cssi/SciSciGPT-SciSciNet huggingface dataset:
Lung Nodule Classification Using Biomarkers, Volumetric Radiomics, and 3D CNNs

We present a hybrid algorithm to estimate lung nodule malignancy that combines imaging biomarkers from Radiologist’s annotation with image classification of CT scans. Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and volumetric radiomic features. We analyze and compare the performance of the algorithm using only imagery, only biomarkers, combined imagery + biomarkers, combined imagery + volumetric radiomic features, and finally the combination of imagery + biomarkers + volumetric features in order to classify the suspicion level of nodule malignancy. The National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) IDRI dataset is used to train and evaluate the classification task. We show that the incorporation of semi-supervised learning by means of K-Nearest-Neighbors (KNN) can increase the available training sample size of the LIDC-IDRI, thereby further improving the accuracy of malignancy estimation of most of the models tested although there is no significant improvement with the use of KNN semi-supervised learning if image classification with CNNs and volumetric features is combined with descriptive biomarkers. Unexpectedly, we also show that a model using image biomarkers alone is more accurate than one that combines biomarkers with volumetric radiomics, 3D CNNs, and semi-supervised learning. We discuss the possibility that this result may be influenced by cognitive bias in LIDC-IDRI because malignancy estimates were recorded by the same radiologist panel as biomarkers, as well as future work to incorporate pathology information over a subset of study participants.
Forecasting macroeconomic risks

Abstract We construct risks around consensus forecasts of real GDP growth, unemployment, and inflation. We find that risks are time-varying, asymmetric, and partly predictable. Tight financial conditions forecast downside growth risk, upside unemployment risk, and increased uncertainty around the inflation forecast. Growth vulnerability arises as the conditional mean and conditional variance of GDP growth are negatively correlated: downside risks are driven by lower mean and higher variance when financial conditions tighten. Similarly, employment vulnerability arises as the conditional mean and conditional variance of unemployment are positively correlated, with tighter financial conditions corresponding to higher forecasted unemployment and higher variance around the consensus forecast.
Quarkonium Semiclassical Transport in Quark-Gluon Plasma: Factorization and Quantum Correction

We study quarkonium transport in the quark-gluon plasma by using the potential nonrelativistic QCD (pNRQCD) effective field theory and the framework of open quantum systems. We argue that the coupling between quarkonium and the thermal bath is weak using separation of scales, so the initial density matrix of the total system factorizes and the time evolution of the subsystem is Markovian. We derive the semiclassical Boltzmann equation for quarkonium by applying a Wigner transform to the Lindblad equation and carrying out a semiclassical expansion. We resum relevant interactions to all orders in the coupling constant at leading power of the nonrelativistic and multipole expansions. The derivation is valid for both weakly coupled and strongly coupled quark-gluon plasmas. We find reaction rates in the transport equation factorize into a quarkonium dipole transition function and a chromoelectric gluon distribution function. For the differential reaction rate, the definition of the momentum dependent chromoelectric gluon distribution function involves staple-shaped Wilson lines. For the inclusive reaction rate, the Wilson lines collapse into a straight line along the real time axis and the distribution becomes momentum independent. The relation between the two Wilson lines is analogous to the relation between the Wilson lines appearing in the gluon parton distribution function (PDF) and the gluon transverse momentum dependent parton distribution function (TMDPDF). The centrality dependence of the quarkonium nuclear modification factor measured by experiments probes the momentum independent distribution while the transverse momentum dependence and measurements of the azimuthal angular anisotropy may be able to probe the momentum dependent one. We discuss one way to indirectly constrain the quarkonium in-medium real potential by using the factorization formula and lattice calculations. The leading quantum correction to the semiclassical transport equation of quarkonium is also worked out. The study can be easily generalized to quarkonium transport in cold nuclear matter, which is relevant for quarkonium production in eA collisions in the future Electron-Ion Collider.
Outcomes for Patients With IDH-Mutated Acute Myeloid Leukemia Undergoing Allogeneic Hematopoietic Cell Transplantation.

ABSTRACT Disease relapse after hematopoietic cell transplantation (HCT) is a major cause of treatment failure for patients with acute myeloid leukemia (AML). Maintenance therapy after HCT for patients with targetable mutations such as mutated IDH1 or IDH2 may improve outcomes, and clinical trials evaluating this strategy are ongoing. However, clinical outcomes of IDH1- and IDH2-mutated AML patients after HCT have not been well described. The primary objective of this study was to describe the clinical characteristics and post-HCT outcomes of IDH-mutated AML patients. Survival outcomes included progression-free survival (PFS), overall survival, and cumulative incidences of relapse and nonrelapse mortality. In this multicenter retrospective analysis, 112 adult patients with IDH1- or IDH2-mutated AML who underwent HCT and did not receive an IDH inhibitor as maintenance therapy after HCT were identified at Massachusetts General Hospital, Dana Farber Cancer Institute, and Ohio State University. Mutation testing was performed using next-generation sequencing panels. Patient characteristics were collected retrospectively, and their survival outcomes were analyzed. Univariate and multivariate analyses were performed. The median patient age was 64.1 years. The median follow-up was 27.5 months. Among patients, 78.5% had intermediate- or adverse-risk disease by European LeukemiaNET criteria. Fifty-eight percent of patients received intensive induction chemotherapy, 82% of patients underwent HCT during first complete remission (CR) or CR with incomplete hematologic recovery (CRi), and 34% of patients received myeloablative conditioning. Frequently detected co-mutations were DNMT3A (35.7%), NPM1 (33.1%), and FLT3-ITD (13.4%); TP53 mutations were detected in 3.6% of patients. For IDH1-mutated patients transplanted during first CR/CRi, the 1- and 2-year PFS was 75% and 58%, respectively. For IDH2-mutated patients transplanted in first CR/CRi, the 1- and 2-year PFS was 64% and 58%, respectively. The 2-year cumulative incidence of relapse was 31% and 25% for IDH1- and IDH2-mutated cohorts, respectively. Multivariable analysis suggested first CR/CRi and age ≤60 was associated with improved outcomes for IDH2-mutated patients. To date, this is the largest multicenter study of outcomes of IDH-mutated AML patients after HCT. Our analysis provides important benchmarks for analysis and interpretation of results emerging from clinical trials evaluating maintenance IDH1 and IDH2 inhibitor therapy for AML patients after HCT.
On the Feynman-alpha method for reflected fissile assemblies

Abstract The Feynman-alpha method is a neutron noise technique that is used to estimate the prompt neutron period of fissile assemblies. The method and quantity are of widespread interest including in applications such as nuclear criticality safety, safeguards and nonproliferation, and stockpile stewardship; the prompt neutron period may also be used to infer the k eff multiplication factor. The Feynman-alpha method is predicated on time-correlated neutron detections that deviate from a Poisson random variable due to multiplication. Traditionally, such measurements are diagnosed with one-region point kinetics, but two-region models are required when the fissile assembly is reflected. This paper presents a derivation of the two-region point kinetics Feynman-alpha equations based on a double integration of the Rossi-alpha equations, develops novel propagation of measurement uncertainty, and validates the theory. Validation is achieved with organic scintillator measurements of weapons-grade plutonium reflected by various amounts of copper to achieve k eff values of 0.83–0.94 and prompt periods of 5–75 ns. The results demonstrate that Feynman-alpha measurements should use the two-region model instead of the one-region model. The simplified one-region model deviates from the validated two-region models by as much as 10% in the estimate of the prompt neutron period, and the two-region model reduces to the one-region model for small amounts of reflector. The Feynman-alpha estimates of the prompt neutron period are compared to those of the Rossi-alpha approach. The comparative results demonstrate that the Feynman-alpha method is more precise than the Rossi-alpha method and more accurate for k eff 0.92 , whereas the Rossi-alpha method is generally more accurate for higher multiplications. The uncertainty propagation developed in this work should be used for all Feynman-alpha measurements and will therein improve fitting accuracy and appropriate precision estimates.
Stability Analysis of a Signaling Circuit with Dual Species of GTPase Switches

GTPases are molecular switches that regulate a wide range of cellular processes, such as organelle biogenesis, position, shape, function, vesicular transport between organelles, and signal transduction. These hydrolase enzymes operate by toggling between an active ("ON") guanosine triphosphate (GTP)-bound state and an inactive ("OFF") guanosine diphosphate (GDP)-bound state; such a toggle is regulated by GEFs (guanine nucleotide exchange factors) and GAPs (GTPase activating proteins). Here we propose a model for a network motif between monomeric (m) and trimeric (t) GTPases assembled exclusively in eukaryotic cells of multicellular organisms. We develop a system of ordinary differential equations in which these two classes of GTPases are interlinked conditional to their ON/OFF states within a motif through coupling and feedback loops. We provide explicit formulae for the steady states of the system and perform classical local stability analysis to systematically investigate the role of the different connections between the GTPase switches. Interestingly, a coupling of the active mGTPase to the GEF of the tGTPase was sufficient to provide two locally stable states: one where both active/inactive forms of the mGTPase can be interpreted as having low concentrations and the other where both m- and tGTPase have high concentrations. Moreover, when a feedback loop from the GEF of the tGTPase to the GAP of the mGTPase was added to the coupled system, two other locally stable states emerged. In both states the tGTPase is inactivated and active tGTPase concentrations are low. Finally, the addition of a second feedback loop, from the active tGTPase to the GAP of the mGTPase, gives rise to a family of steady states that can be parametrized by a range of inactive tGTPase concentrations. Our findings reveal that the coupling of these two different GTPase motifs can dramatically change their steady-state behaviors and shed light on how such coupling may impact signaling mechanisms in eukaryotic cells.
SREC: Proactive Self-Remedy of Energy-Constrained UAV-Based Networks via Deep Reinforcement Learning

Energy-aware control for multiple unmanned aerial vehicles (UAVs) is one of the major research interests in UAV based networking. Yet few existing works have focused on how the network should react around the timing when the UAV lineup is changed. In this work, we study proactive self-remedy of energy-constrained UAV networks when one or more UAVs are short of energy and about to quit for charging. We target at an energy-aware optimal UAV control policy which proactively relocates the UAVs when any UAV is about to quit the network, rather than passively dispatches the remaining UAVs after the quit. Specifically, a deep reinforcement learning (DRL)-based self remedy approach, named SREC-DRL, is proposed to maximize the accumulated user satisfaction scores for a certain period within which at least one UAV will quit the network. To handle the continuous state and action space in the problem, the state-of-the-art algorithm of the actor-critic DRL, i.e., deep deterministic policy gradient (DDPG), is applied with better convergence stability. Numerical results demonstrate that compared with the passive reaction method, the proposed SREC-DRL approach shows a 12.12% gain in accumulative user satisfaction score during the remedy period.
Radon measures and Lipschitz graphs

For all $1\leq m\leq n-1$, we investigate the interaction of locally finite measures in $\mathbb{R}^n$ with the family of $m$-dimensional Lipschitz graphs. For instance, we characterize Radon measures $\mu$, which are carried by Lipschitz graphs in the sense that there exist graphs $\Gamma_1,\Gamma_2,\dots$ such that $\mu(\mathbb{R}^n\setminus\bigcup_1^\infty\Gamma_i)=0$, using only countably many evaluations of the measure. This problem in geometric measure theory was classically studied within smaller classes of measures, e.g.~for the restrictions of $m$-dimensional Hausdorff measure $\mathcal{H}^m$ to $E\subseteq \mathbb{R}^n$ with $0<\mathcal{H}^m(E)<\infty$. However, an example of Csornyei, Kaenmaki, Rajala, and Suomala shows that classical methods are insufficient to detect when a general measure charges a Lipschitz graph. To develop a characterization of Lipschitz graph rectifiability for arbitrary Radon measures, we look at the behavior of coarse doubling ratios of the measure on dyadic cubes that intersect conical annuli. This extends a characterization of graph rectifiability for pointwise doubling measures by Naples by mimicking the approach used in the characterization of Radon measures carried by rectifiable curves by Badger and Schul.
Learning Task-General Representations with Generative Neuro-Symbolic Modeling

People can learn rich, general-purpose conceptual representations from only raw perceptual inputs. Current machine learning approaches fall well short of these human standards, although different modeling traditions often have complementary strengths. Symbolic models can capture the compositional and causal knowledge that enables flexible generalization, but they struggle to learn from raw inputs, relying on strong abstractions and simplifying assumptions. Neural network models can learn directly from raw data, but they struggle to capture compositional and causal structure and typically must retrain to tackle new tasks. We bring together these two traditions to learn generative models of concepts that capture rich compositional and causal structure, while learning from raw data. We develop a generative neuro-symbolic (GNS) model of handwritten character concepts that uses the control flow of a probabilistic program, coupled with symbolic stroke primitives and a symbolic image renderer, to represent the causal and compositional processes by which characters are formed. The distributions of parts (strokes), and correlations between parts, are modeled with neural network subroutines, allowing the model to learn directly from raw data and express nonparametric statistical relationships. We apply our model to the Omniglot challenge of human-level concept learning, using a background set of alphabets to learn an expressive prior distribution over character drawings. In a subsequent evaluation, our GNS model uses probabilistic inference to learn rich conceptual representations from a single training image that generalize to 4 unique tasks, succeeding where previous work has fallen short.
Detecting cell-type-specific allelic expression imbalance by integrative analysis of bulk and single-cell RNA sequencing data.

Allelic expression imbalance (AEI), quantified by the relative expression of two alleles of a gene in a diploid organism, can help explain phenotypic variations among individuals. Traditional methods detect AEI using bulk RNA sequencing (RNA-seq) data, a data type that averages out cell-to-cell heterogeneity in gene expression across cell types. Since the patterns of AEI may vary across different cell types, it is desirable to study AEI in a cell-type-specific manner. Although this can be achieved by single-cell RNA sequencing (scRNA-seq), it requires full-length transcript to be sequenced in single cells of a large number of individuals, which are still cost prohibitive to generate. To overcome this limitation and utilize the vast amount of existing disease relevant bulk tissue RNA-seq data, we developed BSCET, which enables the characterization of cell-type-specific AEI in bulk RNA-seq data by integrating cell type composition information inferred from a small set of scRNA-seq samples, possibly obtained from an external dataset. By modeling covariate effect, BSCET can also detect genes whose cell-type-specific AEI are associated with clinical factors. Through extensive benchmark evaluations, we show that BSCET correctly detected genes with cell-type-specific AEI and differential AEI between healthy and diseased samples using bulk RNA-seq data. BSCET also uncovered cell-type-specific AEIs that were missed in bulk data analysis when the directions of AEI are opposite in different cell types. We further applied BSCET to two pancreatic islet bulk RNA-seq datasets, and detected genes showing cell-type-specific AEI that are related to the progression of type 2 diabetes. Since bulk RNA-seq data are easily accessible, BSCET provided a convenient tool to integrate information from scRNA-seq data to gain insight on AEI with cell type resolution. Results from such analysis will advance our understanding of cell type contributions in human diseases.
Newton Polytopes and Relative Entropy Optimization

Certifying function nonnegativity is a ubiquitous problem in computational mathematics, with especially notable applications in optimization. We study the question of certifying nonnegativity of signomials based on the recently proposed approach of Sums-of-AM/GM-Exponentials (SAGE) decomposition due to the second author and Shah. The existence of a SAGE decomposition is a sufficient condition for nonnegativity of a signomial, and it can be verified by solving a tractable convex relative entropy program. We present new structural properties of SAGE certificates such as a characterization of the extreme rays of the cones associated to these decompositions as well as an appealing form of sparsity preservation. These lead to a number of important consequences such as conditions under which signomial nonnegativity is equivalent to the existence of a SAGE decomposition; our results represent the broadest-known class of nonconvex signomial optimization problems that can be solved efficiently via convex relaxation. The analysis in this paper proceeds by leveraging the interaction between the convex duality underlying SAGE certificates and the face structure of Newton polytopes. After proving our main signomial results, we direct our machinery toward the topic of globally nonnegative polynomials. This leads to (among other things) efficient methods for certifying polynomial nonnegativity, with complexity independent of the degree of a polynomial.
Do “Made in USA” Claims Matter?

Observational study and field experiments show that some consumers will pay more for products advertising themselves as American made but just a little.
Energy on spheres and discreteness of minimizing measures

Abstract In the present paper we study the minimization of energy integrals on the sphere with a focus on an interesting clustering phenomenon: for certain types of potentials, optimal measures are discrete or are supported on small sets. In particular, we prove that the support of any minimizer of the p-frame energy has empty interior whenever p is not an even integer. A similar effect is also demonstrated for energies with analytic potentials which are not positive definite. In addition, we establish the existence of discrete minimizers for a large class of energies, which includes energies with polynomial potentials.
Splitting the atom of property: Rights experimentalism as obligation to future generations

The Framers split the atom of sovereignty. It was the genius of their idea that our citizens would have two political capacities, one state and one federal, each protected from incursion by the other. The resulting Constitution created a legal system unprecedented in form and design, establishing two orders of government, each with its own direct relationship, its own privity, its own set of mutual rights and obligations to the people who sustain it and are governed by it.1
Limit theorems for singular Skorohod integrals

In this paper we prove the convergence in distribution of sequences of It\^o and Skorohod integrals with integrands having singular asymptotic behavior. These sequences include stochastic convolutions and generalize the example $\sqrt n\int _0^1 t^n B_tdB_t$ first studied by Peccati and Yor in 2004.
Bargaining Inside the Black Box

When jurors are presented with a menu of criminal verdict options and they cannot reach a consensus among them, what should they do? Available evidence suggests they are prone to compromise – that is, jurors will negotiate with each other and settle on a verdict in the middle, often on a lesser-included offense. The suggestion that jurors compromise is not new; it is supported by empirical evidence, well-accepted by courts and commentators, and unsurprising given the incentives jurors feel to reach agreement and the different individual views they likely hold. There are, however, some who say intrajury negotiation represents a failure of the jury process. Conventional wisdom clings to the notion that criminal verdicts reflect a jury’s unanimous factual assessment. That notion is thwarted when a juror votes for a verdict as a compromise, as a second choice to the one he thinks best reflects reality. To date, therefore, compromise verdicts are typically dismissed as examples of maverick jurors dishonoring their oath to apply the law and seek the truth. This article challenges that conventional wisdom by way of a new analogy. If jurors each view the case differently and nonetheless negotiate with each other to reach a deal, why is that wrong when 95% of criminal convictions are the result of a similar process? I seek a new understanding of compromise verdicts by making a novel comparison to plea bargaining. I argue that the former should be understood in the context of the latter, and that the best way to evaluate intrajury negotiation is to juxtapose it with the negotiation that dominates our criminal justice system. Instead of dismissing intrajury negotiation as illegitimate, I argue that we should accept it as a reality and from there seek to improve it with lessons drawn from plea negotiations. 2 Bargaining Inside The Black Box August 2010 BARGAINING INSIDE THE BLACK BOX
{CACTI}: Captcha Avoidance via Client-side {TEE} Integration

Preventing abuse of web services by bots is an increasingly important problem, as abusive activities grow in both volume and variety. CAPTCHAs are the most common way for thwarting bot activities. However, they are often ineffective against bots and frustrating for humans. In addition, some recent CAPTCHA techniques diminish user privacy. Meanwhile, client-side Trusted Execution Environments (TEEs) are becoming increasingly widespread (notably, ARM TrustZone and Intel SGX), allowing establishment of trust in a small part (trust anchor or TCB) of client-side hardware. This prompts the question: can a TEE help reduce (or remove entirely) user burden of solving CAPTCHAs? In this paper, we design CACTI: CAPTCHA Avoidance via Client-side TEE Integration. Using client-side TEEs, CACTI allows legitimate clients to generate unforgeable rate-proofs demonstrating how frequently they have performed specific actions. These rate-proofs can be sent to web servers in lieu of solving CAPTCHAs. CACTI provides strong client privacy guarantees, since the information is only sent to the visited website and authenticated using a group signature scheme. Our evaluations show that overall latency of generating and verifying a CACTI rate-proof is less than 0.25 sec, while CACTI's bandwidth overhead is over 98% lower than that of current CAPTCHA systems.
The R-matrix bootstrap for the 2d O(N) bosonic model with a boundary

The S-matrix bootstrap is extended to a 1+1d theory with O(N) symmetry and a boundary in what we call the R-matrix bootstrap since the quantity of interest is the reflection matrix (R-matrix). Given a bulk S-matrix, the space of allowed R-matrices is an infinite dimensional convex space from which we plot a two dimensional section given by a convex domain on a 2d plane. In certain cases, at the boundary of the domain, we find vertices corresponding to integrable R-matrices with no free parameters. In other cases, when there is a one-parameter family of integrable R-matrices, the whole boundary represents integrable theories. We also consider R-matrices which are analytic in an extended region beyond the physical cuts, thus forbidding poles (resonances) in that region. In certain models, this drastically reduces the allowed space of R-matrices leading to new vertices that again correspond to integrable theories. We also work out the dual problem, in particular in the case of extended analyticity, the dual function has cuts on the physical line whenever unitarity is saturated. For the periodic Yang-Baxter solution that has zero transmission, we computed the R-matrix initially using the bootstrap and then derived its previously unknown analytic form.
Towards the Gravituhedron: new expressions for NMHV gravity amplitudes

In this paper, we present new expressions for n-point NMHV tree-level gravity amplitudes. We introduce a method of factorization diagrams which is a simple graphical representation of R-invariants in Yang-Mills theory. We define the gravity analogues which we call $$ \mathcal{G} $$ -invariants, and expand the NMHV gravity amplitudes in terms of these objects. We provide explicit formulas of NMHV gravity amplitudes up to eight points in terms of $$ \mathcal{G} $$ -invariants, and give the general definition for any number of points. We discuss the connection to BCFW representation, special behavior under large momentum shift, the role of momentum twistors and the intricate web of spurious poles cancelation. Because of the close connection between R-invariants and the (tree-level) Amplituhedron for Yang-Mills amplitudes, we speculate that the new expansion for gravity amplitudes should correspond to the triangulation of the putative Gravituhedron geometry.
VETA x-ray data acquisition and control system

We describe the X-ray Data Acquisition and Control System (XDACS) used together with the X-ray Detection System (XDS) to characterize the X-ray image during testing of the AXAF P1/H1 mirror pair at the MSFC X-ray Calibration Facility. A variety of X-ray data were acquired, analyzed and archived during the testing including: mirror alignment, encircled energy, effective area, point spread function, system housekeeping and proportional counter window uniformity data. The system architecture is presented with emphasis placed on key features that include a layered UNIX tool approach, dedicated subsystem controllers, real-time X-window displays, flexibility in combining tools, network connectivity and system extensibility. The VETA test data archive is also described.
Contractual Restrictions and Debt Traps

Microcredit and other forms of small-scale finance have failed to catalyze entrepreneurship in developing countries. In these credit markets, borrowers and lenders often bargain over not only the interest rate but also implicit restrictions on types of investment. We build a dynamic model of informal lending and show this may lead to endogenous debt traps. Lenders constrain business growth for poor borrowers yet richer borrowers may grow their businesses faster than they could have without credit. The theory offers nuanced comparative statics and rationalizes the low average impact and low demand of microfinance despite its high impact on larger businesses.
Positivity and Nonadditivity of Quantum Capacities Using Generalized Erasure Channels

We consider various forms of a process, which we call gluing , for combining two or more complementary quantum channel pairs $({\mathcal B}, {\mathcal C})$ to form a composite. One type of gluing combines a perfect channel with a second channel to produce a generalized erasure channel pair $({\mathcal B}_{g}, {\mathcal C}_{g})$ . We consider two cases in which the second channel is (i) an amplitude-damping, or (ii) a phase-damping qubit channel; (ii) is the dephrasure channel of Leditzky et al. For both (i) and (ii), $({\mathcal B}_{g}, {\mathcal C}_{g})$ depends on the damping parameter $0\leq p\leq 1$ and a parameter $0 \leq \lambda \leq 1$ that characterizes the gluing process. In both cases we study $Q^{(1)}({\mathcal B}_{g})$ and $Q^{(1)}({\mathcal C}_{g})$ , where $Q^{(1)}$ is the channel coherent information, and determine the regions in the $(p, \lambda)$ plane where each is zero or positive, confirming previous results for (ii). A somewhat surprising result for which we lack any intuitive explanation is that $Q^{(1)}({\mathcal C}_{g})$ is zero for $\lambda \leq 1/2$ when $p=0$ , but is strictly positive (though perhaps extremely small) for all values of $\lambda > 0$ when $p$ is positive by even the smallest amount. In addition we study the nonadditivity of $Q^{(1)}({\mathcal B}_{g})$ for two identical channels in parallel. It occurs in a well-defined region of the $(p, \lambda)$ plane in case (i). In case (ii) we have extended previous results for the dephrasure channel without, however, identifying the full range of $(p, \lambda)$ values where nonadditivity occurs. Again, an intuitive explanation is lacking.
Regression with Deep Learning for Sensor Performance Optimization

Neural networks with at least two hidden layers are called deep networks [1]. Recent developments in AI and computer programming in general has led to development of tools such as Tensorflow, Keras, NumPy etc. making it easier to model and draw conclusions from data. In this work we re-approach non-linear regression with deep learning enabled by Keras and Tensorflow. In particular, we use deep learning to parametrize a non-linear multivariate relationship between inputs and outputs of an industrial sensor with an intent to optimize the sensor performance based on selected key metrics.
Sorting probability of Catalan posets

Abstract We show that the sorting probability of the Catalan poset P n satisfies δ ( P n ) = O ( n − 5 / 4 ) .
Design and optimization of a GaAs-based sub-7-μm quantum cascade laser based on multivalley Monte Carlo simulation

We present the design and optimization of a deep-well GaAs-based quantum cascade laser (QCL) emitting at 6.7 μm, the shortest wavelength in GaAs-based QCLs, using a multivalley Monte Carlo simulation. Simulation results provide direct insights into optimizing the layer sequence design. The optimized structure exhibits sufficient gain for lasing at both 77 and 300 K. The calculated threshold-current densities are 5 kA/cm2 at 77 K and 14 kA/cm2 at 300 K.
Gaussian Approximation of Quantization Error for Estimation From Compressed Data

We consider the distributional connection between the lossy compressed representation of a high-dimensional signal $X$ using a random spherical code and the observation of $X$ under an additive white Gaussian noise (AWGN). We show that the Wasserstein distance between a bitrate- $R$ compressed version of $X$ and its observation under an AWGN-channel of signal-to-noise ratio $2^{2R}-1$ is bounded in the problem dimension. We utilize this fact to connect the risk of an estimator based on the compressed version of $X$ to the risk attained by the same estimator when fed the AWGN-corrupted version of $X$ . We demonstrate the usefulness of this connection by deriving various novel results for inference problems under compression constraints, including minimax estimation, sparse regression, compressed sensing, and universality of linear estimation in remote source coding.
Non-interactive Zero Knowledge from Sub-exponential DDH

We provide the first constructions of non-interactive zero-knowledge and Zap arguments for NP based on the sub-exponential hardness of Decisional Diffie-Hellman against polynomial time adversaries (without use of groups with pairings).
Linear time algorithm for the conjugacy problem in the first Grigorchuk group

We prove that the conjugacy problem in the first Grigorchuck group $\Gamma$ can be solved in linear time. Furthermore, the problem to decide if a list of elements $w_1,\ldots,w_k\in\Gamma$ contains a pair of conjugate elements can be solved in linear time. We also show that a conjugator for a pair of conjugate element $u,v\in\Gamma$ can be found in polynomial time.
The ChvG-ChvI and NtrY-NtrX Two-Component Systems Coordinately Regulate Growth of Caulobacter crescentus.

Two-component signaling systems (TCSs) are comprised of a sensory histidine kinase and a response regulator protein. In response to environmental changes, sensor kinases directly phosphorylate their cognate response regulator to affect gene expression. Bacteria typically express multiple TCSs that are insulated from one another and regulate distinct physiological processes. There are certainly examples of cross-regulation between TCSs, but this phenomenon remains relatively unexplored. We have identified regulatory links between the ChvG-ChvI (ChvGI) and NtrY-NtrX (NtrYX) TCSs, which control important and often overlapping processes in α-proteobacteria, including maintenance of the cell envelope. Deletion of chvG and chvI in Caulobacter crescentus limited growth in defined medium and a selection for genetic suppressors of this growth phenotype uncovered interactions among chvGI, ntrYX, and ntrZ, which encodes a previously uncharacterized periplasmic protein. Significant overlap in the experimentally-defined ChvI and NtrX transcriptional regulons provided support for the observed genetic connections between ntrYX and chvGI. Moreover, we present evidence that the growth defect of strains lacking chvGI is influenced by the phosphorylation state of NtrX and, to some extent, by levels of the TonB-dependent receptor ChvT. Measurements of NtrX phosphorylation in vivo indicated that NtrZ is an upstream regulator of NtrY, and that NtrY primarily functions as an NtrX phosphatase. We propose a model in which NtrZ functions in the periplasm to inhibit NtrY phosphatase activity; regulation of phosphorylated NtrX levels by NtrZ and NtrY provides a mechanism to modulate and balance expression of the NtrX and ChvI regulons under different growth conditions. Importance Two-component signaling systems (TCSs) enable bacteria to regulate gene expression in response to physiochemical changes in their environment. The ChvGI and NtrYX TCSs regulate diverse pathways associated with pathogenesis, growth, and cell envelope function in many α-proteobacteria. We used Caulobacter crescentus as a model to investigate regulatory connections between ChvGI and NtrYX. Our work defined the ChvI transcriptional regulon in C. crescentus and revealed a genetic interaction between ChvGI and NtrYX, whereby modulation of NtrYX signaling affects the survival of cells lacking ChvGI. In addition, we identified NtrZ as a periplasmic inhibitor of NtrY phosphatase activity in vivo. Our work establishes C. crescentus as an excellent model to investigate multi-level regulatory connections between ChvGI and NtrYX in α-proteobacteria.
Value Alignment Verification

As humans interact with autonomous agents to perform increasingly complicated, potentially risky tasks, it is important to be able to efficiently evaluate an agent's performance and correctness. In this paper we formalize and theoretically analyze the problem of efficient value alignment verification: how to efficiently test whether the behavior of another agent is aligned with a human's values. The goal is to construct a kind of "driver's test" that a human can give to any agent which will verify value alignment via a minimal number of queries. We study alignment verification problems with both idealized humans that have an explicit reward function as well as problems where they have implicit values. We analyze verification of exact value alignment for rational agents and propose and analyze heuristic and approximate value alignment verification tests in a wide range of gridworlds and a continuous autonomous driving domain. Finally, we prove that there exist sufficient conditions such that we can verify exact and approximate alignment across an infinite set of test environments via a constant-query-complexity alignment test.
Optimal mixing of Glauber dynamics: entropy factorization via high-dimensional expansion

We prove an optimal mixing time bound for the single-site update Markov chain known as the Glauber dynamics or Gibbs sampling in a variety of settings. Our work presents an improved version of the spectral independence approach of Anari et al. (2020) and shows O(nlogn) mixing time on any n-vertex graph of bounded degree when the maximum eigenvalue of an associated influence matrix is bounded. As an application of our results, for the hard-core model on independent sets weighted by a fugacity λ, we establish O(nlogn) mixing time for the Glauber dynamics on any n-vertex graph of constant maximum degree Δ when λc(Δ) where λc(Δ) is the critical point for the uniqueness/non-uniqueness phase transition on the Δ-regular tree. More generally, for any antiferromagnetic 2-spin system we prove O(nlogn) mixing time of the Glauber dynamics on any bounded degree graph in the corresponding tree uniqueness region. Our results apply more broadly; for example, we also obtain O(nlogn) mixing for q-colorings of triangle-free graphs of maximum degree Δ when the number of colors satisfies q > α Δ where α ≈ 1.763, and O(mlogn) mixing for generating random matchings of any graph with bounded degree and m edges. Our approach is based on two steps. First, we show that the approximate tensorization of entropy (i.e., factorizing entropy into single vertices), which is a key step for establishing the modified log-Sobolev inequality in many previous works, can be deduced from entropy factorization into blocks of fixed linear size. Second, we adapt the local-to-global scheme of Alev and Lau (2020) to establish such block factorization of entropy in a more general setting of pure weighted simplicial complexes satisfying local spectral expansion; this also substantially generalizes the result of Cryan et al. (2019).
Measuring the Local ISM along the Sight Lines of the Two Voyager Spacecraft with HST/STIS

In 2012, Voyager 1 crossed the heliopause, becoming the first human-made object to exit the solar system. This milestone signifies the beginning of an important new era for local interstellar medium (LISM) exploration. We present measurements of the structure and composition of the LISM in the immediate path of the Voyager spacecraft by using high-resolution Hubble Space Telescope (HST) spectra of nearby stars that lie along the same lines of sight. We provide a comprehensive inventory of LISM absorption in the near-ultraviolet (2600-2800 Angstroms) and far-ultraviolet (1200-1500 Angstroms). The LISM absorption profiles are used to make comparisons between each pair of closely spaced (<15 deg) sight lines. With fits to several absorption lines, we make measurements of the physical properties of the LISM. We estimate electron density along the Voyager 2 sight line, and our values are consistent with recent measurements by Voyager 1. Excess absorption in the HI Ly-alpha line displays the presence of both the heliosphere and an astrosphere around GJ 780. This is only the 14th detection of an astrosphere, and the large mass-loss rate ($\dot{M} = 10\dot{M}_\odot$) is consistent with other subgiant stars. The heliospheric absorption matches the predicted strength for a sight line 58 deg from the upwind direction. As both HST and Voyager reach the end of their lifetimes, we have the opportunity to synthesize their respective observations, combining in situ measurements with the shortest possible line-of-sight measurements to study the Galactic ISM surrounding the Sun.
Information-Based Disentangled Representation Learning for Unsupervised MR Harmonization

Accuracy and consistency are two key factors in computer-assisted magnetic resonance (MR) image analysis. However, contrast variation from site to site caused by lack of standardization in MR acquisition impedes consistent measurements. In recent years, image harmonization approaches have been proposed to compensate for contrast variation in MR images. Current harmonization approaches either require cross-site traveling subjects for supervised training or heavily rely on site-specific harmonization models to encourage harmonization accuracy. These requirements potentially limit the application of current harmonization methods in large-scale multi-site studies. In this work, we propose an unsupervised MR harmonization framework, CALAMITI (Contrast Anatomy Learning and Analysis for MR Intensity Translation and Integration), based on information bottleneck theory. CALAMITI learns a disentangled latent space using a unified structure for multi-site harmonization without the need for traveling subjects. Our model is also able to adapt itself to harmonize MR images from a new site with fine tuning solely on images from the new site. Both qualitative and quantitative results show that the proposed method achieves superior performance compared with other unsupervised harmonization approaches.
Isoform-specific roles of the Drosophila filamin-type protein Jitterbug (Jbug) during development

Filamins are highly conserved actin-crosslinking proteins that regulate organization of the actin cytoskeleton. As key components of versatile signaling scaffolds, filamins are implicated in developmental anomalies and cancer. Multiple isoforms of filamins exist, raising the possibility of distinct functions for each isoform during development and in disease. Here, we provide an initial characterization of jitterbug (jbug), which encodes one of the two filamin-type proteins in Drosophila. We generate Jbug antiserum that recognizes all of the spliced forms and reveals differential expression of different Jbug isoforms during development, and a significant maternal contribution of Jbug protein. To reveal the function of Jbug isoforms, we create new genetic tools, including a null allele that deletes all isoforms, hypomorphic alleles that affect only a subset, and UAS lines for Gal4-driven expression of the major isoforms. Using these tools, we demonstrate that Jbug is required for viability and that specific isoforms are required in the formation of actin-rich protrusions including thoracic bristles in adults and ventral denticles in the embryo. We also show that specific isoforms of Jbug show differential localization within epithelia and that maternal and zygotic loss of jbug disrupts Crumbs (Crb) localization in several epithelial cell types.
Robust learning under clean-label attack

We study the problem of robust learning under clean-label data-poisoning attacks, where the attacker injects (an arbitrary set of) \emph{correctly-labeled} examples to the training set to fool the algorithm into making mistakes on \emph{specific} test instances at test time. The learning goal is to minimize the attackable rate (the probability mass of attackable test instances), which is more difficult than optimal PAC learning. As we show, any robust algorithm with diminishing attackable rate can achieve the optimal dependence on $\epsilon$ in its PAC sample complexity, i.e., $O(1/\epsilon)$. On the other hand, the attackable rate might be large even for some optimal PAC learners, e.g., SVM for linear classifiers. Furthermore, we show that the class of linear hypotheses is not robustly learnable when the data distribution has zero margin and is robustly learnable in the case of positive margin but requires sample complexity exponential in the dimension. For a general hypothesis class with bounded VC dimension, if the attacker is limited to add at most $t=O(1/\epsilon)$ poison examples, the optimal robust learning sample complexity grows linearly with $t$.
VisualSparta: An Embarrassingly Simple Approach to Large-scale Text-to-Image Search with Weighted Bag-of-words

Text-to-image retrieval is an essential task in cross-modal information retrieval, i.e., retrieving relevant images from a large and unlabelled dataset given textual queries. In this paper, we propose VisualSparta, a novel (Visual-text Sparse Transformer Matching) model that shows significant improvement in terms of both accuracy and efficiency. VisualSparta is capable of outperforming previous state-of-the-art scalable methods in MSCOCO and Flickr30K. We also show that it achieves substantial retrieving speed advantages, i.e., for a 1 million image index, VisualSparta using CPU gets ~391X speedup compared to CPU vector search and ~5.4X speedup compared to vector search with GPU acceleration. Experiments show that this speed advantage even gets bigger for larger datasets because VisualSparta can be efficiently implemented as an inverted index. To the best of our knowledge, VisualSparta is the first transformer-based text-to-image retrieval model that can achieve real-time searching for large-scale datasets, with significant accuracy improvement compared to previous state-of-the-art methods.
On q-analogs of descent and peak polynomials

Abstract Descent polynomials and peak polynomials, which enumerate permutations π ∈ S n with given descent and peak sets respectively, have recently received considerable attention (Billey et al., 1989;Diaz-Lopez et al., 2019). We give several formulas for q -analogs of these polynomials which refine the enumeration by the length of π . In the case of q -descent polynomials we prove that the coefficients in one basis are strongly q -log concave, and conjecture this property in another basis. For peaks, we prove that the q -peak polynomial is palindromic in q , resolving a conjecture of Diaz-Lopez et al. (2021).
Effectiveness of smearing and tetrahedron methods: best practices in DFT codes

Density functional theory (DFT) codes are commonly treated as a "black box" in high-throughput screening of materials, with users opting for the default values of the input parameters. Often, non-experts may not sufficiently consider the effect of these parameters on prediction quality. In this work, we attempt to identify a robust set of parameters related to smearing and tetrahedron methods that return numerically accurate and efficient results for a wide variety of metallic systems. The effects of smearing and tetrahedron methods on the total energy, number of self-consistent field cycles, and forces on atoms are studied in two popular DFT codes: the Vienna Ab initio Simulation Package (VASP) and Quantum Espresso (QE). From nearly 40,000 computations, it is apparent that the optimal smearing depends on the system, smearing method, smearing parameter, and k-point density. The benefit of smearing is a minor reduction in the number of self-consistent field cycles, which is independent of the smearing method or parameter. A large smearing parameter---what is considered large is system dependent---leads to inaccurate total energies and forces. Blochl's tetrahedron method leads to small improvements in total energies. When treating diverse systems with the same input parameters, we suggest using as little smearing as possible due to the system dependence of smearing and the risk of selecting a parameter that gives inaccurate energies and forces.