cssi/SciSciGPT-SciSciNet huggingface dataset:
On the structure and mechanism of two-pore channels.

In eukaryotes, two-pore channels (TPC1-3) comprise a family of ion channels that regulate the conductance of Na+ and Ca2+ ions across cellular membranes. TPC1-3 form endolysosomal channels, but TPC3 can also function in the plasma membrane. TPC1/3 are voltage-gated channels, but TPC2 opens in response to binding endolysosome-specific lipid phosphatidylinositol-3,5-diphosphate (PI(3,5)P2 ). Filoviruses, such as Ebola, exploit TPC-mediated ion release as a means of escape from the endolysosome during infection. Antagonists that block TPC1/2 channel conductance abrogate filoviral infections. TPC1/2 form complexes with the mechanistic target of rapamycin complex 1 (mTORC1) at the endolysosomal surface that couple cellular metabolic state and cytosolic nutrient concentrations to the control of membrane potential and pH. We determined the X-ray structure of TPC1 from Arabidopsis thaliana (AtTPC1) to 2.87A resolution-one of the two first reports of a TPC channel structure. Here, we summarize these findings and the implications that the structure may have for understanding endolysosomal control mechanisms and their role in human health.
Probabilistic Key Predistribution in Mobile Networks Resilient to Node-Capture Attacks

We present a comprehensive analysis on connectivity and resilience of secure sensor networks under the widely studied $q$ -composite key predistribution scheme. For network connectivity, which ensures that any two sensors can find a path in between for secure communication, we derive the conditions to guarantee connectivity in consideration of: 1) node-capture attacks, where the adversary may capture a set of sensors and compromise keys in their memory; 2) sensor mobility, meaning that sensors can move around so that the network topology may change over time; 3) physical transmission constraints, under which two sensors have to be within each other’s transmission range for communication; 4) the boundary effect of network fields; and 5) link unreliability, meaning that links are allowed to be unreliable. In contrast, many prior connectivity analyses of secure sensor networks often ignore the above issues. For resilience, although limited studies have presented formal analysis, it is often assumed that the adversary captures a random set of sensors, whereas this paper allows the adversary to capture an arbitrary set of sensors. We present conditions to ensure unassailability and unsplittability in secure sensor networks under the $q$ -composite scheme. Unassailability ensures that an adversary capturing any set consisting of a negligible fraction of sensors can compromise only a negligible fraction of communication links although the adversary may compromise communications between non-captured nodes, which happen to use keys that are shared by captured nodes. Unsplittability means that when a negligible fraction of sensors are captured, almost all of the remaining nodes are still securely connected. Based on the results of connectivity, unassailability, and unsplittability, we provide useful guidelines for the design of secure sensor networks.
The Value of the Anterior Rectus Sheath in the Transverse Abdominal Island Flap

We report a major modification of technique for the transverse abdominal island flap, namely, taking the anterior rectus fascia with the muscle. We believe that this protects the fine vascular network supplying the flap. This has resulted in a more reliable flap, confirmed by increased fluorescein uptake, and significant reduction in operating time.
Renin Dependency of Blood Pressure in Isolated Systolic Hypertension

The role of the renin system in the maintenance of the elevated systolic blood pressure in isolated systolic hypertension was investigated in 31 patients who received long-term treatment with propranolol (120 mg daily) and in another group of 22 patients with isolated systolic hypertension who received a test dose of captopril (25 or 50 mg). The greatest systolic blood pressure decrease (35 +/- 5 mm Hg) by propranolol occurred in the high-renin group (n = 9), and the smallest decrease (3 +/- 2 mm Hg) in the low-renin group (n = 9), whereas in the normal-renin group (n = 13), systolic blood pressure was decreased by propranolol by 22 +/- 5 mm Hg. For all the propranolol-treated patients, the decrement in the systolic blood pressure by propranolol was related to the control plasma renin activity (r = 0.63, p less than 0.01) and to the concurrent change in plasma renin activity (r = 0.70, p less than 0.001). Captopril decreased the systolic blood pressure by 55 +/- 10 mm Hg in the high-renin group (n = 11) and by 17 +/- 5 mm Hg in the normal-renin group (n = 6), whereas the smallest decrease (12 +/- 5 mm Hg) in systolic blood pressure occurred in the low-renin group (n = 5). In all the captopril-tested patients (n = 22), the decrease in systolic blood pressure by captopril was related to the control plasma renin activity (r = 0.75, p less than 0.001). These results indicate that the plasma renin activity value indicates the participation of the renin-angiotensin system in the maintenance of the elevated systolic blood pressure in patients with isolated systolic hypertension.
Sharpening Jensen's Inequality

This article proposes a new sharpened version of Jensen's inequality. The proposed new bound is simple and insightful, is broadly applicable by imposing minimum assumptions, and provides fairly acc...
Variational Deep Semantic Hashing for Text Documents

As the amount of textual data has been rapidly increasing over the past decade, efficient similarity search methods have become a crucial component of large-scale information retrieval systems. A popular strategy is to represent original data samples by compact binary codes through hashing. A spectrum of machine learning methods have been utilized, but they often lack expressiveness and flexibility in modeling to learn effective representations. The recent advances of deep learning in a wide range of applications has demonstrated its capability to learn robust and powerful feature representations for complex data. Especially, deep generative models naturally combine the expressiveness of probabilistic generative models with the high capacity of deep neural networks, which is very suitable for text modeling. However, little work has leveraged the recent progress in deep learning for text hashing. In this paper, we propose a series of novel deep document generative models for text hashing. The first proposed model is unsupervised while the second one is supervised by utilizing document labels/tags for hashing. The third model further considers document-specific factors that affect the generation of words. The probabilistic generative formulation of the proposed models provides a principled framework for model extension, uncertainty estimation, simulation, and interpretability. Based on variational inference and reparameterization, the proposed models can be interpreted as encoder-decoder deep neural networks and thus they are capable of learning complex nonlinear distributed representations of the original documents. We conduct a comprehensive set of experiments on four public testbeds. The experimental results have demonstrated the effectiveness of the proposed supervised learning models for text hashing.
Epigenetic regulation of hepatitis B virus covalently closed circular DNA: Implications for epigenetic therapy against chronic hepatitis B

Hepatitis B virus (HBV) infection represents a significant public health burden worldwide. Although current therapeutics manage to control the disease progression, lifelong treatment and surveillance are required because drug resistance develops during treatment and reactivations frequently occur following medication cessation. Thus, the occurrence of hepatocellular carcinoma is decreased, but not eliminated. One major reason for failure of HBV treatment is the inability to eradicate or inactivate the viral covalently closed circular DNA (cccDNA), which is a stable episomal form of the viral genome decorated with host histones and nonhistone proteins. Accumulating evidence suggests that epigenetic modifications of cccDNA contribute to viral replication and the outcome of chronic HBV infection. Here, we summarize current progress on HBV epigenetics research and the therapeutic implications for chronic HBV infection by learning from the epigenetic therapies for cancer and other viral diseases, which may open a new venue to cure chronic hepatitis B. (Hepatology 2017;66:2066-2077).
Cervical cancer metastasis to the brain: A case report and review of literature.

Background Intracranial metastasis from cervical cancer is a rare occurrence. Methods In this study we describe a case of cervical cancer metastasis to the brain and perform an extensive review of literature from 1956 to 2016, to characterize clearly the clinical presentation, treatment options, molecular markers, targeted therapies, and survival of patients with this condition. Results An elderly woman with history of cervical cancer in remission, presented 2 years later with a right temporo-parietal tumor, which was treated with surgery and subsequent stereotactic radiosurgery (SRS) to the resection cavity. She then returned 5 months later with a second solitary right lesion; she again underwent surgery and SRS to the resection cavity with no signs of recurrence 6 months later. According to the reviewed literature, the most common clinical presentation included females with median age of 48 years; presenting symptoms such as headache, weakness/hemiplegia/hemiparesis, seizure, and altered mental status (AMS)/confusion; multiple lesions mostly supratentorially located; poorly differentiated squamous cell carcinoma; and additional recurrences at other sites. The best approach to treatment is a multimodal plan, consisting of SRS or whole brain radiation therapy (WBRT) for solitary brain metastases followed by chemotherapy for systemic disease, surgery and WBRT for solitary brain lesions without systemic disease, and SRS or WBRT followed by chemotherapy for palliative care. The overall prognosis is poor with a mean and median survival time from diagnosis of brain metastasis of 7 and 4.6 months, respectively. Conclusion Future efforts through large prospective randomized trials are warranted to better describe the clinical presentation and identify more effective treatment plans.
CONVERTIBLE IMPLANTS: SIMPLIFYING REVISION OF ANATOMIC TO REVERSE TOTAL SHOULDER ARTHROPLASTY

Modern total shoulder arthroplasty seeks to produce a construct that reproduces the kinematics and stability of the native glenohumeral joint. The latest 4th generation implants are modular, adaptable, and capable of use as either anatomic or reverse shoulder arthroplasty components. During surgery, these implants are “universal”; post-operatively, they are “convertible”. Recent work has demonstrated that reverse shoulder arthroplasty components may indeed be the emerging standard of care for most (if not all) shoulder arthroplasty indications. As this new frontier develops, the use of a convertible/universal implant creates the flexibility to individually choose the best surgical option for each patient.
Mechanisms of Breast Implant Toxicity: Will the Real Ringmaster Please Stand Up

The past five years have witnessed a recurrent public health debacle encompassing illness caused by silicone gel-filled breast implants. In an attempt to explain this epidemiologic reality, researchers from many institutions have resurrected the faulty device-related autoimmune theories of the early 1990â€™s. These researchers are genuinely convinced that breast implants initiate a novel disease, but their methodology utilized to prove this premise is unsound. The inability to consider mechanisms of widespread biochemical disruptions caused by in vivo exposure to organosilicon molecules is coexisting with multiple deficiencies in detecting the voluminous clinical manifestations of ailing implant recipients. Chief among the latter are the use of truncated data bases to record symptoms and signs, coupled with the lack of appreciation that silicone-induced disease evolves chronologically in a manner simulating a dose-response curve. This failure to correlate the diverse clinical features present in toxic breast implant recipients with logical mechanisms of disease causation provides a clear understanding of why the silicone breast implant chaos of the early 1990â€™s was inevitably destined to repeat itself.
Chapter 6 – The Laplace Transform

This chapter presents the main properties and applications of the Laplace transform. One important applicative strength of the Laplace method is the conversion of linear ordinary differential equations (ODE) into algebraic equations by means of the direct Laplace transform, which enables calculation of the transformed solution with relative ease. Subsequent use of the inverse Laplace transform allows retrieving the original, time-domain solution. The Laplace transform is utilized for linear ODE with constant coefficients in this chapter. This chapter includes the application of Laplace transform to dynamic models defined by systems of linear ODEs, which are expressed in vector-matrix form. Integral and integral–differential equations and systems can also be solved by the same two-phase Laplace approach. Another topic is time-domain system identification from Laplace-domain information.
Differential voltage amplification from ferroelectric negative capacitance

We demonstrate that a ferroelectric can cause a differential amplification without needing such an external energy source. As the ferroelectric switches from one polarization state to the other, a transfer of energy takes place from the ferroelectric to the dielectric, determined by the ratio of their capacitances, which, in turn, leads to the differential amplification. This amplification is very different in nature from conventional inductor-capacitor based circuits where an oscillatory amplification can be observed. The demonstration of differential voltage amplification from completely passive capacitor elements only has fundamental ramifications for next generation electronics.
Impact of flow, gradient, and left ventricular function on outcomes after transcatheter aortic valve replacement.

OBJECTIVES To assess the impact of low flow with and without preserved left ventricular ejection fraction (LVEF) on outcomes after transcatheter aortic valve replacement (TAVR). BACKGROUND Prior studies have shown that patients with low flow, AVG, and LVEF have worse outcomes after TAVR. It is unclear whether low AVG and LVEF remain prognostic after adjusting for flow, and how the outcomes of patients with low flow with and without preserved LVEF compare after TAVR. The goal of this study was to provide insight into these open questions. METHODS Data from 340 TAVR patients at Brigham and Women's Hospital from 2011 through 2015 were analyzed. Low flow was defined as stroke volume index (SVI) ≤35 mL/m2 , low AVG as mean gradient < 40 mmHg, and reduced LVEF as < 50%. RESULTS Low flow was present in 96 (28.2%) patients, 48 (50.0%) of whom also had reduced LVEF. At 1 year, low flow was associated with increased mortality (21.9 vs 7.4%; P = 0.0002) and heart failure (HF) (20.8 vs 5.3%; P = 0.0113). Among patients with low flow, those with preserved LVEF had increased mortality (HR 5.17, 95% CI 2.73-9.80; P < 0.001) and HF (HR 7.69, 95% CI 3.86-15.31; P < 0.001). After adjusting for clinical factors, patients with low flow had increased mortality (HR 6.51, 95% CI 2.98-14.22; P < 0.001) and HF (HR 5.52, 95% CI 2.34-12.98; P < 0.001), while neither low AVG nor low LVEF were associated with increases in mortality or HF. CONCLUSIONS In patients undergoing TAVR, low flow was an independent predictor of 1-year mortality and HF, and a stronger predictor than either low AVG or LVEF. Patients with low flow and preserved EF had increased mortality and HF at 1-year, while those with low flow and reduced EF had outcomes similar to patients with normal flow.
EpiModel: An R Package for Mathematical Modeling of Infectious Disease over Networks

Package EpiModel provides tools for building, simulating, and analyzing mathematical models for the population dynamics of infectious disease transmission in R. Several classes of models are included, but the unique contribution of this software package is a general stochastic framework for modeling the spread of epidemics on networks. EpiModel integrates recent advances in statistical methods for network analysis (temporal exponential random graph models) that allow the epidemic modeling to be grounded in empirical data on contacts that can spread infection. This article provides an overview of both the modeling tools built into EpiModel, designed to facilitate learning for students new to modeling, and the application programming interface for extending package EpiModel, designed to facilitate the exploration of novel research questions for advanced modelers.
Food Waste and Food Retail Density

This paper examines the relationship between food retail density and municipal solid waste. We test for correlations between the volume of solid waste and the number of food-at-home retailers (e.g....
Open Fractures of the Hand: Review of Pathogenesis and Introduction of a New Classification System

Open fractures of the hand are a common and varied group of injuries. Although at increased risk for infection, open fractures of the hand are more resistant to infection than other open fractures. Numerous unique factors in the hand may play a role in the altered risk of postinjury infection. Current systems for the classification of open fractures fail to address the unique qualities of the hand. This article proposes a novel classification system for open fractures of the hand, taking into account the factors unique to the hand that affect its risk for developing infection after an open fracture.
Appraisal econometrics for proposed transport corridors : optimal placement, intervention design, and wider economic benefits

Transport corridors to stimulate regional integration and trade have become a popular development tool. But when they fail to generate the expected wider economic benefits, they can become wasteful or unequitable investments. This paper evaluates the relative strengths and weaknesses of econometric methods as applied today to appraise the proposed placement of transport corridors and the project design to distribute benefits more widely; the future potential of these methods; and an emerging synthesis in some recent studies. The review focuses on network, reduced-form, and structural (general equilibrium) econometrics. At the current juncture, from the policy-making perspective, combining network econometrics based on the notion of centrality with reduced-form regressions comprising interactive effects could be the most practical approach. Future research could focus on advancing structural general equilibrium econometrics to cover multiple markets together with network econometrics to consider the interaction of heterogeneous agents.
Randomizing Religion: the Impact of Protestant Evangelism on Economic Outcomes

We study the causal impact of religiosity through a randomized evaluation of an evangelical Protestant Christian values and theology education program delivered to thousands of ultra-poor Filipino households. Six months after the program ended, treated households have higher religiosity and income; no statistically significant differences in total labor supply, consumption, food security, or life satisfaction; and lower perceived relative economic status. Exploratory analysis suggests that the income treatment effect may operate through increasing grit. Thirty months after the program ended, significant differences in the intensity of religiosity disappear, but those in the treatment group are less likely to be Catholic and more likely to be Protestant, and there is some mixed evidence that their consumption and perceived relative economic status are higher. We conclude that this church-based program may represent a method of increasing noncognitive skills and reducing poverty among adults in developing countries.
Effects of substrate on the AC performance of submicron GaN HEMTs

This paper presents a comparative study of AC performance of AlN/GaN/AlGaN high electron mobility transistor (HEMT) on Si and SiC substrates. It has been demonstrated that Si/SiC substrate offers a nominal change in the AC performance of the device. Also, Si/SiC based AlN/GaN/AlGaN HEMTs AC performance with and without substrate distributed network is assessed using particle swarm optimization technique and compared with experimental data. Equivalent intrinsic small signal parameters are extracted for both the substrates, which showed a minor change in the intrinsic small signal parameters values. Thus, Si substrate may comfortably be employed in the fabrication of AlN/GaN/AlGaN HEMT to make it cost effective.
A 2.6-GHz/5.2-GHz frequency synthesizer in 0.4-/spl mu/m CMOS technology

This paper describes the design of a CMOS frequency synthesizer targeting wireless local-area network applications in the 5-GHz range. Based on an integer-N architecture, the synthesizer produces a 5.2-GHz output as well as the quadrature phases of a 2.6-GHz carrier. Fabricated in a 0.4-/spl mu/m digital CMOS technology, the circuit provides a channel spacing of 23.5 MHz at 5.2 GHz while exhibiting a phase noise of -115 dBc/Hz at 2.6 GHz and -100 dBc/Hz at 5.2 GHz (both at 10-MHz offset). The reference sidebands are at -53 dBc at 2.6 GHz, and the power dissipation from a 2.6-V supply is 47 mW.
Addressing people and place microenvironments in weight loss disparities (APP-Me): Design of a randomized controlled trial testing timely messages for weight loss behavior in low income Black and White Women.

Abstract Background Behavioral interventions for weight loss have been less effective in lower income and black women. These poorer outcomes may in part be related to these women having more frequent exposures to social and physical situations that are obesogenic, i.e., eating and sedentary cues or situations. Objectives Working with obese, lower income Black and White Women, Addressing People and Place Microenvironments (APP-Me) was designed to create awareness of self-behavior at times and places of frequent eating and sedentary behavior. Design APP-Me is being evaluated in a randomized controlled trial with 240 participants recruited from federally qualified health centers located in a single Midwestern city. All participants complete four weeks of ecological momentary assessments (EMA) of situations and behavior. At the end of the four weeks, participants are randomized to enhanced usual care (UC) or UC plus APP-Me. Methods APP-Me is an automated short messaging system (SMS). Messages are text, image, audio, or a combination, and are delivered to participants' mobile devices with the intent of creating awareness at the times and places of frequent eating or sedentary behavior. The EMA data inform the timing of message deliveries. Summary This project aims to create and test timely awareness messages in a subpopulation that has not responded well to traditional behavioral interventions for weight loss. Novel aspects of the study include the involvement of a low income population, the use of data on time and place of obesogenic behavior, and message delivery time tailored to an individual's behavioral patterns.
Pick your Poison: Examining Adolescent Substance Use Through Opportunity Theory.

The present study examines substance use behaviors of middle and high school students, focusing on how varying influences of opportunity measures impact use of specific types of substances. The dat...
Examining Spillover Effects from Teach for America Corps Members in Miami-Dade County Public Schools.

This article examines Teach For America’s (TFA) placement strategy in Miami-Dade County Public Schools, in which large numbers of TFA corps members are placed as clusters into a targeted set of dis...
Universal stabilization of single-qubit states using a tunable coupler

We theoretically analyze a scheme for fast stabilization of arbitrary qubit states with high fidelities, extending a protocol recently demonstrated experimentally [Lu et al., Phys. Rev. Lett. 119, 150502 (2017)]. That experiment utilized red and blue sideband transitions in a system composed of a fluxonium qubit, a low-$Q$ $LC$ oscillator, and a coupler enabling us to tune the interaction between them. Under parametric modulations of the coupling strength, the qubit can be steered into any desired pure or mixed single-qubit state. For realistic circuit parameters, we predict that stabilization can be achieved within $100\phantom{\rule{0.28em}{0ex}}\mathrm{ns}$. By varying the ratio between the oscillator's damping rate and the effective qubit-oscillator coupling strength, we can switch between underdamped, critically damped, and overdamped stabilization and find optimal working points. We further analyze the effect of thermal fluctuations and show that the stabilization scheme remains robust for realistic temperatures.
Variation in sleep and metabolic function is associated with latitude and average temperature in Drosophila melanogaster

Regulation of sleep and metabolic homeostasis is critical to an animal's survival and under stringent evolutionary pressure. Animals display remarkable diversity in sleep and metabolic phenotypes; however, an understanding of the ecological forces that select for, and maintain, these phenotypic differences remains poorly understood. The fruit fly, Drosophila melanogaster, is a powerful model for investigating the genetic regulation of sleep and metabolic function, and screening in inbred fly lines has led to the identification of novel genetic regulators of sleep. Nevertheless, little is known about the contributions of naturally occurring genetic differences to sleep, metabolic phenotypes, and their relationship with geographic or environmental gradients. Here, we quantified sleep and metabolic phenotypes in 24 D. melanogaster populations collected from diverse geographic localities. These studies reveal remarkable variation in sleep, starvation resistance, and energy stores. We found that increased sleep duration is associated with proximity to the equator and elevated average annual temperature, suggesting that environmental gradients strongly influence natural variation in sleep. Further, we found variation in metabolic regulation of sleep to be associated with free glucose levels, while starvation resistance associates with glycogen and triglyceride stores. Taken together, these findings reveal robust naturally occurring variation in sleep and metabolic traits in D. melanogaster, providing a model to investigate how evolutionary and ecological history modulate these complex traits.
Comparison of deep learning based concept representations for biomedical document clustering

In this research, document representations based on distributed representations of the concepts along with new weighting schemes for the documents are explored. The baseline weighting scheme is the traditional Term Frequency-Inverse Document Frequency (TF-IDF) of the concepts, whereas, the other two newly proposed ones consider both local content using the TF-IDF and associations between concepts. The distributed representations of the concepts are measured using a deep learning algorithm. The evaluation of the proposed document representations is based on the k-means clustering results. The results show that document representation based on TF-IDF in combination with the term based distributed representations for concepts outperforms the other two based on the returned evaluation metrics — F1-measure (80.21%) and Purity (77.1%).
Feedback synthesis for underactuated systems using sequential second-order needle variations:

This paper derives nonlinear feedback control synthesis for general control affine systems using second-order actions, the second-order needle variations of optimal control, as the basis for choosing each control response to the current state. A second result of this paper is that the method provably exploits the nonlinear controllability of a system by virtue of an explicit dependence of the second-order needle variation on the Lie bracket between vector fields. As a result, each control decision necessarily decreases the objective when the system is nonlinearly controllable using first-order Lie brackets. Simulation results using a differential drive cart, an underactuated kinematic vehicle in three dimensions, and an underactuated dynamic model of an underwater vehicle demonstrate that the method finds control solutions when the first-order analysis is singular. Finally, the underactuated dynamic underwater vehicle model demonstrates convergence even in the presence of a velocity field.
Do demographics matter for African child poverty

The effect of demographics on poverty measurement based on per capita consumption is well known. The size and composition of the household can affect the well-being of everyone in the household, with respect to total consumption within that household. Failure to address this issue may often lead to an underestimation or overestimation of poverty, especially for children. Many studies have tried to address the issue, using the generic approach of equivalence scales. However, the choice of scale is controversial and may lead to comparability problems between countries because of the different demographic structures and choice of the pivot household for establishing the per capita poverty line. Based on the World Bank's African poverty database, this study estimates poverty rates for African children using the new international poverty line of $1.90 a day defined in terms of 2011 purchasing power parity. The equivalence scales approach (Food and Agriculture Organization/World Health Organization) is used with the adjustment suggested by Deaton after the identification of the pivot household, which is defined as the household whose per capita consumption is around the international poverty line. This study shows that taking account of demographics results in downward adjustments of child poverty, adult poverty, and child-adult poverty gaps. Moreover, breakdowns by country show that poverty may vary significantly depending on demographics, which may cause some reranking when comparing poverty between African countries. Finally, sensitivity analyses reveal that child poverty is not sensitive to the child discount factor, unlike adult poverty, but, overall, taking account of demographics is helpful for better identifying poor children.
Mapping algorithms, agricultural futures, and the relationship between commodity investment flows and crude oil futures prices

Abstract Several studies employ mapping algorithms to infer index positions in WTI crude oil futures from positions in agricultural futures and report an economically large and statistically significant impact of index positions on crude oil futures prices. In this article, we provide direct evidence that the apparent impact of index investment based on mapping algorithms is spurious. Specifically, an idiosyncratic spike in agricultural index positions during 2007–08, coupled with the spike in oil prices, causes the spurious impact of index investment on crude oil futures prices found in these earlier studies.
Should We Fear the Robot Revolution? (The Correct Answer is Yes)

Abstract Advances in artificial intelligence and robotics may be leading to a new industrial revolution. This paper presents a model with the minimum necessary features to analyze the implications for inequality and output. Two assumptions are key: “robot” capital is distinct from traditional capital in its degree of substitutability with human labor; and only capitalists and skilled workers save. We analyze a range of variants that reflect widely different views of how automation may transform the labor market. Our main results are surprisingly robust: automation is good for growth and bad for equality; in the benchmark model real wages fall in the short run and eventually rise, but “eventually” can easily take generations.
Price Stickiness along the Income Distribution and the Effects of Monetary Policy

Abstract Monetary shocks have distributional consequences if they affect relative prices across goods consumed by different households. We document that the prices of the goods consumed by high-income households are stickier and less volatile than those of the goods consumed by middle-income households. Following a monetary policy shock, the estimated impulse responses of high-income households’ consumer price indices are about one-third smaller than those of the middle-income households. We evaluate the implications of these findings in a quantitative multi-sector New–Keynesian model featuring heterogeneous households. The distributional consequences of monetary policy shocks are large and similar to those in the econometric model.
Joint Strategy Fictitious Play With Inertia for Potential Games

We consider multi-player repeated games involving a large number of players with large strategy spaces and enmeshed utility structures. In these ldquolarge-scalerdquo games, players are inherently faced with limitations in both their observational and computational capabilities. Accordingly, players in large-scale games need to make their decisions using algorithms that accommodate limitations in information gathering and processing. This disqualifies some of the well known decision making models such as ldquoFictitious Playrdquo (FP), in which each player must monitor the individual actions of every other player and must optimize over a high dimensional probability space. We will show that Joint Strategy Fictitious Play (JSFP), a close variant of FP, alleviates both the informational and computational burden of FP. Furthermore, we introduce JSFP with inertia, i.e., a probabilistic reluctance to change strategies, and establish the convergence to a pure Nash equilibrium in all generalized ordinal potential games in both cases of averaged or exponentially discounted historical data. We illustrate JSFP with inertia on the specific class of congestion games, a subset of generalized ordinal potential games. In particular, we illustrate the main results on a distributed traffic routing problem and derive tolling procedures that can lead to optimized total traffic congestion.
Using Electronic Art to Define Navigation Paradigms for Hypermedia Communication

AbstractHypermedia computer programs (including the World Wide Web) are an important part of today’s educational curricula, and the computer interface plays a critical role in the interpretation of information in these programs. Students are fascinated with the amount of information that is available, but they often feel overwhelmed because they don’t know how to organize the information into a coherent whole. They get caught up in the process of navigating to various locations in a multimedia environment, and the continuity between ideas is obscured. This paper discusses the semantic structures in various forms of electronic art and makes recommendations for using these structures to create interface designs that symbolize the integration of multiple relationships in complex information spaces.
Knowledge Integration for Disease Characterization: A Breast Cancer Example

With the rapid advancements in cancer research, the information that is useful for characterizing disease, staging tumors, and creating treatment and survivorship plans has been changing at a pace that creates challenges when physicians try to remain current. One example involves increasing usage of biomarkers when characterizing the pathologic prognostic stage of a breast tumor. We present our semantic technology approach to support cancer characterization and demonstrate it in our end-to-end prototype system that collects the newest breast cancer staging criteria from authoritative oncology manuals to construct an ontology for breast cancer. Using a tool we developed that utilizes this ontology, physician-facing applications can be used to quickly stage a new patient to support identifying risks, treatment options, and monitoring plans based on authoritative and best practice guidelines. Physicians can also re-stage existing patients or patient populations, allowing them to find patients whose stage has changed in a given patient cohort. As new guidelines emerge, using our proposed mechanism, which is grounded by semantic technologies for ingesting new data from staging manuals, we have created an enriched cancer staging ontology that integrates relevant data from several sources with very little human intervention.
Another Look at the New York City School Voucher Experiment

This article reexamines data from the New York City school choice program, the largest and best-implemented private school scholarship experiment yet conducted. In the experiment, low-income public school students in kindergarten to Grade 4 were eligible to participate in a series of lotteries for a private school scholarship in May 1997. Data were collected from students and their parents at baseline and in the spring of each of the next 3 years. Students with missing baseline test scores, which encompasses all those who were initially in kindergarten and 11% of those initially in Grades 1 to 4, were excluded from previous analyses of achievement, even thoughthese students were tested in the follow-up years. In principle, random assignment would be expected to lead treatment status to be uncorrelated with all baseline characteristics. In addition, it was found that the effect of vouchers was sensitive to the particular way race/ethnicity was defined.
Designing Remedies to Compensate Plaintiffs for Unobservable Harms

Despite the vast sums transferred through the legal system, the foundations of the procedures used to compensate plaintiffs for unobservable losses remain unclear. Standard remedies can compensate plaintiffs for unknown harms, but it is expensive to do so. Damage awards will generally undercompensate or overcompensate a plaintiff whose true harm is unknown, while equitable remedies that provide more tailored compensation are generally wasteful. In this article I develop a novel remedy that compensates plaintiffs for unobservable private values at the lowest possible cost to the defendant. This remedy consists of offering the plaintiff the choice between intermediate damages and an inalienable injunction that restores the underlying harm at the conclusion of the trial. I show that this remedy is robust to errors by the court and potential post judgment renegotiation. Furthermore, I demonstrate that this remedy reduces litigants’ incentives to lie during trial. Finally, I consider ex ante deterrence and show conditions under which the remedy improves social welfare relative to optimal damages.
Using the new products margin to predict the industry-level impact of trade reform

This paper develops a methodology for predicting the impact of trade liberalization on exports by industry (3-digit ISIC) based on the pre-liberalization distribution of exports by product (5-digit SITC). Using the results of Kehoe and Ruhl (2013) that much of the growth in trade after trade liberalization is in products that are traded very little or not at all, we predict that industries with a higher share of exports generated by least traded products will experience more growth. Using our methodology, we develop predictions for industry-level changes in trade for the United States and Korea following the U.S.-Korea Free Trade Agreement (KORUS). As a test for our methodology, we show that it performs significantly better than the applied general equilibrium models originally used for the policy evaluation of the North American Free Trade Agreement (NAFTA).
Multi-Panel Kendall plot in light of an ROC curve analysis applied to measuring dependence

ABSTRACTThe Kendall plot (K-plot) is a plot measuring dependence between the components of a bivariate random variable. The K-plot graphs the Kendall distribution function against the distribution ...
Pension Reform and Return to Work Policies

For many people, working after beginning retirement benefit collection is a way to enhance financial security by increasing income. Existing research has shown that retirees are sensitive to the Social Security earnings test, which restricts the amount of earnings some beneficiaries can receive. However, little is known about the effects of other types of policies on post-retirement employment. Instead of restricting earnings, many public pension plans restrict the number of hours beneficiaries can work. I use return-to-work rules limiting the number of hours of employment in a state's public pension plan and administrative data on employment and retirement to determine the rules’ effects on retirement decisions and post-retirement labor supply. I find that the increases in the maximum number of hours of post-retirement employment lead to no change in retirement benefit collection and to increases in part-time work among retirees. As such, these policies appear to be binding on the labor supply decisions of some employees. These results are relevant for designing policies aimed at extending work-lives or improving the health of pension systems.
Mandatory IFRS Adoption and the Role of Accounting Earnings in CEO Turnover

We study whether mandatory adoption of International Financial Reporting Standards (IFRS) is associated with changes in the sensitivity of CEO turnover to accounting earnings and how the impact of IFRS adoption varies with country‐level institutions and firm‐level incentives. We find that CEO turnover responds more to a firm's accounting performance after adoption. This increase in turnover‐to‐earnings sensitivity is concentrated in countries with stronger enforcement of financial reporting and is more prominent for mandatory adopters that have strong firm‐level compliance incentives. In addition, we link the change in turnover‐to‐earnings sensitivity directly to accounting changes due to IFRS adoption and find a stronger adoption effect when firms report large overall accounting changes and large de‐recognition of loss provisions upon adoption. Some of the above findings are sensitive to the exclusion of UK firms, which account for more than half of our sample. Adoption obligatoire des IFRS et role des resultats comptables dans la rotation des chefs de la direction Les auteures se demandent si l'adoption obligatoire des normes internationales d'information financiere (IFRS) est associee a des changements dans la sensibilite de la rotation des chefs de la direction aux resultats comptables (sensibilite rotation‐resultats) et comment l'incidence de l'adoption des IFRS varie selon les institutions, a l’echelle du pays, et les motivations, a l’echelle de l'entreprise. Elles constatent que la rotation des chefs de la direction est davantage influencee par la performance comptable de l'entreprise apres l'adoption. Cette augmentation de la sensibilite rotation‐resultats se concentre dans les pays ou l'application des principes d'information financiere est plus rigoureuse, et elle est plus marquee chez les adoptants obliges ayant de fortes motivations a la conformite, a l’echelle de l'entreprise. De plus, les auteures relient directement les changements dans la sensibilite rotation‐resultats aux changements comptables decoulant de l'adoption des IFRS; elles notent egalement que les repercussions de l'adoption sont plus importantes lorsque les entreprises font globalement etat d'amples changements comptables et d'operations substantielles de decomptabilisation des provisions pour perte au moment de l'adoption. Certaines de ces constatations sont sensibles a l'exclusion des societes du Royaume‐Uni, qui constituent plus de la moitie de l’echantillon etudie par les auteures.
Nested satisfiability

A special case of the satisfiability problem, in which the clauses have a hierarchical structure, is shown to be solvable in linear time, assuming that the clauses have been represented in a convenient way.
Sex, Lies, and Surveys : The Role of Interviewer Characteristics

This paper examines how easily observable interviewer characteristics, such as gender and physical attractiveness, and more difficult to observe characteristics, such as attitudes and beliefs, affect adolescent girls' disclosure of sexual behavior during a baseline survey for an adolescent girls program in Liberia. The study finds that girls are more likely to report sexual activity to better-looking interviewers, and less likely to do so to interviewers holding more discriminatory gender attitudes and greater expectations about the program. The study finds no evidence that the gender of the interviewer matters.
A Flow-Extension Tethered Particle Motion Assay for Single-Molecule Proteolysis.

Regulated proteolysis of signaling proteins under mechanical tension enables cells to communicate with their environment in a variety of developmental and physiologic contexts. The role of force in...
Reliable Deep Grade Prediction with Uncertainty Estimation

Currently, college-going students are taking longer to graduate than their parental generations. Further, in the United States, the six-year graduation rate has been 59% for decades. Improving the educational quality by training better-prepared students who can successfully graduate in a timely manner is critical. Accurately predicting students' grades in future courses has attracted much attention as it can help identify at-risk students early so that personalized feedback can be provided to them on time by advisors. Prior research on students' grade prediction include shallow linear models; however, students' learning is a highly complex process that involves the accumulation of knowledge across a sequence of courses that can not be sufficiently modeled by these linear models. In addition to that, prior approaches focus on prediction accuracy without considering prediction uncertainty, which is essential for advising and decision making. In this work, we present two types of Bayesian deep learning models for grade prediction under a course-specific framework: i)Multilayer Perceptron (MLP) and ii) Recurrent Neural Network (RNN). These course-specific models are based on the assumption that prior courses can provide students with knowledge for future courses so that grades of prior courses can be used to predict grades in a future course. The MLP ignores the temporal dynamics of students' knowledge evolution. Hence, we propose RNN for students' performance prediction. To evaluate the performance of the proposed models, we performed extensive experiments on data collected from a large public university. The experimental results show that the proposed models achieve better performance than prior state-of-the-art approaches. Besides more accurate results, Bayesian deep learning models estimate uncertainty associated with the predictions. We explore how uncertainty estimation can be applied towards developing a reliable educational early warning system. In addition to uncertainty, we also develop an approach to explain the prediction results, which is useful for advisors to provide personalized feedback to students.
System level synthesis

Abstract This article surveys the System Level Synthesis framework, which presents a novel perspective on constrained robust and optimal controller synthesis for linear systems. We show how SLS shifts the controller synthesis task from the design of a controller to the design of the entire closed loop system, and highlight the benefits of this approach in terms of scalability and transparency. We emphasize two particular applications of SLS, namely large-scale distributed optimal control and robust control. In the case of distributed control, we show how SLS allows for localized controllers to be computed, extending robust and optimal control methods to large-scale systems under practical and realistic assumptions. In the case of robust control, we show how SLS allows for novel design methodologies that, for the first time, quantify the degradation in performance of a robust controller due to model uncertainty – such transparency is key in allowing robust control methods to interact, in a principled way, with modern techniques from machine learning and statistical inference. Throughout, we emphasize practical and efficient computational solutions, and demonstrate our methods on easy to understand case studies.