cssi/SciSciGPT-SciSciNet huggingface dataset:
Minority Admissions to Law School: More Trouble Ahead, and Two Solutions

For decades, the Law School Admission Test (“LSAT”) has played a key role in the admissions process at many law schools. Pressures from the rankings published by U.S. News and World Report (“USNAWR”) have increased its importance in recent years, to the point that many schools admit most applicants that have LSAT scores above the school median. The emphasis on LSAT in admissions has narrowed the range of LSATs at many schools. This stratification could have negative effects on the law school experience for students and may have already decreased the number of minority students admitted. The future looks worse. Now that schools have nearly maximized their LSATs for a good portion of the class, they will turn their attentions to the undergraduate grade point average (“UGPA”). If schools maximize both the UGPA and the LSAT, there will be fewer minorities, and especially fewer blacks, at the law schools ranked in the top half of the USNAWR rankings.
A Tale of Two Cities: Business Trust Listings and Capital Markets in Singapore and Hong Kong

Business trusts have been utilized for over a hundred years in the Anglo-American legal world as an alternative business organizational form to the traditional corporation; for example, they were very strong competitors to the corporation as a way of business organization in early 20th century America (John D. Rockefeller’s Standard Oil, the largest oil refiner in the world during its time, was notably organized as a business trust).1 Academic interest in business trusts flourished as well in the early twentieth century.2 Numerous civil law countries – including Japan, Korea, and continental European nations – also now have business trust laws.3 Business trusts emerged out of private trusts4 and historically were used to reap the benefits of incorporation while escaping certain regulatory and tax requirements that were usually levied against corporations.5 To provide a basic definition by way of introduction, business trusts are similar to traditional, private, donative trusts in that certain trustees are given legal title to property owned by the trust; these trustees then manage the property for the trust’s beneficiaries who hold title to the property, usually through transferable certificates of beneficial interest. A written declaration, which creates the trust, sets forth the specific details of the trust, such as its terms, the obligations and powers of the trustees, and the precise interests of the beneficiaries.6 Business trusts were historically governed under traditional common law principles,
Taxes and quality: A market-level analysis

A conventional assumption of product homogeneity when the commodity of interest is actually heterogeneous will lead to errors in an analysis of the incidence of policies, such as taxes. In this article, an equilibrium displacement model is used to derive analytical solutions for price, quantity, and quality effects of ad valorem and per unit taxes. The results show how parameters determine the effects of tax policies on quality. The potential for tax–induced distortions in quality, and the distributive consequences of those distortions, are illustrated in a case study of the market for Australian wine.
Grounding Nike: Exposing Nike's Quest for A Constitutional Right to Lie

Although the Nike v. Kasky case was widely reported to involve Nike's attempt to "defend" itself against its critics, in this article I demonstrate that according to textbook descriptions of the practice Nike was actually engaged in a form of marketing – reactive marketing PR. This marketing was designed to improve sales of its products in the wake of criticism of its labor practices and consumer backlash in response to those criticisms. Kasky claimed in his lawsuit that many of the statements Nike's representatives made in its defense were knowingly and intentionally false and he charged that they constituted fraud. The pleadings in the case reveal (despite widespread reportage to the contrary) that Nike hoped to raise a First Amendment defense to fraud.
Interrogating the State's Role in Human Trafficking

This Essay identifies some examples of and interrogates the state’s role in human trafficking. The state is not a blameless onlooker with respect to trafficking in human beings; nor is it a whole-heartedly committed crusader against this profitable illicit trade. Instead, States create the preconditions for and profit from human trafficking. Through the state’s power to legislate, it defines and re-defines reality – it is the creator and enforcer of paradigms of subordination and exploitation that normalize the exploitation of the individuals and groups it makes vulnerable. The state’s roles in this area arise from (i) foundational concepts, such as doctrines supporting and protecting the sovereignty of state actors; (ii) organizational principles, such as the implementation and policing of the concepts of belonging and non-belonging; and (iii) economic and political policies, such as the tension arising from the state’s simultaneously conflicted flirtation with and resistance to globalization. Anti-human trafficking campaigns, while emotionally gratifying and optically pleasing, do not address the structural foundations of state-created and state-implemented systems of exploitation.
New Ideas on Public Deliberation from Young Scholars. Introduction: Innovations in Deliberative Electoral Designs

In this introduction, we discuss the importance of innovation in advancing deliberative democratic theory and practice. Prior examples reviewed include the Citizens’ Jury, the Deliberative Poll, the British Columbia Citizens’ Assembly, and particularly the Oregon Citizens’ Initiative Review. We then preview the five essays that make up this special section of the Journal of Public Deliberation, which collects writings by graduate students who participated in a summer seminar on deliberative democracy. These essays propose ways to advance electoral deliberation in the U.S. and Guam, in classrooms, newsrooms, meeting rooms, and online discussions. The innovations suggest new ways to think about lobbying, teaching, reporting, campaigning, and voting, and many combine different deliberative mechanisms to maximize the potential for public talk to create a more inclusive and influential deliberative politics.
Measurement of High-temperature Thermophysical Properties of Bulk and Coatings Using Modulated Photothermal Radiometry

Abstract This paper presents the development of instrumentation for the measurement of high-temperature thermal conductivity of bulk and coatings using a modulated photothermal radiometry (MPR) method, where a sample is heated by an intensity-modulated laser to probe into different layers of the sample. While MPR has been previously established, most of the prior studies only focus on the measurement at room temperature. The MPR has not been well studied for measurements of bulk and coating materials at high temperatures, which are increasingly important for a multitude of applications, such as materials used in the concentrating solar power (CSP) plants and the nuclear reactors. MPR is a non-contact technique that utilizes the intrinsic thermal emission from the specimens for thermometry, which is favorable for measurements at high temperatures in harsh environment. The authors designed and utilized a sample holder suitable for high temperature measurement up to 973 K with good temperature uniformity within the sample. The high-temperature MPR setup was validated by measuring bulk materials with known thermal conductivity. The setup and technique were then extended to the measurement of black solar-absorbing coatings of 10 to 50 μm thick on various substrates by modulating the frequency of the laser heating beam and the thermal penetration depth. The studies showed that thermal conductivities of typical solar-absorbing coatings are 0.4 ~ 0.8 W m−1 K−1, indicating a possibly large temperature drop within the coating under high solar irradiation flux, such as over 1000-sun for central solar towers in CSP plants.
A Machine Learning Pipeline Stage for Adaptive Frequency Adjustment

A machine learning (ML) design framework is proposed for adaptively adjusting clock frequency based on propagation delay of individual instructions. A random forest model is trained to classify propagation delays in real time, utilizing current operation type, current operands, and computation history as ML features. The trained model is implemented in Verilog as an additional pipeline stage within TigerMIPS processor. The modified system is experimentally tested at the gate level in 45 nm CMOS technology, exhibiting simultaneously a speedup of 70% and an energy reduction of 30% with coarse-grained ML classification as compared with the baseline TigerMIPS. A speedup of 89% is demonstrated with finer granularities with a simultaneous 15.5% reduction in energy consumption.
From Queer to Paternity: How Primary Gay Fathers Are Changing Fatherhood and Gay Identity

In this symposium paper, I reflect upon how public images of gay fathers who raise children from the start as a gay couple are likely to impact the identity both of gay men and of fatherhood. I consider both the perceptions of the larger society as well as the self-images of gay men and their perceptions of fatherhood. For ease of reference, I refer to such gay fathers who begin fathering while in a gay relationship as primary gay fathers. I contrast primary gay fathers with the first generation of gay fathers to come to American public consciousness - gay fathers of heterosexual marriages. I theorize that the circumstances surrounding the coming to public awareness of the latter class of gay fathers - typically divorce and custody litigation following the gay father's coming out to his wife - helped to reinforce and perpetuate the identity of gay men as deceitful and untrustworthy, particularly in intimate relations, unable to commit to or enjoy a stable relationship, selfish, self-absorbed, and hyper-sexual. These circumstances, I argue, also helped to strengthen traditional masculine images of fatherhood by setting the gay father apart from what is considered the ideal father. I further theorize that, in contrast, the increased visibility of primary gay fathers, particularly in the context of the struggle for recognition of and protection for their intact families, is likely to help to break down these negative stereotypes and self-images of gay men. Moreover, I argue that this increased visibility also has the potential to weaken traditional gender roles associated with the fatherhood identity and to undermine the perception that gay men are unsuited for fatherhood.
Detection of Tumor Multifocality in Resectable Intrahepatic Cholangiocarcinoma: Defining the Optimal Pre-operative Imaging Modality.

Multiple tumor foci (MTF) in intrahepatic cholangiocarcinoma (ICC), including satellitosis and true multifocality, is a known negative prognostic factor and can inform pre-operative decision-making. Lack of standardized pre-operative liver staging practices may contribute to undiagnosed MTF and poor outcomes. We sought to investigate the sensitivity of different cross-sectional imaging modalities for MTF at our institution. We identified n = 52 patients with ICC who underwent curative-intent resection from 2004 to 2017 in a multidisciplinary hepato-pancreato-biliary cancer program. Timing and modality of pre-operative imaging were recorded. Blinded review of imaging was performed and modalities were evaluated for false-negative rate (FNR) in detecting MTF, satellitosis, and true multifocality. Forty-one (79%) patients underwent CT and 20 (38%) underwent MRI prior to hepatectomy. MTF was pre-operatively identified in six (12%) patients. An additional seven patients had MTF discovered on final surgical pathology, despite a median interval from CT/MRI to surgery of 20 days. On blinded review the FNR of MRI compared to CT for multifocality was 0% vs. 38%, 50% vs 80% for satellitosis, and 22% vs 46% for MTF as a whole. CT is inadequate for pre-operative diagnosis of MTF in resectable ICC, even when performed within 30 days of hepatectomy. We recommend liver-protocol MRI as the standard pre-operative imaging modality in non-metastatic ICC.
Low-input ATAC&mRNA-Seq: a simple and robust method for simultaneous dual-omics profiling with low cell number

Abstract Deciphering epigenetic regulation of gene expression requires measuring the epigenome and transcriptome jointly. Single-cell multi-omics technologies have been developed for concurrent profiling of chromatin accessibility and gene expression. However, multi-omics profiling of low-input bulk samples remains challenging. Therefore, we developed low-input ATAC&mRNA-seq, a simple and robust method for studying the role of chromatin structure in gene regulation in a single experiment with thousands of cells, to maximize insights from limited input material by obtaining ATAC-seq and mRNA-seq data simultaneously from the same cells with data quality comparable to conventional mono-omics assays. Integrative data analysis revealed similar strong association between promoter accessibility and gene expression using the data of low-input ATAC&mRNA-seq as using single-assay data, underscoring the accuracy and reliability of our dual-omics assay to generate both data types simultaneously with just thousands of cells. We envision our method to be widely applied in many biological disciplines with limited materials.
REITS: Reflective Surface for Intelligent Transportation Systems

Autonomous vehicles are predicted to dominate the transportation industry in the foreseeable future. Safety is one of the major challenges to the early deployment of self-driving systems. To ensure safety, self-driving vehicles must sense and detect humans, other vehicles, and road infrastructure accurately, robustly, and timely. However, existing sensing techniques used by self-driving vehicles may not be absolutely reliable. In this paper, we design REITS, a system to improve the reliability of RF-based sensing modules for autonomous vehicles. We conduct theoretical analysis on possible failures of existing RF-based sensing systems. Based on the analysis, REITS adopts a multi-antenna design, which enables constructive blind beamforming to return an enhanced radar signal in the incident direction. REITS can also let the existing radar system sense identification information by switching between constructive beamforming state and destructive beamforming state. Preliminary results show that REITS improves the detection distance of a self-driving car radar by a factor of 3.63.
Use of Ultrasound for Navigating the Internal Carotid Artery in Revision Endoscopic Endonasal Skull Base Surgery.

While the use of image-guided navigation is an excellent adjunct to the use of anatomical landmarks, dynamic changes that may occur in the position of critical structures are not accounted for during and after tumor resection. Unlike navigation, Doppler ultrasonography provides real-time imaging of the anterior skull base and can be used to accurately identify the location of vital structures during skull base surgery. A 56-year-old female initially presented with new onset left eye visual deficits. She previously underwent sublabial transsphenoidal subtotal resection of the tumor, confirmed as clival chordoma. She subsequently presented to our institution. She was treated with an expanded endonasal resection of the remaining chordoma followed by CyberKnife radiosurgery. Two years later, surveillance imaging identified tumor recurrence within the right clivus posterior to the carotid artery. Intraoperatively, in the previously operated irradiated skull base, the normal bony architecture of the sella was absent, resulting in the inability to distinguish the anterior genu of the internal carotid artery (ICA) from the adjacent tumor. Using Doppler ultrasonography, the course of the ICA was imaged in real time, allowing for safe, gross total tumor resection. In the setting of prior operation, radiation, or extensive disease, the normal bony architecture of the sella may be disrupted, placing the cavernous ICA at risk. We report what we believe is the first use of intraoperative ultrasound during the endoscopic endonasal approach in the setting of a previously operated, radiated sella.
Bound States in the Continuum in Elasticity

Abstract It is shown that Bound States in the Continuum (BSC) exist in an elastic structure consisting of a periodic double array of thin (subwavelength) cylindrical scatterers embedded in a background material, where the distance between the arrays is taken to be large in comparison to both the wavelength and the period of the array. The background medium supports waves with all polarizations, one longitudinal and two transverse, each of which has a different phase velocity. The transverse mode polarized parallel to the cylinders is decoupled from the other two and the corresponding BSCs are similar to those found earlier in the electromagnetic theory. The longitudinal and transverse modes in the plane perpendicular to the array remain coupled, and the corresponding BSCs are shown to exist only for specific distances between the arrays and for specific values of the Bloch phase or ratio of the phase velocities. The latter condition stems from the fact that both the longitudinal and transverse modes acquire different round-trip phases as they propagate between the two arrays. An analytic form of such BSCs is obtained by solving the Lippmann–Schwinger equation in the dipole approximation with arbitrary mass densities and Lame coefficients.
Using a Personal Health Library-Enabled mHealth Recommender System for Self-Management of Diabetes Among Underserved Populations: Use Case for Knowledge Graphs and Linked Data.

Background: Traditionally, digital health data management has been based on electronic health record (EHR) systems and has been handled primarily by centralized health providers. New mechanisms are needed to give patients more control over their digital health data. Personal health libraries (PHLs) provide a single point of secure access to patients' digital health data and enable the integration of knowledge stored in their digital health profiles with other sources of global knowledge. PHLs can help empower caregivers and health care providers to make informed decisions about patients’ health by understanding medical events in the context of their lives. Objective: This paper reports the implementation of a mobile health digital intervention that incorporates both digital health data stored in patients’ PHLs and other sources of contextual knowledge to deliver tailored recommendations for improving self-care behaviors in diabetic adults. Methods: We conducted a thematic assessment of patient functional and nonfunctional requirements that are missing from current EHRs based on evidence from the literature. We used the results to identify the technologies needed to address those requirements. We describe the technological infrastructures used to construct, manage, and integrate the types of knowledge stored in the PHL. We leverage the Social Linked Data (Solid) platform to design a fully decentralized and privacy-aware platform that supports interoperability and care integration. We provided an initial prototype design of a PHL and drafted a use case scenario that involves four actors to demonstrate how the proposed prototype can be used to address user requirements, including the construction and management of the PHL and its utilization for developing a mobile app that queries the knowledge stored and integrated into the PHL in a private and fully decentralized manner to provide better recommendations. Results: To showcase the main features of the mobile health app and the PHL, we mapped those features onto a framework comprising the user requirements identified in a use case scenario that features a preventive intervention from the diabetes self-management domain. Ongoing development of the app requires a formative evaluation study and a clinical trial to assess the impact of the digital intervention on patient-users. We provide synopses of both study protocols. Conclusions: The proposed PHL helps patients and their caregivers take a central role in making decisions regarding their health and equips their health care providers with informatics tools that support the collection and interpretation of the collected knowledge. By exposing the PHL functionality as an open service, we foster the development of third-party applications or services and provide motivational technological support in several projects crossing different domains of interest.
End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning

Current autonomous driving systems are composed of a perception system and a decision system. Both of them are divided into multiple subsystems built up with lots of human heuristics. An end-to-end approach might clean up the system and avoid huge efforts of human engineering, as well as obtain better performance with increasing data and computation resources. Compared to the decision system, the perception system is more suitable to be designed in an end-to-end framework, since it does not require online driving exploration. In this paper, we propose a novel end-to-end approach for autonomous driving perception. A latent space is introduced to capture all relevant features useful for perception, which is learned through sequential latent representation learning. The learned end-to-end perception model is able to solve the detection, tracking, localization and mapping problems altogether with only minimum human engineering efforts and without storing any maps online. The proposed method is evaluated in a realistic urban driving simulator, with both camera image and lidar point cloud as sensor inputs. The codes and videos of this work are available at our github repo† and project website‡.
A topic analysis of traditional and social media news coverage of the early COVID-19 pandemic and implications for public health communication.

OBJECTIVE: To characterize and compare early coverage of coronavirus disease 2019 (COVID-19) in newspapers, television, and social media, and discuss implications for public health communication strategies that are relevant to an initial pandemic response. METHODS: Latent Dirichlet allocation (LDA), an unsupervised topic modeling technique, analysis of 3271 newspaper articles, 40 cable news shows transcripts, 96,000 Twitter posts, and 1000 Reddit posts during March 4-12, 2020, a period chronologically early in the timeframe of the COVID-19 pandemic. RESULTS: Coverage of COVID-19 clustered on topics such as epidemic, politics, and the economy, and these varied across media sources. Topics dominating news were not predominantly health-related, suggesting a limited presence of public health in news coverage in traditional and social media. Examples of misinformation were identified, particularly in social media. CONCLUSIONS: Public health entities should use communication specialists to create engaging informational content to be shared on social media sites. Public health officials should be attuned to their target audience to anticipate and prevent spread of common myths likely to exist within a population. This may help control misinformation in early stages of pandemics.
What do consumers learn from regulator ratings? Evidence from restaurant hygiene quality disclosures

Abstract Regulators use ratings to inform consumers about firms and products across a range of industries, but little is known about what consumers learn from such ratings. We propose and directly study two pathways through which consumers form beliefs about products and firms based on these ratings: inference about the absolute implications of a rating (e.g., does a good rating mean a firm is of high quality?) and inference about the relative implications of a rating (e.g., do few firms have a rating this good?). In the context of restaurant hygiene ratings, we find that consumers form incorrect beliefs along both pathways and that their mis-calibrated beliefs are strongly related to their willingness to pay for a restaurant meal. We also find that their misperceptions can be partially reduced with informational interventions that impact their willingness to pay as well.
Virtual Cell Clustering With Optimal Resource Allocation to Maximize Capacity

The deployment of small cells in cellular networks increases their overall capacity, however, the proximity of small cells to one another also causes significant interference. To reduce interference and increase capacity, this work proposes a new resource allocation optimization and network management framework for wireless networks using neighborhood-based optimization rather than fully centralized or fully decentralized methods. We first utilize hierarchical clustering with a minimax linkage criterion for forming the virtual cells. Once the virtual cells are formed we consider two cooperation models: the interference coordination model and the coordinated multi-point decoding model. In the first model, base stations in a virtual cell decode their signals independently but allocate the communication resources cooperatively. In the second model, base stations in the same virtual cell allocate the communication resources and decode their signals cooperatively. We address the resource allocation problem for each of these cooperation models. Our numerical results indicate that the proper design of the neighborhood-based optimization leads to significant gains in sum rate over fully decentralized optimization. Nonetheless, they may have a significant sum rate penalty compared to fully centralized optimization. In particular, neighborhood-based optimization has a significant sum rate penalty compared to fully centralized optimization in the coordinated multi-point model, but not in the interference coordination model.
Minimizing migrations in fair multiprocessor scheduling of persistent tasks

Suppose that we are given n persistent tasks (jobs) that need to be executed in an equitable way on m processors (machines). Each machine is capable of performing one unit of work in each integral time unit and each job may be executed on at most one machine at a time. The schedule needs to specify which job is to be executed on each machine in each time window. The goal is to find a schedule that minimizes job migrations between machines while guaranteeing a fair schedule. We measure the fairness by the drift d defined as the maximum difference between the execution times accumulated by any two jobs. As jobs are persistent we measure the quality of the schedule by the ratio of the number of migrations to time windows. We show a tradeoff between the drift and the number of migrations. Let n = qm + r with 0 0; namely, it asymptotically requires r(m ? r) job migrations every n(q(d ? 1) + 1) time windows. We show how to implement the schedule efficiently. We prove that our algorithm is almost optimal by proving a lower bound of r(m ? r)/(nqd) on the migration ratio. We also give a more complicated schedule that matches the lower bound for a special case when 2q ? d and m = 2r. Our algorithms can be extended to the dynamic case in which jobs enter and leave the system over time.
Dense Point Sets Have Sparse Delaunay Triangulations or ... But Not Too Nasty

The spread of a finite set of points is the ratio between the longest and shortest pairwise distances. We prove that the Delaunay triangulation of any set of $n$ points in~$\Real^3$ with spread $\Delta$ has complexity $O(\Delta^3)$. This bound is tight in the worst case for all $\Delta = O(\sqrt{n})$. In particular, the Delaunay triangulation of any dense point set has linear complexity. We also generalize this upper bound to regular triangulations of $k$-ply systems of balls, unions of several dense point sets, and uniform samples of smooth surfaces. On the other hand, for any $n$ and $\Delta = O(n)$, we construct a regular triangulation of complexity $\Omega(n\Delta)$ whose $n$ vertices have spread $\Delta$.
The Value of Supply Chain Disruption Duration Information

Companies that experience a disruption in their supply chain often face a difficult decision—either accept the information that they have regarding the duration of the disruption, or invest in collecting better information. This choice is not clear since better information may not be attainable, and if it is attainable, it may not improve operational decision‐making. In light of this dilemma, we collaborate with a multinational division of a Fortune 500 manufacturing firm to develop stochastic linear programming models that quantify the value of disruption duration information. Our models allow us to examine characteristics of the disrupted part that may be associated with the value of better information. We focus on characteristics that are knowable at the outset of the disruption, as those can help the firm decide whether to invest in collecting better information. Using our research partner's supply chain and production data, we find that the value of information can vary materially—from
Random Gap Processes and Asymptotically Complete Sequences

We study a process of generating random positive integer weight sequences $$\{ W_n \}$$ where the gaps between the weights $$\{ X_n = W_n - W_{n-1} \}$$ are i.i.d. positive integer-valued random variables. The main result of the paper is that if the gap distribution has a moment generating function with large enough radius of convergence, then the weight sequence is almost surely asymptotically m-complete for every $$m\ge 2$$ , i.e. every large enough multiple of the greatest common divisor (gcd) of gap values can be written as a sum of m distinct weights for any fixed $$m \ge 2$$ . Under the weaker assumption of finite $$\frac{1}{2}$$ -moment for the gap distribution, we also show the simpler result that, almost surely, the resulting weight sequence is asymptotically complete, i.e. all large enough multiples of the gcd of the possible gap values can be written as a sum of distinct weights.
Asymptotic dimension and coarse embeddings in the quantum setting

We generalize the notions of asymptotic dimension and coarse embeddings from metric spaces to quantum metric spaces in the sense of Kuperberg and Weaver [A von Neumann algebra approach to quantum m...
LOCATER: Cleaning WiFi Connectivity Datasets for Semantic Localization

This paper explores the data cleaning challenges that arise in using WiFi connectivity data to locate users to semantic indoor locations such as buildings, regions, rooms. WiFi connectivity data consists of sporadic connections between devices and nearby WiFi access points (APs), each of which may cover a relatively large area within a building. Our system, entitled semantic LOCATion cleanER (LOCATER), postulates semantic localization as a series of data cleaning tasks - first, it treats the problem of determining the AP to which a device is connected between any two of its connection events as a missing value detection and repair problem. It then associates the device with the semantic subregion (e.g., a conference room in the region) by postulating it as a location disambiguation problem. LOCATER uses a bootstrapping semi-supervised learning method for coarse localization and a probabilistic method to achieve finer localization. The paper shows that LOCATER can achieve significantly high accuracy at both the coarse and fine levels.
Ladder determinantal rings over normal domains

We explicitly describe the divisor class groups and semidualizing modules for ladder determinantal rings with coefficients in an arbitrary normal domain for arbitrary ladders, not necessarily conne...
A Review of Thromboembolic Events in Hospitalized COVID-19 Patients

A higher incidence of thromboembolic disorders in COVID-19 has been reported by many clinicians worldwide. Selected studies found in PubMed that reported thromboembolic events were included for meta-analysis using weighted fixed and random effects. Data from 19 articles on cohort studies in patients diagnosed with COVID-19 and thromboembolic events, including thrombosis and embolism were included in this review. The likelihood for developing thromboembolic disorders in hospitalized COVID-19 patients was 0.28 (95% CI 0.21–0.36). This study further validates the increased risk of VTE in COVID-19 patients when compared to healthy, non-hospitalized people, and hospitalized patients. These findings will be useful to researchers and medical practitioners caring for COVID-19 patients.
What Gideon Did

Many accounts of Gideon v. Wainwright’s legacy focus on what Gideon did not do — its doctrinal and practical limits. For constitutional theorists, Gideon imposed a preexisting national consensus upon a few “outlier” states, and therefore did not represent a dramatic doctrinal shift. For criminal procedure scholars, advocates, and journalists, Gideon has failed, in practice, to guarantee meaningful legal help for poor people charged with crimes.Drawing on original historical research, this Article instead chronicles what Gideon did — the doctrinal and institutional changes it inspired between 1963 and the early 1970s. Gideon shifted the legal profession’s policy consensus on indigent defense away from a charity model toward a public model. By 1973, this new consensus had transformed criminal practice nationwide through the establishment of hundreds of public defender offices and the expansion of lawyers’ presence in low-level criminal proceedings. This Article describes these changes primarily through the example of Massachusetts, while contextualizing that example with national comparisons.The broad outlines of these post-Gideon changes are familiar to legal scholars. But situating these changes in a longer historical context and tracing them in detail from the perspective of lawyers on the ground in the 1960s yields two insights that help to explain the seemingly permanent post-Gideon crisis in indigent defense. First, the post-Gideon transformation was indeed limited in its practical effects, but its limits derived not only from politics but also from history — and from the legal profession itself. Lawyers themselves, long before Gideon, framed indigent defense as low-status, low-pay, less-than-fully-professional legal work. That framing survived even as private charities became post-Gideon public defenders. Second, the post-Gideon transformation was also limited — or, perhaps, destined to be perceived as limited — by tensions inherent in the attempt to provide large-scale legal assistance through government bureaucracies. Characteristics now identified as symptoms of crisis — such as politically determined funding, ever-expanding caseloads, and triage advocacy — first appeared as innovations that lawyers perceived Gideon to require. As public defenders proliferated, so too did complaints that they were underfunded and overworked, and that they encouraged guilty pleas over trials.The origins of the indigent defense crisis lie not only in Gideon’s neglect but also, paradoxically, in Gideon’s transformative influence. This history lends some support to recent scholarly expressions of skepticism about Gideon, but it also provides some reasons for optimism: If the indigent defense crisis derives not only from intransigent political indifference but also from contingent choices made by lawyers, then lawyers may retain more power than they realize to mitigate the crisis.
On Polyhedral Realization with Isosceles Triangles

Answering a question posed by Joseph Malkevitch, we prove that there exists a polyhedral graph, with triangular faces, such that every realization of it as the graph of a convex polyhedron includes at least one face that is a scalene triangle. Our construction is based on Kleetopes, and shows that there exists an integer i such that all convex i-iterated Kleetopes have a scalene face. However, we also show that all Kleetopes of triangulated polyhedral graphs have non-convex non-self-crossing realizations in which all faces are isosceles. We answer another question of Malkevitch by observing that a spherical tiling of Dawson (Renaissance Banff, Bridges Conference, pp. 489–496, 2005) leads to a fourth infinite family of convex polyhedra in which all faces are congruent isosceles triangles, adding one to the three families previously known to Malkevitch. We prove that the graphs of convex polyhedra with congruent isosceles faces have bounded diameter and have dominating sets of bounded size.
The contact mappings of a flat (2,3,5)-distribution

Let Ω and Ω′ be open subsets of a flat (2,3,5)-distribution. We show that a C1-smooth contact mapping f: Ω → Ω′ is a $$C^\infty$$ -smooth contact mapping. Ultimately, this is a consequence of the rigidity of the associated stratified Lie group. (The Tanaka prolongation of the Lie algebra is of finite type.) The conclusion is reached through a careful study of some differential identities satisfied by components of the Pansu derivative of a C1-smooth contact mapping.
TenIPS: Inverse Propensity Sampling for Tensor Completion

Tensors are widely used to represent multiway arrays of data. The recovery of missing entries in a tensor has been extensively studied, generally under the assumption that entries are missing completely at random (MCAR). However, in most practical settings, observations are missing not at random (MNAR): the probability that a given entry is observed (also called the propensity) may depend on other entries in the tensor or even on the value of the missing entry. In this paper, we study the problem of completing a partially observed tensor with MNAR observations, without prior information about the propensities. To complete the tensor, we assume that both the original tensor and the tensor of propensities have low multilinear rank. The algorithm first estimates the propensities using a convex relaxation and then predicts missing values using a higher-order SVD approach, reweighting the observed tensor by the inverse propensities. We provide finite-sample error bounds on the resulting complete tensor. Numerical experiments demonstrate the effectiveness of our approach.
An atomic approach to the design and implementation of a research data warehouse.

Objective As a long-standing Clinical and Translational Science Awards (CTSA) Program hub, the University of Pittsburgh and the University of Pittsburgh Medical Center (UPMC) developed and implemented a modern research data warehouse (RDW) to efficiently provision electronic patient data for clinical and translational research. Materials and methods We designed and implemented an RDW named Neptune to serve the specific needs of our CTSA. Neptune uses an atomic design where data are stored at a high level of granularity as represented in source systems. Neptune contains robust patient identity management tailored for research; integrates patient data from multiple sources, including electronic health records (EHRs), health plans, and research studies; and includes knowledge for mapping to standard terminologies. Results Neptune contains data for more than 5 million patients longitudinally organized as Health Insurance Portability and Accountability Act (HIPAA) Limited Data with dates and includes structured EHR data, clinical documents, health insurance claims, and research data. Neptune is used as a source for patient data for hundreds of institutional review board-approved research projects by local investigators and for national projects. Discussion The design of Neptune was heavily influenced by the large size of UPMC, the varied data sources, and the rich partnership between the University and the healthcare system. It includes several unique aspects, including the physical warehouse straddling the University and UPMC networks and management under an HIPAA Business Associates Agreement. Conclusion We describe the design and implementation of an RDW at a large academic healthcare system that uses a distinctive atomic design where data are stored at a high level of granularity.
Packaging deals in the entertainment industry: a bargaining approach

Creation of a television or movie project requires the bundling of inputs – writers, actors, directors – into a coherent package that can be produced and distributed. Traditionally this bundling wa...
Epithelial Morphogenesis Driven by Cell-Matrix vs. Cell-Cell Adhesion

Many embryonic organs undergo epithelial morphogenesis to form tree-like hierarchical structures. However, it remains unclear what drives the budding and branching of stratified epithelia, such as in embryonic salivary gland and pancreas. Here, we performed live-organ imaging of mouse embryonic salivary glands at single-cell resolution to reveal that budding morphogenesis is driven by expansion and folding of a distinct epithelial surface cell sheet characterized by strong cell-matrix adhesions and weak cell-cell adhesions. Profiling of single-cell transcriptomes of this epithelium revealed spatial patterns of transcription underlying these cell adhesion differences. We then synthetically reconstituted budding morphogenesis by experimentally suppressing E-cadherin expression and inducing basement membrane formation in 3D spheroid cultures of engineered cells, which required β1 integrin-mediated cell-matrix adhesion for successful budding. Thus, stratified epithelial budding, the key first step of branching morphogenesis, is driven by an overall combination of strong cell-matrix adhesion and weak cell-cell adhesion by peripheral epithelial cells.
Spatial Capacity Planning

We study the relationship between capacity and performance for a service firm with spatial operations, in the sense that requests arrive with origin-destination pairs. An example of such a system i...
Adaptive Influence Maximization: If Influential Node Unwilling to Be the Seed

Influence maximization problem attempts to find a small subset of nodes that makes the expected influence spread maximized, which has been researched intensively before. They all assumed that each user in the seed set we select is activated successfully and then spread the influence. However, in the real scenario, not all users in the seed set are willing to be an influencer. Based on that, we consider each user associated with a probability with which we can activate her as a seed, and we can attempt to activate her many times. In this article, we study the adaptive influence maximization with multiple activations (Adaptive-IMMA) problem, where we select a node in each iteration, observe whether she accepts to be a seed, if yes, wait to observe the influence diffusion process; if no, we can attempt to activate her again with a higher cost or select another node as a seed. We model the multiple activations mathematically and define it on the domain of integer lattice. We propose a new concept, adaptive dr-submodularity, and show our Adaptive-IMMA is the problem that maximizing an adaptive monotone and dr-submodular function under the expected knapsack constraint. Adaptive dr-submodular maximization problem is never covered by any existing studies. Thus, we summarize its properties and study its approximability comprehensively, which is a non-trivial generalization of existing analysis about adaptive submodularity. Besides, to overcome the difficulty to estimate the expected influence spread, we combine our adaptive greedy policy with sampling techniques without losing the approximation ratio but reducing the time complexity. Finally, we conduct experiments on several real datasets to evaluate the effectiveness and efficiency of our proposed policies.
Sandslash: a two-level framework for efficient graph pattern mining

Graph pattern mining (GPM) is a key building block in diverse applications, including bioinformatics, chemical engineering, social network analysis, recommender systems and security. Existing GPM frameworks either provide high-level interfaces for productivity at the cost of expressiveness or provide low-level interfaces that can express a wide variety of GPM algorithms at the cost of increased programming complexity. Moreover, existing systems lack the flexibility to explore combinations of optimizations to achieve performance competitive with hand-optimized applications. We present Sandslash, an in-memory graph pattern mining framework that uses a novel programming interface to support productive, expressive, and efficient GPM on large graphs. Sandslash provides a high-level API that needs only a specification of the GPM problem from the user, and the system implements fast subgraph enumeration, provides efficient data structures, and applies high-level optimizations automatically. To achieve performance competitive with expert-optimized implementations, Sandslash also provides a low-level API that allows users to express algorithm-specific optimizations. This enables Sandslash to support both high-productivity and high-efficiency without losing expressiveness. We evaluate Sandslash on shared-memory machines using five GPM applications and a wide range of large real-world graphs. Experimental results demonstrate that applications written using Sandslash high-level or low-level API outperform those in state-of-the-art GPM systems AutoMine, Pangolin, and Peregrine on average by 13.8x, 7.9x, and 5.4x, respectively. We also show that these Sandslash applications outperform expert-optimized GPM implementations by 2.3x on average with less programming effort.
Cyber-physical-social convergence in smart living: Challenges and opportunities

We live in an era in which our physical and personal environments are becoming increasingly intertwined and smarter due to the embedding of pervasive sensing, wireless communications, computing, and actuation capabilities. Our daily lives in smart cities and connected communities depend upon a wide variety of smart service systems and cyber-physical infrastructures, such as smart energy, transportation, healthcare, logistics, and so on. Indeed the availability of wireless sensor networks (WSNs) and rich mobile devices (e.g., smartphones) are also empowering humans with finegrained information and opinion collection through participatory (or crowd) sensing about events of interest, resulting in actionable inferences and decisions. This synergy has led to cyber-physical-social convergence with human in the loop that exhibits complex interactions, interdependencies and adaptations between systems and users with a goal to improve quality of life and experience in what is called smart living. However, the main challenges are posed by the scale, heterogeneity, big data, and resource limitations (such as energy) in determining multi-modal context and situation awareness. This seminar talk will first highlight unique research issues and challenges in smart living and cyber-physical-social systems. Next it will discuss novel solutions for energy-efficient data gathering and fusion, lifetime optimization and security in WSNs, and tradeoff between energy and information quality in multimodal context recognition. The foundations of our research are based on online and randomized algorithms, graph theory, game theory, trust model, and information theory. Case studies and experimental results will be presented for energy efficient homes and smart healthcare applications. The talk will be concluded with directions for future research.
Robust Inference for High-Dimensional Linear Models via Residual Randomization

We propose a residual randomization procedure designed for robust Lasso-based inference in the high-dimensional setting. Compared to earlier work that focuses on sub-Gaussian errors, the proposed procedure is designed to work robustly in settings that also include heavy-tailed covariates and errors. Moreover, our procedure can be valid under clustered errors, which is important in practice, but has been largely overlooked by earlier work. Through extensive simulations, we illustrate our method's wider range of applicability as suggested by theory. In particular, we show that our method outperforms state-of-art methods in challenging, yet more realistic, settings where the distribution of covariates is heavy-tailed or the sample size is small, while it remains competitive in standard, ``well behaved" settings previously studied in the literature.