
Safety of Intranasal Ketamine for Reducing Uncontrolled Cancer-Related Pain: Protocol of a Phase I/II Clinical Trial.

Background: Approximately 12 million Americans are affected with cancer. Of these, 53% experience pain at all stages of cancer. Pain may remain uncontrolled despite high-dose opioid therapy, and opioids have many well-documented harmful side effects. Intranasal ketamine has been shown to be effective in controlling breakthrough noncancer pain in a double-blind randomized control trial (DBRCT) by Carr et al in 2003 as well as to help with depression in a DBRCT by Lapidus et al in 2014. We seek to obtain preliminary data on the safety, feasibility, and utility of this novel technique for the treatment of uncontrolled cancer pain. Objective: This study aimed to obtain preliminary data via a clinical trial addressing the safety, feasibility, pharmacokinetics, and pharmacodynamics of intranasal ketamine. These initial findings will be applied to a subsequent trial to determine the effectiveness and associated toxicities of ketamine in a larger sample of cancer patients and to address the compelling need to identify new, successful management therapies for cancer pain. Methods: This is an institutional review board– and investigational new drug–approved, prospective phase I/II trial to investigate the safety and use of intranasal ketamine in patients with uncontrolled pain related to cancer or cancer treatment. Informed consent will be obtained prior to all study procedures. All patients will be assigned to the same investigational treatment arm. After patient selection via inclusion/exclusion criteria, patients will be seen over 5 visits, with each visit conducted 2-7 days apart. Patients will be administered ketamine on visits 1-4 and monitored for 240 minutes with continuous pulse oximetry and regular blood pressure checks. Blood samples as well as patient-reported outcomes will be collected at set time points at baseline and after drug delivery. Patients will receive 10 mg intranasal ketamine on visit 1, 10 mg intravenous ketamine on visit 2, 30 mg intranasal ketamine on visit 3, and 50 mg intranasal ketamine on visit 4. On visit 5, an addition blood sample will be drawn. Results: As of March 2019, enrollment is in progress, and a total of 7 subjects have completed the study. Enrollment is expected to be completed by April 2019. Final data analysis will commence soon after, and the results are expected to be submitted for publication in 2019. Conclusions: If intranasal ketamine can be utilized for pain control in cancer patients, it could provide superior analgesia and better quality of life, without the risk of significant respiratory depression and constipation associated with opioid medications. These findings will be an important initial step toward testing the effectiveness of intranasal ketamine as a nonopioid medication for cancer pain and as potential maintenance outpatient therapy. Trial Registration: ClinicalTrials.gov NCT03146806; https://clinicaltrials.gov/ct2/show/NCT03146806. International Registered Report Identifier (IRRID): DERR1-10.2196/12125
AUDIT STATE DEPENDENT TAXPAYER COMPLIANCE: THEORY AND EVIDENCE FROM COLOMBIA

We develop and analyze a dynamic model of individual taxpayer compliance choice that predicts “audit state dependent taxpayer compliance,” by distinguishing between forward‐looking versus myopic versus naive behavior. We then test experimentally the audit state dependent model by reporting the results from the first tax compliance experiment run in Colombia. We find that subjects' compliance rates increase with greater enforcement. We also find more novel results: fine rates should be increased after an audit, and “nudging” myopic individuals toward reporting a constant rather than a fluctuating proportion of income would benefit both the taxpayer and the tax authority.(JEL H26, C91)
Poincaré/Koszul Duality

We prove a duality for factorization homology which generalizes both usual Poincare duality for manifolds and Koszul duality for $${\mathcal{E}_n}$$ -algebras. The duality has application to the Hochschild homology of associative algebras and enveloping algebras of Lie algebras. We interpret our result at the level of topological quantum field theory.
Monolithic Integration of Micron-scale Piezoelectric Materials with CMOS for Biomedical Applications

We present the development of micro-fabrication techniques achieving the monolithic integration of micron-scale piezoelectric ultrasonic transducers in both polyvinylidene difluoride (PVDF) and lead zirconate titanate (PZT) with complementary metal-oxide-semiconductor (CMOS) integrated circuits (ICs). PVDF-CMOS integration is driven by applications in energy harvesting and data telemetry for medical implants, while PZT-CMOS integration is applied to high-resolution two-dimensional (2D) ultrasound imaging. Both of these applications benefit from reduced parasitic capacitances and higher levels of integration possible with monolithic CMOS integration. Characterization results of micro-fabricated devices reveal the preservation of the piezoelectric properties of PVDF and PZT and transducer functionality with co-designed CMOS ICs.
Free-Boolean independence for pairs of algebras

Abstract We construct pairs of algebras with mixed independence relations by using truncations of reduced free products of algebras. For example, we construct free-Boolean pairs of algebras and free-monotone pairs of algebras. We also introduce free-Boolean cumulants and show that free-Boolean independence is equivalent to the vanishing of mixed cumulants. In addition, we provide a moments-condition for free-Boolean independence. A central limit law for free-Boolean independence is studied.
Numerical simulation of coupled fluid-solid interaction at the pore scale: A digital rock-physics technology

ABSTRACTWe have used numerical modeling to capture the physics related to coupled fluid-solid interaction (FSI) and the frequency dependence of pore scale fluid flow in response to pore pressure he...
On the existence of universal families of marked irreducible holomorphic symplectic manifolds

We prove the existence of a global family with natural universal properties over every component of the moduli space of marked irreducible holomorphic symplectic manifolds. The analogous result follows for Teichmuller spaces.
Human-Computer Insurrection: Notes on an Anarchist HCI

The HCI community has worked to expand and improve our consideration of the societal implications of our work and our corresponding responsibilities. Despite this increased engagement, HCI continues to lack an explicitly articulated politic, which we argue re-inscribes and amplifies systemic oppression. In this paper, we set out an explicit political vision of an HCI grounded in emancipatory autonomy-an anarchist HCI, aimed at dismantling all oppressive systems by mandating suspicion of and a reckoning with imbalanced distributions of power. We outline some of the principles and accountability mechanisms that constitute an anarchist HCI. We offer a potential framework for radically reorienting the field towards creating prefigurative counterpower-systems and spaces that exemplify the world we wish to see, as we go about building the revolution in increment.
Acceptance of Behavior Guidance Techniques Used in Pediatric Dentistry by Parents From Diverse Backgrounds

Objective. To investigate if parental background affects acceptance of behavior guidance techniques. Background. Behavior guidance techniques are used for the safe and effective treatment of pediatric patients. Acceptance of these techniques may vary by racial and ethnic background. Methods. A total of 142 parents were recruited and asked to rate videos showing: active restraint/protective stabilization (AR), general anesthesia (GA), nitrous oxide sedation (N2O), oral premedication/sedation (OP), passive restraint/protective stabilization (PR), tell-show-do (TSD), and voice control (VC) techniques. Results. Hispanic parents rated VC most acceptable, followed by TSD, PR, and pharmacologic techniques. Black and white parents rated TSD, followed by N2O, as most acceptable, and AR and PR as least favorable. Hispanics found GA significantly less acceptable than whites or blacks. Hispanics were less accepting of AR than blacks; but more accepting of PR than whites. TSD was highly rated among all 3 cohorts. Parental background affected acceptance of the techniques in this study.
Can Successful Schools Replicate? Scaling Up Boston’s Charter School Sector

Can schools that boost student outcomes reproduce their success at new campuses? We study a policy reform that allowed effective charter schools in Boston, Massachusetts to replicate their school models at new locations. Estimates based on randomized admission lotteries show that replication charter schools generate large achievement gains on par with those produced by their parent campuses. The average effectiveness of Boston's charter middle school sector increased after the reform despite a doubling of charter market share. An exploration of mechanisms shows that Boston charter schools compress the distribution of teacher effectiveness and may reduce the returns to teacher experience, suggesting the highly standardized practices in place at charter schools may facilitate replicability.