cssi/SciSciGPT-SciSciNet huggingface dataset:
Discovery of Activities via Statistical Clustering of Fixation Patterns

Human behavior often consists of a series of distinct activities, each characterized by a unique pattern of interaction with the visual environment. This is true even in a restricted domain, such as a pilot flying an airplane; in this case, activities with distinct visual signatures might be things like communicating, navigating, monitoring, etc. We propose a novel analysis method for gaze-tracking data, to perform blind discovery of these hypothetical activities. We compare, not individual fixations, but groups of fixations aggregated over a fixed time interval (Tau). We assume that the environment has been divided into a finite set of discrete areas-of-interest (AOIs). For a given time interval, we compute the proportion of time spent fixating each AOI, resulting in an N-dimensional vector, where N is the number of AOIs. These proportions can be converted to integer counts by multiplying by Tau divided by the average fixation duration, a parameter that we fix at 283 milliseconds. We compare different intervals by computing the chi-squared statistic. The p-value associated with the statistic is the likelihood of observing the data under the hypothesis that the data in the two intervals were generated by a single process with a single set of probabilities governing the fixation of each AOI. We cluster the intervals, first by merging adjacent intervals that are sufficiently similar, optionally shifting the boundary between non-merged intervals to maximize the difference. Then we compare and cluster non-adjacent intervals. The method is evaluated using synthetic data generated by a hand-crafted set of activities. While the method generally finds more activities than put into the simulation, we have obtained agreement as high as 80 percent between the inferred activity labels and ground truth.
Stable distributions and green’s functions for fractional diffusions

Stable distributions are a class of distributions that have important uses in probability theory. They also have a applications in the theory of fractional diffusions: symmetric stable density functions are the Green’s functions of the fractional heat equation. We describe efficient numerical representations for these Green’s functions, enabling their use in numerical solutions of fractional heat equations.
What You Don't Know Won't Hurt You, Unless You Don't Know You're Wrong.

The purpose of this investigation was to determine the impact of students’ background knowledge and how they utilized “don’t know” affordances to comprehend and learn from text. In two studies, ove...
Numerical Analysis on Diffuser of Simulated Altitude Test Stand

Numerical methods were used to solve the flow field of the diffuser in the active injector altitude simulated test system of solid rocket engine.The influences of the results of containing Al2O3 particles or not on the flow field of the diffuser were compared.The calculated results agree well with the test results,which shows that the numerical methods can be used for the study of the diffuser.
Brain Segmentation from k-Space with End-to-End Recurrent Attention Network.

The task of medical image segmentation commonly involves an image reconstruction step to convert acquired raw data to images before any analysis. However, noises, artifacts and loss of information due to the reconstruction process are almost inevitable, which compromises the final performance of segmentation. We present a novel learning framework that performs magnetic resonance brain image segmentation directly from k-space data. The end-to-end framework consists of a unique task-driven attention module that recurrently utilizes intermediate segmentation estimation to facilitate image-domain feature extraction from the raw data, thus closely bridging the reconstruction and the segmentation tasks. In addition, to address the challenge of manual labeling, we introduce a novel workflow to generate labeled training data for segmentation by exploiting imaging modality simulators and digital phantoms. Extensive experimental results show that the proposed method outperforms several state-of-the-art methods.
Integrating Neural Networks and Dictionary Learning for Multidimensional Clinical Characterizations from Functional Connectomics Data

We propose a unified optimization framework that combines neural networks with dictionary learning to model complex interactions between resting state functional MRI and behavioral data. The dictionary learning objective decomposes patient correlation matrices into a collection of shared basis networks and subject-specific loadings. These subject-specific features are simultaneously input into a neural network that predicts multidimensional clinical information. Our novel optimization framework combines the gradient information from the neural network with that of a conventional matrix factorization objective. This procedure collectively estimates the basis networks, subject loadings, and neural network weights most informative of clinical severity. We evaluate our combined model on a multi-score prediction task using 52 patients diagnosed with Autism Spectrum Disorder (ASD). Our integrated framework outperforms state-of-the-art methods in a ten-fold cross validated setting to predict three different measures of clinical severity.
Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors

Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group...
An Empirical Study of GraphQL Schemas

GraphQL is a query language for APIs and a runtime to execute queries. Using GraphQL queries, clients define precisely what data they wish to retrieve or mutate on a server, leading to fewer round trips and reduced response sizes. Although interest in GraphQL is on the rise, with increasing adoption at major organizations, little is known about what GraphQL interfaces look like in practice. This lack of knowledge makes it hard for providers to understand what practices promote idiomatic, easy-to-use APIs, and what pitfalls to avoid.
Time-Slice Rationality and Self-Locating Belief

The epistemology of self-locating belief concerns itself with how rational agents ought to respond to certain kinds of indexical information. I argue that those who endorse the thesis of Time-Slice Rationality ought to endorse a particular view about the epistemology of self-locating belief, according to which ‘essentially indexical’ information is never evidentially relevant to non-indexical matters. I close by offering some independent motivations for endorsing Time-Slice Rationality in the context of the epistemology of self-locating belief.
Customized Nudging to Improve FAFSA Completion and Income Verification

Informational and behavioral barriers hinder social benefit take-up. We investigate the impact of mitigating these barriers through providing personalized information on benefits application status...
Mathematical software: past, present, and future

This paper provides some reflections on the field of mathematical software on the occasion of John Rice's 65th birthday. I describe some of the common themes of research in this field and recall some significant events in its evolution. Finally, I raise a number of issues that are of concern to future developments.
MathDL: mathematical deep learning for D3R Grand Challenge 4

We present the performances of our mathematical deep learning (MathDL) models for D3R Grand Challenge 4 (GC4). This challenge involves pose prediction, affinity ranking, and free energy estimation for beta secretase 1 (BACE) as well as affinity ranking and free energy estimation for Cathepsin S (CatS). We have developed advanced mathematics, namely differential geometry, algebraic graph, and/or algebraic topology, to accurately and efficiently encode high dimensional physical/chemical interactions into scalable low-dimensional rotational and translational invariant representations. These representations are integrated with deep learning models, such as generative adversarial networks (GAN) and convolutional neural networks (CNN) for pose prediction and energy evaluation, respectively. Overall, our MathDL models achieved the top place in pose prediction for BACE ligands in Stage 1a. Moreover, our submissions obtained the highest Spearman correlation coefficient on the affinity ranking of 460 CatS compounds, and the smallest centered root mean square error on the free energy set of 39 CatS molecules. It is worthy to mention that our method on docking pose predictions has significantly improved from our previous ones.
Caring for Alzheimer's Disease Caregivers: A Qualitative Study Investigating Opportunities for Exergame Innovation

The number of informal caregivers for family members with Alzheimer's Disease (AD) is rising dramatically in the United States. AD caregivers disproportionately experience numerous health problems and are often isolated with little support. An active lifestyle can help prevent and mitigate physical and psychological health concerns amongst AD caregivers. Research has demonstrated how pervasive exergames can encourage physical activity (PA) in the general population, yet little work has explored how these tools can address the significant PA barriers that AD caregivers face. To identify opportunities for design, we conducted semi-structured interviews and participatory design sessions with 14 informal caregivers of family members with AD. Our findings characterize how becoming an AD caregiver profoundly impacts one's ability to be active, perspectives on being active, and the ways that exergames might best support this population.We discuss implications for design and howour findings challenge existing technological approaches to PA promotion.
Choosing Among Pro-Poor Policy Options in the Delivery of Municipal Water Services

The poor are most likely to suffer from a service provider that cannot reliably supply high-quality water through the piped network. Effectively assisting the poor is a key component of a successfu...
Boltzmann and Statistical Mechanics

I received two invitations to this meeting: in the first unofficial one I was asked to speak ”on the transport properties of dense gases”. This involves the generalization of the Boltzmann equation to higher densities, a topic on which I have worked for more than 35 years. Later, I also received an official invitation, in which I was asked to give a lecture of “a generalized character”. Although the first topic would be a natural and relatively easy one, since I have spoken on it often and thought about it a lot, the second one seemed much more difficult but irresistibly challenging, in allowing me to view Boltzmann’s work in the last century from the perspective of the end of this century. This seems at first sight to be a precarious undertaking for a research scientist, but, as I hope to make clear to you, there may be advantages to this. While the historian of science is able to place the work of a scientist of the past in the context of that of his contemporaries, the research scientist can place the work of that scientist in the context of present day research and, up to a point, identify with his difficulties and achievements in the past on the basis of his own experience in the present day. I embark then on my perilous self-imposed task in the hope of providing some new perspectives on Boltzmann and his work, which are, I hope, historically not too inaccurate as far as the past is concerned, and stimulating, if not provocative, as far as the future is concerned.
3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders

We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs. The approach learns an encoding of the samples in the training corpus using an unsupervised variational autoencoder-decoder architecture, without the need for an explicit parametric representation of the original designs. To facilitate the generation of smooth final surfaces, we develop a 3D shape representation based on a distance transformation of the original 3D data, rather than using the commonly utilized binary voxel representation. Once established, the generator maps the latent space representations to the high-dimensional distance transformation fields, which are then automatically surfaced to produce 3D representations amenable to physics simulations or other objective function evaluation modules. We demonstrate our approach for the computational design of gliders that are optimized to attain prescribed performance scores. Our results show that when combined with genetic optimization, the proposed approach can generate a rich set of candidate concept designs that achieve prescribed functional goals, even when the original dataset has only a few or no solutions that achieve these goals.
Noncommutative Catalan Numbers

The goal of this paper is to introduce and study noncommutative Catalan numbers$$C_n$$ which belong to the free Laurent polynomial algebra $$\mathcal {L}_n$$ in n generators. Our noncommutative numbers admit interesting (commutative and noncommutative) specializations, one of them related to Garsia–Haiman (q, t)-versions, another—to solving noncommutative quadratic equations. We also establish total positivity of the corresponding (noncommutative) Hankel matrices $$H_n$$ and introduce accompanying noncommutative binomial coefficients.
Clinical correlates of hand‐held ultrasound‐guided assessments of the inferior vena cava in patients with acute decompensated heart failure

BACKGROUND Accurately assessing volume status in acutely decompensated heart failure (ADHF) can be challenging. Inferior vena cava (IVC) dynamics by echocardiography allow indirect assessment of volume status in these patients. Recently introduced hand-held ultrasound devices are promising. We aimed to describe the clinical correlates of volume status assessment using a hand-held ultrasound device in ADHF. METHODS In this prospective study, we evaluated 106 patients admitted with ADHF. First scan was performed within 24 hours of admission and timed in reference to first dose of intravenous diuretic. Daily resting and inspiratory (sniff) IVC diameters were measured according to standard echocardiography methods during hospitalization including the day of discharge. IVC collapsibility index (IVC-CI = Maximum IVC diameter-Inspiratory IVC diameter/maximum diameter; <0.5 representing hypervolemia) was calculated. Primary study endpoint was 30-day readmission. Research activities were independent of clinical decision-making. RESULTS Data for 106 patients was analyzed. Mean age was 66.7 ± 13.8 years, of which 53.8% were females, and a mean ejection fraction was 39 ± 18%. Initial scan of the IVC was obtained at an average time of 5.2 ± 8.04 hours from first diuretic dose. 81.2% of patients at admission had an IVC-CI <0.5. 63.2% patients had an IVC-CI <0.5 at discharge. There were no significant differences in age, length of stay, diuretic dose, or 30-day readmissions between patients with a discharge IVC-CI <0.5 vs ≥ 0.5. CONCLUSION Hand-held ultrasound assessment of IVC-CI in ADHF patients, although a feasible concept, is unable to predict 30-day readmissions in our study. Further prospective studies are necessary.
Asymptotically Optimal Stochastic Encryption for Quantized Sequential Detection in the Presence of Eavesdroppers

We consider sequential detection based on quantized data in the presence of eavesdropper. Stochastic encryption is employed as a counter measure that flips the quantization bits at each sensor according to certain probabilities, and the flipping probabilities are only known to the legitimate fusion center (LFC) but not the eavesdropping fusion center (EFC). As a result, the LFC employs the optimal sequential probability ratio test (SPRT) for sequential detection whereas the EFC employs a mismatched SPRT (MSPRT). We characterize the asymptotic performance of the MSPRT in terms of the expected sample size as a function of the vanishing error probabilities. We show that when the detection error probabilities are set to be the same at the LFC and EFC, every symmetric stochastic encryption is ineffective in the sense that it leads to the same expected sample size at the LFC and EFC. Next, in the asymptotic regime of small detection error probabilities, we show that every stochastic encryption degrades the performance of the quantized sequential detection at the LFC by increasing the expected sample size, and the expected sample size required at the EFC is no fewer than that is required at the LFC. Then the optimal stochastic encryption is investigated in the sense of maximizing the difference between the expected sample sizes required at the EFC and LFC. Although this optimization problem is nonconvex, we show that if the acceptable tolerance of the increase in the expected sample size at the LFC induced by the stochastic encryption is small enough, then the globally optimal stochastic encryption can be analytically obtained; and moreover, the optimal scheme only flips one type of quantized bits (i.e., 1 or 0) and keeps the other type unchanged.
Classification With the Matrix-Variate-t Distribution

Matrix-variate distributions can intuitively model the dependence structure of matrix-valued observations that arise in applications with multivariate time series, spatio-temporal, or repeated meas...
Delay-Dependent Output-Feedback Control for Blood Pressure Regulation Using LPV Techniques

This paper presents a delay-dependent parameter-varying control design approach to address the automated blood pressure regulation problem in the critical patient resuscitation using closed-loop administration of vasopressors. The mean arterial pressure (MAP) response of a patient subject to the intravenous vasoactive drug treatment is modeled as a linear parameter-varying (LPV) model, where varying model parameters and varying time-delay are considered as scheduling parameters of the system. Parameter-dependent Lyapunov-Krasovskii functionals are used to design an output-feedback dynamic controller to satisfy the closed-loop stability and reference MAP tracking requirements. The synthesis conditions are formulated in terms of Linear Matrix Inequalities (LMIs) that characterize the induced $\mathcal{L}_2$-norm performance specification of the closed-loop system. The main objectives of the proposed control method in the presence of limitations posed by the time-varying model parameters and the large time-varying delay are to track the MAP reference command and maintain the blood pressure within the permissible range of commanded set-point, avoid undesirable overshoot and slow response, and to provide a smooth drug injection. Finally, to evaluate the performance of the proposed LPV blood pressure regulation approach, closed-loop simulations are conducted and the results confirm the effectiveness of the proposed control method against various simulated scenarios.
L1-norm Tucker Tensor Decomposition

Tucker decomposition is a standard multi-way generalization of Principal-Component Analysis (PCA), appropriate for processing tensor data. Similar to PCA, Tucker decomposition has been shown to be sensitive against faulty data, due to its L2-norm-based formulation which places squared emphasis to peripheral/outlying entries. In this work, we explore L1-Tucker, an L1-norm based reformulation of Tucker decomposition, and present two algorithms for its solution, namely L1-norm Higher-Order Singular Value Decomposition (L1-HOSVD) and L1-norm Higher-Order Orthogonal Iterations (L1-HOOI). The proposed algorithms are accompanied by complexity and convergence analysis. Our numerical studies on tensor reconstruction and classification corroborate that L1-Tucker decomposition, implemented by means of the proposed algorithms, attains similar performance to standard Tucker when the processed data are corruption-free, while it exhibits sturdy resistance against heavily corrupted entries.
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control

In this paper, we introduce Symplectic ODE-Net (SymODEN), a deep learning framework which can infer the dynamics of a physical system from observed state trajectories. To achieve better generalization with fewer training samples, SymODEN incorporates appropriate inductive bias by designing the associated computation graph in a physics-informed manner. In particular, we enforce Hamiltonian dynamics with control to learn the underlying dynamics in a transparent way which can then be leveraged to draw insight about relevant physical aspects of the system, such as mass and potential energy. In addition, we propose a parametrization which can enforce this Hamiltonian formalism even when the generalized coordinate data is embedded in a high-dimensional space or we can only access velocity data instead of generalized momentum. This framework, by offering interpretable, physically-consistent models for physical systems, opens up new possibilities for synthesizing model-based control strategies.
Norm-square localization and the quantization of Hamiltonian loop group spaces

Abstract In an earlier article we introduced a new definition for the ‘quantization’ of a Hamiltonian loop group space M , involving the equivariant L 2 -index of a Dirac-type operator D on a non-compact finite dimensional submanifold Y of M . In this article we study a deformation of this operator, similar to the work of Tian-Zhang and Ma-Zhang. We obtain a formula for the index with infinitely many non-trivial contributions, indexed by the components of the critical set of the norm-square of the moment map. This is the main part of a new proof of the [ Q , R ] = 0 theorem for Hamiltonian loop group spaces.
The Dynamics of Cytoplasmic mRNA Metabolism

For all but a few mRNAs, the dynamics of metabolism are unknown. Here, we developed an experimental and analytical framework for examining these dynamics for mRNAs from thousands of genes. mRNAs of mouse fibroblasts exit the nucleus with diverse intragenic and intergenic poly(A)-tail lengths. Once in the cytoplasm, they have a broad (1000-fold) range of deadenylation rate constants, which correspond to cytoplasmic lifetimes. Indeed, with few exceptions, degradation appears to occur primarily through deadenylation-linked mechanisms, with little contribution from either endonucleolytic cleavage or deadenylation-independent decapping. Most mRNA molecules degrade only after their tail lengths fall below 25 nt. Decay rate constants of short-tailed mRNAs vary broadly (1000-fold) and are larger for short-tailed mRNAs that have previously undergone more rapid deadenylation. This coupling helps clear rapidly deadenylated mRNAs, enabling the large range in deadenylation rate constants to impart a similarly large range in stabilities.
Large-scale relation extraction from web documents and knowledge graphs with human-in-the-loop

Abstract The Semantic Web movement has produced a wealth of curated collections of entities and facts, often referred as Knowledge Graphs. Creating and maintaining such Knowledge Graphs is far from being a solved problem: it is crucial to constantly extract new information from the vast amount of heterogeneous sources of data on the Web. In this work we address the task of Knowledge Graph population. Specifically, given any target relation between two entities, we propose an approach to extract positive instances of the relation from various Web sources. Our relation extraction approach introduces a human-in-the-loop component in the extraction pipeline, which delivers significant advantage with respect to other solely automatic approaches. We test our solution on the ISWC 2018 Semantic Web Challenge, with the objective to identify supply-chain relations among organizations in the Thomson Reuters Knowledge Graph. Our human-in-the-loop extraction pipeline achieves top performance among all competing systems.
U1 snRNP regulates cancer cell migration and invasion in vitro

Stimulated cells and cancer cells have widespread shortening of mRNA 3'-untranslated regions (3'UTRs) and switches to shorter mRNA isoforms due to usage of more proximal polyadenylation signals (PASs) in introns and last exons. U1 snRNP (U1), vertebrates' most abundant non-coding (spliceosomal) small nuclear RNA, silences proximal PASs and its inhibition with antisense morpholino oligonucleotides (U1 AMO) triggers widespread premature transcription termination and mRNA shortening. Here we show that low U1 AMO doses increase cancer cells' migration and invasion in vitro by up to 500%, whereas U1 over-expression has the opposite effect. In addition to 3'UTR length, numerous transcriptome changes that could contribute to this phenotype are observed, including alternative splicing, and mRNA expression levels of proto-oncogenes and tumor suppressors. These findings reveal an unexpected role for U1 homeostasis (available U1 relative to transcription) in oncogenic and activated cell states, and suggest U1 as a potential target for their modulation.
Conformity in scientific networks

Scientists are generally subject to social pressures, including pressures to conform with others in their communities, that affect achievement of their epistemic goals. Here we analyze a network epistemology model in which agents, all else being equal, prefer to take actions that conform with those of their neighbors. This preference for conformity interacts with the agents’ beliefs about which of two (or more) possible actions yields the better result. We find a range of possible outcomes, including stable polarization in belief and action. The model results are sensitive to network structure. In general, though, conformity has a negative effect on a community’s ability to reach accurate consensus about the world.
Birational superrigidity is not a locally closed property

We prove an optimal result on the birational rigidity and K-stability of index 1 hypersurfaces in $$\mathbb {P}^{n+1}$$ with ordinary singularities when $$n\gg 0$$ and also study the birational superrigidity and K-stability of certain weighted complete intersections. As an application, we show that birational superrigidity is not a locally closed property in moduli. We also prove (in the Appendix) that the alpha invariant function is constructible in some families of complete intersections.
A Mobile Extendable Robot Arm: Singularity Analysis and Design*

Inspection and maintenance of equipment inside buildings, such as exit signs, bared pipelines, air vents, and fire alarms often requires a robot to reach high, hidden, or confined areas that are difficult for humans to access. Even though these tasks are easy and repeatable, they are still not automated. The Mobile Extendable Robot Arm (MERA) is a movable robot arm with a novel 2-DOF scissor mechanism for reaching a high place and positioning an end-effector. MERA is composed of a locomotion vehicle with a rotation table and a 4-DOF extender arm, itself made of two layers of the 2-DOF scissor mechanism arranged in series. Placing the end-effector at an arbitrary point in space, the 4-DOF arm possesses two degrees of redundancy, allowing access to a point from various directions and enabling obstacle avoidance. In this paper, we present the design and analysis of the 2-DOF scissor mechanism. The 2-DOF scissor mechanism has two rotary actuators for driving the base links individually; consequently, the mechanism can elongate the entire body and tilt at the center of the base shaft. However, we found that the 2-DOF scissor mechanism had a singularity; after analyzing the singularity, we propose two novel solutions to the problem.
A Novel Resistance Pathway for Calcineurin Inhibitors in the Human-Pathogenic Mucorales Mucor circinelloides.

ABSTRACT Mucormycosis is an emerging lethal fungal infection in immunocompromised patients. Mucor circinelloides is a causal agent of mucormycosis and serves as a model system to understand genetics in Mucorales. Calcineurin is a conserved virulence factor in many pathogenic fungi, and calcineurin inhibition or deletion of the calcineurin regulatory subunit (CnbR) in Mucor results in a shift from hyphal to yeast growth. We analyzed 36 calcineurin inhibitor-resistant or bypass mutants that exhibited hyphal growth in the presence of calcineurin inhibitors or in the yeast-locked cnbRΔ mutant background without carrying any mutations in known calcineurin components. We found that a majority of the mutants had altered sequence in a gene, named here bycA (bypass of calcineurin). bycA encodes an amino acid permease. We verified that both the bycAΔ single mutant and the bycAΔ cnbRΔ double mutant are resistant to calcineurin inhibitor FK506, thereby demonstrating a novel mechanism of resistance against calcineurin inhibitors. We also found that the level of expression of bycA was significantly higher in the wild-type strain treated with FK506 and in the cnbRΔ mutants but was significantly lower in the wild-type strain without FK506 treatment. These findings suggest that bycA is a negative regulator of hyphal growth and/or a positive regulator of yeast growth in Mucor and that calcineurin suppresses expression of the bycA gene at the mRNA level to promote hyphal growth. BycA is involved in the Mucor hypha-yeast transition as our data demonstrate positive correlations among bycA expression, protein kinase A activity, and Mucor yeast growth. Also, calcineurin, independently of its role in morphogenesis, contributes to virulence traits, including phagosome maturation blockade, host cell damages, and proangiogenic growth factor induction during interactions with hosts. IMPORTANCEMucor is intrinsically resistant to most known antifungals, which makes mucormycosis treatment challenging. Calcineurin is a serine/threonine phosphatase that is widely conserved across eukaryotes. When calcineurin function is inhibited in Mucor, growth shifts to a less virulent yeast growth form, which makes calcineurin an attractive target for development of new antifungal drugs. Previously, we identified two distinct mechanisms through which Mucor can become resistant to calcineurin inhibitors involving Mendelian mutations in the gene for FKBP12, including mechanisms corresponding to calcineurin A or B subunits and epimutations silencing the FKBP12 gene. Here, we identified a third novel mechanism where loss-of-function mutations in the amino acid permease corresponding to the bycA gene contribute to resistance against calcineurin inhibitors. When calcineurin activity is absent, BycA can activate protein kinase A (PKA) to promote yeast growth via a cAMP-independent pathway. Our data also show that calcineurin activity contributes to host-pathogen interactions primarily in the pathogenesis of Mucor.
Occlusion-robust Deformable Object Tracking without Physics Simulation

Estimating the state of a deformable object is crucial for robotic manipulation, yet accurate tracking is challenging when the object is partially-occluded. To address this problem, we propose an occlusion-robust RGBD sequence tracking framework based on Coherent Point Drift (CPD). To mitigate the effects of occlusion, our method 1) Uses a combination of locally linear embedding and constrained optimization to regularize the output of CPD, thus enforcing topological consistency when occlusions create disconnected pieces of the object; 2) Reasons about the free-space visible by an RGBD sensor to better estimate the prior on point location and to detect tracking failures during occlusion; and 3) Uses shape descriptors to find the most relevant previous state of the object to use for tracking after a severe occlusion. Our method does not rely on physics simulation or a physical model of the object, which can be difficult to obtain in unstructured environments. Despite having no physical model, our experiments demonstrate that our method achieves improved accuracy in the presence of occlusion as compared to a physics-based CPD method while maintaining adequate run-time.
First Steps Towards Full Model Based Motion Planning and Control of Quadrupeds: A Hybrid Zero Dynamics Approach

The hybrid zero dynamics (HZD) approach has become a powerful tool for the gait planning and control of bipedal robots. This paper aims to extend the HZD methods to address walking, ambling and trotting behaviors on a quadrupedal robot. We present a framework that systematically generates a wide range of optimal trajectories and then provably stabilizes them for the full-order, nonlinear and hybrid dynamical models of quadrupedal locomotion. The gait planning is addressed through a scalable nonlinear programming using direct collocation and HZD. The controller synthesis for the exponential stability is then achieved through the Poincare sections analysis. In particular, we employ an iterative optimization algorithm involving linear and bilinear matrix inequalities (LMIs and BMIs) to design HZD-based controllers that guarantee the exponential stability of the fixed points for the Poincare return map. The power of the framework is demonstrated through gait generation and HZD-based controller synthesis for an advanced quadruped robot, —Vision 60, with 36 state variables and 12 control inputs. The numerical simulations as well as real world experiments confirm the validity of the proposed framework.
Advances in understanding and intervening in teacher stress and coping: The Coping-Competence-Context Theory

Teacher stress and burnout are associated with many adverse outcomes for teachers, students, and the educational system. This paper describes the Coping-Competence-Context (3C) Theory of Teacher Stress. The theory is based on empirical research on teacher stress and coping highlighted within this special issue and attempts to more explicitly highlight three critical interconnected pathways to teacher stress development and intervention. The 3C model also highlights why teacher stress is important and should be the topic of future inquiry by showing clear links between teacher stress and adverse student and teacher outcomes. Lastly, this paper provides guidance for leverage points to intervene and describes a future research agenda in three domains: measurement, conceptual, and intervention issues and challenges.
Systematic Managed Floating

A majority of countries neither freely float their currencies nor firmly peg. But most of the remainder in practice also don’t obey such well-defined intermediate exchange rate regimes as target zones. This paper proposes to define an intermediate regime, to be called “systematic managed floating,” as an arrangement where the central bank regularly responds to changes in total exchange market pressure by allowing some fraction to be reflected as a change in the exchange rate and the remaining fraction to be absorbed as a change in foreign exchange reserves. An operational criterion for judging systematic managed floaters is a high correlation between exchange rate changes and reserve changes. The paper rejects the view that exchange rate regimes make no difference. In regressions to test effects on real exchange rates, we find that positive external shocks tend to cause real appreciation for most systematic managed-floaters; more strongly so for pure floaters; and not at all for most firm peggers. Two measures of exogenous external shocks are used: (i) for commodity-exporters, a country-specific index of global prices of the export commodities and (ii) for other Asian emerging market economies, the VIX.
On non-uniqueness in mean field games

We analyze an $N+1$-player game and the corresponding mean field game with state space $\{0,1\}$. The transition rate of $j$-th player is the sum of his control $\alpha^j$ plus a minimum jumping rate $\eta$. Instead of working under monotonicity conditions, here we consider an anti-monotone running cost. We show that the mean field game equation may have multiple solutions if $\eta < \frac{1}{2}$. We also prove that that although multiple solutions exist, only the one coming from the entropy solution is charged (when $\eta=0$), and therefore resolve a conjecture of ArXiv: 1903.05788.
Distinct gene expression patterns in vector-residing Leishmania infantum identify parasite stage-enriched markers.

Background Leishmaniasis is a vector-borne neglected disease. Inside the natural sand fly vector, the promastigote forms of Leishmania undergo a series of extracellular developmental stages to reach the infectious stage, the metacyclic promastigote. There is limited information regarding the expression profile of L. infantum developmental stages inside the sand fly vector, and molecular markers that can distinguish the different parasite stages are lacking. Methodology/principal findings We performed RNAseq on unaltered midguts of the sand fly Lutzomyia longipalpis after infection with L. infantum parasites. RNAseq was carried out at various time points throughout parasite development. Principal component analysis separated the transcripts corresponding to the different Leishmania promastigote stages, the procyclic, nectomonad, leptomonad and metacyclics. Importantly, there were a significant number of differentially expressed genes when comparing the sequential development of the various Leishmania stages in the sand fly. There were 836 differentially expressed (DE) genes between procyclic and long nectomonad promastigotes; 113 DE genes between nectomonad and leptomonad promastigotes; and 302 DE genes between leptomonad and metacyclic promastigotes. Most of the DE genes do not overlap across stages, highlighting the uniqueness of each Leishmania stage. Furthermore, the different stages of Leishmania parasites exhibited specific transcriptional enrichment across chromosomes. Using the transcriptional signatures exhibited by distinct Leishmania stages during their development in the sand fly midgut, we determined the genes predominantly enriched in each stage, identifying multiple potential stage-specific markers for L. infantum. Conclusions Overall, these findings demonstrate the transcriptional plasticity of the Leishmania parasite inside the sand fly vector and provide a repertoire of potential stage-specific markers for further development as molecular tools for epidemiological studies.
Structured Mappings and Conferencing Common Information for Multiple-Access Channels

In this work, we study two problems: three-user Multiple-Access Channel (MAC) with correlated sources, and MAC with Feedback (MAC-FB) with independent messages. For the first problem, we identify a structure in the joint probability distribution of discrete memoryless sources, and define a new common information called “conferencing common information”. We develop a multi-user joint-source channel coding methodology based on structured mappings to encode this common information efficiently and to transmit it over a MAC. We derive a new set of sufficient conditions for this coding strategy using single-letter information quantities for arbitrary sources and channel distributions. Next, we make a fundamental connection between this problem and the problem of communication of independent messages over three-user MAC-FB. In the latter problem, although the messages are independent to begin with, they become progressively correlated given the channel output feedback. Subsequent communication can be modeled as transmission of correlated sources over MAC. Exploiting this connection, we develop a new coding scheme for the problem. We characterize its performance using single-letter information quantities, and derive an inner bound to the capacity region. For both problems, we provide a set of examples where these rate regions are shown to be optimal. Moreover, we analytically prove that this performance is not achievable using random unstructured random mappings/codes.
Fusion of Deep Neural Networks for Activity Recognition: A Regular Vine Copula Based Approach

In this paper, we propose regular vine copula based fusion of multiple deep neural network classifiers for the problem of multi-sensor based human activity recognition. We take the cross-modal dependence into account by employing regular vine copulas that are extremely flexible and powerful graphical models to characterize complex dependence among multiple modalities. Multiple deep neural networks are used to extract high-level features from multi-sensing modalities, with each deep neural network processing the data collected from a single sensor. The extracted high-level features are then combined using a regular vine copula model. Numerical experiments are conducted to demonstrate the effectiveness of our approach.
Punishing the Crime of Forgetting

Although many people think that forgetting is a problemin life, and it often is, they would do well to appreciate thebenefits of forgetting. Fawcett and Hulbert (2020) have mar-shaled a powerful argument for the adaptive value of forgetting,highlighting the many ways that forgetting is both a commonand essential feature of cognition. Their arguments have impli-cations for memory as it plays out in real life, but here wefocus on implications in the legal realm. We make two majorpoints. First, within the legal system, forgetting on the partof criminal defendants can transcend mere embarrassment orinconvenience, and can actually implicate defendants in crimi-nal acts. Second, forgetting in relation to trauma and victimhoodcan be complicated and controversial, with some victims pre-ferring to retain their painful memories, even at a personalcost.
Entangledness in Suslin lines and trees

Abstract We introduce the idea of a weakly entangled linear order, and show that it is consistent for a Suslin line to be weakly entangled. We generalize the notion of entangled linear orders to ω 1 -trees, and prove that an ω 1 -tree is entangled iff it is free. We force the existence of a Suslin tree which is n-entangled, but all of whose derived trees of dimension n + 1 are special, for any positive n ω .
Controlling chemical oscillations in heterogeneous Belousov-Zhabotinsky gels via mechanical strain

We performed theoretical and computational studies to determine the effect of an applied mechanical strain on the dynamic behavior of heterogeneous polymer gels undergoing the oscillatory Belousov-Zhabotinsky (BZ) reaction. In these spatially heterogeneous gels, the catalyst for the reaction is localized in specific patches within the polymer network and the BZ reaction only occurs within these catalyst-containing patches, which we refer to as BZ patches. We focused on a model for a one-dimensional system, and further assumed that the BZ reaction did not affect the degree of swelling within the gel. For gels having one and two BZ patches, we found that a tensile or compressive strain could induce transitions between the oscillatory and nonoscillatory, steady-state regimes of the system. For certain values of the BZ stoichiometric parameter $f$, these transitions could exhibit a hysteresis. In systems having two oscillating BZ patches, an applied strain could cause a switching between the in-phase and out-of-phase synchronization of the oscillations. The ability to controllably alter the dynamic behavior of BZ gels through mechanical deformations opens up the possibility of using these materials in the design of chemo-mechanical sensors.
Enhanced inference of ecological networks by parameterizing ensembles of population dynamics models constrained with prior knowledge

Accurate network models of species interaction could be used to predict population dynamics and be applied to manage real world ecosystems. Most relevant models are nonlinear, however, and data available from real world ecosystems are too noisy and sparsely sampled for common inference approaches. Here we improved the inference of generalized Lotka–Volterra (gLV) ecological networks by using a new optimization algorithm to constrain parameter signs with prior knowledge and a perturbation-based ensemble method. We applied the new inference to long-term species abundance data from the freshwater fish community in the Illinois River, United States. We constructed an ensemble of 668 gLV models that explained 79% of the data on average. The models indicated (at a 70% level of confidence) a strong positive interaction from emerald shiner (Notropis atherinoides) to channel catfish (Ictalurus punctatus), which we could validate using data from a nearby observation site, and predicted that the relative abundances of most fish species will continue to fluctuate temporally and concordantly in the near future. The network shows that the invasive silver carp (Hypophthalmichthys molitrix) has much stronger impacts on native predators than on prey, supporting the notion that the invader perturbs the native food chain by replacing the diets of predators. Ensemble approaches constrained by prior knowledge can improve inference and produce networks from noisy and sparsely sampled time series data to fill knowledge gaps on real world ecosystems. Such network models could aid efforts to conserve ecosystems such as the Illinois River, which is threatened by the invasion of the silver carp.
AMTree: an active approach to multicasting in mobile networks

Active networks (ANs) are a new paradigm in computer networking. In ANs, programs can be injected into routers and switches to extend the functionalities of the network. This allows programmers to enhance existing protocols and enables the rapid deploymentof new protocols. Little work has been done in the area of multicast routing in heterogeneous environments. In this paper, we propose AMTree, an AN-based multicast tree that is bidirectional, optimizable on demand and adaptive to source migration. We show how ANs can be exploited to enable multicast tree to be modified and optimized efficiently. By filtering unnecessary signaling messages, maintaining minimal storage at routers and incorporating features from shared-tree methods we are able to achieve a scalable solution. Furthermore, we introduce an AN-based optimisation algorithm that is executed on demand by receivers. Besides that we introduce a fast rejoin protocol for receiver migration that makes no assumptions about the existence of multicast services in foreign networks. The performance of AMTree is compared to those of the bidirectional home agent (HA) method and the remote subscription method. We found that compared to the bidirectional HA method AMTree has a much lower handoff and end-to-end latency. Unlike the bidirectional HA method where end-to-end latency increases as the mobile host (MH) migrates further away from its HA, AMTree's latency remains fairly constant. We found that after optimization, the resulting tree's end-to-end latency to be comparable to the remote subscription method but without the need for building a new multicast tree after each handoff.
Standard-Model Extension Constraints on Lorentz and CPT Violation from Optical Polarimetry of Active Galactic Nuclei

Vacuum birefringence from Lorentz and CPT violation in the Standard-Model Extension can be constrained using ground-based optical polarimetry of extragalactic sources. We describe results from a pilot program with an automated system that can perform simultaneous optical polarimetry in multiple passbands on different telescopes with an effective 0.45 m aperture. Despite the limited collecting area, our polarization measurements of AGN using a wider effective optical passband than previous studies yielded individual line-of-sight constraints for Standard-Model Extension mass dimension $d = 5$ operators within a factor of about one to ten of comparable broadband polarimetric bounds obtained using data from a 3.6 m telescope with roughly 64 times the collecting area. Constraining more general anisotropic Standard-Model Extension coefficients at higher $d$ would require more AGN along different lines of sight. This motivates a future dedicated ground-based, multi-band, optical polarimetry AGN survey with $\gtrsim 1$ m-class telescopes, to obtain state-of-the-art anisotropic Standard-Model Extension $d = 4, 5, 6$ constraints, while also using complementary archival polarimetry. This could happen more quickly and cost-effectively than via spectropolarimetry and long before more competitive constraints from space- or balloon-based x-ray/$\gamma$-ray polarization measurements.