cssi/SciSciGPT-SciSciNet huggingface dataset:
System inference via field inversion for the spatio-temporal progression of infectious diseases: Studies of COVID-19 in Michigan and Mexico

We present an approach to studying and predicting the spatio-temporal progression of infectious diseases. We treat the problem by adopting a partial differential equation (PDE) version of the Susceptible, Infected, Recovered, Deceased (SIRD) compartmental model of epidemiology, which is achieved by replacing compartmental populations by their densities. Building on our recent work (Computational Mechanics, 66, 1177, 2020), we replace our earlier use of global polynomial basis functions with those having local support, as epitomized in the finite element method, for the spatial representation of the SIRD parameters. The time dependence is treated by inferring constant parameters over time intervals that coincide with the time step in semi-discrete numerical implementations. In combination, this amounts to a scheme of field inversion of the SIRD parameters over each time step. Applied to data over ten months of 2020 for the pandemic in the US state of Michigan and to all of Mexico, our system inference via field inversion infers spatio-temporally varying PDE SIRD parameters that replicate the progression of the pandemic with high accuracy. It also produces accurate predictions, when compared against data, for a three week period into 2021. Of note is the insight that is suggested on the spatio-temporal variation of infection, recovery and death rates, as well as patterns of the populations mobility revealed by diffusivities of the compartments.
Strongly obtuse rational lattice triangles

We classify rational triangles which unfold to Veech surfaces when the largest angle is at least $\frac{3\pi}{4}$. When the largest angle is greater than $\frac{2\pi}{3}$, we show that the unfolding is not Veech except possibly if it belongs to one of six infinite families. Our methods include a criterion of Mirzakhani and Wright that built on work of Moller and McMullen, and in most cases show that the orbit closure of the unfolding cannot have rank 1.
Electric field effect on the thermal conductivity of wurtzite GaN

Gallium nitride (GaN), a wide bandgap semiconductor, has been broadly used in power electronic devices due to its high electron mobility and high breakdown voltage. Its relatively high thermal conductivity makes GaN a favorable material for such applications, where heat dissipation is a major concern for device efficiency and long-term stability. However, in GaN-based transistors, where the active region can withstand extremely strong electric fields, the field effect on the thermal transport properties has drawn little attention so far. In this work, we apply first-principles methods to investigate phonon properties of wurtzite GaN in the presence of a near-breakdown electric field applied along different crystallographic directions. We find that the electric field changes thermal conductivity considerably via impacting the bond stiffness, ionicity, anharmonicity, and the crystal symmetry, although it has little effect on phonon dispersions. The presence of an out-of-plane electric field increases (decreases) the thermal conductivity parallel (perpendicular) to the electric field, which is attributed to different changes in the Ga–N bond stiffness and ionicity and the scattering rates of phonons traveling along different directions. When an in-plane electric field is applied, the sizable decrease in thermal conductivities along all directions is attributed to the crystal symmetry breaking that enhances the phonon–phonon scattering. Our study provides insight into the effect of extreme external electric fields on phonon transport properties in wide-gap semiconductors.
PALMNUT: An Enhanced Proximal Alternating Linearized Minimization Algorithm With Application to Separate Regularization of Magnitude and Phase

We introduce a new algorithm for complex image reconstruction with separate regularization of the image magnitude and phase. This optimization problem is interesting in many different image reconstruction contexts, although is nonconvex and can be difficult to solve. In this work, we first describe a novel implementation of the previous proximal alternating linearized minimization (PALM) algorithm to solve this optimization problem. We then make enhancements to PALM, leading to a new algorithm named PALMNUT that combines the PALM together with Nesterov's momentum and a novel approach that relies on uncoupled coordinatewise step sizes derived from coordinatewise Lipschitz-like bounds. Theoretically, we establish that a version of PALMNUT (without Nesterov's momentum) monotonically decreases the objective function, guaranteeing convergence of the cost function value. Empirical results obtained in the context of magnetic resonance imaging demonstrate that PALMNUT has computational advantages over common existing approaches like alternating minimization. Although our focus is on the application to separate magnitude and phase regularization, we expect that the same approach may also be useful in other nonconvex optimization problems with similar objective function structure.
Can Program Synthesis be Used to Learn Merge Conflict Resolutions?: An Empirical Analysis

Forking structure is widespread in the open-source repositories and that causes a significant number of merge conflicts. In this paper, we study the problem of textual merge conflicts from the perspective of Microsoft Edge, a large, highly collaborative fork off the main Chromium branch with significant merge conflicts. Broadly, this study is divided into two sections. First, we empirically evaluate textual merge conflicts in Microsoft Edge and classify them based on the type of files, location of conflicts in a file, and the size of conflicts. We found that ~28% of the merge conflicts are 1-2 line changes, and many resolutions have frequent patterns. Second, driven by these findings, we explore Program Synthesis (for the first time) to learn patterns and resolve structural merge conflicts. We propose a novel domain-specific language (DSL) that captures many of the repetitive merge conflict resolution patterns and learn resolution strategies as programs in this DSL from example resolutions. We found that the learned strategies can resolve 11.4% of the conflicts (~41% of 1-2 line changes) that arise in the C++ files with 93.2% accuracy.
Measuring the Impact of Crowdsourcing Features on Mobile App User Engagement and Retention: A Randomized Field Experiment

The most commonly cited issues with mobile apps are low user engagement and retention levels. In this paper, we use a randomized control trial to test the efficacy of crowdsourcing on enhancing use...
The Multiplicative Ideal Theory of Leavitt Path Algebras of Directed Graphs—A Survey

Let L be the Leavitt path algebra of an arbitrary directed graph E over a field K. This survey article describes how this highly non-commutative ring L shares a number of the characterizing properties of a Dedekind domain or a Prufer domain expressed in terms of their ideal lattices. Special types of ideals such as the prime, the primary, the irreducible, and the radical ideals of L are described in terms of the graphical properties of E. The existence and the uniqueness of the factorization of a non-zero ideal of L as an irredundant product of prime or primary or irreducible ideals are established. Such factorization always exists for every ideal in L if the graph E is finite or if L is two-sided Artinian or two-sided Noetherian. In all these factorizations, the graded ideals of L seem to play an important role. Necessary and sufficient conditions are given under which L is a generalized ZPI ring, that is, when every ideal of L is a product of prime ideals. Intersections of various special types of ideals are investigated and an analogue of Krull’s theorem on the intersection of powers of an ideal in L is established.
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps

We consider the learning and prediction of nonlinear time series generated by a latent symplectic map. A special case is (not necessarily separable) Hamiltonian systems, whose solution flows give such symplectic maps. For this special case, both generic approaches based on learning the vector field of the latent ODE and specialized approaches based on learning the Hamiltonian that generates the vector field exist. Our method, however, is different as it does not rely on the vector field nor assume its existence; instead, it directly learns the symplectic evolution map in discrete time. Moreover, we do so by representing the symplectic map via a generating function, which we approximate by a neural network (hence the name GFNN). This way, our approximation of the evolution map is always \emph{exactly} symplectic. This additional geometric structure allows the local prediction error at each step to accumulate in a controlled fashion, and we will prove, under reasonable assumptions, that the global prediction error grows at most \emph{linearly} with long prediction time, which significantly improves an otherwise exponential growth. In addition, as a map-based and thus purely data-driven method, GFNN avoids two additional sources of inaccuracies common in vector-field based approaches, namely the error in approximating the vector field by finite difference of the data, and the error in numerical integration of the vector field for making predictions. Numerical experiments further demonstrate our claims.
Search for the Identification of 3EG J1835+5918: Evidence for a New Type of High-Energy Gamma-Ray Source

The EGRET source 3EG J1835+5918 is the brightest and most accurately positioned of the as-yet unidentified high-energy γ-ray sources at high Galactic latitude (l,b = 89°,25°). We present a multiwavelength study of the region around it, including X-ray, radio, and optical imaging surveys, as well as optical spectroscopic classification of most of the active objects in this area. Identifications are made of all but one of the ROSAT and ASCA sources in this region to a flux limit of approximately 5 × 10-14 ergs cm-2 s-1, which is 10-4 of the γ-ray flux. The identified X-ray sources in or near the EGRET error ellipse are radio-quiet QSOs, a galaxy cluster, and coronal emitting stars. We also find nine quasars using purely optical color selection, and we have monitored the entire field for variable optical objects on short and long timescales without any notable discoveries. The radio sources inside the error ellipse are all fainter than 4 mJy at 1.4 GHz. There are no flat-spectrum radio sources in the vicinity; the brightest neighboring radio sources are steep-spectrum radio galaxies or quasars. Since no blazar-like or pulsar-like candidate has been found as a result of these searches, 3EG J1835+5918 must be lacking one or more of the physically essential attributes of these known classes of γ-ray emitters. If it is an AGN it lacks the beamed emission radio of blazars by at least a factor of 100 relative to identified EGRET blazars. If it is an isolated neutron star, it lacks the steady thermal X-rays from a cooling surface and the magnetospheric nonthermal X-ray emission that is characteristic of all EGRET pulsars. If a pulsar, 3EG J1835+5918 must be either older or more distant than Geminga, and probably an even more efficient or beamed γ-ray engine. One intermittent ROSAT source falls on a blank optical field to a limit of B > 23.4, V > 25.2, and R > 24.5. In view of this conspicuous absence, RX J1836.2+5925 should be examined further as a candidate for identification with 3EG J1835+5918 and possibly the prototype of a new class of high-energy γ-ray source.
A Context-Dependent Gated Module for Incorporating Symbolic Semantics into Event Coreference Resolution

Event coreference resolution is an important research problem with many applications. Despite the recent remarkable success of pre-trained language models, we argue that it is still highly beneficial to utilize symbolic features for the task. However, as the input for coreference resolution typically comes from upstream components in the information extraction pipeline, the automatically extracted symbolic features can be noisy and contain errors. Also, depending on the specific context, some features can be more informative than others. Motivated by these observations, we propose a novel context-dependent gated module to adaptively control the information flows from the input symbolic features. Combined with a simple noisy training method, our best models achieve state-of-the-art results on two datasets: ACE 2005 and KBP 2016.
A Thermodynamic Perspective of Negative-Capacitance Field-Effect Transistors

Physical phenomena, underlying operation of negative capacitance field-effect transistors (NCFETs), are treated within a unified simulation framework. The framework incorporates the Landau mean-field treatment of free energy of a ferroelectric (FE) and the polarization dynamics according to Landau–Khalatnikov (LK) equation. These equations are self-consistently solved with the 1-D metal–oxide–semiconductor structure electrostatics and the drift-diffusion solution for currents in the semiconductor channel. Numerical simulations show that, depending on the strength of depolarization field, both regimes of hysteresis switching and of higher on-currents and steeper subthreshold slope with a negligible hysteresis can be delivered by NCFETs under the steady-state condition due to either multiple local energy minima or a single global energy minimum in the free energy landscape. The transient behavior of NCFETs is also studied by the viscosity coefficient in LK equation, and it is found that when the FE response becomes too slow, a significant hysteresis effect on currents shows up even for a free energy landscape with a single global energy minimum. Finally, recent experimental measurements on transient NC in an FE capacitor are discussed. It is shown that those results on transient NC can be due to the mismatch between free charge and polarization driven by the negative curvature of FE thermodynamic energy profile during polarization switching and cannot lead to a transient charge-boost in a hysteresis-free NCFETs.
Bounded Risk-Sensitive Markov Games: Forward Policy Design and Inverse Reward Learning with Iterative Reasoning and Cumulative Prospect Theory.

Classical game-theoretic approaches for multi-agent systems in both the forward policy design problem and the inverse reward learning problem often make strong rationality assumptions: agents perfectly maximize expected utilities under uncertainties. Such assumptions, however, substantially mismatch with observed human behaviors such as satisficing with sub-optimal, risk-seeking, and loss-aversion decisions. Drawing on iterative reasoning models and cumulative prospect theory, we propose a new game-theoretic framework, bounded risk-sensitive Markov Game (BRSMG), that captures two aspects of realistic human behaviors: bounded intelligence and risk-sensitivity. General solutions to both the forward policy design problem and the inverse reward learning problem are provided with theoretical analysis and simulation verification. We validate the proposed forward policy design algorithm and the inverse reward learning algorithm in a two-player navigation scenario. The results show that agents demonstrate bounded-intelligence, risk-averse and risk-seeking behaviors in our framework. Moreover, in the inverse reward learning task, the proposed bounded risk-sensitive inverse learning algorithm outperforms a baseline risk-neutral inverse learning algorithm by effectively learning not only more accurate reward values but also the intelligence levels and the risk-measure parameters of agents from demonstrations.
{MAGE}: Nearly Zero-Cost Virtual Memory for Secure Computation

Secure Computation (SC) is a family of cryptographic primitives for computing on encrypted data in single-party and multi-party settings. SC is being increasingly adopted by industry for a variety of applications. A significant obstacle to using SC for practical applications is the memory overhead of the underlying cryptography. We develop MAGE, an execution engine for SC that efficiently runs SC computations that do not fit in memory. We observe that, due to their intended security guarantees, SC schemes are inherently oblivious -- their memory access patterns are independent of the input data. Using this property, MAGE calculates the memory access pattern ahead of time and uses it to produce a memory management plan. This formulation of memory management, which we call memory programming, is a generalization of paging that allows MAGE to provide a highly efficient virtual memory abstraction for SC. MAGE outperforms the OS virtual memory system by up to an order of magnitude, and in many cases, runs SC computations that do not fit in memory at nearly the same speed as if the underlying machines had unbounded physical memory to fit the entire computation.
Exploring spin symmetry-breaking effects for static field ionization of atoms: Is there an analog to the Coulson-Fischer point in bond dissociation?

Lowdin’s symmetry dilemma is an ubiquitous issue in approximate quantum chemistry. In the context of Hartree–Fock (HF) theory, the use of Slater determinants with some imposed constraints to preserve symmetries of the exact problem may lead to physically unreasonable potential energy surfaces. On the other hand, lifting these constraints leads to the so-called broken symmetry solutions that usually provide better energetics, at the cost of losing information about good quantum numbers that describe the state of the system. This behavior has previously been extensively studied in the context of bond dissociation. This paper studies the behavior of different classes of HF spin polarized solutions (restricted, unrestricted, and generalized) in the context of ionization by strong static electric fields. We find that, for simple two electron systems, unrestricted Hartree–Fock (UHF) is able to provide a qualitatively good description of states involved during the ionization process (neutral, singly ionized, and doubly ionized states), whereas RHF fails to describe the singly ionized state. For more complex systems, even though UHF is able to capture some of the expected characteristics of the ionized states, it is constrained to a single Ms (diabatic) manifold in the energy surface as a function of field intensity. In this case, a better qualitative picture can be painted by using generalized Hartree–Fock as it is able to explore different spin manifolds and follow the lowest solution due to lack of collinearity constraints on the spin quantization axis.
Experiments with Tractable Feedback in Robotic Planning Under Uncertainty: Insights over a Wide Range of Noise Regimes

We consider the problem of robotic planning under uncertainty. This problem may be posed as a stochastic optimal control problem, complete solution to which is fundamentally intractable owing to the infamous curse of dimensionality. We report the results of an extensive simulation study in which we have compared two methods, both of which aim to salvage tractability by using alternative, albeit inexact, means for treating feedback. The first is a recently proposed method based on a near-optimal “decoupling principle” for tractable feedback design, wherein a nominal open-loop problem is solved, followed by a linear feedback design around the open-loop. The second is Model Predictive Control (MPC), a widely-employed method that uses repeated re-computation of the nominal open-loop problem during execution to correct for noise, though when interpreted as feedback, this can only said to be an implicit form. We examine a much wider range of noise levels than have been previously reported and empirical evidence suggests that the decoupling method allows for tractable planning over a wide range of uncertainty conditions without unduly sacrificing performance.
Nailed It: Autonomous Roofing with a Nailgun-Equipped Octocopter

This paper presents the first demonstration of autonomous roofing with a multicopter. A DJI S1000 octocopter equipped with an off-the-shelf nailgun and an adjustableslope roof mock-up were used. The nailgun was modified to allow triggering from the vehicle and tooltip compression feedback. A mount was designed to adjust the angle to match representative roof slopes. An open-source octocopter autopilot facilitated controller adaptation for the roofing application. A state machine managed autonomous nailing sequences using smooth trajectories designed to apply prescribed contact forces for reliable nail deployment. Experimental results showed that the system is capable of nailing within a required three centimeter gap on the shingle. Extensions to achieve a complete autonomous roofing system are discussed as future work.
PLS for V2I Communications Using Friendly Jammer and Double kappa-mu Shadowed Fading

The concept of intelligent transportation systems (ITS) is considered to be a highly promising area of research due to its diversity of unique features. It is based mainly on the wireless vehicular network (WVN), where vehicles can perform sophisticated services such as sharing real-time safety information. To ensure high-quality service, WVN needs to solve the security challenges like eavesdropping, where malicious entities try to intercept the confidential transmitted signal. In this paper, we are going to provide a security scheme under the Double kappa-mu Shadowed fading. Our solution is based on the use of a friendly jammer that will transmit an artificial noise (AN) to jam the attacker’s link and decrease its eavesdropping performances. To evaluate the efficiency of our solution, we investigated the outage probability for two special cases: Nakagami-m and Rician shadowed while taking into consideration the density of the blockage and the shadowing effects. We also studied the average secrecy capacity via deriving closed-form expressions of the ergodic capacity at the legitimate receiver and the attacker for the special case: Nakagami-m fading distribution.
Targeting credit through community members.

Decentralizing the allocation of resources to community members is an increasingly popular form of delivering public resources in developing countries. However, this approach is associated with the tradeoff between improved information about potential beneficiaries and favoritism towards local elites, which could be strengthened in the context of credit. Unlike targeting cash transfers at poor households, allocating publicly-provided credit is a more complex problem involving issues of risk, neediness, productivity, and market responses: This paper analyzes this problem using a large-scale lending program, the Thai Million Baht Credit Fund, which decentralizes the allocation of loans to an elected group of community members, and provides three main results. First, exploiting a long and detailed panel, I recover pre-program structural estimates of household productivity and find that neither repayment, nor poverty, nor productivity explains program participation. Second, using socioeconomic networks data, I show that actual targeting is strongly driven by connections to village elites and is related to lower program profitability, which suggests favoritism as a reason for mistargeting. Finally, I exploit quasi-experimental variation in the rollout of the program and uncover evidence that, in general equilibrium, informal credit markets partially compensate for targeting distortions by redirecting credit towards unconnected households, albeit at higher interest rates than those provided by the program. The results highlight the limitations of community-driven approaches to program delivery and the role of markets in attenuating potential targeting errors.
Pulse sharpening in a uniform LC ladder network containing nonlinear ferroelectric capacitors

A new method of pulse sharpening is described in which the risetime of a high-voltage pulse is progressively reduced as it propagates along a uniform lumped-element delay line containing nonlinear ferroelectric capacitors. The capacitors are made from a barium-titanate-based ceramic dielectric whose relative permittivity reduces with applied voltage stress. The resultant drop in capacitance causes the phase velocity of an electrical pulse propagating on the delay line to increase with increasing pulse amplitude. Therefore, the time delay of each section of the line is also amplitude dependent, causing the crest of the pulse to catch up with the low-amplitude portion of its leading edge. In the experiment described, the risetime of the leading edge of a 28-kV voltage pulse was reduced from 280 to 50 ns as it propagated along a 15-section ladder network. It is found that the impedance of the ladder is amplitude dependent, and the problem of matching the line with a nonreactive linear load resistance is discussed. Techniques are described for characterizing the nonlinearity of the capacitors and also for measuring their loss. >
Silica Raman scattering probe for absolute calibration of Thomson scattering spectrometers

We have developed a solid state probe for an absolute irradiance calibration of the Thomson scattering system on the Large Plasma Device (LAPD), based on Raman scattering off silica. Measurements performed with a triple-grating spectrometer have investigated the intensities of a pulsed laser beam Raman scattered off crystalline and amorphous silica over a range of temperatures of relevance to the LAPD (299 - 498 K). The data were compared with Rayleigh and Raman scattering intensities in gaseous nitrogen. The measurements show that Raman scattering off quartz allows rapid and accurate alignment and calibration of Thomson scattering systems in plasma physics experiments that cannot be calibrated using conventional methods.
Disparate Treatment as a Theory of Discrimination: The Need for a Restatement Not a Revolution

Disparate treatment, a seemingly straightforward basis for identifying discrimination and establishing liability under federal antidiscrimination laws, has become mired in controversy and debate. The debate, triggered by the Supreme Court's 2003 decision in Desert Palace v. Costa, has seen several prominent scholars argue that the case has revolutionary implications, and warrants an overhaul of thirty years of disparate treatment jurisprudence and the methodologies courts employ in assessing proof of discrimination. This article takes a contrary position, rejecting the rush to discard these methods of proof, while seeking to recast them on a sounder conceptual basis. The article accomplishes this by proposing a template of proof patterns that provide a more rational and reliable way of identifying and distinguishing the traditional proof schemes in disparate treatment cases and by offering a restatement of principles to serve as a guide for assessing disparate treatment cases in the post-Desert Palace era. The outcome of this disparate treatment debate will have significant implications for the enforcement of federal anti-discrimination laws. Methods of proof directly affect the outcome of cases. Critics who call for an overhaul of today's methods see in their proposals a single open-ended standard that will work for the benefit of plaintiffs and enhance their chances of surviving legal challenges to their proof. This article contends that this assumption is flawed, that discrimination plaintiffs are generally hurt not helped by broad schemes that afford judges wide discretion under general standards. The article's proposals should benefit both plaintiffs and defendants by providing a rational and more reliable framework and set of principles for assessing proof of disparate treatment claims.
Multitasking Deep Learning Model for Detection of Five Stages of Diabetic Retinopathy

Early diagnosis and treatment of diabetic retinopathy (DR) can reduce the risk of vision loss. There are five stages of DR consisting of no DR, mild DR, moderate DR, severe DR, and proliferate DR. This paper presents a multitask deep learning model to detect all the five stages of DR more accurately than existing methods. The developed multitask model consists of one classification model and one regression model, each with its own loss function. After training the regression model and the classification model separately, the features extracted by these two models are concatenated and inputted to a multilayer perceptron network to classify the five stages of DR. A modified Squeeze Excitation Densely Connected deep neural network is also developed as part of this multitasking approach. The developed multitask model is applied to the two large Kaggle datasets of APTOS and EyePACS. The results obtained indicate that the developed multitask model achieved a weighted Kappa score of 0.90 and 0.88 for the APTOS and EyePACS datasets, respectively. In addition, the micro and macro average area under the receiver operating characteristic (ROC) curve was found to be 0.96, and 0.93, respectively, which are higher than existing methods for detecting the five stages of DR.
Representing Law Faculty Scholarly Impact: Strategies for Improving Citation Metrics Accuracy and Promoting Scholarly Visibility

This article examines the new U.S. News scholarly impact ranking and its use of HeinOnline as a data source. It compares law journal and interdisciplinary scholarship citation metrics and explores ...
Splitting Brauer classes using the universal Albanese

We prove that every Brauer class over a field splits over a torsor under an abelian variety. If the index of the class is not congruent to 2 modulo 4, we show that the Albanese variety of any smooth curve of positive genus that splits the class also splits the class, and there exist many such curves splitting the class. We show that this can be false when the index is congruent to 2 modulo 4, but adding a single genus 1 factor to the Albanese suffices to split the class.
Domain Analysis and Motif Matcher (DAMM): A Program to Predict Selectivity Determinants in Monosiga brevicollis PDZ Domains Using Human PDZ Data.

Choanoflagellates are single-celled eukaryotes with complex signaling pathways. They are considered the closest non-metazoan ancestors to mammals and other metazoans and form multicellular-like states called rosettes. The choanoflagellate Monosiga brevicollis contains over 150 PDZ domains, an important peptide-binding domain in all three domains of life (Archaea, Bacteria, and Eukarya). Therefore, an understanding of PDZ domain signaling pathways in choanoflagellates may provide insight into the origins of multicellularity. PDZ domains recognize the C-terminus of target proteins and regulate signaling and trafficking pathways, as well as cellular adhesion. Here, we developed a computational software suite, Domain Analysis and Motif Matcher (DAMM), that analyzes peptide-binding cleft sequence identity as compared with human PDZ domains and that can be used in combination with literature searches of known human PDZ-interacting sequences to predict target specificity in choanoflagellate PDZ domains. We used this program, protein biochemistry, fluorescence polarization, and structural analyses to characterize the specificity of A9UPE9_MONBE, a M. brevicollis PDZ domain-containing protein with no homology to any metazoan protein, finding that its PDZ domain is most similar to those of the DLG family. We then identified two endogenous sequences that bind A9UPE9 PDZ with <100 μM affinity, a value commonly considered the threshold for cellular PDZ–peptide interactions. Taken together, this approach can be used to predict cellular targets of previously uncharacterized PDZ domains in choanoflagellates and other organisms. Our data contribute to investigations into choanoflagellate signaling and how it informs metazoan evolution.
The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization

Among the most successful methods for sparsifying deep (neural) networks are those that adaptively mask the network weights throughout training. By examining this masking, or dropout, in the linear case, we uncover a duality between such adaptive methods and regularization through the so-called "$\eta$-trick" that casts both as iteratively reweighted optimizations. We show that any dropout strategy that adapts to the weights in a monotonic way corresponds to an effective subquadratic regularization penalty, and therefore leads to sparse solutions. We obtain the effective penalties for several popular sparsification strategies, which are remarkably similar to classical penalties commonly used in sparse optimization. Considering variational dropout as a case study, we demonstrate similar empirical behavior between the adaptive dropout method and classical methods on the task of deep network sparsification, validating our theory.
Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning

We show that bringing intermediate layers' representations of two augmented versions of an image closer together in self-supervised learning helps to improve the momentum contrastive (MoCo) method. To this end, in addition to the contrastive loss, we minimize the mean squared error between the intermediate layer representations or make their cross-correlation matrix closer to an identity matrix. Both loss objectives either outperform standard MoCo, or achieve similar performances on three diverse medical imaging datasets: NIH-Chest Xrays, Breast Cancer Histopathology, and Diabetic Retinopathy. The gains of the improved MoCo are especially large in a low-labeled data regime (e.g. 1% labeled data) with an average gain of 5% across three datasets. We analyze the models trained using our novel approach via feature similarity analysis and layer-wise probing. Our analysis reveals that models trained via our approach have higher feature reuse compared to a standard MoCo and learn informative features earlier in the network. Finally, by comparing the output probability distribution of models fine-tuned on small versus large labeled data, we conclude that our proposed method of pre-training leads to lower Kolmogorov-Smirnov distance, as compared to a standard MoCo. This provides additional evidence that our proposed method learns more informative features in the pre-training phase which could be leveraged in a low-labeled data regime.
Blurred boundaries: A flexible approach for segmentation applied to the car market

Prominent features of differentiated product markets are segmentation and product proliferation blurring the boundaries between segments. I develop a tractable demand model, the Ordered Nested Logit, which allows for asymmetric substitution between segments. I apply the model to the automobile market where segments are ordered from small to luxury. I find that consumers, when substituting outside their vehicle segment, are more likely to switch to a neighboring segment. Accounting for such asymmetric substitution matters when evaluating the impact of new product introduction or the effect of subsidies on fuel‐efficient cars. Discrete choice model Generalized Extreme Value Ordered Nested Logit D11 D12 L62 M3
Securing LSB embedding against structural steganalysis

This work explores the extent to which LSB embedding can be made secure against structural steganalysis through a modification of cover image statistics prior to message embedding. Natural images possess symmetries that are expressed through approximately equal cardinalities of certain sets of $k$-tuples of consecutive pixels. LSB embedding disturbs this balance and a $k^{\rm th}$-order structural attack infers the presence of a hidden message with a length in proportion to the size of the imbalance amongst sets of $k$-tuples. To protect against $k^{\rm th}$-order structural attacks, cover modifications involve the redistribution of $k$-tuples among the different sets so that symmetries of the cover image are broken, then repaired through the act of LSB embedding so that the stego image bears the statistics of the original cover. To protect against all orders up to some order $k$, the statistics of $n$-tuples must be preserved where $n$ is the least common multiple of all orders $\leq k$. We find that this is only feasible for securing against up to $3^{\rm rd}$-order attacks (Sample Pairs and Triples analyses) since higher-order protections result in virtually zero embedding capacities. Securing up to $3^{\rm rd}$-order requires redistribution of sextuplets: rather than perform these $6^{\rm th}$-order cover modifications, which result in tiny embedding capacities, we reduce the problem to the redistribution of triplets in a manner that also preserves the statistics of pairs. This is done by embedding into only certain pixels of each sextuplet, constraining the maximum embedding rate to be $\leq 2/3$ bits per channel. Testing on a variety of image formats, we report best performance for JPEG-compressed images with a mean maximum embedding rate undetectable by $2^{\rm nd}$- and $3^{\rm rd}$-order attacks of 0.21 bits per channel.
Accelerator-level parallelism

Charging computer scientists to develop the science needed to best achieve the performance and cost goals of accelerator-level parallelism hardware and software.
Fixed and Random Zeros

Not infrequently, one encounters a table in which a number of the cell counts are O’s. These cells sometimes cause problems when fitting log-linear models. Recall that, in our discussion of logistic regression in Section VII.3, we had a 2 × 200 table that was riddled with 0 counts. Except for the fact that some asymptotic results did not hold, the zeros caused no problems. Cells with zero counts merely have the potential to cause problems.
Acquired Immune Deficiency Syndrome and the Developing Nervous System

Publisher Summary The study of AIDS has contributed substantially to a growing awareness of the relationship between the immune and nervous systems. This chapter discusses a comparison of the impairments in the nervous and immune systems that arise from HIV infection. The chapter also emphasizes the examination of the evidence for the role of the major envelope glycoprotein of HIV—gp120—as a causative agent for the pathology of AIDS, which may occur as the result of an interference with neuroimmune effectors. The latter idea serves to highlight the thesis that the nervous and immune systems share many common chemical mediators. Although the identity and action of these substances are uncertain, interference in their action may contribute to, if not define, the development and course of AIDS. The chapter focuses on the identification of such substances as well as possible roles for peptides as protective agents against the deleterious effects of HIV in the nervous system.
A Markov chain Monte Carlo method for the groundwater inverse problem

In this study, we develop a Markov Chain Monte Carlo method (MCMC) to estimate the hydraulic conductivity field conditioned on the direct measurements of hydraulic conductivity and indirect measurements of dependent variables such as hydraulic head for saturated flow in randomly heterogeneous porous media. The log hydraulic conductivity field is represented (parameterized) by the combination of some basis kernels centered at fixed spatial locations. The prior distribution for the vector of coefficients {theta} are taken from a posterior distribution {pi}({theta}/d) that is proportional to the product of the likelihood function of measurements d given parameter vector {theta} and the prior distribution of {theta}. Starting from any initial setting, a partial realization of a Markov chain is generated by updating only one component of {theta} at a time according to Metropolis rules. This ensures that output from this chain has {pi}({theta}/d) as its stationary distribution. The posterior mean of the parameter {theta} (and thus the mean log hydraulic conductivity conditional to measurements on hydraulic conductivity, and hydraulic head) can be estimated from the Markov chain realizations (ignoring some early realizations). The uncertainty associated with the mean filed can also be assessed from these realizations. In addition, the MCMC approach provides anmore » alternative for estimating conditional predictions of hydraulic head and concentration and their associated uncertainties. Numerical examples for flow in a hypothetic random porous medium show that estimated log hydraulic conductivity field from the MCMC approach is closer to the original hypothetical random field than those obtained using kriging or cokriging methods.« less
Kuroshio Transport East of Taiwan and the Effect of Mesoscale Eddies

Abstract : To determine the time-varying Kuroshio transport and velocity structure east of Taiwan, in situ measurements were collected with moorings deployed for 2 years. This work was carried out as a collaboration between the Woods Hole Oceanographic Institution and National Taiwan University (NTU), whose companion projects were funded by the Taiwanese National Science Council (now the Ministry of Science and Technology, MOST). The long-term goal of this project is to improve understanding and predictability of the regional circulation in the western North Pacific. The objective of this project is to characterize variability in the Kuroshio east of Taiwan and to understand (1) how this variability is related to variability in the upstream region, where the North Equatorial Current bifurcates forming the northward-flowing Kuroshio and the southward-flowing Mindanao Current and (2) how westward-propagating mesoscale eddies that arrive east of Taiwan from the ocean interior affect Kuroshio variability. This will establish the advective versus the eddy-driven contributions to Kuroshio variability east of Taiwan.
Functional Diversity of Stem and Progenitor Cells with B-Lymphopoietic Potential

Technical advances have made it possible to separate hematopoietic tissues such as the bone marrow into ever smaller populations, complicating our understanding of immune system replenishment. Patterns of surface marker expression and transcription profiles as well as results obtained with reporter mice suggest that lymphopoietic cells are not closely synchronized, and there is considerable cell to cell variation. Loss of differentiation options is gradual, and ultimate fate can be established at different stages of lineage progression. For example, individual hematopoietic stem cells can be biased such that some are very poor sources of lymphocytes as contrasted to ones with balanced outputs. Still other hematopoietic stem cells are effective at generating B and T cells but are defective with respect to expansion and difficult to distinguish from early lymphoid progenitors. That diversity carries forward to later events, and similar appearing cells in the immune system can arise from alternate differentiation pathways. In fact, new categories of lymphoid progenitors are still being discovered. Heterogeneity provides adaptability as hematopoiesis can be dramatically altered during infections, influencing numbers and types of cells that are produced.
Effects of pre-performance massage before isokinetic exercise

Arroyo-Morales M, Fernandez-Lao C, Ariza-Garcia A, Toro-Velasco C, Winters M, Diaz-Rodriguez L, Cantarero-Villanueva I, Huijbregts P, Fernandez-De-Las-Penas C. Psychophysiological effects of preperformance massage before isokinetic exercise. J Strength Cond Res 2010; 25: 481–8.
Diminishing the Dread: Exploring service learning and student motivation

Dread is an unfortunate, yet all too common occurrence in the classroom. It often arises when a student has little motivation toward a class or its content. As an instructor of a traditionally “dreaded” class (Design History I), my goal was to seek ways to increase student motivation for the topic of design history and historic preservation. To do so, I designed a service learning project that incorporated Deci and Ryan’s Self-Determination Theory (1985, 2000), and its constructs of: autonomy, competence, and relatedness. During the project students worked with the local City Planner on designing a series of informational placemats to be used in downtown restaurants during Historic Preservation Month. Students were tasked with searching the town for buildings that merited inclusion, then photographing, and documenting their history. To do this, they worked in groups and applied for specific roles on the project (i.e. Editor-in-Chief, Copy Editor, Graphic Editor, or Information Scout). Following project completion, I collected surveys and student reflections to understand the project’s successes, failures, and any resulting changes in student motivation. This article describes my process of designing the project, its implementation, and my student’s perceptions following its completion. It is hoped that this design case may provide instructors in “dreaded” courses tools to increase long-term student motivation.
Two unilateral complete cleft lip and palate orthodontic cases treated from birth to adolescence

Abstract These two cases, one extraction and one nonextraction, demonstrate the present day status of a treatment protocol initiated, refined, and modified for the past 30 years. Using the combined and coordinated services of surgeon and orthodontist from the time of birth, individuals born with complete clefts of the lip and palate now have a better possibility of full esthetic and functional use of all of their maxillary permanent anterior dentition. (Am J Orthod Dentofacial Orthop 1999;115:61-71)
Scheduling Tree-Dags Using FIFO Queues

We study a combinatorial problem that is motivated by “client?server” schedulers for parallel computations. Such schedulers are often used, for instance, when computations are being done by a cooperating network of workstations. Our results expose and quantify a control?memory trade-off for such schedulers, when the computation being scheduled has the structure of a binary tree, with all arcs oriented either root-toward-leaves or leaves-toward-root. The combinatorial problem for the root-toward-leaves case takes the following form. (The leaves-toward-root case gives rise to a dual formulation, which yields the same trade-offs.) Consider, for integersk,N> 0, an algorithm that employskFIFO queues in order to schedule anN-leaf binary tree in such a way that each nonleaf node of the tree is executed before its children. We establish a trade-off between the number of queues used by the algorithm?which we view as measuring thecontrol complexityof the algorithm?and thememory requirementsof the algorithm, as embodied in the required capacity of the largest-capacity queue. Specifically, for each integerk? {1, 2, ..., log2N}, letQk(N) denote the minimax per-queue capacity for ak-queue algorithm that schedules allN-leaf binary trees; letQ*k(N) denote the analogous quantity forcompletebinary trees. We establish the following bounds: For generalN-leaf binary trees, for allk,formula]?Qk(N) ? 2N1/k+ 1.For complete binary trees, we derive tighter bounds. We prove that for all constantk,Q*k(N) = ?formula].For generalk, we obtain the following bounds:formula]?Q*k(N) ? (4k)1?1/kformula].Similar trade-offs are readily established for trees of any fixed branching factor.
Effects of island geometry in postdeposition island growth

The results of kinetic Monte Carlo simulations of a realistic model of postdeposition island growth that takes into account the spatial extent of islands are presented. Simulations were carried out on oneand twodimensional substrates for different values of the critical island size i and were compared with previous results for a point-island model. The use of a realistic island geometry results in enhanced island aggregation and coalescence. This leads to an increase in the average island size S as well as the exponent z describing the dependence of S on coverage. The shape of the island-size distribution for i53 also changes dramatically due to the existence of ‘‘magic’’ islands.