cssi/SciSciGPT-SciSciNet huggingface dataset:
Expanding the Natural Laminar Flow Boundary for Supersonic Transports

A computational design and analysis methodology is being developed to design a vehicle that can support significant regions of natural laminar flow (NLF) at supersonic flight conditions. The methodology is built in the CDISC design module to be used in this paper with the flow solvers Cart3D and USM3D, and the transition prediction modules BLSTA3D and LASTRAC. The NLF design technique prescribes a target pressure distribution for an existing geometry based on relationships between modal instability wave growth and pressure gradients. The modal instability wave growths (both on- and off-axes crossflow and Tollmien-Schlichting) are balanced to produce a pressure distribution that will have a theoretical maximum NLF region for a given streamwise wing station. An example application is presented showing the methodology on a generic supersonic transport wingbody configuration. The configuration has been successfully redesigned to support significant regions of NLF (approximately 40% of the wing upper surface by surface area). Computational analysis predicts NLF with transition Reynolds numbers (ReT) as high as 36 million with 72 degrees of leading-edge sweep (ΛLE), significantly expanding the current boundary of ReT - ΛLE combinations for NLF. This NLF geometry provides a total drag savings of 4.3 counts compared to the baseline wing-body configuration (approximately 5% of total drag). Off-design evaluations at near-cruise and low-speed, high-lift conditions are discussed, as well as attachment line contamination/transition concerns. This computational NLF design effort is a part of an ongoing cooperative agreement between NASA and JAXA researchers.
NewsEmbed: Modeling News through Pre-trained Document Representations

Effectively modeling text-rich fresh content such as news articles at document-level is a challenging problem. To ensure a content-based model generalize well to a broad range of applications, it is critical to have a training dataset that is large beyond the scale of human labels while achieving desired quality. In this work, we address those two challenges by proposing a novel approach to mine semantically-relevant fresh documents, and their topic labels, with little human supervision. Meanwhile, we design a multitask model called NewsEmbed that alternatively trains a contrastive learning with a multi-label classification to derive a universal document encoder. We show that the proposed approach can provide billions of high quality organic training examples and can be naturally extended to multilingual setting where texts in different languages are encoded in the same semantic space. We experimentally demonstrate NewsEmbed's competitive performance across multiple natural language understanding tasks, both supervised and unsupervised.
Cost-Aware Robust Tree Ensembles for Security Applications.

There are various costs for attackers to manipulate the features of security classifiers. The costs are asymmetric across features and to the directions of changes, which cannot be precisely captured by existing cost models based on $L_p$-norm robustness. In this paper, we utilize such domain knowledge to increase the attack cost of evading classifiers, specifically, tree ensemble models that are widely used by security tasks. We propose a new cost modeling method to capture the feature manipulation cost as constraint, and then we integrate the cost-driven constraint into the node construction process to train robust tree ensembles. During the training process, we use the constraint to find data points that are likely to be perturbed given the feature manipulation cost, and we use a new robust training algorithm to optimize the quality of the trees. Our cost-aware training method can be applied to different types of tree ensembles, including gradient boosted decision trees and random forest models. Using Twitter spam detection as the case study, our evaluation results show that we can increase the attack cost by 10.6X compared to the baseline. Moreover, our robust training method using cost-driven constraint can achieve higher accuracy, lower false positive rate, and stronger cost-aware robustness than the state-of-the-art training method using $L_\infty$-norm cost model. Our code is available at this https URL.
Adaptive Shape Control for Aerodynamic Design

We present an approach to aerodynamic optimization in which the shape control is adaptively parameterized. Starting from a coarse set of design variables, a sequence of higher-dimensional nested search spaces is automatically generated. Refinement can be either uniform or adaptive, in which case only the most important shape control is added. The relative importance of candidate design variables is determined by comparing objective and constraint gradients, computed at low cost via adjoint solutions. A search procedure for finding an effective ensemble of shape parameters is also given. We first demonstrate this system on a multipoint drag miminization problem in 2D with many constraints, showing that an adaptive parameterization approach consistently achieves smoother, more robust, and faster design improvement than fixed parameterizations. We also establish a 3D shape- matching benchmark, where we demonstrate that our approach automatically discovers the necessary parameters to match a target shape. By largely automating shape parameterization, this work also aims to remove a time-consuming aspect of shape optimization.
Development, implementation, and experimental outdoor evaluation of quadcopter controllers for computationally limited embedded systems

Abstract Quadcopters are increasingly used for applications ranging from hobby to industrial products and services. This paper serves as a tutorial on the design, simulation, implementation, and experimental outdoor testing of digital quadcopter flight controllers, including Explicit Model Predictive Control, Linear Quadratic Regulator, and Proportional Integral Derivative. A quadcopter was flown in an outdoor testing facility and made to track an inclined, circular path at different tangential velocities under ambient wind conditions. Controller performance was evaluated via multiple metrics, such as position tracking error, velocity tracking error, and onboard computation time. Challenges related to the use of computationally limited embedded hardware and flight in an outdoor environment are addressed with proposed solutions.
Consistency Regularization with High-dimensional Non-adversarial Source-guided Perturbation for Unsupervised Domain Adaptation in Segmentation.

Unsupervised domain adaptation for semantic segmentation has been intensively studied due to the low cost of the pixel-level annotation for synthetic data. The most common approaches try to generate images or features mimicking the distribution in the target domain while preserving the semantic contents in the source domain so that a model can be trained with annotations from the latter. However, such methods highly rely on an image translator or feature extractor trained in an elaborated mechanism including adversarial training, which brings in extra complexity and instability in the adaptation process. Furthermore, these methods mainly focus on taking advantage of the labeled source dataset, leaving the unlabeled target dataset not fully utilized. In this paper, we propose a bidirectional style-induced domain adaptation method, called BiSIDA, that employs consistency regularization to efficiently exploit information from the unlabeled target domain dataset, requiring only a simple neural style transfer model. BiSIDA aligns domains by not only transferring source images into the style of target images but also transferring target images into the style of source images to perform high-dimensional perturbation on the unlabeled target images, which is crucial to the success in applying consistency regularization in segmentation tasks. Extensive experiments show that our BiSIDA achieves new state-of-the-art on two commonly-used synthetic-to-real domain adaptation benchmarks: GTA5-to-CityScapes and SYNTHIA-to-CityScapes. Code and pretrained style transfer model are available at: https://github.com/wangkaihong/BiSIDA.
Hodge theory of the Turaev cobracket and the Kashiwara–Vergne problem

In this paper we show that, after completing in the $I$-adic topology, the Turaev cobracket on the vector space freely generated by the closed geodesics on a smooth, complex algebraic curve $X$ with an algebraic framing is a morphism of mixed Hodge structure. We combine this with results of a previous paper (arXiv:1710.06053) on the Goldman bracket to construct torsors of solutions of the Kashiwara--Vergne problem in all genera. The solutions so constructed form a torsor under a prounipotent group that depends only on the topology of the framed surface. We give a partial presentation of these groups. Along the way, we give a homological description of the Turaev cobracket.
Identification and Mitigation of False Data Injection using Multi State Implicative Interdependency Model (MSIIM) for Smart Grid

Smart grid monitoring, automation and control will completely rely on PMU based sensor data soon. Accordingly, a high throughput, low latency Information and Communication Technology (ICT) infrastructure should be opted in this regard. Due to the low cost, low power profile, dynamic nature, improved accuracy and scalability, wireless sensor networks (WSNs) can be a good choice. Yet, the efficiency of a WSN depends a lot on the network design and the routing technique. In this paper a new design of the ICT network for smart grid using WSN is proposed. In order to understand the interactions between different entities, detect their operational levels, design the routing scheme and identify false data injection by particular ICT entities, a new model of interdependency called the Multi State Implicative Interdependency Model (MSIIM) is proposed in this paper, which is an updated version of the Modified Implicative Interdependency Model (MIIM) [1]. MSIIM considers the data dependency and operational accuracy of entities together with structural and functional dependencies between them. A multi-path secure routing technique is also proposed in this paper which relies on the MSIIM model for its functioning. Simulation results prove that MSIIM based False Data Injection (FDI) detection and mitigation works better and faster than existing methods.
GazeBase, a large-scale, multi-stimulus, longitudinal eye movement dataset.

This manuscript presents GazeBase, a large-scale longitudinal dataset containing 12,334 monocular eye-movement recordings captured from 322 college-aged participants. Participants completed a battery of seven tasks in two contiguous sessions during each round of recording, including a – (1) fixation task, (2) horizontal saccade task, (3) random oblique saccade task, (4) reading task, (5/6) free viewing of cinematic video task, and (7) gaze-driven gaming task. Nine rounds of recording were conducted over a 37 month period, with participants in each subsequent round recruited exclusively from prior rounds. All data was collected using an EyeLink 1000 eye tracker at a 1,000 Hz sampling rate, with a calibration and validation protocol performed before each task to ensure data quality. Due to its large number of participants and longitudinal nature, GazeBase is well suited for exploring research hypotheses in eye movement biometrics, along with other applications applying machine learning to eye movement signal analysis. Classification labels produced by the instrument’s real-time parser are provided for a subset of GazeBase, along with pupil area. Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14761866
Transition to strong coupling regime in hybrid plasmonic systems: exciton-induced transparency and Fano interference

We present a microscopic model describing the transition to strong coupling regime for an emitter resonantly coupled to a surface plasmon in a metal-dielectric structure. We demonstrate that the shape of scattering spectra is determined by an interplay of two distinct mechanisms. First is the near-field coupling between the emitter and the plasmon mode which underpins energy exchange between the system components and gives rise to exciton-induced transparency minimum in scattering spectra prior the transition to strong coupling regime. The second mechanism is Fano interference between the plasmon dipole and the plasmon-induced emitter's dipole as the system interacts with the radiation field. We show that the Fano interference can strongly affect the overall shape of scattering spectra, leading to the inversion of spectral asymmetry that was recently reported in the experiment.
Testing Permanent Oracles - Revisited.

Suppose we are given an oracle that claims to approximate the permanent for most matrices X, where X is chosen from the Gaussian ensemble (the matrix entries are i.i.d. univariate complex Gaussians). Can we test that the oracle satisfies this claim? This paper gives a polynomial-time algorithm for the task. The oracle-testing problem is of interest because a recent paper of Aaronson and Arkhipov showed that if there is a polynomial-time algorithm for simulating boson-boson interactions in quantum mechanics, then an approximation oracle for the permanent (of the type described above) exists in BPP^NP. Since computing the permanent of even 0/1 matrices is #P-complete, this seems to demonstrate more computational power in quantum mechanics than Shor's factoring algorithm does. However, unlike factoring, which is in NP, it was unclear previously how to test the correctness of an approximation oracle for the permanent, and this is the contribution of the paper. The technical difficulty overcome here is that univariate polynomial self-correction, which underlies similar oracle-testing algorithms for permanent over finite fields --- and whose discovery led to a revolution in complexity theory --- does not seem to generalize to complex (or even, real) numbers. We believe that this tester will motivate further progress on understanding the permanent of Gaussian matrices.
Of Cops and Bumper Stickers: Notes toward a Theory of Selective Prosecution

The author, Professor Richard Delgado, takes as his point of departure a remark by the chair of the Colorado committee that voted academic sanctions against Ward Churchill. This essay explores the role of retaliatory motives in academic miscon-
Lossy Event Compression Based On Image-Derived Quad Trees And Poisson Disk Sampling

With several advantages over conventional RGB cameras, event cameras have provided new opportunities for tackling visual tasks under challenging scenarios with fast motion, high dynamic range, and/or power constraint. Yet unlike image/video compression, the performance of event compression algorithm is far from satisfying and practical. The main challenge for compressing events is the unique event data form, i.e., a stream of asynchronously fired event tuples each encoding the 2D spatial location, timestamp, and polarity (denoting an increase or decrease in brightness). Since events only encode temporal variations, they lack spatial structure which is crucial for compression. To address this problem, we propose a novel event compression algorithm based on a quad tree (QT) segmentation map derived from the adjacent intensity images. The QT informs 2D spatial priority within the 3D space-time volume. In the event encoding step, events are first aggregated over time to form polarity-based event histograms. The histograms are then variably sampled via Poisson Disk Sampling prioritized by the QT based segmentation map. Next, differential encoding and run length encoding are employed for encoding the spatial and polarity information of the sampled events, respectively, followed by Huffman encoding to produce the final encoded events. Our Poisson Disk Sampling based Lossy Event Compression (PDS-LEC) algorithm performs rate-distortion based optimal allocation. On average, our algorithm achieves greater than 6x compression compared to the state of the art.
Properly discontinuous actions versus uniform embeddings

Whenever a finitely generated group $G$ acts properly discontinuously by isometries on a metric space $X$, there is an induced uniform embedding (a Lipschitz and uniformly proper map) $\rho: G \rightarrow X$ given by mapping $G$ to an orbit. We study when there is a difference between a finitely generated group $G$ acting properly on a contractible $n$-manifold and uniformly embedding into a contractible $n$-manifold. For example, Kapovich and Kleiner showed that there are torsion-free hyperbolic groups that uniformly embed into a contractible $3$-manifold but only virtually act on a contractible $3$-manifold. We show that $k$-fold products of these examples do not act on a contractible $3k$-manifold.
Bounds on Point Configurations Determined by Distances and Dot Products

We study a family of variants of Erdő’s unit distance problem, concerning distances and dot products between pairs of points chosen from a large finite point set. Specifically, given a large finite set of n points E, we look for bounds on how many subsets of k points satisfy a set of relationships between point pairs based on distances or dot products. We survey some of the recent work in the area and present several new, more general families of bounds.
Personalizing User Engagement Dynamics in a Non-Verbal Communication Game for Cerebral Palsy

Children and adults with cerebral palsy (CP) can have involuntary upper limb movements as a consequence of the symptoms that characterize their motor disability, leading to difficulties in communicating with caretakers and peers. We describe how a socially assistive robot may help individuals with CP to practice non-verbal communicative gestures using an active orthosis in a one-on-one number-guessing game. We performed a user study and data collection with participants with CP; we found that participants preferred an embodied robot over a screen-based agent, and we used the participant data to train personalized models of participant engagement dynamics that can be used to select personalized robot actions. Our work highlights the benefit of personalized models in the engagement of users with CP with a socially assistive robot and offers design insights for future work in this area.
Longitudinal auditory pathophysiology following mild blast-induced trauma.

Few studies on blast-induced hearing deficits go beyond simple sounds and sparsely track postexposure. Therefore, the recovery arc for potential therapies and real-world listening is poorly underst...
Novel Theorems and Algorithms Relating to the Collatz Conjecture

Proposed in 1937, the Collatz conjecture has remained in the spotlight for mathematicians and computer scientists alike due to its simple proposal, yet intractable proof. In this paper, we propose several novel theorems, corollaries, and algorithms that explore relationships and properties between the natural numbers, their peak values, and the conjecture. These contributions primarily analyze the number of Collatz iterations it takes for a given integer to reach 1 or a number less than itself, or the relationship between a starting number and its peak value.
Learning Robust Hybrid Control Barrier Functions for Uncertain Systems

Abstract The need for robust control laws is especially important in safety-critical applications. We propose robust hybrid control barrier functions as a means to synthesize control laws that ensure robust safety. Based on this notion, we formulate an optimization problem for learning robust hybrid control barrier functions from data. We identify sufficient conditions on the data such that feasibility of the optimization problem ensures correctness of the learned robust hybrid control barrier functions. Our techniques allow us to safely expand the region of attraction of a compass gait walker that is subject to model uncertainty.
Fourier-like Thermal Relaxation of Nanoscale Explosive Hot Spots

We develop here an approach to directly determine the thermal transport properties of explosive hot spots with realistic initial structures through a combination of molecular dynamics (MD) and diffusive heat equation (HEq) modeling. Effective thermal conductivity values are determined by fitting HEq models to MD predictions of long timescale hot spot relaxation. The approach is applied to model hot spots in the molecular crystalline explosive TATB for a range of shock strengths and two limiting cases for impact orientation. Conductivity is found to be a strong function of density, which parametrically captures dependence on temperature, pressure, and material state. The approach provides a convenient foundation for determining the effective thermal conductivity for hot spot problems in other explosives and directly yields information on reasonable approximations that might be taken in higher-level models for those materials.
Species Distribution Modeling for Machine Learning Practitioners: A Review

Conservation science depends on an accurate understanding of what’s happening in a given ecosystem. How many species live there? What is the makeup of the population? How is that changing over time? Species Distribution Modeling (SDM) seeks to predict the spatial (and sometimes temporal) patterns of species occurrence, i.e. where a species is likely to be found. The last few years have seen a surge of interest in applying powerful machine learning tools to challenging problems in ecology [2, 5, 8]. Despite its considerable importance, SDM has received relatively little attention from the computer science community. Our goal in this work is to provide computer scientists with the necessary background to read the SDM literature and develop ecologically useful ML-based SDM algorithms. In particular, we introduce key SDM concepts and terminology, review standard models, discuss data availability, and highlight technical challenges and pitfalls.
Simulating spin waves in entropy stabilized oxides

The entropy stabilized oxide ${\mathrm{Mg}}_{0.2}{\mathrm{Co}}_{0.2}{\mathrm{Ni}}_{0.2}{\mathrm{Cu}}_{0.2}{\mathrm{Zn}}_{0.2}\mathrm{O}$ exhibits antiferromagnetic order and magnetic excitations, as revealed by recent neutron scattering experiments. This observation raises the question of the nature of spin-wave excitations in such disordered systems. Here, we investigate theoretically the magnetic ground state and the spin-wave excitations using linear spin-wave theory in combination with the supercell approximation to take into account the extreme disorder in this magnetic system. We find that the experimentally observed antiferromagnetic structure can be stabilized by a rhombohedral distortion together with large second-nearest-neighbor interactions. Our calculations show that the spin-wave spectrum consists of a well-defined low energy coherent spectrum in the background of an incoherent continuum that extends to higher energies.
Rapamycin-induced hyperglycemia is associated with exacerbated age-related osteoarthritis.

Background The objective of this study was to determine if mechanistic target of rapamycin (mTOR) inhibition with or without AMP-activated protein kinase (AMPK) activation can protect against primary, age-related OA. Design Dunkin-Hartley guinea pigs develop mild primary OA pathology by 5 months of age that progresses to moderate OA by 8 months of age. At 5 months, guinea pigs served as young control (n = 3) or were fed either a control diet (n = 8), a diet enriched with the mTOR-inhibitor rapamycin (Rap, 14 ppm, n = 8), or Rap with the AMPK-activator metformin (Rap+Met, 1000 ppm, n = 8) for 12 weeks. Knee joints were evaluated by OARSI scoring, micro-computed tomography, and immunohistochemistry. Glenohumeral articular cartilage was collected for western blotting. Results Rap- and Rap+Met-treated guinea pigs displayed lower body weight than control. Rap and Rap+Met inhibited articular cartilage mTORC1 but not mTORC2 signaling. Rap+Met, but not Rap alone, stimulated AMPK. Despite lower body weight and articular cartilage mTORC1 inhibition, Rap- and Rap+Met-treated guinea pigs had greater OA severity in the medial tibial plateau due to articular cartilage structural damage and/or proteoglycan loss. Rap and Rap+Met increased plasma glucose compared to control. Plasma glucose concentration was positively correlated with proteoglycan loss, suggesting hyperglycemic stress after Rap treatment was related to worsened OA. Conclusions This is the first study to show that Rap induced increase in plasma glucose was associated with greater OA severity. Further, articular cartilage mTORC1 inhibition and bodyweight reduction by dietary Rap and Rap+Met did not appear to protect against primary OA during the prevailing hyperglycemia.
Characterizing attitudinal network graphs through frustration cloud

Attitudinal network graphs are signed graphs where edges capture an expressed opinion; two vertices connected by an edge can be agreeable (positive) or antagonistic (negative). A signed graph is called balanced if each of its cycles includes an even number of negative edges. Balance is often characterized by the frustration index or by finding a single convergent balanced state of network consensus. In this paper, we propose to expand the measures of consensus from a single balanced state associated with the frustration index to the set of nearest balanced states. We introduce the frustration cloud as a set of all nearest balanced states and use a graph-balancing algorithm to find all nearest balanced states in a deterministic way. Computational concerns are addressed by measuring consensus probabilistically, and we introduce new vertex and edge metrics to quantify status, agreement, and influence. We also introduce a new global measure of controversy for a given signed graph and show that vertex status is a zero-sum game in the signed network. We propose an efficient scalable algorithm for calculating frustration cloud-based measures in social network and survey data of up to 80,000 vertices and half-a-million edges. We also demonstrate the power of the proposed approach to provide discriminant features for community discovery when compared to spectral clustering and to automatically identify dominant vertices and anomalous decisions in the network.
Transcriptome analysis of heterogeneity in mouse model of metastatic breast cancer.

Background Cancer metastasis is a complex process involving the spread of malignant cells from a primary tumor to distal organs. Understanding this cascade at a mechanistic level could provide critical new insights into the disease and potentially reveal new avenues for treatment. Transcriptome profiling of spontaneous cancer models is an attractive method to examine the dynamic changes accompanying tumor cell spread. However, such studies are complicated by the underlying heterogeneity of the cell types involved. The purpose of this study was to examine the transcriptomes of metastatic breast cancer cells using the well-established MMTV-PyMT mouse model. Methods Organ-derived metastatic cell lines were harvested from 10 female MMTV-PyMT mice. Cancer cells were isolated and sorted based on the expression of CD44low/EpCAMhigh or CD44high/EpCAMhigh surface markers. RNA from each cell line was extracted and sequenced using the NextSeq 500 Illumina platform. Tissue-specific genes were compared across the different metastatic and primary tumor samples. Reads were mapped to the mouse genome using STAR, and gene expression was quantified using RSEM. Single-cell RNA-seq (scRNA-seq) was performed on select samples using the ddSeq platform by BioRad and analyzed using Seurat v3.2.3. Monocle2 was used to infer pseudo-time progression. Results Comparison of RNA sequencing data across all cell populations produced distinct gene clusters. Differential gene expression patterns related to CD44 expression, organ tropism, and immunomodulatory signatures were observed. scRNA-seq identified expression profiles based on tissue-dependent niches and clonal heterogeneity. These cohorts of data were narrowed down to identify subsets of genes with high expression and known metastatic propensity. Dot plot analyses further revealed clusters expressing cancer stem cell and cancer dormancy markers. Changes in relevant genes were investigated across pseudo-time and tissue origin using Monocle2. These data revealed transcriptomes that may contribute to sub-clonal evolution and treatment evasion during cancer progression. Conclusions We performed a comprehensive transcriptome analysis of tumor heterogeneity and organ tropism during breast cancer metastasis. These data add to our understanding of metastatic progression and highlight targets for breast cancer treatment. These markers could also be used to image the impact of tumor heterogeneity on metastases.
Detection and Characterization of Thrombosis in Humans Using Fibrin-Targeted Positron Emission Tomography and Magnetic Resonance.

Abstract Objectives The authors present a novel technique to detect and characterize LAA thrombus in humans using combined positron emission tomography (PET)/cardiac magnetic resonance (CMR) of a fibrin-binding radiotracer, [64Cu]FBP8. Background The detection of thrombus in the left atrial appendage (LAA) is vital in the prevention of stroke and is currently performed using transesophageal echocardiography (TEE). Methods The metabolism and pharmacokinetics of [64Cu]FBP8 were studied in 8 healthy volunteers. Patients with atrial fibrillation and recent TEEs of the LAA (positive n = 12, negative n = 12) were injected with [64Cu]FBP8 and imaged with PET/CMR, including mapping the longitudinal magnetic relaxation time (T1) in the LAA. Results [64Cu]FBP8 was stable to metabolism and was rapidly eliminated. The maximum standardized uptake value (SUVMax) in the LAA was significantly higher in the TEE-positive than TEE-negative subjects (median of 4.0 [interquartile range (IQR): 3.0-6.0] vs 2.3 [IQR: 2.1-2.5]; P Conclusions PET/CMR of [64Cu]FBP8 is able to detect acute as well as older platelet-poor thrombi with excellent accuracy. Furthermore, the integrated PET/CMR approach provides useful information on the biological properties of thrombus such as fibrin and methemoglobin content. (Imaging of LAA Thrombosis; NCT03830320 )
Data-driven abductive inference of library specifications

Programmers often leverage data structure libraries that provide useful and reusable abstractions. Modular verification of programs that make use of these libraries naturally rely on specifications that capture important properties about how the library expects these data structures to be accessed and manipulated. However, these specifications are often missing or incomplete, making it hard for clients to be confident they are using the library safely. When library source code is also unavailable, as is often the case, the challenge to infer meaningful specifications is further exacerbated. In this paper, we present a novel data-driven abductive inference mechanism that infers specifications for library methods sufficient to enable verification of the library's clients. Our technique combines a data-driven learning-based framework to postulate candidate specifications, along with SMT-provided counterexamples to refine these candidates, taking special care to prevent generating specifications that overfit to sampled tests. The resulting specifications form a minimal set of requirements on the behavior of library implementations that ensures safety of a particular client program. Our solution thus provides a new multi-abduction procedure for precise specification inference of data structure libraries guided by client-side verification tasks. Experimental results on a wide range of realistic OCaml data structure programs demonstrate the effectiveness of the approach.
A safety factor approach to designing urban infrastructure for dynamic conditions

Current approaches to design flood-sensitive infrastructure typically assume a stationary rainfall distribution and neglect many uncertainties. These assumptions are inconsistent with observations that suggest intensifying extreme precipitation events and the uncertainties surrounding projections of the coupled natural-human systems. Here we show that assuming climate stationarity and neglecting deep uncertainties can drastically underestimate flood risks and lead to poor infrastructure design choices. We find that climate uncertainty dominates the socioeconomic and engineering uncertainties that impact the hydraulic reliability in stormwater drainage systems. We quantify the upfront costs needed to achieve higher hydraulic reliability and robustness against the deep uncertainties surrounding projections of rainfall, surface runoff characteristics, and infrastructure lifetime. Depending on the location, we find that adding safety factors of 1.4 to 1.7 to the standard stormwater pipe design guidance produces robust performance to the considered deep uncertainties.
Pollution-Development Tradeoffs in Nigeria: An Agent-based Model

Like many developing countries, Nigeria, is faced with a number of tradeoffs that pit rapid economic development against environmental preservation. Environmentally sustainable,“green" economic development is slower, more costly, and more difficult than unrestricted, unregulated economic growth. In this paper we develop an agent-based computational model of agents (who can be individuals or organizations) in a population who make successive choices that have economic and environmental impacts. Simulations based on the model suggest that widespread public awareness of environmental issues is insufficient to prevent the tendency towards sacrificing the environment for the sake of growth. Even if people have an understanding of negative impacts and always choose to act in their own self-interest, they may still act collectively in such a way as to bring down the quality of life for the entire society. We conclude that besides raising public awareness, economic intervention by the government (in the form of incentives or penalties) may be the only way to achieve an optimal balance between economic and environmental factors.
Asymmetric temperature equilibration with heat flow from cold to hot in a quantum thermodynamic system

A model computational quantum thermodynamic network is constructed with two variable temperature baths coupled by a linker system, with an asymmetry in the coupling of the linker to the two baths. It is found in computational simulations that the baths come to ``thermal equilibrium'' at different bath energies and temperatures. In a sense, heat is observed to flow from cold to hot. A description is given in which a recently defined quantum entropy ${S}_{\mathrm{univ}}^{Q}$ for a pure state ``universe'' continues to increase after passing through the classical equilibrium point of equal temperatures, reaching a maximum at the asymmetric equilibrium. Thus, a second law account $\mathrm{\ensuremath{\Delta}}{S}_{\mathrm{univ}}^{Q}\ensuremath{\ge}0$ holds for the asymmetric quantum process. In contrast, a von Neumann entropy description fails to uphold the entropy law, with a maximum near when the two temperatures are equal, then a decrease $\mathrm{\ensuremath{\Delta}}{S}^{vN}l0$ on the way to the asymmetric equilibrium.
MetaPix: Domain transfer for semantic segmentation by meta pixel weighting

Abstract Training a deep neural model for semantic segmentation requires collecting a large amount of pixel-level labeled data. To alleviate the data scarcity problem presented in the real world, one could utilize synthetic data whose label is easy to obtain. Previous work has shown that the performance of a semantic segmentation model can be improved by training jointly with real and synthetic examples with a proper weighting on the synthetic data. Such weighting was learned by a heuristic to maximize the similarity between synthetic and real examples. In our work, we instead learn a pixel-level weighting of the synthetic data by meta-learning, i.e., the learning of weighting should only be minimizing the loss on the target task. We achieve this by gradient-on-gradient technique to propagate the target loss back into the parameters of the weighting model. The experiments show that our method with only one single meta module can outperform a complicated combination of an adversarial feature alignment, a reconstruction loss, plus a hierarchical heuristic weighting at pixel, region and image levels.
USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problems

Real-world decision-making systems are often subject to uncertainties that have to be resolved through observational data. Therefore, we are frequently confronted with combinatorial optimization problems of which the objective function is unknown and thus has to be debunked using empirical evidence. In contrast to the common practice that relies on a learning-and-optimization strategy, we consider the regression between combinatorial spaces, aiming to infer high-quality optimization solutions from samples of input-solution pairs -- without the need to learn the objective function. Our main deliverable is a universal solver that is able to handle abstract undetermined stochastic combinatorial optimization problems. For learning foundations, we present learning-error analysis under the PAC-Bayesian framework using a new margin-based analysis. In empirical studies, we demonstrate our design using proof-of-concept experiments, and compare it with other methods that are potentially applicable. Overall, we obtain highly encouraging experimental results for several classic combinatorial problems on both synthetic and real-world datasets.
Automatic Tuning of Tensorflow’s CPU Backend Using Gradient-Free Optimization Algorithms

Modern deep learning (DL) applications are built using DL libraries and frameworks such as TensorFlow and PyTorch. These frameworks have complex parameters and tuning them to obtain good training and inference performance is challenging for typical users, such as DL developers and data scientists. Manual tuning requires deep knowledge of the user-controllable parameters of DL frameworks as well as the underlying hardware. It is a slow and tedious process, and it typically delivers sub-optimal solutions. In this paper, we treat the problem of tuning parameters of DL frameworks to improve training and inference performance as a black-box optimization problem. We then investigate applicability and effectiveness of Bayesian optimization, genetic algorithm, and Nelder-Mead simplex to tune the parameters of TensorFlow’s CPU backend. While prior work has already investigated the use of Nelder-Mead simplex for a similar problem, it does not provide insights into the applicability of other more popular algorithms. Towards that end, we provide a systematic comparative analysis of all three algorithms in tuning TensorFlow’s CPU backend on a variety of DL models. Our findings reveal that Bayesian optimization performs the best on the majority of models. There are, however, cases where it does not deliver the best results.
A microwave communication link with self-heterodyne direct down conversion and system predistortion

A novel design of a microwave communications link operating at 5.8 GHz is presented based on self-heterodyne direct downconversion (SHDDC) and system predistortion. With the SHDDC scheme, the need of any local oscillators at the receive ends is eliminated, and the spectrum usage is minimized; however, the transmitter power efficiency is low, and there exists high mixer intermodulation levels in the receiver. To overcome these drawbacks, a system predistortion approach is proposed. A two-tone measurement is performed to validate the idea. It shows 6.83 dBc overall improvement in the signal-to-intermodulation ratio (SIMR) by applying a simple second-order predistortion technique. Further, successful transmission of a digitally modulated signal is also demonstrated.
Emmetropization During Early Childhood.

Abstract Degree of emmetropization in late infancy varies greatly with the type and magnitude of refractive error, with the majority of high astigmatism, and to a lesser extent high hyperopia, resolving before 2 years of age.
Virginia Woolf and the Politics of Language

In the context of today's significant struggles with 'fundamentalisms', media consolidation, and the stifling of dissent, Allen's close readings of Woolf's writings focus on their relevance to our current political situation. Judith Allen approaches Woolf as a theorist of language as well as a theorist of reading, and shows how her writing strategies - sometimes single, resonant words - function to express and enact her politics. Allen also shows how Woolf's complex arguments serve to awaken her readers to the lack of transparency in the dissemination of information, the complexities and power of language, and the urgent need for critical thinking. Key features: close readings of Woolf's essays include "Montaigne", "A Room of One's Own", "Craftsmanship", "Three Guineas" and "Thoughts on Peace in an Air Raid"; sources range from Michel de Montaigne to the Dixie Chicks, from the Northcliffe Press newspaper empire of World War I to today's mainstream newspapers, Rupert Murdoch's empire, satirical news shows like "The Colbert Report" and "The Daily Show", and social media and the blogosphere.
Prostatic Scanning with Gray Scale Ultrasound

The evaluation of the prostate gland by ultrasonography has been described previously with both suprapubic and perineal scanning as well as transrectal and transurethral scanning. These studies have included evaluations of prostatic size as well as prostatic carcinomas. To our knowledge, the application of gray scale ultrasound to the evaluation of prostatic conditions has not been described. We have evaluated a series of patients with benign prostatic hyperplasia, prostatic malignancies and prostatitis.
An analysis of the relationship between somatotype and personality traits of college women.

Abstract The underlying rationale for the study was based on the conviction that biological factors are important in accounting for human behavior. This study was undertaken to compare the results of selected personality traits with the somatotypes of college women. Somatotypes were determined by the use of somatotype ratings as established by Sheldon. The personality profiles were determined by two personality measuring instruments: (a) the Sixteen Personality Factor Questionnaire, and (b) the California Psychological Inventory. Of the 23 hypotheses which were arbitrarily derived from Sheldon's work on physique and temperament of men, only one proved to be tenable. This tenable hypothesis stated that the component of mesomorphy is significantly correlated with the second order Factor I of the 16 PF (Low Anxiety).
Roadblocks to Reform: The Influence of HIPAA and HITECH on the Affordable Care Act

When it comes to health care, our nation has reached a crossroads. President Obama’s fledgling Affordable Care Act is a multifaceted, aggressive program designed to overhaul the delivery of health care by effectively restructuring its foundations from the inside out. In doing so, it seeks to reduce the number of uninsured patients who have for so long been a burden to a struggling health care system that must provide medical care as a service while also turning a profit as a business. But such a far-reaching plan has little chance of success if it is forced to evolve while fettered with the restrictions placed upon providers by certain grandfathered programs, most notably the Health Insurance Portability and Accountability Act (HIPAA) and the Health Information Technology for Economic and Clinical Health Act (HITECH). As a result, we as a nation must now consider whether the ACA’s fundamental mission or patient privacy should rule the day.
The Performance of Variable Sampling Plans When the Normal Distribution is Truncated

The robustness of standard variable sampling plans due to Lieberman and Resnikoff is considered with respect to truncation of the normal distribution. It is shown how variable sampling plans can be designed if the truncation point and the normal populat..
Environmental crazing and intrinsic tensile deformation in polymethylmethacrylate

The tensile stress-strain behaviour of PMMA was measured from 77 to 350 K in He which provides an inert environment and in N2, Ar, O2, CO2, and water which cause crazing. The effect of the partial pressure of the crazing agent on the tensile properties was measured. It was found that the ratio of the tensile strength in a crazing environment to that in an inert environment depended on temperature and pressure as follows: σc/σi={P exp (Q/RT)/P*}−0.13 where P* and Q depend on the crazing agent. The critical temperature and pressure which determine when a gas ceases to produce an environmental effect on the tensile strength is given by the condition that P exp (Q/RT)<P*. Above 273 K the intrinsic deformation changed from brittle fracture to a combination of intrinsic crazing and shear.
Mechanisms of Stratospheric and Tropospheric Circulation Response to Projected Arctic Sea Ice Loss

The impact of projected Arctic sea ice loss on the atmospheric circulation is investigated using the Whole Atmosphere Community Climate Model (WACCM), a model with a well-resolved stratosphere. Two 160-yr simulations are conducted: one with surface boundary conditions fixed at late twentieth-century values and the other with identical conditions except for Arctic sea ice, which is prescribed at late twenty-first-century values. Their difference isolates the impact of future Arctic sea ice loss upon the atmosphere. The tropospheric circulation response to the imposed ice loss resembles the negative phase of the northern annular mode, with the largest amplitude in winter, while the less well-known stratospheric response transitions from a slight weakening of the polar vortex in winter to a strengthening of the vortex in spring. The lack of a significant winter stratospheric circulation response is shown to be a consequence of largely cancelling effects from sea ice loss in the Atlantic and Pacific sectors, which drive opposite-signed changes in upward wave propagation from the troposphere to the stratosphere. Identical experiments conducted with Community Atmosphere Model, version 4, WACCM’s low-top counterpart, show a weaker tropospheric response and a different stratospheric response compared to WACCM. An additional WACCM experiment in which the imposed ice loss is limited to August‐November reveals that autumn ice loss weakens the stratospheric polar vortexin January,followedbyasmallbutsignificanttropospheric responsein latewinterandearlyspringthat resembles the negative phase of the North Atlantic Oscillation, with attendant surface climate impacts.