cssi/SciSciGPT-SciSciNet huggingface dataset:
Measuring dependence powerfully and equitably

Given a high-dimensional data set, we often wish to find the strongest relationships within it. A common strategy is to evaluate a measure of dependence on every variable pair and retain the highest-scoring pairs for follow-up. This strategy works well if the statistic used (a) has good power to detect non-trivial relationships, and (b) is equitable, meaning that for some measure of noise it assigns similar scores to equally noisy relationships regardless of relationship type (e.g., linear, exponential, periodic). In this paper, we define and theoretically characterize two new statistics that together yield an efficient approach for obtaining both power and equitability. To do this, we first introduce a new population measure of dependence and show three equivalent ways that it can be viewed, including as a canonical "smoothing" of mutual information. We then introduce an efficiently computable consistent estimator of our population measure of dependence, and we empirically establish its equitability on a large class of noisy functional relationships. This new statistic has better bias/variance properties and better runtime complexity than a previous heuristic approach. Next, we derive a second, related statistic whose computation is a trivial side-product of our algorithm and whose goal is powerful independence testing rather than equitability. We prove that this statistic yields a consistent independence test and show in simulations that the test has good power against independence. Taken together, our results suggest that these two statistics are a valuable pair of tools for exploratory data analysis.
On the hourglass model

Used in the design of the Internet and Unix, the layered services of the hourglass model have enabled viral adoption and deployment scalability.
A Challenge Set Approach to Evaluating Machine Translation

Neural machine translation represents an exciting leap forward in translation quality. But what longstanding weaknesses does it resolve, and which remain? We address these questions with a challenge set approach to translation evaluation and error analysis. A challenge set consists of a small set of sentences, each hand-designed to probe a system’s capacity to bridge a particular structural divergence between languages. To exemplify this approach, we present an English-French challenge set, and use it to analyze phrase-based and neural systems. The resulting analysis provides not only a more fine-grained picture of the strengths of neural systems, but also insight into which linguistic phenomena remain out of reach.
The complexity of simulating local measurements on quantum systems

An important task in quantum physics is the estimation of local quantities for ground states of local Hamiltonians. Recently, Ambainis defined the complexity class P^QMA[log], and motivated its study by showing that the physical task of estimating the expectation value of a local observable against the ground state of a local Hamiltonian is P^QMA[log]-complete. In this paper, we continue the study of P^QMA[log], obtaining the following results. The P^QMA[log]-completeness result of Ambainis requires O(log n)-local observ- ables and Hamiltonians. We show that simulating even a single qubit measurement on ground states of 5-local Hamiltonians is P^QMA[log]-complete, resolving an open question of Ambainis. We formalize the complexity theoretic study of estimating two-point correlation functions against ground states, and show that this task is similarly P^QMA[log]-complete. P^QMA[log] is thought of as "slightly harder" than QMA. We justify this formally by exploiting the hierarchical voting technique of Beigel, Hemachandra, and Wechsung to show P^QMA[log] \subseteq PP. This improves the containment QMA \subseteq PP from Kitaev and Watrous. A central theme of this work is the subtlety involved in the study of oracle classes in which the oracle solves a promise problem. In this vein, we identify a flaw in Ambainis' prior work regarding a P^UQMA[log]-hardness proof for estimating spectral gaps of local Hamiltonians. By introducing a "query validation" technique, we build on his prior work to obtain P^UQMA[log]-hardness for estimating spectral gaps under polynomial-time Turing reductions.
The Spatial Outage Capacity of Wireless Networks

We address a fundamental question in wireless networks that, surprisingly, has not been studied before: what is the maximum density of concurrently active links that satisfy a certain outage constraint? We call this quantity the spatial outage capacity (SOC), give a rigorous definition, and analyze it for Poisson bipolar networks with ALOHA. Specifically, we provide exact analytical and approximate expressions for the density of links satisfying an outage constraint and give simple upper and lower bounds on the SOC. In the high-reliability regime where the target outage probability is close to zero, we obtain an exact closed-form expression of the SOC, which reveals the interesting and perhaps counter-intuitive result that all transmitters need to be always active to achieve the SOC, i.e., the transmit probability needs to be set to 1 to achieve the SOC.
Inclusion Matrices and the MDS Conjecture

Let $\mathbb{F}_{q}$ be a finite field of order $q$ with characteristic $p$. An arc is an ordered family of at least $k$ vectors in $\mathbb{F}_{q}^{k}$ in which every subfamily of size $k$ is a basis of $\mathbb{F}_{q}^{k}$. The MDS conjecture, which was posed by Segre in 1955, states that if $k \leq q$, then an arc in $\mathbb{F}_{q}^{k}$ has size at most $q+1$, unless $q$ is even and $k=3$ or $k=q-1$, in which case it has size at most $q+2$. We propose a conjecture which would imply that the MDS conjecture is true for almost all values of $k$ when $q$ is odd. We prove our conjecture in two cases and thus give simpler proofs of the MDS conjecture when $k \leq p$, and if $q$ is not prime, for $k \leq 2p-2$. To accomplish this, given an arc $G \subset \mathbb{F}_{q}^{k}$ and a nonnegative integer $n$, we construct a matrix $M_{G}^{\uparrow n}$, which is related to an inclusion matrix, a well-studied object in combinatorics. Our main results relate algebraic properties of the matrix $M_{G}^{\uparrow n}$ to properties of the arc $G$ and may provide new tools in the computational classification of large arcs.
Module structure of the center of the universal central extension of a genus zero Krichever–Novikov algebra

Abstract We describe how the center of the universal central extension of the genus zero Krichever–Novikov current algebra decomposes as a direct sum of irreducible modules for automorphism group of the coordinate ring of this algebra.
Sequence training and adaptation of highway deep neural networks

Highway deep neural network (HDNN) is a type of depth-gated feedforward neural network, which has shown to be easier to train with more hidden layers and also generalise better compared to conventional plain deep neural networks (DNNs). Previously, we investigated a structured HDNN architecture for speech recognition, in which the two gate functions were tied across all the hidden layers, and we were able to train a much smaller model without sacrificing the recognition accuracy. In this paper, we carry on the study of this architecture with sequence-discriminative training criterion and speaker adaptation techniques on the AMI meeting speech recognition corpus. We show that these two techniques improve speech recognition accuracy on top of the model trained with the cross entropy criterion. Furthermore, we demonstrate that the two gate functions that are tied across all the hidden layers are able to control the information flow over the whole network, and we can achieve considerable improvements by only updating these gate functions in both sequence training and adaptation experiments.
Transforming the content, pedagogy and structure of an introductory physics course for life sciences majors

In this paper, we describe how we transformed our large-enrollment introductory physics sequence for the life sciences to a Lecture/Studio format and aligned the physics concepts with authentic biological applications. We modified the pedagogy to include research-validated practices in interactive engagement, and accomplished our goals of enhanced learning gains and institutionalization of our course transformations. The active engagement at the heart of the Lecture/Studio format results in comparable or enhanced learning gains (as measured by validated concept surveys) when compared to traditional instruction. As a result of our efforts, the current format of these courses is sustainable, requiring no greater financial or human resources than does the traditional mode of teaching such courses. We developed a complete suite of active-engagement instructional materials and made them available to the physics education community for adoption outside our institution.
Topological conformal dimension

We investigate a quasisymmetrically invariant counterpart of the topological Hausdorff dimension of a metric space. This invariant, called the topological conformal dimension, gives a lower bound on the topological Hausdorff dimension of quasisymmetric images of the space. We obtain results concerning the behavior of this quantity under products and unions, and compute it for some classical fractals. The range of possible values of the topological conformal dimension is also considered, and we show that this quantity can be fractional.
Generating Markov equivalent maximal ancestral graphs by single edge replacement

Maximal ancestral graphs (MAGs) are used to encode conditional independence relations in DAG models with hidden variables. Different MAGs may represent the same set of conditional independences and are called Markov equivalent. This paper considers MAGs without undirected edges and shows conditions under which an arrow in a MAG can be reversed or interchanged with a bi-directed edge so as to yield a Markov equivalent MAG.
Rademacher sums, moonshine and gravity

In 1939 Rademacher derived a conditionally convergent series expression for the elliptic modular invariant, and used this expression—the first Rademacher sum—to verify its modular invariance. By generalizing Rademacher’s approach we construct bases for the spaces of automorphic integrals of arbitrary even integer weight, for groups commensurable with the modular group. Our methods provide explicit expressions for the Fourier expansions of the Rademacher sums we construct at arbitrary cusps, and illuminate various aspects of the structure of the spaces of automorphic integrals, including the actions of Hecke operators. We give a moduli interpretation for a class of groups commensurable with the modular group which includes all those that are associated to the Monster via monstrous moonshine. We show that within this class the monstrous groups can be characterized just in terms of the behavior of their Rademacher sums. In particular, the genus zero property of monstrous moonshine is encoded naturally in the properties of Rademacher sums. Just as the ellptic modular invariant gives the graded dimension of the moonshine module, the exponential generating function of the Rademacher sums associated to the modular group furnishes the bi-graded dimension of the Verma module for the Monster Lie algebra. This result generalizes naturally to all the groups of monstrous moonshine, and recovers a certain family of monstrous Lie algebras recently introduced by Carnahan. Our constructions suggest conjectures relating monstrous moonshine to a distinguished family of chiral three dimensional quantum gravities, and relating monstrous Lie algebras and their Verma modules to the second quantization of this family of chiral three dimensional quantum gravities.
Optimal experimental designs for estimating Henry's law constants via the method of phase ratio variation.

When measuring Henry's law constants (kH) using the phase ratio variation (PRV) method via headspace gas chromatography (GC), the value of kH of the compound under investigation is calculated from the ratio of the slope to the intercept of a linear regression of the inverse GC response versus the ratio of gas to liquid volumes of a series of vials drawn from the same parent solution. Thus, an experimenter collects measurements consisting of the independent variable (the gas/liquid volume ratio) and dependent variable (the GC−1 peak area). A review of the literature found that the common design is a simple uniform spacing of liquid volumes. We present an optimal experimental design which estimates kH with minimum error and provides multiple means for building confidence intervals for such estimates. We illustrate performance improvements of our design with an example measuring the kH for Naphthalene in aqueous solution as well as simulations on previous studies. Our designs are most applicable after a trial run defines the linear GC response and the linear phase ratio to the GC−1 region (where the PRV method is suitable) after which a practitioner can collect measurements in bulk. The designs can be easily computed using our open source software optDesignSlopeInt, an R package on CRAN.
The intimate relationship between cavitation and fracture

Nearly three decades ago, the field of mechanics was cautioned of the obscure nature of cavitation processes in soft materials [A. Gent, Cavitation in rubber: a cautionary tale, Rubber Chem. Technol., 1990, 63, 49-53]. Since then, the debate on the mechanisms that drive this failure process is ongoing. Using a high precision volume controlled cavity expansion procedure, this paper reveals the intimate relationship between cavitation and fracture. Combining a Griffith inspired formulation for crack propagation, and a Gent inspired formulation for cavity expansion, we show that despite the apparent complexity of the fracture patterns, the pressure-volume response follows a predictable path. In contrast to available studies, both the model and our experiments are able to track the entire process including the unstable branch, by controlling the volume of the cavity. Moreover, this minimal theoretical framework is able to explain the ambiguity in previous experiments by revealing the presence of metastable states that can lead to first order transitions at onset of fracture. The agreement between the simple theory and all of the experimental results conducted in PDMS samples with shear moduli in the range of 25-246 [kPa] confirms that cavitation and fracture work together in driving the expansion process. Through this study we also determine the fracture energy of PDMS and show its significant dependence on strain stiffening.
Provable Indefinite-Horizon Real-Time Planning for Repetitive Tasks.

In many robotic manipulation scenarios, robots often have to perform highly-repetitive tasks in structured environments e.g. sorting mail in a mailroom or pick and place objects on a conveyor belt. In this work we are interested in settings where the tasks are similar, yet not identical (e.g., due to uncertain orientation of objects) and motion planning needs to be extremely fast. Preprocessing-based approaches prove to be very beneficial in these settings—they analyze the configuration-space offline to generate some auxiliary information which can then be used in the query phase to speedup planning times. Typically, the tighter the requirement is on query times the larger the memory footprint will be. In particular, for high-dimensional spaces, providing real-time planning capabilities is extremely challenging. While there are planners that guarantee real-time performance by limiting the planning horizon, we are not aware of general-purpose planners capable of doing it for indefinite horizon (i.e., planning to the goal). To this end, we propose a preprocessingbased method that provides provable bounds on the query time while incurring only a small amount of memory overhead in the query phase. We evaluate our method on a 7-DOF robot arm and show a speedup of over tenfold in query time when compared to the PRM algorithm.
An Examination of Measurement Procedures and Characteristics of Baseline Outcome Data in Single-Case Research:

There has been growing interest in using statistical methods to analyze data and estimate effect size indices from studies that use single-case designs (SCDs), as a complement to traditional visual inspection methods. The validity of a statistical method rests on whether its assumptions are plausible representations of the process by which the data were collected, yet there is evidence that some assumptions-particularly regarding normality of error distributions-may be inappropriate for single-case data. To develop more appropriate modeling assumptions and statistical methods, researchers must attend to the features of real SCD data. In this study, we examine several features of SCDs with behavioral outcome measures in order to inform development of statistical methods. Drawing on a corpus of over 300 studies, including approximately 1,800 cases, from seven systematic reviews that cover a range of interventions and outcome constructs, we report the distribution of study designs, distribution of outcome measurement procedures, and features of baseline outcome data distributions for the most common types of measurements used in single-case research. We discuss implications for the development of more realistic assumptions regarding outcome distributions in SCD studies, as well as the design of Monte Carlo simulation studies evaluating the performance of statistical analysis techniques for SCD data.
People Infer Recursive Visual Concepts from Just a Few Examples

Machine learning has made major advances in categorizing objects in images, yet the best algorithms miss important aspects of how people learn and think about categories. People can learn richer concepts from fewer examples, including causal models that explain how members of a category are formed. Here, we explore the limits of this human ability to infer causal “programs”—latent generating processes with nontrivial algorithmic properties—from one, two, or three visual examples. People were asked to extrapolate the programs in several ways, for both classifying and generating new examples. As a theory of these inductive abilities, we present a Bayesian program learning model that searches the space of programs for the best explanation of the observations. Although variable, people’s judgments are broadly consistent with the model and inconsistent with several alternatives, including a pretrained deep neural network for object recognition, indicating that people can learn and reason with rich algorithmic abstractions from sparse input data.
On the Performance of Thompson Sampling on Logistic Bandits

We study the logistic bandit, in which rewards are binary with success probability $\exp(\beta a^\top \theta) / (1 + \exp(\beta a^\top \theta))$ and actions $a$ and coefficients $\theta$ are within the $d$-dimensional unit ball. While prior regret bounds for algorithms that address the logistic bandit exhibit exponential dependence on the slope parameter $\beta$, we establish a regret bound for Thompson sampling that is independent of $\beta$. Specifically, we establish that, when the set of feasible actions is identical to the set of possible coefficient vectors, the Bayesian regret of Thompson sampling is $\tilde{O}(d\sqrt{T})$. We also establish a $\tilde{O}(\sqrt{d\eta T}/\lambda)$ bound that applies more broadly, where $\lambda$ is the worst-case optimal log-odds and $\eta$ is the "fragility dimension," a new statistic we define to capture the degree to which an optimal action for one model fails to satisfice for others. We demonstrate that the fragility dimension plays an essential role by showing that, for any $\epsilon > 0$, no algorithm can achieve $\mathrm{poly}(d, 1/\lambda)\cdot T^{1-\epsilon}$ regret.
In vitro demodicidal activity of commercial lid hygiene products.

Purpose Demodex folliculorum is a ubiquitous mite that can infest the eyelash follicles. Two commercial lid hygiene products have asserted their effectiveness in killing Demodex mites, yet there has never been a comparative trial between these two products. This study evaluated the demodicidal activity of 0.01% hypochlorous acid (HOCl) solution (Avenova; NovaBay Pharmaceuticals; Emeryville, CA) and 4% terpinen-4-ol (T4O) solution (Cliradex, Bio-Tissue; Miami, FL) in comparison to mineral oil (MO), a negative control. Methods Live Demodex mites were obtained from volunteers. Samples were immersed in 1-2 drops of test solution: 0.01% HOCl, 4% T4O, or 100% MO. Samples were observed under the microscope every 10 mins for up to 90 mins. Kill time was defined as the elapsed time between the addition of test solution and all cessation of movement of the body, legs, mouth and pedipalps for a minimum of 60 seconds. Results T4O demonstrated a mean kill time of 40±0.0 mins. HOCl had a mean kill time of 87.86±4.23 mins, with 79% of samples surviving the full 90 mins. In the MO group, all samples survived through the 90 min mark. Kill time was statistically significant in favor of T4O as compared to HOCl (p=0.0005). There was no statistically significant difference in kill time between HOCl and MO (p=0.25). Conclusion 4% T4O effectively killed all adult mite samples within 40 mins of exposure. In contrast, the demodicidal activity of 0.01% HOCl was minimal, and comparatively similar to 100% MO.
WarpGAN: Automatic Caricature Generation

We propose, WarpGAN, a fully automatic network that can generate caricatures given an input face photo. Besides transferring rich texture styles, WarpGAN learns to automatically predict a set of control points that can warp the photo into a caricature, while preserving identity. We introduce an identity-preserving adversarial loss that aids the discriminator to distinguish between different subjects. Moreover, WarpGAN allows customization of the generated caricatures by controlling the exaggeration extent and the visual styles. Experimental results on a public domain dataset, WebCaricature, show that WarpGAN is capable of generating caricatures that not only preserve the identities but also outputs a diverse set of caricatures for each input photo. Five caricature experts suggest that caricatures generated by WarpGAN are visually similar to hand-drawn ones and only prominent facial features are exaggerated.
Endoscopic transpterygoid approach for resection of trigeminal neurotropic melanoma: Case report and technical note

Abstract Background The endoscopic transpterygoid approach to Meckel's cave is an established technique for resection of trigeminal schwannomas. Modern endoscopes provide excellent intraoperative visualization of anatomic structures and relevant pathology while the minimally-invasive nature of the procedure allows for rapid postoperative recovery. Neurotropic melanoma is a rare clinical entity that often involves the head and neck and can lead to cranial neuropathies when nerve invasion occurs. Pathological diagnosis of this lesion can be challenging due to its rarity and lack of classic melanoma markers such as Melan-A and HMB-45. Case description In this article, the authors describe the endoscopic transpterygoid approach to a neurotropic melanoma involving the maxillary and infraorbital nerves. To our knowledge, this is the first use of this surgical approach for resection of neurotropic melanoma. Conclusions Endoscopic approaches to the trigeminal nerve allow for safe and effective resection of these lesions. However, a strong understanding of the microsurgical anatomy is necessary prior to such an undertaking.
Transient crosslinking kinetics optimize gene cluster interactions

Our understanding of how chromosomes structurally organize and dynamically interact has been revolutionized through the lens of long-chain polymer physics. Major protein contributors to chromosome structure and dynamics are condensin and cohesin that stochastically generate loops within and between chains, and entrap proximal strands of sister chromatids. In this paper, we explore the ability of transient, protein-mediated, gene-gene crosslinks to induce clusters of genes, thereby dynamic architecture, within the highly repeated ribosomal DNA that comprises the nucleolus of budding yeast. We implement three approaches: live cell microscopy; computational modeling of the full genome during G1 in budding yeast, exploring four decades of timescales for transient crosslinks between 5kbp domains (genes) in the nucleolus on Chromosome XII; and, temporal network models with automated community (cluster) detection algorithms applied to the full range of 4D modeling datasets. The data analysis tools detect and track gene clusters, their size, number, persistence time, and their plasticity (deformation). Of biological significance, our analysis reveals an optimal mean crosslink lifetime that promotes pairwise and cluster gene interactions through “flexible” clustering. In this state, large gene clusters self-assemble yet frequently interact (merge and separate), marked by gene exchanges between clusters, which in turn maximizes global gene interactions in the nucleolus. This regime stands between two limiting cases each with far less global gene interactions: with shorter crosslink lifetimes, “rigid” clustering emerges with clusters that interact infrequently; with longer crosslink lifetimes, there is a dissolution of clusters. These observations are compared with imaging experiments on a normal yeast strain and two condensin-modified mutant cell strains. We apply the same image analysis pipeline to the experimental and simulated datasets, providing support for the modeling predictions.
Training Image Estimators without Image Ground-Truth

Deep neural networks have been very successful in compressive-sensing and image restoration applications, as a means to estimate images from partial, blurry, or otherwise degraded measurements. These networks are trained on a large number of corresponding pairs of measurements and ground-truth images, and thus implicitly learn to exploit domain-specific image statistics. But unlike measurement data, it is often expensive or impractical to collect a large training set of ground-truth images in many application settings. In this paper, we introduce an unsupervised framework for training image estimation networks, from a training set that contains only measurements---with two varied measurements per image---but no ground-truth for the full images desired as output. We demonstrate that our framework can be applied for both regular and blind image estimation tasks, where in the latter case parameters of the measurement model (e.g., the blur kernel) are unknown: during inference, and potentially, also during training. We evaluate our framework for training networks for compressive-sensing and blind deconvolution, considering both non-blind and blind training for the latter. Our framework yields models that are nearly as accurate as those from fully supervised training, despite not having access to any ground-truth images.
Thermal Boundary Conductance and Phonon Transmission in Hexagonal Boron Nitride/Graphene Heterostructures

Increased power density in modern microelectronics has led to thermal management challenges which can cause degradation in performance and reliability. In many high-power electronic devices, the power consumption and heat removal are limited by the thermal boundary conductance (TBC) at the interfaces of dissimilar materials. Two-dimensional (2D) materials such as graphene and hexagonal boron nitride (h-BN) have attracted interest as a conductor/insulator pair in next-generation devices because of their unique physical properties; however, the thermal transport at the interfaces must be understood to accurately predict the performance of heterostructures composed of these materials. We use time-domain thermoreflectance (TDTR) to estimate the TBC at the interface of h-BN and graphene to be 35.1 MW/m2-K. We compare the phonon transmission and TBC at the h-BN/graphene interface predicted by two different formulations of the diffuse mismatch model (DMM) for anisotropic materials. The piecewise anisotropic DMM model, which uses two different phonon velocities near the center and at edge of the first Brillouin zone, results in better prediction of phonon transmission rates. The phonon transmission and temperature dependence of TBC confirms the flexural branch in ab-plane and c-plane longitudinal acoustic branch of graphene and h-BN are the dominant contributor when implementing both the A-DMM and PWA-DMM models. The methodology used here can be employed to heterostructures of other 2D materials.
On the Gevrey ultradifferentiability of weak solutions of an abstract evolution equation with a scalar type spectral operator on the open semi-axis

Given the abstract evolution equation \[ y'(t)=Ay(t),\ t\in \mathbb{R}, \] with scalar type spectral operator $A$ in a complex Banach space, found are conditions necessary and sufficient for all weak solutions of the equation, which a priori need not be strongly differentiable, to be strongly infinite differentiable on $\mathbb{R}$. The important case of the equation with a normal operator $A$ in a complex Hilbert space is obtained immediately as a particular case. Also, proved is the following inherent smoothness improvement effect explaining why the case of the strong finite differentiability of the weak solutions is superfluous: if every weak solution of the equation is strongly differentiable at $0$, then all of them are strongly infinite differentiable on $\mathbb{R}$.
A Tunable Loss Function for Binary Classification

We present α-loss, α ∈ [1, ∞], a tunable loss function for binary classification that bridges log-loss (α = 1) and 0-1 loss (α = ∞). We prove that α-loss has an equivalent margin-based form and is classification-calibrated, two desirable properties for a good surrogate loss function for the ideal yet intractable 0-1 loss. For logistic regression-based classification, we provide an upper bound on the difference between the empirical and expected risk for α-loss at the critical points of the empirical risk by exploiting its Lipschitzianity along with recent results on the landscape features of empirical risk functions. Finally, we show that α-loss with α = 2 performs better than log-loss on MNIST for logistic regression.
Relative dielectric constants and selectivity ratios in open ionic channels

We investigate the effects of the relative dielectric coefficient on ionic flows in open ion channels, using mathematical analysis of reasonably general Poisson-Nernst-Planck type models that can include the finite sizes of ions. The value of the relative dielectric coefficient is of course a crucial parameter for ionic behavior in general. Using the powerful theory of singularly perturbed problems in applied mathematics, we show that some properties of open channels are quite insensitive to variation in the relative dielectric coefficient, thereby explaining such effects seen unexpectedly in simulations. The ratio between the total number of one ion species and that of another ion species, and the ratio between the flux of one ion species and that of another ion species do not depend significantly on the relative dielectric coefficient.
Verdachtsdiagnosen bestätigen, Differenzialdiagnosen ausschließen

Neben der Anamnese und klinischen Untersuchung sind Laboruntersuchungen wesentlicher Bestandteil der Abklarung rheumatischer Beschwerden bei Kindern. Allerdings ist eine Rheumaerkrankung durch Labordiagnostik weder zu beweisen noch auszuschliesen. Jedoch kann sie klinisch vermutete Diagnosen verifizieren, Krankheitsaktivitat uberwachen, Prognosehinweise liefern und Therapietoxizitat erfassen.
Where Economics Went Wrong : A Review Essay

In their recent book, Where Economics Went Wrong, David Colander and Craig Freedman (2019) argue that economics went wrong when it abandoned the Classical liberal firewall that demanded separation of scientific theory from the art of policy making. Colander has long advanced the idea that applied economics should be classified neither as positive nor as normative economics. Instead, it should be placed in a third category, "the art of economics"; art requires vision and acumen in addition to knowledge and technique, and is thus more akin to engineering than the natural sciences. The primary contribution of Where Economics Went Wrong is thus to advance Colander's general argument through the specific story of Chicago economics. This essay make two interconnected claims. First, while I agree with Colander and Freedman that applied economics would benefit from more art and less calculation, the Chicago school is not the best vehicle by which to tell a convincing story. Second, a thicker history of the Chicago school reminds us of the importance of institutions and rules, not only for understanding the economy but also for thinking about how economists have constructed our discipline and how internal institutions and incentives affect our behavioral choices.
CORN YIELD DYNAMICS AND WEATHER SHOCKS: CLIMATE CHANGE IMPLICATIONS FOR THE U.S. CORN BELT

Unmitigated climate change is projected to reduce average U.S. corn yields. Many of these projections rely on stable model coefficients, which may not hold in practice. We investigate the assumptio...
Compounding of Wealth in Proof-of-Stake Cryptocurrencies

Proof-of-stake (PoS) is a promising approach for designing efficient blockchains, where block proposers are randomly chosen with probability proportional to their stake. A primary concern in PoS systems is the “rich getting richer” effect, whereby wealthier nodes are more likely to get elected, and hence reap the block reward, making them even wealthier. In this paper, we introduce the notion of equitability, which quantifies how much a proposer can amplify her stake compared to her initial investment. Even with everyone following protocol (i.e., honest behavior), we show that existing methods of allocating block rewards lead to poor equitability, as does initializing systems with small stake pools and/or large rewards relative to the stake pool. We identify a geometric reward function, which we prove is maximally equitable over all choices of reward functions under honest behavior and bound the deviation for strategic actions; the proofs involve the study of optimization problems and stochastic dominances of Polya urn processes. These results allow us to provide a systematic framework to choose the parameters of a practical incentive system for PoS cryptocurrencies.
Measuring Belief and Risk Attitude.

Ramsey (1926) sketches a proposal for measuring the subjective probabilities of an agent by their observable preferences, assuming that the agent is an expected utility maximizer. I show how to extend the spirit of Ramsey's method to a strictly wider class of agents: risk-weighted expected utility maximizers (Buchak 2013). In particular, I show how we can measure the risk attitudes of an agent by their observable preferences, assuming that the agent is a risk-weighted expected utility maximizer. Further, we can leverage this method to measure the subjective probabilities of a risk-weighted expected utility maximizer.
A DELTA-DISTRIBUTION DERIVATION OF THE ELECTRIC FIELD IN THE SOURCE REGION

ABSTRACT The sequential approach to distribution theory is used to rigorously derive the expression, which remains valid in the source region, for electric field in terms of the electric dyadic Green's function. Sufficient conditions for the behavior of the current density that insure that the electric field obeys Maxwell's equations are determined explicitly.
Real Signal Equalization for OQAM

This correspondence proposes the use of a real-only equalizer (ROE), which acts on real signals derived from the received offset quadrature amplitude modulation (OQAM) symbols. For the same fading channel, we prove that both ROE and the widely linear equalizer (WLE) yield equivalent outputs. Hence, these exhibit the same performance. Our complexity analysis finds that depending on the frame length, ROE can be computationally less complex, and save significant signal processing time over WLE. In the adaptive normalized least mean square implementation, ROE performs better with lower complexity than its counterpart, for a given number of pilot bits.
Modified U-Net (mU-Net) With Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images

Segmentation of livers and liver tumors is one of the most important steps in radiation therapy of hepatocellular carcinoma. The segmentation task is often done manually, making it tedious, labor intensive, and subject to intra-/inter- operator variations. While various algorithms for delineating organ-at-risks (OARs) and tumor targets have been proposed, automatic segmentation of livers and liver tumors remains intractable due to their low tissue contrast with respect to the surrounding organs and their deformable shape in CT images. The U-Net has gained increasing popularity recently for image analysis tasks and has shown promising results. Conventional U-Net architectures, however, suffer from three major drawbacks. First, skip connections allow for the duplicated transfer of low resolution information in feature maps to improve efficiency in learning, but this often leads to blurring of extracted image features. Secondly, high level features extracted by the network often do not contain enough high resolution edge information of the input, leading to greater uncertainty where high resolution edge dominantly affects the network’s decisions such as liver and liver-tumor segmentation. Thirdly, it is generally difficult to optimize the number of pooling operations in order to extract high level global features, since the number of pooling operations used depends on the object size. To cope with these problems, we added a residual path with deconvolution and activation operations to the skip connection of the U-Net to avoid duplication of low resolution information of features. In the case of small object inputs, features in the skip connection are not incorporated with features in the residual path. Furthermore, the proposed architecture has additional convolution layers in the skip connection in order to extract high level global features of small object inputs as well as high level features of high resolution edge information of large object inputs. Efficacy of the modified U-Net (mU-Net) was demonstrated using the public dataset of Liver tumor segmentation (LiTS) challenge 2017. For liver-tumor segmentation, Dice similarity coefficient (DSC) of 89.72 %, volume of error (VOE) of 21.93 %, and relative volume difference (RVD) of − 0.49 % were obtained. For liver segmentation, DSC of 98.51 %, VOE of 3.07 %, and RVD of 0.26 % were calculated. For the public 3D Image Reconstruction for Comparison of Algorithm Database (3Dircadb), DSCs were 96.01 % for the liver and 68.14 % for liver-tumor segmentations, respectively. The proposed mU-Net outperformed existing state-of-art networks.
Mitigation of tipping point transitions by time-delay feedback control

In stochastic multistable systems driven by the gradient of a potential, transitions between equilibria are possible because of noise. We study the ability of linear delay feedback control to mitigate these transitions, ensuring that the system stays near a desirable equilibrium. For small delays, we show that the control term has two effects: (i) a stabilizing effect by deepening the potential well around the desirable equilibrium and (ii) a destabilizing effect by intensifying the noise by a factor of (1-τα)-1/2, where τ and α denote the delay and the control gain, respectively. As a result, successful mitigation depends on the competition between these two factors. We also derive analytical results that elucidate the choice of the appropriate control gain and delay that ensure successful mitigations. These results eliminate the need for any Monte Carlo simulations of the stochastic differential equations and, therefore, significantly reduce the computational cost of determining the suitable control parameters. We demonstrate the application of our results on two examples.
Gradient Flow Algorithms for Density Propagation in Stochastic Systems

We develop a new computational framework to solve the partial differential equations (PDEs) governing the flow of the joint probability density functions (PDFs) in continuous-time stochastic nonlinear systems. The need for computing the transient joint PDFs subject to prior dynamics arises in uncertainty propagation, nonlinear filtering, and stochastic control. Our methodology breaks away from the traditional approach of spatial discretization or function approximation—both of which, in general, suffer from the “curse-of-dimensionality.” In the proposed framework, we discretize time but not the state space. We solve infinite dimensional proximal recursions in the manifold of joint PDFs, which in the small time-step limit, is theoretically equivalent to solving the underlying transport PDEs. The resulting computation has the geometric interpretation of gradient flow of certain free energy functional with respect to the Wasserstein metric arising from the theory of optimal mass transport. We show that dualization along with an entropic regularization, leads to a cone-preserving fixed point recursion that is proved to be contractive in Thompson metric. A block co-ordinate iteration scheme is proposed to solve the resulting nonlinear recursions with guaranteed convergence. This approach enables remarkably fast computation for nonparametric transient joint PDF propagation. Numerical examples and various extensions are provided to illustrate the scope and efficacy of the proposed approach.
Ceramic Production and Exchange among the Virgin Anasazi, 30 Years Later

At the 1988 SAA annual meeting in Phoenix, Margaret Lyneis presented a paper with the title Ceramic Production and Exchange among the Virgin Anasazi. In that paper, she presented evidence that much...
DHHC20 Palmitoyl-Transferase Reshapes the Membrane to Foster Catalysis

Cysteine palmitoylation, a form of S-acylation, is a key posttranslational modification in cellular signaling. This type of reversible lipidation occurs in both plasma and organellar membranes, and is catalyzed by a family of integral membrane proteins known as DHHC acyltransferases. The first step in the S-acylation process is the recognition of free acyl coenzyme A (acyl-CoA) from the lipid bilayer. The DHHC enzyme then becomes autoacylated at a site defined by a conserved Asp-His-His-Cys motif. This reaction entails ionization of the catalytic Cys. Intriguingly, in known DHHC structures, this catalytic Cys appears to be exposed to the hydrophobic interior of the lipid membrane, which would be highly unfavorable for a negatively charged nucleophile, thus hindering autoacylation. Here, we use biochemical and computational methods to reconcile these seemingly contradictory facts. First, we experimentally demonstrate that human DHHC20 is active when reconstituted in POPC nanodiscs. Microsecond-long all-atom molecular dynamics simulations are then calculated for human DHHC20 and for different acyl-CoA forms, also in a POPC membrane. Strikingly, we observe that human DHHC20 induces a drastic deformation in the membrane, particularly on the cytoplasmic side, where autoacylation occurs. As a result, the catalytic Cys becomes hydrated and optimally positioned to encounter the cleavage site in acyl-CoA. In summary, we hypothesize that DHHC enzymes locally reshape the membrane to foster a morphology that is specifically adapted for acyl-CoA recognition and autoacylation.
The Role of Publicly Available Data in MICCAI Papers from 2014 to 2018

Widely-used public benchmarks are of huge importance to computer vision and machine learning research, especially with the computational resources required to reproduce state of the art results quickly becoming untenable. In medical image computing, the wide variety of image modalities and problem formulations yields a huge task-space for benchmarks to cover, and thus the widespread adoption of standard benchmarks has been slow, and barriers to releasing medical data exacerbate this issue. In this paper, we examine the role that publicly available data has played in MICCAI papers from the past five years. We find that more than half of these papers are based on private data alone, although this proportion seems to be decreasing over time. Additionally, we observed that after controlling for open access publication and the release of code, papers based on public data were cited over 60% more per year than their private-data counterparts. Further, we found that more than 20% of papers using public data did not provide a citation to the dataset or associated manuscript, highlighting the “second-rate” status that data contributions often take compared to theoretical ones. We conclude by making recommendations for MICCAI policies which could help to better incentivise data sharing and move the field toward more efficient and reproducible science.
ManiNetCluster: a novel manifold learning approach to reveal the functional links between gene networks

The coordination of genomic functions is a critical and complex process across biological systems such as phenotypes or states (e.g., time, disease, organism, environmental perturbation). Understanding how the complexity of genomic function relates to these states remains a challenge. To address this, we have developed a novel computational method, ManiNetCluster, which simultaneously aligns and clusters gene networks (e.g., co-expression) to systematically reveal the links of genomic function between different conditions. Specifically, ManiNetCluster employs manifold learning to uncover and match local and non-linear structures among networks, and identifies cross-network functional links. We demonstrated that ManiNetCluster better aligns the orthologous genes from their developmental expression profiles across model organisms than state-of-the-art methods (p-value <2.2×10−16). This indicates the potential non-linear interactions of evolutionarily conserved genes across species in development. Furthermore, we applied ManiNetCluster to time series transcriptome data measured in the green alga Chlamydomonas reinhardtii to discover the genomic functions linking various metabolic processes between the light and dark periods of a diurnally cycling culture. We identified a number of genes putatively regulating processes across each lighting regime. ManiNetCluster provides a novel computational tool to uncover the genes linking various functions from different networks, providing new insight on how gene functions coordinate across different conditions. ManiNetCluster is publicly available as an R package at https://github.com/daifengwanglab/ManiNetCluster.