cssi/SciSciGPT-SciSciNet huggingface dataset:
Huntington’s Disease

Abstract Early in 1993, an unstable, expanded trinucleotide repeat in a novel gene of unknown function was identified on HD chromosomes. This discovery unleased a flurry of experimentation that has established the expanded CAG repeat at the almost universal cause of the characteristic neurologic symptoms and pathology of this neurodegenerative disorder of midlife onset. The biochemical basis for the specific neuronal loss of HD remains uncertain, but the genetic lesion probably acts via its consequent polyglutamine segment in the protein product, huntingtin. This review will describe the basic parameters of the HD repeat's behavior and the knowledge that has accumulated concerning its potential mechanisms of action.
Enhanced Microgrid Power Flow Incorporating Hierarchical Control

An enhanced microgrid power flow (EMPF) is devised to incorporate hierarchical control effects. The new contributions are threefold: 1) an advanced-hierarchical-control-based Newton approach is established to accurately assess power sharing and voltage regulation effects; 2) a modified Jacobian matrix is derived to incorporate droop control and various secondary control modes; and 3) the secondary adjustment is calculated on top of the droop-control-based power flow results to ensure a robust Newton solution. Case studies validate that EMPF is efficacious and efficient and can serve as a powerful tool for microgrid operation and monitoring, especially for those highly meshed microgrids in urban areas.
Comparing the performance of mScarlet-I, mRuby3, and mCherry as FRET acceptors for mNeonGreen

Forster Resonance Energy Transfer (FRET) has become an immensely powerful tool to profile intra- and inter-molecular interactions. Through fusion of genetically encoded fluorescent proteins (FPs) researchers have been able to detect protein oligomerization, receptor activation, and protein translocation among other biophysical phenomena. Recently, two bright monomeric red fluorescent proteins, mRuby3 and mScarlet-I, have been developed. These proteins offer much improved physical properties compared to previous generations of monomeric red FPs that should help facilitate more general adoption of Green/Red FRET. Here we assess the ability of these two proteins, along with mCherry, to act as a FRET acceptor for the bright, monomeric, green-yellow FP mNeonGreen using intensiometric FRET and 2-photon Fluorescent Lifetime Imaging Microscopy (FLIM) FRET techniques. We first determined that mNeonGreen was a stable donor for 2-photon FLIM experiments under a variety of imaging conditions. We then tested the red FP's ability to act as FRET acceptors using mNeonGreen-Red FP tandem construct. With these constructs we found that mScarlet-I and mCherry are able to efficiently FRET with mNeonGreen in spectroscopic and FLIM FRET. In contrast, mNeonGreen and mRuby3 FRET with a much lower efficiency than predicted in these same assays. We explore possible explanations for this poor performance and determine mRuby3's protein maturation properties are a major contributor. Overall, we find that mNeonGreen is an excellent FRET donor, and both mCherry and mScarlet-I, but not mRuby3, act as practical FRET acceptors, with the brighter mScarlet-I out performing mCherry in intensiometric studies, but mCherry out performing mScarlet-I in instances where consistent efficiency in a population is critical.
Techniques for efficient detection of rapid weather changes and analysis of their impacts on a highway network

Adverse weather conditions have a significant impact on the safety, mobility, and efficiency of highway networks. Weather contributed to 23 percent of all non-reoccurring delay and approximately 544 million vehicle hours of delay each year (2014). Nearly 2.3 billion dollars each year are spent by transportation agencies for winter maintenance that contribute to close to 20 percent of most DOT’s yearly budgets (2014). These safety and mobility factors make it important to develop new and more effective methods to address road conditions during adverse weather conditions. Given weather and traffic sensors installed along side of the highway networks, how can we automatically detect weather and traffic change events and prevent from the traffic delay or harsh weather accidents? To this end, we propose a novel framework to address this problem. This paper develops techniques for efficiently detecting rapid weather change events and analyzing their impacts on the traffic flow characteristics of a highway network. It is composed of three components, including 1) detection of rapid weather change events in a highway network using the streaming weather information from a sensor network of weather stations; 2) detection of rapid traffic change events on the traffic flow characteristics (e.g., travel time) of the highway network; and 3) analysis of correlations between the detected weather and traffic change events in space and time. The proposed approach was applied to a weather dataset provided by New York State Mesonet and a traffic flow dataset the National Performance Management Research Data Set (NPMRDS) provided by NYSDOT. The empirical results provide potential evidence about the significant impacts of rapid weather change events on traffic flow characteristics of the Interstate 90 (I-90) Highway in the state of New York. We show the quantitative performance evaluation of our change event detection algorithm and three baseline methods on manually labeled the weather dataset and our method outperforms baselines in terms of precision, recall and F-score. We present the analysis of Top K detected change events as case studies and also provide the spatio-temporal correlation statistics of top k weather and traffic change events. The limitations of the proposed approach and the empirical study are also discussed.
Opting out of workers’ compensation: Non-subscription in Texas and its effects

Texas is the only state that does not mandate that employers carry workers’ compensation (WC) insurance coverage. In place of traditional WC, companies can choose to offer alternative “non-subscription” disability plans to workers. Little large-scale empirical research has studied the consequences of switching from traditional WC to non-subscription plans. We use a difference-in-differences estimator along with a novel machine learning approach to compare effects of switching to non-subscription plans for employees in Texas versus contemporaneously measured non-Texas-based employees for 25 large companies. Our results indicate that total medical payments dropped by roughly 40% from switching to non-subscription plans, consisting of reductions in hospital spending, physician spending, and other medical spending. Similarly, indemnity payments dropped by 70% and number of reported lost days dropped by 80%. Accumulating all sources of spending, we find that total expense associated with workplace injury fell by approximately 46% after switching to the non-subscription program.
Resistance and Tolerance to Root Herbivory in Maize Were Mediated by Domestication, Spread, and Breeding

Plants may defend against herbivory and disease through various means. Plant defensive strategies against herbivores include resistance and tolerance, which may have metabolic costs that affect plant growth and reproduction. Thus, expression of these strategies may be mediated by a variety of factors, such as resource availability, herbivory pressure, and plant genetic variation, among others. Additionally, artificial selection by farmers and systematic breeding by scientists may mediate the expression of resistance and tolerance in crop plants. In this study, we tested whether maize defense against Western corn rootworm (WCR) was mediated by the crop's domestication, spread, and modern breeding. We expected to find a trend of decreasing resistance to WCR with maize domestication, spread, and breeding, and a trend of increasing tolerance with decreasing resistance. To test our expectations, we compared resistance and tolerance among four Zea plants spanning those processes: Balsas teosinte, Mexican landrace maize, US landrace maize, and US inbred maize. We measured the performance of WCR larvae as a proxy for plant resistance, and plant growth as affected by WCR feeding as a proxy for plant tolerance. Our results showed that domestication and spread decreased maize resistance to WCR, as expected, whereas breeding increased maize resistance to WCR, contrary to expected. Our results also showed that maize resistance and tolerance to WCR are negatively correlated, as expected. We discussed our findings in relation to ecological-evolutionary hypotheses seeking to explain defense strategy evolution in the contexts of plant resistance-productivity trade-offs, plant tolerance-resistance trade-offs, and varying resource availability vis-a-vis plant physiological stress and herbivory pressure. Finally, we suggested that defense strategy evolution in maize, from domestication to the present, is predicted by those ecological-evolutionary hypotheses.
Analysis of consensus sorting via the cycle metric

Sorting is studied in this paper as an archetypal example to explore the optimizing power of consensus. In conceptualizing the consensus sort, the classical hill-climbing method of optimization is paired with the modern notion that value and fitness can be judged by data mining. Consensus sorting is a randomized sorting algorithm which is based on randomly selecting pairs of elements within an unsorted list (expressed in this paper as a permutation), and deciding whether to swap them based on appeals to a database of other permutations. The permutations in the database are all scored via some adaptive sorting metric, and the decision to swap depends on whether the database consensus suggests a better score as a result of swapping. This uninformed search process does not require the definition of the concept of sorting, but rather depends on selecting a metric which does a good job of distinguishing a good path to the goal, a sorted list. A previous paper has shown that the ability of the algorithm to converge on the goal depends strongly on the metric which is used, and analyzed the performance of the algorithm when number of inversions was used as a metric. This paper continues by analyzing the performance of a much more efficient metric, the number of cycles in the permutation.
The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation

Recently deep learning – namely convolutional neural networks (CNNs) – have yielded impressive performance for the task of building segmentation on large overhead (e.g., satellite) imagery benchmarks. However, these benchmark datasets only capture a small fraction of the variability present in real-world overhead imagery, limiting the ability to properly train, or evaluate, models for real-world application. Unfortunately, developing a dataset that captures even a small fraction of real-world variability is typically infeasible due to the cost of imagery, and manual pixel-wise labeling of the imagery. In this work we develop an approach to rapidly and cheaply generate large and diverse synthetic overhead imagery for training segmentation CNNs. Using this approach, we generate and publicly-release a collection of synthetic overhead imagery, termed Synthinel-1, with full pixel-wise building labels. We use several benchmark datasets to demonstrate that Synthinel-1 is consistently beneficial when used to augment real-world training imagery, especially when CNNs are tested on novel geographic locations or conditions.
Reinforcement Learning for POMDP: Partitioned Rollout and Policy Iteration with Application to Autonomous Sequential Repair Problems

In this letter we consider infinite horizon discounted dynamic programming problems with finite state and control spaces, and partial state observations. We discuss an algorithm that uses multistep lookahead, truncated rollout with a known base policy, and a terminal cost function approximation. This algorithm is also used for policy improvement in an approximate policy iteration scheme, where successive policies are approximated by using a neural network classifier. A novel feature of our approach is that it is well suited for distributed computation through an extended belief space formulation and the use of a partitioned architecture, which is trained with multiple neural networks. We apply our methods in simulation to a class of sequential repair problems where a robot inspects and repairs a pipeline with potentially several rupture sites under partial information about the state of the pipeline.
Supervised Machine Learning based Ensemble Model for Accurate Prediction of Type 2 Diabetes

According to the American Diabetes Association(ADA), 30.3 million people in the United States have diabetes, but only 7.2 million may be undiagnosed and unaware of their condition. Type 2 diabetes is usually diagnosed for most patients later on in life whereas the less common Type 1 diabetes is diagnosed early on in life. People can live healthy and happy lives while living with diabetes, but early detection produces a better overall outcome on most patient’s health. Thus, to test the accurate prediction of Type 2 diabetes, we use the patients’ information from an electronic health records company called Practice Fusion, which has about 10,000 patient records from 2009 to 2012. This data contains individual key biometrics, including age, diastolic and systolic blood pressure, gender, height, and weight. We use this data on popular machine learning algorithms and for each algorithm, we evaluate the performance of every model based on their classification accuracy, precision, sensitivity, specificity/recall, negative predictive value, and F1 score. In our study, we find that all algorithms other than Naive Bayes suffered from very low precision. Hence, we take a step further and incorporate all the algorithms into a weighted average or soft voting ensemble model where each algorithm will count towards a majority vote towards the decision outcome of whether a patient has diabetes or not. The accuracy of the Ensemble model on Practice Fusion is 85%, by far our ensemble approach is new in this space. We firmly believe that the weighted average ensemble model not only performed well in overall metrics but also helped to recover wrong predictions and aid in accurate prediction of Type 2 diabetes. Our accurate novel model can be used as an alert for the patients to seek medical evaluation in time.
Energy-Efficient Moderate Precision Time-Domain Mixed-Signal Vector-by-Matrix Multiplier Exploiting 1T-1R Arrays

The emerging mobile devices in the era of Internet-of-Things (IoT) require a dedicated processor to enable computationally intensive applications such as neuromorphic computing and signal processing. Vector-by-matrix multiplication is the most prominent operation in these applications. Therefore, there is a critical need for compact and ultralow-power vector-by-matrix multiplier (VMM) blocks to perform resource-intensive low-to-moderate precision computations. To this end, in this article, we propose a time-domain mixed-signal VMM exploiting a modified configuration of 1MOSFET-1RRAM (1T-1R) array. The proposed VMM overcomes the energy inefficiency of the current-mode VMM approaches based on RRAMs. A rigorous analysis of different nonideal factors affecting the computational precision indicates that the nonnegligible minimum cell currents, channel length modulation (CLM), and drain-induced barrier lowering (DIBL) are the dominant mechanisms degrading the precision of the proposed VMM. We also show that there exists a tradeoff between the computational precision, dynamic range, and the area- and energy-efficiency of the proposed VMM approach. Therefore, we provide the necessary design guidelines for optimizing the performance. Our preliminary results indicate that an effective computational precision of 6 bits is achievable owing to the inherent compensation effect in the modified 1T-1R blocks. Furthermore, a 4-bit $200\times200$ VMM utilizing the proposed approach exhibits a significantly high energy efficiency of ~1.5 Pops/J and a throughput of 2.5 Tops/s including the contribution from the input/output (I/O) circuitry.
Refinement and Reactivation of a Taste-Responsive Hippocampal Network.

Summary Animals need to remember the locations of nourishing and toxic food sources for survival, a fact that necessitates a mechanism for associating taste experiences with particular places. We have previously identified such responses within hippocampal place cells [ 1 ], the activity of which is thought to aid memory-guided behavior by forming a mental map of an animal’s environment that can be reshaped through experience [ 2 , 3 , 4 , 5 , 6 , 7 ]. It remains unknown, however, whether taste responsiveness is intrinsic to a subset of place cells or emerges as a result of experience that reorganizes spatial maps. Here, we recorded from neurons in the dorsal CA1 region of rats running for palatable tastes delivered via intra-oral cannulae at specific locations on a linear track. We identified a subset of taste-responsive cells that, even prior to taste exposure, had larger place fields than non-taste-responsive cells overlapping with stimulus delivery zones. Taste-responsive cells’ place fields then contracted as a result of taste experience, leading to a stronger representation of stimulus delivery zones on the track. Taste-responsive units exhibited increased sharp-wave ripple co-activation during the taste delivery session and subsequent rest periods, which correlated with the degree of place field contraction. Our results reveal that novel taste experience evokes responses within a preconfigured network of taste-responsive hippocampal place cells with large fields, whose spatial representations are refined by sensory experience to signal areas of behavioral salience. This represents a possible mechanism by which animals identify and remember locations where ecologically relevant stimuli are found within their environment.
Tips for success in pulmonary hypertension treatment: progress in isolating endothelial cells from pulmonary artery catheters.

The paucity of patient-derived pulmonary cells has been a limiting factor in the study of pulmonary hypertension; however, new advances have the potential to increase scientific translatability and identify novel mechanisms driving early diseasehttp://bit.ly/2U1AUEw
Coherency Detection and Network Partitioning based on Hierarchical DBSCAN

After a sudden disturbance, the energy balance of generators is disturbed, and the power outputs of synchronous generators vary as their rotor angles shift from their equilibrium points. This trend essentially presents the versatile response of each machine to the disturbance. Because of this change, the phase angle of the bus also differs. Hence, the versatile response of each machine can be assessed by the phase angles change at the buses close to the synchronous generator. This paper introduces a new methodology for discovering the degree of coherency among buses using the correlation index of the voltage angle between each pair of buses and use the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to partition the network into islands. The proposed approach also provides the network integrity indices (connectivity, splitting, and separation) for studying the dynamic nature of the power network system. The approach is assessed on an IEEE-39 test system with a fully dynamic model. The simulation results presented in this paper demonstrate the efficiency of the proposed approach.
A constant-time channel-assignment algorithm on reconfigurable meshes

The channel-assignment problem is central to the integrated circuit fabrication process. Given a two-sided printed circuit board, we wish to maken pairs of components electrically equivalent. The connections are made using two vertical runs along with a horizontal one. Each horizontal run lies in a channel. The channel-assignment problem involves minimizing the total number of channels used. Recent advances in VLSI have made it possible to build massively parallel machines. To overcome the inefficiency of long distance communications among processors, some parallel architectures have been augmented by bus systems. If such a bus system can be dynamically changed to suit communication needs among processors, it is referred to asreconfigurable. The reconfigurable mesh is one of the practical models featuring a reconfigurable bus system. In this paper, we propose a constant-time algorithm to solve the channel-assignment problem of sizen on a reconfigurable mesh of sizen×n.
Using Audit Programs to Improve Auditor Evidence Collection

Auditors experience difficulty auditing accounts on which it is difficult to identify in advance all the evidence necessary to perform an effective audit. These accounts are challenging because the...
Aberrant cocoons found on honey bee comb cells are found to be Osmia cornifrons (Radoszkowski) (Hymenoptera: Megachillidae)

Potential biological threats to honey bees must be identified quickly, before making disruptive and costly decisions. Here we describe numerous Osmia cornifrons (Hymenoptera: Megachillidae) cocoons...
ASIC1a plays a key role in evoking the metabolic component of the exercise pressor reflex in rats

The role played by acid-sensing ion channel 1a (ASIC1a) in evoking the exercise pressor reflex remains unknown. In decerebrated rats with freely perfused femoral arteries, blocking ASIC1a with psal...
Spacetime Foam and the Cosmological Constant

In the saddle point approximation, the Euclidean path integral for quantum gravity closely resembles a thermodynamic partition function, with the cosmological constant Λ playing the role of temperature and the “density of topologies” acting as an effective density of states. For Λ < 0, the density of topologies grows superexponentially, and the sum over topologies diverges. In thermodynamics, such a divergence can signal the existence of a maximum temperature. The same may be true in quantum gravity: the effective cosmological constant may be driven to zero by a rapid rise in the density of topologies. PACS numbers: 04.60.Gw, 98.80.Hw email: carlip@dirac.ucdavis.edu The cosmological constant Λ—in modern language, the energy density of the vacuum—is observed to be less than 10−47GeV, or 10−120 in Planck units. The cosmological constant problem [1,2], the problem of explaining the smallness of this number, is one of the central puzzles of modern physics. A natural guess is that some symmetry forces Λ to vanish, but the two obvious candidates, supersymmetry and conformal symmetry, are both badly broken. One can, of course, set Λ to zero by fiat, but this requires fine-tuning over a vast range of energies, and is in any case time-dependent, since phase transitions in the early universe can change the value of Λ. One can search for dynamical mechanisms to relax the cosmological constant to zero, but such attempts typically involve the implicit use of conformal invariance, and fail when the symmetry is broken [1]. This leaves quantum gravity as a tempting place to look for an explanation. Perhaps the most intriguing proposal to date has been Coleman’s wormhole model [3], in which topological fluctuations of spacetime induce effective nonlocal interactions that smear Λ into a probabilistic distribution peaked sharply at zero. The proposal presented in this paper is similar in spirit to Coleman’s, but different in detail: I consider a different set of topologies, with metrics that (unlike Coleman’s) are exact saddle points of the functional integral, and I interpret the resulting partition function rather differently. In particular, I will argue that a rapidly growing density of topologies may drive the cosmological constant to zero, as processes that could increase |Λ| instead merely produce more complicated “spacetime foam.” 1. The Euclidean Gravitational Partition Function I shall work in Euclidean quantum gravity, that is, quantum gravity “analytically continued” to Riemannian (positive-definite) metrics, since this seems to be the most natural setting in which to consider fluctuations of spacetime topology. The partition function for the volume canonical ensemble is [4, 5]
Mechanical Behavior Of Energetic Materials During High Acceleration

The mechanical behavior of explosives subjected to high acceleration has been studied in an ultracentrifuge at –10°C and 25°C. Melt-cast TNAZ and pressed TNAZ, LX-14, Composition A3 Type II, PAX-2A, and PAX-3 have been studied. Failure occurs when the shear or tensile strength of the explosive is exceeded. The fracture acceleration of melt-cast TNAZ is greater than that of pressed TNAZ at –10°C and 25°C. The fracture acceleration of PAX-3 is greater than that of Composition A3 Type II at –10°C and 25°C. The fracture acceleration of melt-cast TNAZ and pressed TNAZ at –10°C is about 10less than at 25°C. The fracture acceleration of PAX-3 at –10°C is about 2.6 times that at 25°C. The fracture acceleration of Composition A3 Type II at –10°C is about 1.7 times that at 25°C.
The New Business Entities in Evolutionary Perspective

The many legal forms for business organisations that first appeared in the United States during the last thirty years — the limited liability company (LLC), the limited liability partnership (LLP), the limited liability limited partnership (LLLP) and the statutory business trust — all combine the pattern of creditors’ rights, or asset partitioning, that is traditional to the business organisation with the freedom of contract among investors and managers that is traditional to the partnership. To view these new entities as partnership-like is to treat the degree of freedom of contract as the essential difference between the traditional corporation and partnership forms; to view them as corporation-like is to treat the pattern of creditors’ rights as the essential difference. While recent scholarship often takes the former view, the latter seems more accurate. History shows that much of the contractual inflexibility in the traditional corporation served merely to buttress its pattern of creditors’ rights and that this inflexibility fell away upon the development of substitute sources of investor protection. The new forms are thus better understood as part of the continuing development of the corporate form rather than as entities more akin to the traditional partnership, which has in fact been evolving in a different direction. This article first develops this argument in terms of the trade-off between contractual freedom and the form of asset partitioning that to date has received the most scholarly attention, that is, limited liability. It then explores the evolution of the new forms from a less familiar perspective, focusing on the entity shielding component of asset partitioning.
Output Gap Uncertainty and Real-Time Monetary Policy

Abstract Output gap estimates are subject to a wide range of uncertainty owing principally to the difficulty in distinguishing between cycle and trend in real time. We show that country desks tend to overestimate economic slack, especially during recessions, and that uncertainty in initial output gap estimates persists several years. Only a small share of output gap revisions is predictable based on output dynamics, data quality, and policy frameworks. We also show that for a group of Latin American inflation targeters the prescriptions from monetary policy rules are subject to large changes due to revised output gap estimates. These explain a sizable proportion of the deviation of inflation from target, suggesting this information is not accounted for in real-time policy decisions.
Using a Generative Adversarial Network for CT Normalization and Its Impact on Radiomic Features

Computer-Aided-Diagnosis (CADx) systems assist radiologists with identifying and classifying potentially malignant pulmonary nodules on chest CT scans using morphology and texture-based (radiomic) features. However, radiomic features are sensitive to differences in acquisitions due to variations in dose levels and slice thickness. This study investigates the feasibility of generating a normalized scan from heterogeneous CT scans as input. We obtained projection data from 40 low-dose chest CT scans, simulating acquisitions at 10%, 25% and 50% dose and reconstructing the scans at 1.0mm and 2.0mm slice thickness. A 3D generative adversarial network (GAN) was used to simultaneously normalize reduced dose, thick slice (2.0mm) images to normal dose (100%), thinner slice (1.0mm) images. We evaluated the normalized image quality using peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and Learned Perceptual Image Patch Similarity (LPIPS). Our GAN improved perceptual similarity by 35%, compared to a baseline CNN method. Our analysis also shows that the GAN-based approach led to a significantly smaller error (p-value < 0.05) in nine studied radiomic features. These results indicated that GANs could be used to normalize heterogeneous CT images and reduce the variability in radiomic feature values.
Frameshift mutations of YPEL3 alter the sensory circuit function in Drosophila.

A frameshift mutation in Yippee-like (YPEL) 3 was recently found from a rare human disorder with peripheral neurological conditions including hypotonia and areflexia. The YPEL gene family is highly conserved from yeast to human, but their functions are poorly defined. Moreover, the pathogenicity of the human YPEL3 variant is completely unknown. We generated a Drosophila model of human YPEL3 variant and a genetic null allele of Drosophila YPEL3 (dYPEL3). Gene-trap analysis suggests that dYPEL3 is predominantly expressed in subsets of neurons, including larval nociceptors. Analysis on chemical nociception induced by allyl-isothiocyanate (AITC), a natural chemical stimulant, revealed reduced nociceptive responses in both dYPEL3 frameshift and null mutants. Subsequent circuit analysis showed reduced activation of second-order neurons (SONs) in the pathway without affecting nociceptor activation upon AITC treatment. Although the gross axonal and dendritic development of nociceptors was not affected, the synaptic contact between nociceptors and SONs were decreased by the dYPEL3 mutations. Furthermore, expressing dYPEL3 in larval nociceptors rescued the behavioral deficit in dYPEL3 frameshift mutants, suggesting a presynaptic origin of the deficit. Together, these findings suggest that the frameshift mutation results in YPEL3 loss-of-function and may cause neurological conditions by weakening synaptic connections through presynaptic mechanisms.
New Notions of Security: Achieving Universal Composability without Trusted Setup.

We propose a modification to the framework of Universally Composable (UC) security [3]. Our new notion, involves comparing the protocol executions with an ideal execution involving ideal functionalities (just as in UC-security), but allowing the environment and adversary access to some super-polynomial computational power. We argue the meaningfulness of the new notion, which in particular subsumes many of the traditional notions of security. We generalize the Universal Composition theorem of [3] to the new setting. Then under new computational assumptions, we realize secure multi-party computation (for static adversaries) without a common reference string or any other set-up assumptions, in the new framework. This is known to be impossible under the UC framework.
Convergence and consistency of recursive least squares with variable-rate forgetting

Abstract A recursive least squares algorithm with variable rate forgetting (VRF) is derived by minimizing a quadratic cost function. Under persistent excitation, the minimizer given by VRF is shown to converge to the true parameters. In addition, under persistent excitation and with noisy measurements, where the noise is uncorrelated with the regressor, conditions are given under which the minimizer given by VRF is a consistent estimator of the true parameters. The results are illustrated by a numerical example involving abruptly changing parameters.
Projected changes in the terrestrial and oceanic regulators of climate variability across sub-Saharan Africa

Future changes in the sign and intensity of ocean–land–atmosphere interactions have been insufficiently studied, despite implications for regional climate change projections, extreme event statistics, and seasonal climate predictability. In response to this deficiency, the present study focuses on projected responses to the enhanced greenhouse effect in: (1) the mean state of the atmosphere and land surface; (2) oceanic and terrestrial drivers of sub-Saharan climate variability; and (3) total seasonal climate predictability of sub-Saharan Africa, a region known for its pronounced land–atmosphere coupling. Analysis focuses on output from 23 Earth System Models in the Coupled Model Intercomparison Project Phase Five for the late twentieth and twenty-first centuries. It is projected that the greatest warming across sub-Saharan Africa will occur over the Sahel, the monsoon season will become more persistent into late summer and autumn, short rains in the Horn of Africa (HOA) will intensify, and leaf area index will increase across the HOA. Stepwise Generalized Equilibrium Feedback Assessment, i.e. a multivariate statistical approach, is applied to the model output over sub-Saharan Africa in order to explore the oceanic and terrestrial drivers of regional climate. The models indicate that the study region’s climate variability is dominated by oceanic drivers, with secondary contributions from soil moisture and very modest impacts from vegetation. Overall, the general model consensus of future projections indicates a concerning diminished seasonal predictability of sub-Saharan African regional climate based on key oceanic and terrestrial predictors and an elevated role of the land surface (associated with soil moisture anomalies) compared to oceanic drivers in regulating regional climate variability.
Bivariate Order Polynomials

Motivated by Dohmen–Ponitz–Tittmann’s bivariate chromatic polynomial $$\chi _G(x,y)$$, which counts all x-colorings of a graph G such that adjacent vertices get different colors if they are $$\le y$$, we introduce a bivarate version of Stanley’s order polynomial, which counts order preserving maps from a given poset to a chain. Our results include decomposition formulas in terms of linear extensions, a combinatorial reciprocity theorem, and connections to bivariate chromatic polynomials.
A homotopy category for graphs

We show that the category of graphs has the structure of a 2-category with homotopy as the 2-cells. We then develop an explicit description of homotopies for finite graphs, in terms of what we call ‘spider moves.’ We then create a category by modding out by the 2-cells of our 2-category and use the spider moves to show that for finite graphs, this category is a homotopy category in the sense that it satisfies the universal property for localizing homotopy equivalences. We then show that finite stiff graphs form a skeleton of this homotopy category.
Spectrum Dependent Learning Curves in Kernel Regression and Wide Neural Networks

We derive analytical expressions for the generalization performance of kernel regression as a function of the number of training samples using theoretical methods from Gaussian processes and statistical physics. Our expressions apply to wide neural networks due to an equivalence between training them and kernel regression with the Neural Tangent Kernel (NTK). By computing the decomposition of the total generalization error due to different spectral components of the kernel, we identify a new spectral principle: as the size of the training set grows, kernel machines and neural networks fit successively higher spectral modes of the target function. When data are sampled from a uniform distribution on a high-dimensional hypersphere, dot product kernels, including NTK, exhibit learning stages where different frequency modes of the target function are learned. We verify our theory with simulations on synthetic data and MNIST dataset.
Improving Robustness of Deep-Learning-Based Image Reconstruction

Deep-learning-based methods for different applications have been shown vulnerable to adversarial examples. These examples make deployment of such models in safety-critical tasks questionable. Use of deep neural networks as inverse problem solvers has generated much excitement for medical imaging including CT and MRI, but recently a similar vulnerability has also been demonstrated for these tasks. We show that for such inverse problem solvers, one should analyze and study the effect of adversaries in the measurement-space, instead of the signal-space as in previous work. In this paper, we propose to modify the training strategy of end-to-end deep-learning-based inverse problem solvers to improve robustness. We introduce an auxiliary network to generate adversarial examples, which is used in a min-max formulation to build robust image reconstruction networks. Theoretically, we show for a linear reconstruction scheme the min-max formulation results in a singular-value(s) filter regularized solution, which suppresses the effect of adversarial examples occurring because of ill-conditioning in the measurement matrix. We find that a linear network using the proposed min-max learning scheme indeed converges to the same solution. In addition, for non-linear Compressed Sensing (CS) reconstruction using deep networks, we show significant improvement in robustness using the proposed approach over other methods. We complement the theory by experiments for CS on two different datasets and evaluate the effect of increasing perturbations on trained networks. We find the behavior for ill-conditioned and well-conditioned measurement matrices to be qualitatively different.
Conventional dose rate spatially-fractionated radiation therapy (SFRT) treatment response and its association with dosimetric parameters-A preclinical study in a Fischer 344 rat model.

Purpose To identify key dosimetric parameters that have close associations with tumor treatment response and body weight change in SFRT treatments with a large range of spatial-fractionation scale at dose rates of several Gy/min. Methods Six study arms using uniform tumor radiation, half-tumor radiation, 2mm beam array radiation, 0.3mm minibeam radiation, and an untreated arm were used. All treatments were delivered on a 320kV x-ray irradiator. Forty-two female Fischer 344 rats with fibrosarcoma tumor allografts were used. Dosimetric parameters studied are peak dose and width, valley dose and width, peak-to-valley-dose-ratio (PVDR), volumetric average dose, percentage volume directly irradiated, and tumor- and normal-tissue EUD. Animal survival, tumor volume change, and body weight change (indicative of treatment toxicity) are tested for association with the dosimetric parameters using linear regression and Cox Proportional Hazards models. Results The dosimetric parameters most closely associated with tumor response are tumor EUD (R2 = 0.7923, F-stat = 15.26*; z-test = -4.07***), valley (minimum) dose (R2 = 0.7636, F-stat = 12.92*; z-test = -4.338***), and percentage tumor directly irradiated (R2 = 0.7153, F-stat = 10.05*; z-test = -3.837***) per the linear regression and Cox Proportional Hazards models, respectively. Tumor response is linearly proportional to valley (minimum) doses and tumor EUD. Average dose (R2 = 0.2745, F-stat = 1.514 (no sig.); z-test = -2.811**) and peak dose (R2 = 0.04472, F-stat = 0.6874 (not sig.); z-test = -0.786 (not sig.)) show the weakest associations to tumor response. Only the uniform radiation arm did not gain body weight post-radiation, indicative of treatment toxicity; however, body weight change in general shows weak association with all dosimetric parameters except for valley (minimum) dose (R2 = 0.3814, F-stat = 13.56**), valley width (R2 = 0.2853, F-stat = 8.783**), and peak width (R2 = 0.2759, F-stat = 8.382**). Conclusions For a single-fraction SFRT at conventional dose rates, valley, not peak, dose is closely associated with tumor treatment response and thus should be used for treatment prescription. Tumor EUD, valley (minimum) dose, and percentage tumor directly irradiated are the top three dosimetric parameters that exhibited close associations with tumor response.
Stable and dynamic representations of value in the prefrontal cortex

Optimal decision-making requires that stimulus-value associations are kept up to date by constantly comparing the expected value of a stimulus with its experienced outcome. To do this, value information must be held in mind when a stimulus and outcome are separated in time. However, little is known about the neural mechanisms of working memory (WM) for value. Contradicting theories have suggested WM requires either persistent or transient neuronal activity, with stable or dynamic representations, respectively. To test these hypotheses, we recorded neuronal activity in the orbitofrontal and anterior cingulate cortex of two monkeys performing a valuation task. We found that features of all hypotheses were simultaneously present in prefrontal activity, and no single hypothesis was exclusively supported. Instead, mixed dynamics supported robust, time invariant value representations while also encoding the information in a temporally specific manner. We suggest that this hybrid coding is a critical mechanism supporting flexible cognitive abilities.
Assessing conformer energies using electronic structure and machine learning methods

We have performed a large-scale evaluation of current computational methods, including conventional small-molecule force fields, semiempirical, density functional, ab initio electronic structure methods, and current machine learning (ML) techniques to evaluate relative single-point energies. Using up to 10 local minima geometries across ~700 molecules, each optimized by B3LYP-D3BJ with single-point DLPNO-CCSD(T) triple-zeta energies, we consider over 6,500 single points to compare the correlation between different methods for both relative energies and ordered rankings of minima. We find promise from current ML methods and recommend methods at each tier of the accuracy-time tradeoff, particularly the recent GFN2 semiempirical method, the B97-3c density functional approximation, and RI-MP2 for accurate conformer energies. The ANI family of ML methods shows promise, particularly the ANI-1ccx variant trained in part on coupled-cluster energies. Multiple methods suggest continued improvements should be expected in both performance and accuracy.
Totally geodesic submanifolds of Teichmüller space

Let $\mathcal{T}_{g,n}$ and $\mathcal{M}_{g,n}$ denote the Teichmuller and moduli space respectively of genus $g$ Riemann surfaces with $n$ marked points. The Teichmuller metric on these spaces is a natural Finsler metric that quantifies the failure of two different Riemann surfaces to be conformally equivalent. It is equal to the Kobayashi metric [Roy74], and hence reflects the intrinsic complex geometry of these spaces. There is a unique holomorphic and isometric embedding from the hyperbolic plane to $\mathcal{T}_{g,n}$ whose image passes through any two given points. The images of such maps, called Teichmuller disks or complex geodesics, are much studied in relation to the geometry and dynamics of Riemann surfaces and their moduli spaces. A complex submanifold of $\mathcal{T}_{g,n}$ is called totally geodesic if it contains a complex geodesic through any two of its points, and a subvariety of $\mathcal{M}_g$ is called totally geodesic if a component of its preimage in $\mathcal{T}_{g,n}$ is totally geodesic. Totally geodesic submanifolds of dimension $1$ are exactly the complex geodesics. Almost every complex geodesic in $\mathcal{T}_{g,n}$ has dense image in $\mathcal{M}_{g,n}$ [Mas82, Vee82]. We show that higher dimensional totally geodesic submanifolds are much more rigid.
Minimax Optimal Estimation of KL Divergence for Continuous Distributions

Estimating Kullback-Leibler divergence from identical and independently distributed samples is an important problem in various domains. One simple and effective estimator is based on the $k$ nearest neighbor distances between these samples. In this paper, we analyze the convergence rates of the bias and variance of this estimator. Furthermore, we derive a lower bound of the minimax mean square error and show that kNN method is asymptotically rate optimal.
An updated re-entry analysis of the Hubble Space Telescope

Abstract The Hubble Space Telescope (HST), launched in 1990, has without question given us a better understanding of the Universe [1] . The storied spacecraft has far exceeded its design life and, in spite of four repair missions, is nearing the end of its useful lifespan. Originally designed to be returned by the Space Shuttle, the HST has no on-board propulsion system. A 2012 study estimated that without intervention, the HST will re-enter the atmosphere in approximately 2027 with a 1:240 risk of fatality [2] . This study updates that analysis with more recent de-orbit technologies and updated trajectory information. We propose a design solution to safely perform a targeted de-orbit, assuming a worst-case scenario (a non-functional, tumbling spacecraft). Multiple de-orbit options are assessed to actively capture the satellite. Results frame an approach that could be accomplished with proven technologies at reasonable cost to improve the fatality risk as required by US Government regulation [3] . Moreover, delayed action would significantly increase mission cost and complexity so we recommend a project start in the near future.
Exploring rapid transient detection with the Athena Wide Field Imager

X-ray transients are among the most enigmatic objects in the cosmic sky. In recent years, the unpredictability and underlying nature of their transient behavior has prompted many studies. While significant progress has been made in this field, a more complete understanding of such events is often hampered by the delay in the rapid follow-up of any transient event. An efficient way to mitigate this constraint would be to devise a way for near real-time detection of such transient phenomena. The Advanced Telescope for High-Energy Astrophysics/Wide Field Imager (Athena/WFI), with its 40 ′ × 40 ′ field of view and large effective area, will detect a large number of x-ray variable or transient objects daily. We discuss an algorithm for the rapid onboard or ground-based detection of x-ray transients with WFI. We present a feasibility test of the algorithm using simulated Athena WFI data and show that a fairly simple algorithm can effectively detect transient and variable sources in typical Athena WFI observations.
General witness sets for numerical algebraic geometry

Numerical algebraic geometry has a close relationship to intersection theory from algebraic geometry. We deepen this relationship, explaining how rational or algebraic equivalence gives a homotopy. We present a general notion of witness set for subvarieties of a smooth complete complex algebraic variety using ideas from intersection theory. Under appropriate assumptions, general witness sets enable numerical algorithms such as sampling and membership. These assumptions hold for products of flag manifolds. We introduce Schubert witness sets, which provide general witness sets for Grassmannians and flag manifolds.
Meager sets, games and singular cardinals

Abstract We show that a statement concerning the existence of winning strategies of limited memory in an infinite two-person topological game is equivalent to a weak version of the Singular Cardinals Hypothesis.