cssi/SciSciGPT-SciSciNet huggingface dataset:
Divisors and specializations of Lucas polynomials

Three-term recurrences have infused stupendous amount of research in a broad spectrum of the sciences, such as orthogonal polynomials (in special functions) and lattice paths (in enumerative combinatorics). Among these are the Lucas polynomials, which have seen a recent true revival. In this paper one of the themes of investigation is the specialization to the Pell and Delannoy numbers. The underpinning motivation comprises primarily of divisibility and symmetry. One of the most remarkable findings is a structural decomposition of the Lucas polynomials into what we term as flat and sharp analogs.
End-to-end training of deep visuomotor policies

Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.
A distance for circular Heegaard splittings

For a knot K ⊂ S3, its exterior E(K) = S3\η(K) has a singular foliation by Seifert surfaces of K derived from a circle-valued Morse function f : E(K) → S1. When f is self-indexing and has no critic...
Monte Carlo Syntax Marginals for Exploring and Using Dependency Parses

Dependency parsing research, which has made significant gains in recent years, typically focuses on improving the accuracy of single-tree predictions. However, ambiguity is inherent to natural language syntax, and communicating such ambiguity is important for error analysis and better-informed downstream applications. In this work, we propose a transition sampling algorithm to sample from the full joint distribution of parse trees defined by a transition-based parsing model, and demonstrate the use of the samples in probabilistic dependency analysis. First, we define the new task of dependency path prediction, inferring syntactic substructures over part of a sentence, and provide the first analysis of performance on this task. Second, we demonstrate the usefulness of our Monte Carlo syntax marginal method for parser error analysis and calibration. Finally, we use this method to propagate parse uncertainty to two downstream information extraction applications: identifying persons killed by police and semantic role assignment.
How Deep is Knowledge Tracing

In theoretical cognitive science, there is a tension between highly structured models whose parameters have a direct psychological interpretation and highly complex, generalpurpose models whose parameters and representations are difficult to interpret. The former typically provide more insight into cognition but the latter often perform better. This tension has recently surfaced in the realm of educational data mining, where a deep learning approach to predicting students’ performance as they work through a series of exercises—termed deep knowledge tracing or DKT—has demonstrated a stunning performance advantage over the mainstay of the field, Bayesian knowledge tracing or BKT. In this article, we attempt to understand the basis for DKT’s advantage by considering the sources of statistical regularity in the data that DKT can leverage but which BKT cannot. We hypothesize four forms of regularity that BKT fails to exploit: recency effects, the contextualized trial sequence, inter-skill similarity, and individual variation in ability. We demonstrate that when BKT is extended to allow it more flexibility in modeling statistical regularities—using extensions previously proposed in the literature—BKT achieves a level of performance indistinguishable from that of DKT. We argue that while DKT is a powerful, useful, generalpurpose framework for modeling student learning, its gains do not come from the discovery of novel representations— the fundamental advantage of deep learning. To answer the question posed in our title, knowledge tracing may be a domain that does not require ‘depth’; shallow models like BKT can perform just as well and offer us greater interpretability and explanatory power.
A simple and provable algorithm for sparse diagonal CCA

Given two sets of variables, derived from a common set of samples, sparse Canonical Correlation Analysis (CCA) seeks linear combinations of a small number of variables in each set, such that the induced canonical variables are maximally correlated. Sparse CCA is NP-hard. We propose a novel combinatorial algorithm for sparse diagonal CCA, i.e., sparse CCA under the additional assumption that variables within each set are standardized and uncorrelated. Our algorithm operates on a low rank approximation of the input data and its computational complexity scales linearly with the number of input variables. It is simple to implement, and parallelizable. In contrast to most existing approaches, our algorithm administers precise control on the sparsity of the extracted canonical vectors, and comes with theoretical data-dependent global approximation guarantees, that hinge on the spectrum of the input data. Finally, it can be straightforwardly adapted to other constrained variants of CCA enforcing structure beyond sparsity. We empirically evaluate the proposed scheme and apply it on a real neuroimaging dataset to investigate associations between brain activity and behavior measurements.
Density-Aware Single Image De-raining Using a Multi-stream Dense Network

Single image rain streak removal is an extremely challenging problem due to the presence of non-uniform rain densities in images. We present a novel density-aware multi-stream densely connected convolutional neural network-based algorithm, called DID-MDN, for joint rain density estimation and de-raining. The proposed method enables the network itself to automatically determine the rain-density information and then efficiently remove the corresponding rain-streaks guided by the estimated rain-density label. To better characterize rain-streaks with different scales and shapes, a multi-stream densely connected de-raining network is proposed which efficiently leverages features from different scales. Furthermore, a new dataset containing images with rain-density labels is created and used to train the proposed density-aware network. Extensive experiments on synthetic and real datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods. In addition, an ablation study is performed to demonstrate the improvements obtained by different modules in the proposed method. The code can be downloaded at https://github.com/hezhangsprinter/DID-MDN
Intergalactic shock fronts

When galaxy clusters collide, they generate shock fronts in the hot intracluster medium. Observations of these shocks can provide valuable information on the merger dynamics and physical conditions in the cluster plasma, and even help constrain the nature of dark matter. To study shock fronts, one needs an X-ray telescope with high angular resolution (such as Chandra), and be lucky to see the merger from the right angle and at the right moment. As of this writing, only a handful of merger shock fronts have been discovered and confirmed using both X-ray imaging and gas temperature data -- those in 1E0657-56, A520, A754, and two fronts in A2146. A few more are probable shocks awaiting temperature profile confirmation -- those in A521, RXJ1314-25, A3667, A2744, and Coma. The highest Mach number is 3 in 1E0657-56, while the rest has M=1.6-2. Interestingly, all these relatively weak X-ray shocks coincide with sharp edges in their host cluster's synchrotron radio halos (except in A3667, where it coincides with the distinct radio relic, and A2146, which does not have radio data yet). This is contrary to the common wisdom that weak shocks are inefficient particle accelerators, and may shed light on the mechanisms of relativistic electron production in astrophysical plasmas.
Security-Aware Synthesis Using Delayed-Action Games

Stochastic multiplayer games (SMGs) have gained attention in the field of strategy synthesis for multi-agent reactive systems. However, standard SMGs are limited to modeling systems where all agents have full knowledge of the state of the game. In this paper, we introduce delayed-action games (DAGs) formalism that simulates hidden-information games (HIGs) as SMGs, where hidden information is captured by delaying a player’s actions. The elimination of private variables enables the usage of SMG off-the-shelf model checkers to implement HIGs. Furthermore, we demonstrate how a DAG can be decomposed into subgames that can be independently explored, utilizing parallel computation to reduce the model checking time, while alleviating the state space explosion problem that SMGs are notorious for. In addition, we propose a DAG-based framework for strategy synthesis and analysis. Finally, we demonstrate applicability of the DAG-based synthesis framework on a case study of a human-on-the-loop unmanned-aerial vehicle system under stealthy attacks, where the proposed framework is used to formally model, analyze and synthesize security-aware strategies for the system.
A Quasi Birth-and-Death model for tumor recurrence.

Abstract A major cause of chemoresistance and recurrence in tumors is the presence of dormant tumor foci that survive chemotherapy and can eventually transition to active growth to regenerate the cancer. In this paper, we propose a Quasi Birth-and-Death (QBD) model for the dynamics of tumor growth and recurrence/remission of the cancer. Starting from a discrete-state master equation that describes the time-dependent transition probabilities between states with different numbers of dormant and active tumor foci, we develop a framework based on a continuum-limit approach to determine the time-dependent probability that an undetectable residual tumor will become large enough to be detectable. We derive an exact formula for the probability of recurrence at large times and show that it displays a phase transition as a function of the ratio of the death rate μA of an active tumor focus to its doubling rate λ. We also derive forward and backward Kolmogorov equations for the transition probability density in the continuum limit and, using a first-passage time formalism, we obtain a drift-diffusion equation for the mean recurrence time and solve it analytically to leading order for a large detectable tumor size N. We show that simulations of the discrete-state model agree with the analytical results, except for O(1/N) corrections. As an example of the use of our model in a clinical setting, we show that a range of model parameters can fit Kaplan–Meier recurrence-free survival data for ovarian cancer. Finally, we show in simulations that extending the duration of chemotherapy increases both the mean recurrence time and the asymptotic (large-time) probability of no recurrence. Our results should be useful in planning optimized chemotherapy dosing and duration for cancer treatment, especially in cancer types for which no targeted therapy is available.
Minors of a random binary matroid

Let ${\bf A}={\bf A}_{n,m,k}$ be a random $n\times m$ matrix over $\mathbf{GF}_2$ wher each column consists of $k$ randomly chosen ones. Let $M$ be an arbirary fixed binary matroid. We show that if $m/n$ and $k$ are sufficiently large then as $n\to\infty$ the binary matroid induced by {\bf A} contains $M$ as a minor.
High-Precision Localization Using Ground Texture

Location-aware applications play an increasingly critical role in everyday life. However, satellite-based localization (e.g., GPS) has limited accuracy and can be unusable in dense urban areas and indoors. We introduce an image-based global localization system that is accurate to a few millimeters and performs reliable localization both indoors and outside. The key idea is to capture and index distinctive local keypoints in ground textures. This is based on the observation that ground textures including wood, carpet, tile, concrete, and asphalt may look random and homogeneous, but all contain cracks, scratches, or unique arrangements of fibers. These imperfections are persistent, and can serve as local features. Our system incorporates a downward-facing camera to capture the fine texture of the ground, together with an image processing pipeline that locates the captured texture patch in a compact database constructed offline. We demonstrate the capability of our system to robustly, accurately, and quickly locate test images on various types of outdoor and indoor ground surfaces. This paper contains a supplementary video. All datasets and code are available online at microgps.cs.princeton.edu.
High Quality, Lightweight and Adaptable TTS Using LPCNet.

We present a lightweight adaptable neural TTS system with high quality output. The system is composed of three separate neural network blocks: prosody prediction, acoustic feature prediction and Linear Prediction Coding Net as a neural vocoder. This system can synthesize speech with close to natural quality while running 3 times faster than real-time on a standard CPU. The modular setup of the system allows for simple adaptation to new voices with a small amount of data. We first demonstrate the ability of the system to produce high quality speech when trained on large, high quality datasets. Following that, we demonstrate its adaptability by mimicking unseen voices using 5 to 20 minutes long datasets with lower recording quality. Large scale Mean Opinion Score quality and similarity tests are presented, showing that the system can adapt to unseen voices with quality gap of 0.12 and similarity gap of 3% compared to natural speech for male voices and quality gap of 0.35 and similarity of gap of 9 % for female voices.
Towards Optimal Moment Estimation in Streaming and Distributed Models

One of the oldest problems in the data stream model is to approximate the $p$-th moment $\|\mathcal{X}\|_p^p = \sum_{i=1}^n |\mathcal{X}_i|^p$ of an underlying vector $\mathcal{X} \in \mathbb{R}^n$, which is presented as a sequence of poly$(n)$ updates to its coordinates. Of particular interest is when $p \in (0,2]$. Although a tight space bound of $\Theta(\epsilon^{-2} \log n)$ bits is known for this problem when both positive and negative updates are allowed, surprisingly there is still a gap in the space complexity when all updates are positive. Specifically, the upper bound is $O(\epsilon^{-2} \log n)$ bits, while the lower bound is only $\Omega(\epsilon^{-2} + \log n)$ bits. Recently, an upper bound of $\tilde{O}(\epsilon^{-2} + \log n)$ bits was obtained assuming that the updates arrive in a random order. We show that for $p \in (0, 1]$, the random order assumption is not needed. Namely, we give an upper bound for worst-case streams of $\tilde{O}(\epsilon^{-2} + \log n)$ bits for estimating $\|\mathcal{X}\|_p^p$. Our techniques also give new upper bounds for estimating the empirical entropy in a stream. On the other hand, we show that for $p \in (1,2]$, in the natural coordinator and blackboard communication topologies, there is an $\tilde{O}(\epsilon^{-2})$ bit max-communication upper bound based on a randomized rounding scheme. Our protocols also give rise to protocols for heavy hitters and approximate matrix product. We generalize our results to arbitrary communication topologies $G$, obtaining an $\tilde{O}(\epsilon^{2} \log d)$ max-communication upper bound, where $d$ is the diameter of $G$. Interestingly, our upper bound rules out natural communication complexity-based approaches for proving an $\Omega(\epsilon^{-2} \log n)$ bit lower bound for $p \in (1,2]$ for streaming algorithms. In particular, any such lower bound must come from a topology with large diameter.
Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays

Central venous catheters (CVCs) are commonly used in critical care settings for monitoring body functions and administering medications. They are often described in radiology reports by referring to their presence, identity and placement. In this paper, we address the problem of automatic detection of their presence and identity through automated segmentation using deep learning networks and classification based on their intersection with previously learned shape priors from clinician annotations of CVCs. The results not only outperform existing methods of catheter detection achieving 85.2% accuracy at 91.6% precision, but also enable high precision (95.2%) classification of catheter types on a large dataset of over 10,000 chest X-rays, presenting a robust and practical solution to this problem.
Evolutionary loss of foot muscle during development with characteristics of atrophy and no evidence of cell death.

Intrinsic muscles are a group of muscles deep inside the hands and feet. They help to control the precise movements required, for example, for a pianist to play their instrument or for certain animals to climb with remarkable agility. Some animals, such as horses and deer, have evolved in such a way that they no longer grasp objects with hands and feet. Where intrinsic muscles were once present in the hands and feet of their ancestors, these animals now have strong ligaments that prevent over-extension of the wrist and ankle joints during hard landings. Given their size, it is difficult to study horses and deer in the laboratory and understand how they lost their intrinsic muscles during evolution. Tran et al. therefore focused on a small rodent called the lesser Egyptian jerboa, which also displays long legs with strong ligaments and no intrinsic muscles. Newborn jerboas have foot muscles that look very much like the intrinsic muscles found in mice, but these muscles disappear within 4 days of birth. A mechanism called programmed cell death is often responsible for specific tissues disappearing during development, but the experiments of Tran et al. revealed that this was not the case in jerboas. Instead, their intrinsic muscles were degraded by processes triggered by genes that disassemble underused muscles. In mice and humans, fasting, nerve injuries, or immobility trigger this type of muscle degradation, but in jerboas these processes appear to be a normal part of development. This unexpected discovery shows that development and disease-like processes are linked, and that more studies of nontraditional research animals may help scientists better understand these connections.
Functors and Morphisms Determined by Subcategories

We study the existence and uniqueness of minimal right determiners in various categories. Particularly in a $${{\,\mathrm{Hom}\,}}$$-finite hereditary abelian category with enough projectives, we prove that the Auslander–Reiten–Smalo–Ringel formula of the minimal right determiner still holds. As an application, we give a formula of minimal right determiners in the category of finitely presented representations of strongly locally finite quivers.
Goal-embedded dual hierarchical model for task-oriented dialogue generation

Hierarchical neural networks are often used to model inherent structures within dialogues. For goal-oriented dialogues, these models miss a mechanism adhering to the goals and neglect the distinct conversational patterns between two interlocutors. In this work, we propose Goal-Embedded Dual Hierarchical Attentional Encoder-Decoder (G-DuHA) able to center around goals and capture interlocutor-level disparity while modeling goal-oriented dialogues. Experiments on dialogue generation, response generation, and human evaluations demonstrate that the proposed model successfully generates higher-quality, more diverse and goal-centric dialogues. Moreover, we apply data augmentation via goal-oriented dialogue generation for task-oriented dialog systems with better performance achieved.
Nearest Neighbors for Matrix Estimation Interpreted as Blind Regression for Latent Variable Model

We consider the setup of nonparametric blind regression for estimating the entries of a large ${m} \times {n}$ matrix, when provided with a small, random fraction of noisy measurements. We assume that all rows ${u} \in [{m}]$ and columns ${i} \in [{n}]$ of the matrix are associated to latent features ${x}_{\text {row}}({{u}})$ and ${x}_{\text {col}}({{i}})$ respectively, and the $({\it{ u, i}})$ -th entry of the matrix, ${A}({\it{ u, i}})$ is equal to ${f}({x}_{\text {row}}({{u}}), {x}_{\text {col}}({{i}}))$ for a latent function $f$ . Given noisy observations of a small, random subset of the matrix entries, our goal is to estimate the unobserved entries of the matrix as well as to “de-noise” the observed entries. As the main result of this work, we introduce a nearest-neighbor-based estimation algorithm, and establish its consistency when the underlying latent function $f$ is Lipschitz, the underlying latent space is a bounded diameter Polish space, and the random fraction of observed entries in the matrix is at least $\max \big ({m}^{-1 + \delta }, {n}^{-1/2 + \delta } \big)$ , for any $\delta > 0$ . As an important byproduct, our analysis sheds light into the performance of the classical collaborative filtering algorithm for matrix completion, which has been widely utilized in practice. Experiments with the MovieLens and Netflix datasets suggest that our algorithm provides a principled improvement over basic collaborative filtering and is competitive with matrix factorization methods. Our algorithm has a natural extension to the setting of tensor completion via flattening the tensor to matrix. When applied to the setting of image in-painting, which is a 3-order tensor, we find that our approach is competitive with respect to state-of-art tensor completion algorithms across benchmark images.
Vanishing for Hodge ideals on toric varieties

In this article we construct a Koszul‐type resolution of the pth exterior power of the sheaf of holomorphic differential forms on smooth toric varieties and use this to prove a Nadel‐type vanishing theorem for Hodge ideals associated to effective Q‐divisors on smooth projective toric varieties. This extends earlier results of Mustaţa and Popa.
Homologous Codes for Multiple Access Channels

Building on recent development by Padakandla and Pradhan, and by Lim, Feng, Pastore, Nazer, and Gastpar, this paper studies the potential of structured coset coding as a complete replacement for random coding in network information theory. The roles of two techniques used in coset coding to generate nonuniform codewords, namely, shaping and channel transformation, are clarified and illustrated via the simple example of the two-sender multiple access channel. While individually deficient, the optimal combination of shaping via nested coset codes of the same generator matrix (which we refer to as homologous codes) and channel transformation is shown to achieve the same performance as traditional random codes for the general two-sender multiple access channel. The achievability proof of the capacity region is extended to multiple access channels with more than two senders, and with one or more receivers. A quantization argument adapted to the proposed combination of two techniques is presented to establish the achievability proof for their Gaussian counterparts. It is illustrated by an example that combining shaping and channel transformation is useful even when the goal of transmission for a subset of the receivers is to recover a linear combination of messages. These results open up new possibilities of utilizing homologous codes for a broader class of applications.
School-Based Treatment for Anxiety Research Study (STARS): a Randomized Controlled Effectiveness Trial

The current study compared the effectiveness of a school-clinician administered cognitive behavioral treatment (CBT) to treatment as usual (TAU) at post-treatment (i.e., after 12 weeks) and at a 1 year follow-up. Sixty-two school-based clinicians (37 in CBT; 25 in TAU) and 216 students (148 students in CBT; 68 in TAU) participated. Students were ages 6–18 (mean age 10.87; 64% Caucasian & 29% African American; 48.6% female) and all met DSM-IV diagnostic criteria for a primary anxiety disorder. Independent evaluators (IEs) assessed clinical improvement, global functioning, and loss of anxiety diagnoses; children and parents completed measures of anxiety symptoms. At post-treatment, no significant treatment main effects emerged on the primary outcome; 42% and 37% of youth were classified as treatment responders in CBT and TAU respectively. However, parent-report of child anxiety showed greater improvements in CBT relative to TAU (d = .29). Moderation analyses at post-treatment indicated that older youth, those with social phobia and more severe anxiety at baseline were more likely to be treatment responders in CBT compared to TAU. At the 1 year follow-up, treatment gains were maintained but no treatment group differences or moderators emerged. CBT and TAU for pediatric anxiety disorders, when delivered by school clinicians were generally similar in effectiveness for lowering anxiety and improving functioning at both post-treatment (on all but the parent measure and for specific subgroups) and 1 year follow-up. Implications for disseminating CBT in the school setting are discussed.
The calcium channel subunit α 2 δ-3 organizes synapses via an activity-dependent and autocrine BMP signaling pathway

Synapses are highly specialized for neurotransmitter signaling, yet activity-dependent growth factor release also plays critical roles at synapses. While efficient neurotransmitter signaling relies on precise apposition of release sites and neurotransmitter receptors, molecular mechanisms enabling high-fidelity growth factor signaling within the synaptic microenvironment remain obscure. Here we show that the auxiliary calcium channel subunit α2δ-3 promotes the function of an activity-dependent autocrine Bone Morphogenetic Protein (BMP) signaling pathway at the Drosophila neuromuscular junction (NMJ). α2δ proteins have conserved synaptogenic activity, although how they execute this function has remained elusive. We find that α2δ-3 provides an extracellular scaffold for an autocrine BMP signal, suggesting a mechanistic framework for understanding α2δ's conserved role in synapse organization. We further establish a transcriptional requirement for activity-dependent, autocrine BMP signaling in determining synapse density, structure, and function. We propose that activity-dependent, autocrine signals provide neurons with continuous feedback on their activity state for modulating both synapse structure and function.
Exploring Older Adults' Beliefs About the Use of Intelligent Assistants for Consumer Health Information Management: A Participatory Design Study.

Background: Intelligent assistants (IAs), also known as intelligent agents, use artificial intelligence to help users achieve a goal or complete a task. IAs represent a potential solution for providing older adults with individualized assistance at home, for example, to reduce social isolation, serve as memory aids, or help with disease management. However, to design IAs for health that are beneficial and accepted by older adults, it is important to understand their beliefs about IAs, how they would like to interact with IAs for consumer health, and how they desire to integrate IAs into their homes. Objective: We explore older adults’ mental models and beliefs about IAs, the tasks they want IAs to support, and how they would like to interact with IAs for consumer health. For the purpose of this study, we focus on IAs in the context of consumer health information management and search. Methods: We present findings from an exploratory, qualitative study that investigated older adults’ perspectives of IAs that aid with consumer health information search and management tasks. Eighteen older adults participated in a multiphase, participatory design workshop in which we engaged them in discussion, brainstorming, and design activities that helped us identify their current challenges managing and finding health information at home. We also explored their beliefs and ideas for an IA to assist them with consumer health tasks. We used participatory design activities to identify areas in which they felt IAs might be useful, but also to uncover the reasoning behind the ideas they presented. Discussions were audio-recorded and later transcribed. We compiled design artifacts collected during the study to supplement researcher transcripts and notes. Thematic analysis was used to analyze data. Results: We found that participants saw IAs as potentially useful for providing recommendations, facilitating collaboration between themselves and other caregivers, and for alerts of serious illness. However, they also desired familiar and natural interactions with IAs (eg, using voice) that could, if need be, provide fluid and unconstrained interactions, reason about their symptoms, and provide information or advice. Other participants discussed the need for flexible IAs that could be used by those with low technical resources or skills. Conclusions: From our findings, we present a discussion of three key components of participants’ mental models, including the people, behaviors, and interactions they described that were important for IAs for consumer health information management and seeking. We then discuss the role of access, transparency, caregivers, and autonomy in design for addressing participants’ concerns about privacy and trust as well as its role in assisting others that may interact with an IA on the older adults’ behalf.
Size of nested cities

Models of city systems based on market theories are unable to replicate their size distribution. The stochastic process approach to size distribution, which asserts proportional growth, does not provide a strong economic foundation. Hence an apparent irreconcilability. We propose that since there is a continuum of equilibrium set in market models of cities, as pointed out by Berliant and Kung (2006), proportional growth can work as an equilibrium selection. We compute an urban configuration that has not been presented in the literature before. A small city locates inside a larger city's agricultural supply zone. This gives a larger variation in city size that may contain Zipf's distribution.
Imaging uncompensated moments and exchange-biased emergent ferromagnetism in FeRh thin films

Uncompensated moments (UMs) in antiferromagnets are responsible for exchange bias in antiferromagnet/ ferromagnet heterostructures; however, they are difficult to directly detect because any signal they contribute is typically overwhelmed by the ferromagnetic layer. We use magnetothermal microscopy to image micron-scale uncompensated moments in thin films of FeRh, a room-temperature antiferromagnet that exhibits a first-order phase transition to a ferromagnetic (FM) state near 400 K. FeRh provides the unique opportunity to study both uncompensated moments in the antiferromagnetic (AF) phase and the interaction of uncompensated moments with emergent ferromagnetism within a relatively broad (390--420 K) temperature range near ${T}_{C}$. In the AF phase below ${T}_{C}$, we image both pinned UMs, which cause local vertical exchange bias, and unpinned UMs, which exhibit an enhanced coercive field that reflects exchange-coupling to the AF bulk. Near ${T}_{C}$, where AF and FM order coexist, we find that the emergent FM order is exchange-coupled to the bulk N\'eel order. This exchange coupling leads to the nucleation of unusual configurations in which FM domains are pinned in different in-plane orientations, even in the presence of a nominally saturating magnetic field, before suddenly collapsing into a state uniformly parallel to the field.
Probabilistic Assessment of Nerve Regeneration with Diffusion MRI in Rat Models of Peripheral Nerve Trauma.

Nerve regeneration after injury must occur in a timely fashion to restore function. Unfortunately, current methods (e.g., electrophysiology) provide limited information following trauma, resulting in delayed management and suboptimal outcomes. Herein, we evaluated the ability of diffusion MRI to monitor nerve regeneration after injury/repair. Sprague-Dawley rats were divided into three treatment groups (sham = 21, crush = 23, cut/repair = 19) and ex vivo diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) was performed 1–12 weeks post-surgery. Behavioral data showed a distinction between crush and cut/repair nerves at 4 weeks. This was consistent with DTI, which found that thresholds based on the ratio of radial and axial diffusivities (RD/AD = 0.40 ± 0.02) and fractional anisotropy (FA = 0.53 ± 0.01) differentiated crush from cut/repair injuries. By the 12th week, cut/repair nerves whose behavioral data indicated a partial recovery were below the RD/AD threshold (and above the FA threshold), while nerves that did not recover were on the opposite side of each threshold. Additional morphometric analysis indicated that DTI-derived normalized scalar indices report on axon density (RD/AD: r = −0.54, p < 1e-3; FA: r = 0.56, p < 1e-3). Interestingly, higher-order DKI analyses did not improve our ability classify recovery. These findings suggest that DTI may provide promising biomarkers for distinguishing successful/unsuccessful nerve repairs and potentially identify cases that require reoperation.
Implications of a 300–500 GeV\(/c^2\)\(Z'\) boson on \(p\bar{p}\) collider data at \(\sqrt{s}=1.8\) TeV

Recent analyses of precision low energy electroweak data indicate that the deviation from the Standard Model predictions of the measurement of atomic parity violation (\(2.3 \sigma\)), the effective number of massless neutrinos (\(2 \sigma\)), and \(A_b\) (\(2.7 \sigma\)) could be better described if the existence of an extra \(Z'\) neutral gauge boson is assumed. We investigate the implications of a 300– 00 GeV/\(c^2\) extra \(Z'\) on current \(p\bar{p}\) collider data at \(\sqrt{s}=1.8\) TeV, including the forward-backward charge asymmetry for very high mass \(e^+e^-\) pairs, and the invariant mass specstrum of high mass \(e^+e^-, \mu^+\mu^-, t\overline{t}\) and \(b\overline{b}\) pairs. For example, a 500 GeV\(/c^2\)\(Z'\) with a total production cross section of \(\approx 3 pb\) and enhanced coupling to the third generation, better describes both the low energy and the Tevatron data.
Adaptations to Oral and Pharyngeal Swallowing Function Induced by Injury to the Mylohyoid Muscle

Muscle injury is a frequent side effect of radiation treatment for head and neck cancer. To understand the pathophysiology of injury-related dysfunction, we investigated the effects of a single muscle injury to the mylohyoid on oropharyngeal swallowing function in the rat. The mylohyoid protects the airway from food/liquid via hyolaryngeal elevation and plays an active role during both oral and pharyngeal swallowing. We hypothesized (1) that fibrosis to the mylohyoid alters swallowing bolus flow and licking patterns and (2) that injury to the mylohyoid changes normal activity of submental, laryngeal, and pharyngeal muscles during swallowing. A chilled cryoprobe was applied to the rat mylohyoid muscle to create a localized injury. One and two weeks after injury, swallowing bolus transit was assessed via videofluoroscopy and licking behavior via an electrical lick sensor. The motor activity of five swallow-related muscles was analyzed immediately after injury using electromyography (EMG). Comparisons were made pre- and post-injury. Fibrosis was confirmed in the mylohyoid at 2 weeks after injury by measuring collagen content. One week after injury, bolus size decreased, swallowing rate reduced, and licking patterns were altered. Immediately post-injury, there was a significant depression in mylohyoid and thyropharyngeus EMG amplitudes during swallowing. Our results demonstrated that injury to the mylohyoid is sufficient to cause changes in deglutition. These disruptions in oral and pharyngeal swallowing were detected prior to long-term fibrotic changes, including delays in tongue movement, alterations in bolus flow, and changes in sensorimotor function. Therefore, injuring a single important swallowing muscle can have dramatic clinical effects.
Vehicle Maintenance Contracting in Developing Economies: The Role of Social Enterprise

Problem definition: Difficulties in healthcare delivery in developing economies arise from poor road infrastructure of rural communities, where the bulk of the population resides. Although motorcyc...
Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4

Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) offered a unique opportunity for designing and testing novel methodology for accurate docking and affinity prediction of ligands in an open and blinded manner. We participated in the beta-secretase 1 (BACE) Subchallenge which is comprised of cross-docking and redocking of 20 macrocyclic ligands to BACE and predicting binding affinity for 154 macrocyclic ligands. For this challenge, we developed machine learning models trained specifically on BACE. We developed a deep neural network (DNN) model that used a combination of both structure and ligand-based features that outperformed simpler machine learning models. According to the results released by D3R, we achieved a Spearman's rank correlation coefficient of 0.43(7) for predicting the affinity of 154 ligands. We describe the formulation of our machine learning strategy in detail. We compared the performance of DNN with linear regression, random forest, and support vector machines using ligand-based, structure-based, and combining both ligand and structure-based features. We compared different structures for our DNN and found that performance was highly dependent on fine optimization of the L2 regularization hyperparameter, alpha. We also developed a novel metric of ligand three-dimensional similarity inspired by crystallographic difference density maps to match ligands without crystal structures to similar ligands with known crystal structures. This report demonstrates that detailed parameterization, careful data training and implementation, and extensive feature analysis are necessary to obtain strong performance with more complex machine learning methods. Post hoc analysis shows that scoring functions based only on ligand features are competitive with those also using structural features. Our DNN approach tied for fifth in predicting BACE-ligand binding affinities.
Organizing principles of whole-brain functional connectivity in zebrafish larvae

Network science has begun to reveal the fundamental principles by which large-scale brain networks are organized, including geometric constraints, a balance between segregative and integrative features, and functionally flexible brain areas. However, it remains unknown whether whole-brain networks imaged at the cellular level are organized according to similar principles. Here, we analyze whole-brain functional networks reconstructed from calcium imaging data recorded in larval zebrafish. Our analyses reveal that functional connections are distance-dependent and that networks exhibit hierarchical modular structure and hubs that span module boundaries. We go on to show that spontaneous network structure places constraints on stimulus-evoked reconfigurations of connections and that networks are highly consistent across individuals. Our analyses reveal basic organizing principles of whole-brain functional brain networks at the mesoscale. Our overarching methodological framework provides a blueprint for studying correlated activity at the cellular level using a low-dimensional network representation. Our work forms a conceptual bridge between macro- and mesoscale network neuroscience and opens myriad paths for future studies to investigate network structure of nervous systems at the cellular level.
Mask, a component of the Hippo pathway, is required for Drosophila eye morphogenesis.

Abstract Hippo signaling is an important regulator of tissue size, but it also has a lesser-known role in tissue morphogenesis. Here we use the Drosophila pupal eye to explore the role of the Hippo effector Yki and its cofactor Mask in morphogenesis. We found that Mask is required for the correct distribution and accumulation of adherens junctions and appropriate organization of the cytoskeleton. Accordingly, disrupting mask expression led to severe mis-patterning and similar defects were observed when yki was reduced or in response to ectopic wts. Further, the patterning defects generated by reducing mask expression were modified by Hippo pathway activity. RNA-sequencing revealed a requirement for Mask for appropriate expression of numerous genes during eye morphogenesis. These included genes implicated in cell adhesion and cytoskeletal organization, a comprehensive set of genes that promote cell survival, and numerous signal transduction genes. To validate our transcriptome analyses, we then considered two loci that were modified by Mask activity: FER and Vinc, which have established roles in regulating adhesion. Modulating the expression of either locus modified mask mis-patterning and adhesion phenotypes. Further, expression of FER and Vinc was modified by Yki. It is well-established that the Hippo pathway is responsive to changes in cell adhesion and the cytoskeleton, but our data indicate that Hippo signaling also regulates these structures.
Object Rearrangement with Nested Nonprehensile Manipulation Actions

This paper considers the problem of rearrangement planning, i.e finding a sequence of manipulation actions that displace multiple objects from an initial configuration to a given goal configuration. Rearrangement is a critical skill for robots so that they can effectively operate in confined spaces that contain clutter. Examples of tasks that require rearrangement include packing objects inside a bin, wherein objects need to lay according to a predefined pattern. In tight bins, collision-free grasps are often unavailable. Nonprehensile actions, such as pushing and sliding, are preferred because they can be performed using minimalistic end-effectors that can easily be inserted in the bin. Rearrangement with nonprehensile actions is a challenging problem as it requires reasoning about object interactions in a combinatorially large configuration space of multiple objects. This work revisits several existing rearrangement planning techniques and introduces a new one that exploits nested nonprehensile actions by pushing several similar objects simultaneously along the same path, which removes the need to rearrange each object individually. Experiments in simulation and using a real Kuka robotic arm show the ability of the proposed approach to solve difficult rearrangement tasks while reducing the length of the end-effector’s trajectories.
Cell Fate Forecasting: A Data-Assimilation Approach to Predict Epithelial-Mesenchymal Transition.

Abstract Epithelial-mesenchymal transition (EMT) is a fundamental biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, which is composed of transitions from an epithelial state to intermediate or partial EMT state(s) to a mesenchymal state. Using computational models to predict cell state transitions in a specific experiment is inherently difficult for reasons including model parameter uncertainty and error associated with experimental observations. In this study, we demonstrate that a data-assimilation approach using an ensemble Kalman filter, which combines limited noisy observations with predictions from a computational model of TGFβ-induced EMT, can reconstruct the cell state and predict the timing of state transitions. We used our approach in proof-of-concept “synthetic” in silico experiments, in which experimental observations were produced from a known computational model with the addition of noise. We mimic parameter uncertainty in in vitro experiments by incorporating model error that shifts the TGFβ doses associated with the state transitions and reproduces experimentally observed variability in cell state by either shifting a single parameter or generating “populations” of model parameters. We performed synthetic experiments for a wide range of TGFβ doses, investigating different cell steady-state conditions, and conducted parameter studies varying properties of the data-assimilation approach including the time interval between observations and incorporating multiplicative inflation, a technique to compensate for underestimation of the model uncertainty and mitigate the influence of model error. We find that cell state can be successfully reconstructed and the future cell state predicted in synthetic experiments, even in the setting of model error, when experimental observations are performed at a sufficiently short time interval and incorporate multiplicative inflation. Our study demonstrates the feasibility and utility of a data-assimilation approach to forecasting the fate of cells undergoing EMT.
Outer approximation with conic certificates for mixed-integer convex problems

A mixed-integer convex (MI-convex) optimization problem is one that becomes convex when all integrality constraints are relaxed. We present a branch-and-bound LP outer approximation algorithm for an MI-convex problem transformed to MI-conic form. The polyhedral relaxations are refined with $${{\mathcal {K}}}^*$$cuts derived from conic certificates for continuous primal-dual conic subproblems. Under the assumption that all subproblems are well-posed, the algorithm detects infeasibility or unboundedness or returns an optimal solution in finite time. Using properties of the conic certificates, we show that the $${{\mathcal {K}}}^*$$ cuts imply certain practically-relevant guarantees about the quality of the polyhedral relaxations, and demonstrate how to maintain helpful guarantees when the LP solver uses a positive feasibility tolerance. We discuss how to disaggregate$${{\mathcal {K}}}^*$$ cuts in order to tighten the polyhedral relaxations and thereby improve the speed of convergence, and propose fast heuristic methods of obtaining useful $${{\mathcal {K}}}^*$$ cuts. Our new open source MI-conic solver Pajarito (github.com/JuliaOpt/Pajarito.jl) uses an external mixed-integer linear solver to manage the search tree and an external continuous conic solver for subproblems. Benchmarking on a library of mixed-integer second-order cone (MISOCP) problems, we find that Pajarito greatly outperforms Bonmin (the leading open source alternative) and is competitive with CPLEX’s specialized MISOCP algorithm. We demonstrate the robustness of Pajarito by solving diverse MI-conic problems involving mixtures of positive semidefinite, second-order, and exponential cones, and provide evidence for the practical value of our analyses and enhancements of $${{\mathcal {K}}}^*$$ cuts.