cssi/SciSciGPT-SciSciNet huggingface dataset:
Does Media Stimulate Reform Efforts

This paper investigates to what extent media impacts political decisions. A viable practical approach to test the relationship between mass media and political actions is through the use of the World Bank’s Doing Business data, specifically, by assessing local media coverage of Doing Business and implementation of business regulatory reforms. The tested hypothesis is that countries with higher media coverage of Doing Business tend to carry out more business regulatory reforms, assuming one- and two-year lags between media coverage and reform implementation. To achieve this objective, the study put together a comprehensive data set that encompasses country-specific local media coverage of the Doing Business report in 190 economies. The study finds that local media coverage of Doing Business has a significant influence on regulators’ actions. First, the analysis shows that the number of local media articles tends to increase the probability of whether a country does any reform. Second, countries with greater media coverage of Doing Business indicators tend to have higher numbers of implemented reforms.
Unsupervised Monocular Depth and Ego-Motion Learning With Structure and Semantics

We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion. More specifically we model the motions of individual objects and learn their 3D motion vector jointly with depth and ego-motion. We obtain more accurate results, especially for challenging dynamic scenes not addressed by previous approaches. This is an extended version of Casser et al. Code and models have been open sourced at: https://sites.google.com/corp/view/struct2depth.
The International Committee for Future Accelerators (ICFA): History and the Future

In this paper we trace the origins of the International Committee for Future Accelerators (ICFA), and outline its structure and mandate, its activities and accomplishments. We also discuss ICFA’s a...
Remediating Difficulty with Fractions for Students with Mathematics Learning Difficulties.

Competence with fractions is foundational to acquiring more advanced mathematical skills. However, achieving competency with fractions is challenging for many students, especially for those with mathematics learning difficulties who often lack foundational skill with whole numbers. Teaching fractions is also challenging for many teachers as they often experience gaps in their own fractions knowledge. In this article, the authors explain the sources of difficulty when learning and teaching fractions. Then, the authors describe effective instructional strategies for teaching fractions, derived from three randomized control trials. Implications for practice are discussed. Subscribe to LDMJ
An MILP Approach for Distribution Grid Topology Identification using Inverter Probing

Although knowing the feeder topology and line impedances is a prerequisite for solving any grid optimization task, utilities oftentimes have limited or outdated information on their electric network assets. Given the rampant integration of smart inverters, we have previously advocated perturbing their power injections to unveil the underlying grid topology using the induced voltage responses. Under an approximate grid model, the perturbed power injections and the collected voltage deviations obey a linear regression setup, where the unknown is the vector of line resistances. Building on this model, topology processing can be performed in two steps. Given a candidate radial topology, the line resistances can be estimated via a least-squares (LS) fit on the probing data. The topology attaining the best fit can be then selected. To avoid evaluating the exponentially many candidate topologies, this two-step approach is uniquely formulated as a mixed-integer linear program (MILP) using the McCormick relaxation. If the recovered topology is not radial, a second, computationally more demanding MILP confines the search only within radial topologies. Numerical tests explain how topology recovery depends on the noise level and probing duration, and demonstrate that the first simpler MILP yields a tree topology in 90% of the cases tested.
Venous thromboembolism in breast cancer patients receiving cyclin-dependent kinase inhibitors.

Background Venous thromboembolism (VTE) complicates several anticancer regimens including chemotherapy and antiangiogenic agents. Cyclin-dependent kinase inhibitors (CDKIs) are a new approach for hormone receptor-positive metastatic breast cancer (mBC). Reported VTE rates in randomized trials range from 0.6% to 5%, but these may underestimate actual rates observed in clinical practice. Objectives Evaluate VTE rate and its association with outcomes in CDKIs patients. Patients/methods We conducted a retrospective cohort study of consecutive mBC patients who received any of three Food and Drug Administration (FDA)-approved CDKIs (palbociclib, ribociclib, abemaciclib) from January 2015 through December 2017. Venous thromboembolism including deep venous thrombosis (DVT), pulmonary embolism (PE), and visceral vein thrombosis (VVT) were identified by electronic medical record review. Overall survival (OS) and progression-free survival (PFS) were estimated and evaluated for association with VTE using Cox proportional hazard regression. Results We included 424 patients, with a median age at diagnosis of 55 years. Palbociclib was the most commonly used CDKI (n = 390, 91.8%). Venous thromboembolism during CDKIs occurred in 38 patients, 6.3% at year 1, including DVT in 52.6%, PE in 18.5%, and VVT in 15.8%. Median time to VTE was 314 days. Venous thromboembolism was associated with a trend to worse PFS and OS in multivariate analysis [PFS hazard ratio (HR) 1.40, 95% confidence interval (CI) 0.83-2.38, P = .21], OS (HR 1.70, 95% CI 0.95-2.98, P = .076). Conclusions Venous thromboembolism rates with CDKI treatment in mBC in clinical practice are 2-fold to 5-fold greater than reported in registration trials and may be associated with worse outcomes.
PUMICE: A Multi-Modal Agent that Learns Concepts and Conditionals from Natural Language and Demonstrations

Natural language programming is a promising approach to enable end users to instruct new tasks for intelligent agents. However, our formative study found that end users would often use unclear, ambiguous or vague concepts when naturally instructing tasks in natural language, especially when specifying conditionals. Existing systems have limited support for letting the user teach agents new concepts or explaining unclear concepts. In this paper, we describe a new multi-modal domain-independent approach that combines natural language programming and programming-by-demonstration to allow users to first naturally describe tasks and associated conditions at a high level, and then collaborate with the agent to recursively resolve any ambiguities or vagueness through conversations and demonstrations. Users can also define new procedures and concepts by demonstrating and referring to contents within GUIs of existing mobile apps. We demonstrate this approach in PUMICE, an end-user programmable agent that implements this approach. A lab study with 10 users showed its usability.
Harmonic Maps in Connection of Phase Transitions with Higher Dimensional Potential Wells

This is in the sequel of authors’ paper [Lin, F. H., Pan, X. B. and Wang, C. Y., Phase transition for potentials of high dimensional wells, Comm. Pure Appl. Math., 65(6), 2012, 833-888] in which the authors had set up a program to verify rigorously some formal statements associated with the multiple component phase transitions with higher dimensional wells. The main goal here is to establish a regularity theory for minimizing maps with a rather non-standard boundary condition at the sharp interface of the transition. The authors also present a proof, under simplified geometric assumptions, of existence of local smooth gradient flows under such constraints on interfaces which are in the motion by the mean-curvature. In a forthcoming paper, a general theory for such gradient flows and its relation to Keller-Rubinstein-Sternberg’s work (in 1989) on the fast reaction, slow diffusion and motion by the mean curvature would be addressed.
Gamma-positivity of variations of Eulerian polynomials

An identity of Chung, Graham and Knuth involving binomial coefficients and Eulerian numbers motivates our study of a class of polynomials that we call binomial-Eulerian polynomials. These polynomials share several properties with the Eulerian polynomials. For one thing, they are $h$-polynomials of simplicial polytopes, which gives a geometric interpretation of the fact that they are palindromic and unimodal. A formula of Foata and Schutzenberger shows that the Eulerian polynomials have a stronger property, namely $\gamma$-positivity, and a formula of Postnikov, Reiner and Williams does the same for the binomial-Eulerian polynomials. We obtain $q$-analogs of both the Foata-Schutzenberger formula and an alternative to the Postnikov-Reiner-Williams formula, and we show that these $q$-analogs are specializations of analogous symmetric function identities. Algebro-geometric interpretations of these symmetric function analogs are presented.
Patient-Reported Outcome Measures are not a Valid Proxy for Patient Satisfaction in Total Joint Arthroplasty.

Abstract Background Patient-reported outcome measures (PROMs) are increasingly used as quality benchmarks in total joint arthroplasty. The objective of this study is to investigate whether PROMs correlate with patient satisfaction, which is arguably the most important and desired outcome. Methods Our institutional joint database was queried for patients who underwent primary, elective, unilateral total joint arthroplasty. Eligible patients were asked to complete a satisfaction survey at final follow-up. Correlation coefficients (R) were calculated to quantify the relationship between patient satisfaction and prospectively collected PROMs. We explored a wide range of PROMs including Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Short Form-12, Oxford Hip Score, Knee Society Clinical Rating Score (KSCRS), Single Assessment Numerical Evaluation, and University of California Los Angeles activity level rating. Results In general, there was only weak to moderate correlation between patient satisfaction and PROMs. Querying the absolute postoperative scores had higher correlation with patient satisfaction compared to either preoperative scores or net changes in scores. The correlation was higher with disease-specific PROMs (WOMAC, Oxford Hip Score, KSCRS) compared to general health (Short Form-12), activity level (University of California Los Angeles activity level rating), or perception of normalcy (Single Assessment Numerical Evaluation). Within disease-specific PROMs, the pain domain consistently carried the highest correlation with patient satisfaction (WOMAC pain subscale, R = 0.45, P Conclusion There is only weak to moderate correlation between PROMs and patient satisfaction. PROMs alone are not the optimal way to evaluate patient satisfaction. We recommend directly querying patients about satisfaction and using shorter PROMs, particularly disease-specific PROMs that assess pain perception to better gauge patient satisfaction.
Discovery of aspirin-triggered eicosanoid-like mediators in a Drosophila metainflammation-blood tumor model

Epidemiologic studies have linked the use of aspirin to a decline in chronic inflammation that underlies many human diseases, including some cancers. Aspirin reduces the levels of cyclooxygenase-mediated pro-inflammatory prostaglandins, promotes the production of pro-resolution molecules, and triggers the production of anti-inflammatory electrophilic mono-oxygenated (EFOX) lipid mediators. We investigated the effects of aspirin in fruit fly models of chronic inflammation. Ectopic Toll/NF-κB and JAK/STAT signaling in mutant D. melanogaster results in overproliferation of hematopoietic blood progenitors resulting in the formation of granuloma-like tumors. Ectopic JAK-STAT signaling also leads to metabolic inflammation. We report that aspirin-treated mutant flies experience reduction in metabolic inflammation, mitosis, ectopic immune signaling, and macrophage infiltration. Moreover, these flies synthesize 13-HODE, and aspirin triggers 13-oxoODE (13-EFOX-L2) production. Providing the precursor of 13-HODE, linoleic acid, or performing targeted knockdown of the transcription factor STAT in inflammatory blood cells, boosts 13-EFOX-L2 levels while decreasing metabolic inflammation. Thus, hematopoietic cells regulate metabolic inflammation in flies, and their effects can be reversed by pharmaceutical or dietary intervention, suggesting deep phylogenetic conservation in the ability of animals to resolve inflammation and repair tissue damage. These findings can help identify novel treatment targets in humans.
Towards a Non-Stochastic Information Theory

The δ-mutual information between uncertain variables is introduced as a generalization of Nair’s non-stochastic information functional. Several properties of this new quantity are illustrated, and used to prove a channel coding theorem in a non-stochastic setting. Namely, it is shown that the largest δ mutual information between a metric space and its ϵ-packing equals the (ϵ,δ)-capacity of the space. This notion of capacity generalizes the Kolmogorov ϵ -capacity to packing sets of overlap at most δ, and is a variation of a previous definition proposed by one of the authors. These results provide a framework for developing a non-stochastic information theory motivated by potential applications in control and learning theories. Compared to previous non-stochastic approaches, the theory admits the possibility of decoding errors as in Shannon’s probabilistic setting, while retaining its worst-case non-stochastic character.
Cell-Surface Proteomic Profiling in the Fly Brain Uncovers New Wiring Regulators

SUMMARY Molecular interactions at the cellular interface mediate organized assembly of single cells into tissues, and thus govern the development and physiology of multicellular organisms. Here, we developed a cell-type-specific, spatiotemporally-resolved approach to profile cell-surface proteomes in intact tissues. Quantitative profiling of cell-surface proteomes of Drosophila olfactory projection neurons (PNs) in pupae and adults revealed a global down-regulation of wiring molecules and an up-regulation of synaptic molecules in the transition from developing to mature PNs. A proteome-instructed in vivo screen identified 20 new cell-surface molecules regulating neural circuit assembly, many of which belong to evolutionarily conserved protein families not previously linked to neural development. Genetic analysis further revealed that the lipoprotein receptor LRP1 cell-autonomously controls PN dendrite targeting, contributing to the formation of a precise olfactory map. These findings highlight the power of temporally-resolved in situ cell-surface proteomic profiling in discovering new regulators of brain wiring.
Loss of FBXO9 Enhances Proteasome Activity and Promotes Aggressiveness in Acute Myeloid Leukemia

The hematopoietic system is maintained throughout life by stem cells that are capable of differentiating into all hematopoietic lineages. An intimate balance between self-renewal, differentiation, and quiescence is required to maintain hematopoiesis and disruption of this balance can result in malignant transformation. FBXO9, the substrate recognition component from the SCF E3 ubiquitin ligase family, is downregulated in patients with acute myeloid leukemia (AML) compared to healthy bone marrow, and this downregulation is particularly evident in patients with inv(16) AML. To study FBXO9 in malignant hematopoiesis, we generated a conditional knockout mouse model using a novel CRISPR/Cas9 strategy. Deletion of Fbxo9 in the murine hematopoietic system showed no adverse effects on stem and progenitor cell function but in AML lead to markedly accelerated and aggressive leukemia development in mice with inv(16). Not only did Fbxo9 play a role in leukemia initiation but it also functioned to maintain AML activity and promote disease progression. Quantitative mass spectrometry from primary tumors reveals tumors lacking Fbxo9 highly express proteins associated with metastasis and invasion as well as components of the ubiquitin proteasome system. We confirmed that the loss of FBXO9 leads to increased proteasome activity and tumors cells were more sensitive to in vitro proteasome inhibition with bortezomib, suggesting that FBXO9 expression may predict patients' response to bortezomib.
A neural network for information seeking

Humans and other animals often show a strong desire to know the uncertain rewards their future has in store, even when they cannot use this information to influence the outcome. However, it is unknown how the brain predicts opportunities to gain information and motivates this information-seeking behavior. Here we show that neurons in a network of interconnected subregions of primate anterior cingulate cortex and basal ganglia predict the moment of gaining information about uncertain rewards. Spontaneous increases in their information prediction signals are followed by gaze shifts toward objects associated with resolving uncertainty, and pharmacologically disrupting this network reduces the motivation to seek information. These findings demonstrate a cortico-basal ganglia mechanism responsible for motivating actions to resolve uncertainty by seeking knowledge about the future. Animals resolve uncertainty by seeking knowledge about the future. How the brain controls this is unclear. The authors show that a network including primate anterior cingulate cortex and basal ganglia encodes opportunities to gain information about uncertain rewards and mediates information seeking.
On Acoustic Modeling for Broadband Beamforming

In this work, we describe limitations of the free-field propagation model for designing broadband beamformers for microphone arrays on a rigid surface. Towards this goal, we describe a general frame-work for quantifying the microphone array performance in a general wave-field by directly solving the acoustic wave equation. The model utilizes Finite-Element-Method (FEM) for evaluating the response of the microphone array surface to background 3D planar and spherical waves. The effectiveness of the framework is established by designing and evaluating a representative broadband beamformer under realistic acoustic conditions.
Learning to Map Nearly Anything

Looking at the world from above, it is possible to estimate many properties of a given location, including the type of land cover and the expected land use. Historically, such tasks have relied on relatively coarse-grained categories due to the diffi-culty of obtaining fine-grained annotations. In this work, we propose an easily extensible approach that makes it possible to estimate fine-grained properties from overhead imagery. In particular, we propose a cross-modal distillation strategy to learn to predict the distribution of fine-grained properties from overhead imagery, without requiring any manual annotation of overhead imagery. We show that our learned models can be used directly for applications in mapping and image localization.
The power of random counterexamples

Abstract Learning a target concept from a finite n × m concept space requires Ω ( n ) proper equivalence queries in the worst case. We propose a variation of the usual equivalence query in which the teacher is constrained to choose counterexamples randomly from a known probability distribution on examples. We present and analyze the Max-Min learning algorithm, which identifies an arbitrary target concept in an arbitrary finite n × m concept space using at most an expected log 2 ⁡ n proper equivalence queries with random counterexamples.
Infectious power domination of hypergraphs

Abstract The power domination problem seeks to find the placement of the minimum number of sensors needed to monitor an electric power network. We generalize the power domination problem to hypergraphs using the infection rule from Bergen et al. (2018): given an initial set of observed vertices, S 0 , a set A ⊆ S 0 may infect an edge e if A ⊆ e and for any unobserved vertex v , if A ∪ { v } is contained in an edge, then v ∈ e . We combine a domination step with this infection rule to create infectious power domination. We compare this new parameter to the previous generalization by Chang and Roussel (2015). We provide general bounds and determine the impact of some hypergraph operations.
Iterated functions and the Cantor set in one dimension

In this paper we consider the long-term behavior of points in R under iterations of continuous functions. We show that, given any Cantor set Λ∗ embedded in R, there exists a continuous function F ∗ : R → R such that the points that are bounded under iterations of F ∗ are just those points in Λ∗. In the course of this, we find a striking similarity between the way in which we construct the Cantor middle-thirds set, and the way in which we find the points bounded under iterations of certain continuous functions. Acknowledgements: Thanks to Elizabeth Stanhope for some helpful comments and discussions. Page 80 RHIT Undergrad. Math. J., Vol. 14, no. 2
Testing the automation revolution hypothesis

Abstract Wages and employment predict automation in 832 U.S. jobs, 1999 to 2019, but add little to top 25 O*NET job features, whose best predictive model did not change over this period. Automation changes predict changes in neither wages nor employment.
The importance of capturing late melt season sea ice conditions for modeling the western Arctic ocean boundary layer

To better understand the response of the western Arctic upper ocean to late summer ice-ocean interactions, a range of surface, interior, and basal sea ice conditions were simulated in a 1-D turbulent boundary layer model. In-ice and under-ice autonomous observations from the 2014 Marginal Ice Zone Experiment provided a complete characterization of the late melt-season sea ice and were used to set initial conditions, update boundary conditions, and conduct model validation studies. Results show that underestimates of open water and melt pond fraction at the sea ice surface had the largest influence on ocean-to-ice turbulent heat fluxes reducing basal melt rates by as much as 32%. This substantial reduction in latent heat loss was attributed to underestimates of open water areas and the exclusion of melt ponds by low-resolution synthetic aperture radar imagery. However, the greatest overall effect on the ice-ocean boundary layer came from mischaracterizations of basal roughness, with smooth ice scenarios resulting in 7 m of summer halocline shoaling and preservation of the near-surface temperature maximum. Rough ice conditions showed a 23% deepening of the mixed layer and erosion of heat storage above 40 m. Adjustments of conductive heat fluxes had little effect on the near-interface heat budget due to small internal thermal gradients within the late summer sea ice. Results from the 1-D boundary layer simulations highlight the most influential components of sea ice structure during late summer conditions and provide the magnitude of errors expected when ice conditions are mischaracterized.
Geometric Insights into the Convergence of Nonlinear TD Learning

While there are convergence guarantees for temporal difference (TD) learning when using linear function approximators, the situation for nonlinear models is far less understood, and divergent examples are known. Here we take a first step towards extending theoretical convergence guarantees to TD learning with nonlinear function approximation. More precisely, we consider the expected learning dynamics of the TD(0) algorithm for value estimation. As the step-size converges to zero, these dynamics are defined by a nonlinear ODE which depends on the geometry of the space of function approximators, the structure of the underlying Markov chain, and their interaction. We find a set of function approximators that includes ReLU networks and has geometry amenable to TD learning regardless of environment, so that the solution performs about as well as linear TD in the worst case. Then, we show how environments that are more reversible induce dynamics that are better for TD learning and prove global convergence to the true value function for well-conditioned function approximators. Finally, we generalize a divergent counterexample to a family of divergent problems to demonstrate how the interaction between approximator and environment can go wrong and to motivate the assumptions needed to prove convergence.
Model Compression with Two-stage Multi-teacher Knowledge Distillation for Web Question Answering System

Deep pre-training and fine-tuning models (such as BERT and OpenAI GPT) have demonstrated excellent results in question answering areas. However, due to the sheer amount of model parameters, the inference speed of these models is very slow. How to apply these complex models to real business scenarios becomes a challenging but practical problem. Previous model compression methods usually suffer from information loss during the model compression procedure, leading to inferior models compared with the original one. To tackle this challenge, we propose a Two-stage Multi-teacher Knowledge Distillation (TMKD for short) method for web Question Answering system. We first develop a general Q&A distillation task for student model pre-training, and further fine-tune this pre-trained student model with multi-teacher knowledge distillation on downstream tasks (like Web Q&A task, MNLI, SNLI, RTE tasks from GLUE), which effectively reduces the overfitting bias in individual teacher models, and transfers more general knowledge to the student model. The experiment results show that our method can significantly outperform the baseline methods and even achieve comparable results with the original teacher models, along with substantial speedup of model inference.
Modeling, Simulation and Validation of Supersonic Parachute Inflation Dynamics during Mars Landing

A high fidelity multi-physics Eulerian computational framework is presented for the simulation of supersonic parachute inflation during Mars landing. Unlike previous investigations in this area, the framework takes into account an initial folding pattern of the parachute, the flow compressibility effect on the fabric material porosity, and the interactions between supersonic fluid flows and the suspension lines. Several adaptive mesh refinement (AMR)-enabled, large edge simulation (LES)-based, simulations of a full-size disk-gap-band (DGB) parachute inflating in the low-density, low-pressure, carbon dioxide (CO2) Martian atmosphere are reported. The comparison of the drag histories and the first peak forces between the simulation results and experimental data collected during the NASA Curiosity Rover's Mars atmospheric entry shows reasonable agreements. Furthermore, a rudimentary material failure analysis is performed to provide an estimate of the safety factor for the parachute decelerator system. The proposed framework demonstrates the potential of using Computational Fluid Dynamics (CFD) and Fluid-Structure Interaction (FSI)-based simulation tools for future supersonic parachute design.
Airy Functions in the Thermodynamic Bethe Ansatz

Thermodynamic Bethe ansatz equations are coupled nonlinear integral equations which appear frequently when solving integrable models. Those associated with models with N=2 supersymmetry can be related to differential equations, among them Painleve III and the Toda hierarchy. In the simplest such case, the massless limit of these nonlinear integral equations can be solved in terms of the Airy function. This is the only known closed-form solution of thermodynamic Bethe ansatz equations, outside of free or classical models. This turns out to give the spectral determinant of the Schrodinger equation in a linear potential.
Extrema of the Two-Dimensional Discrete Gaussian Free Field

These lecture notes offer a gentle introduction to the two-dimensional Discrete Gaussian Free Field with particular attention paid to the scaling limits of the level sets at heights proportional to the absolute maximum. The bulk of the text is based on recent joint papers with O. Louidor and with J. Ding and S. Goswami. Still, new proofs of the tightness and distributional convergence of the centered DGFF maximum are presented that by-pass the use of the modified Branching Random Walk. The text contains a wealth of instructive exercises and a list of open questions and conjectures for future research.
Counting independent sets in unbalanced bipartite graphs

Understanding the complexity of approximately counting the number of weighted or unweighted independent sets in a bipartite graph (#BIS) is a central open problem in the field of approximate counting. Here we consider a subclass of this problem and give an FPTAS for approximating the partition function of the hard-core model for bipartite graphs when there is sufficient imbalance in the degrees or fugacities between the sides (L, R) of the bipartition. This includes, among others, the biregular case when λ = 1 (approximating the number of independent sets of G) and [MATH HERE]. Our approximation algorithm is based on truncating the cluster expansion of a polymer model partition function that expresses the hard-core partition function in terms of deviations from independent sets that are empty on one side of the bipartition. Further consequences of this method for unbalanced bipartite graphs include an efficient sampling algorithm for the hard-core model and zero-freeness results for the partition function with complex fugacities. By utilizing connections between the cluster expansion and joint cumulants of certain random variables, we go beyond previous algorithmic applications of the cluster expansion to prove that the hard-core model exhibits exponential decay of correlations for all graphs and fugacities satisfying our conditions. This illustrates the applicability of statistical mechanics tools to algorithmic problems and refines our understanding of the connections between different methods of approximate counting.
Global Pixel Transformers for Virtual Staining of Microscopy Images

Visualizing the details of different cellular structures is of great importance to elucidate cellular functions. However, it is challenging to obtain high quality images of different structures directly due to complex cellular environments. Fluorescence staining is a popular technique to label different structures but has several drawbacks. In particular, label staining is time consuming and may affect cell morphology, and simultaneous labels are inherently limited. This raises the need of building computational models to learn relationships between unlabeled microscopy images and labeled fluorescence images, and to infer fluorescence labels of other microscopy images excluding the physical staining process. We propose to develop a novel deep model for virtual staining of unlabeled microscopy images. We first propose a novel network layer, known as the global pixel transformer layer, that fuses global information from inputs effectively. The proposed global pixel transformer layer can generate outputs with arbitrary dimensions, and can be employed for all the regular, down-sampling, and up-sampling operators. We then incorporate our proposed global pixel transformer layers and dense blocks to build an U-Net like network. We believe such a design can promote feature reusing between layers. In addition, we propose a multi-scale input strategy to encourage networks to capture features at different scales. We conduct evaluations across various fluorescence image prediction tasks to demonstrate the effectiveness of our approach. Both quantitative and qualitative results show that our method outperforms the state-of-the-art approach significantly. It is also shown that our proposed global pixel transformer layer is useful to improve the fluorescence image prediction results.
Insights into the Involvement of Spliceosomal Mutations in Myelodysplastic Disorders from Analysis of SACY-1/DDX41 in Caenorhabditis elegans.

Mutations affecting spliceosomal proteins are frequently found in hematological malignancies, including myelodysplastic syndromes and acute myeloid leukemia (AML). DDX41/Abstrakt is a metazoan-specific spliceosomal DEAD-box RNA helicase that is recurrently mutated in inherited myelodysplastic syndromes and in relapsing cases of AML. The genetic properties and genomic impacts of disease-causing missense mutations in DDX41 and other spliceosomal proteins have been uncertain. Here, we conduct a comprehensive analysis of the Caenorhabditis elegans DDX41 ortholog, SACY-1. Biochemical analyses defined SACY-1 as a component of the C. elegans spliceosome, and genetic analyses revealed synthetic lethal interactions with spliceosomal components. We used the auxin-inducible degradation system to analyze the consequence of SACY-1 depletion on the transcriptome using RNA sequencing. SACY-1 depletion impacts the transcriptome through splicing-dependent and splicing-independent mechanisms. Altered 3′ splice site usage represents the predominant splicing defect observed upon SACY-1 depletion, consistent with a role for SACY-1 in the second step of splicing. Missplicing events appear more prevalent in the soma than the germline, suggesting that surveillance mechanisms protect the germline from aberrant splicing. The transcriptome changes observed after SACY-1 depletion suggest that disruption of the spliceosome induces a stress response, which could contribute to the cellular phenotypes conferred by sacy-1 mutant alleles. Multiple sacy-1/ddx41 missense mutations, including the R525H human oncogenic variant, confer antimorphic activity, suggesting that their incorporation into the spliceosome is detrimental. Antagonistic variants that perturb the function of the spliceosome may be relevant to the disease-causing mutations, including DDX41, affecting highly conserved components of the spliceosome in humans.
Anonymity Mixes as (Partial) Assembly Queues: Modeling and Analysis

Anonymity platforms route the traffic over a network of special routers that are known as mixes and implement various traffic disruption techniques to hide the communicating users’ identities. Batch mixes in particular anonymize communicating peers by allowing message exchange to take place only after a sufficient number of messages (a batch) accumulate, thus introducing delay. We introduce a queueing model for batch mix and study its delay properties. Our analysis shows that delay of a batch mix grows quickly as the batch size gets close to the number of senders connected to the mix. We then propose a randomized batch mixing strategy and show that it achieves much better delay scaling in terms of the batch size. However, randomization is shown to reduce the anonymity preserving capabilities of the mix. We also observe that queueing models are particularly useful to study anonymity metrics that are more practically relevant such as the time-to-deanonymize metric.
A Precision Medicine Approach Uncovers a Unique Signature of Neutrophils in Patients With Brushite Kidney Stones.

Introduction We have previously found that papillary histopathology differs greatly between calcium oxalate and brushite stone formers (SF); the latter have much more papillary mineral deposition, tubular cell injury, and tissue fibrosis. Methods In this study, we applied unbiased orthogonal omics approaches on biopsied renal papillae and extracted stones from patients with brushite or calcium oxalate (CaOx) stones. Our goal was to discover stone type-specific molecular signatures to advance our understanding of the underlying pathogenesis. Results Brushite SF did not differ from CaOx SF with respect to metabolic risk factors for stones but did exhibit increased tubule plugging in their papillae. Brushite SF had upregulation of inflammatory pathways in papillary tissue and increased neutrophil markers in stone matrix compared with those with CaOx stones. Large-scale 3-dimensional tissue cytometry on renal papillary biopsies showed an increase in the number and density of neutrophils in the papillae of patients with brushite versus CaOx, thereby linking the observed inflammatory signatures to the neutrophils in the tissue. To explain how neutrophil proteins appear in the stone matrix, we measured neutrophil extracellular trap (NET) formation—NETosis—and found it significantly increased in the papillae of patients with brushite stones compared with CaOx stones. Conclusion We show that increased neutrophil infiltration and NETosis is an unrecognized factor that differentiates brushite and CaOx SF and may explain the markedly increased scarring and inflammation seen in the papillae of patients with brushite stones. Given the increasing prevalence of brushite stones, the role of neutrophil activation in brushite stone formation requires further study.
On the sojourn of an arbitrary customer in an $M/M/1$ Processor Sharing Queue

In this paper, we consider the number of departures seen by a tagged customer while in service in a classical M/M/1 processor sharing queue. By exploiting the underlying orthogonal structure of thi...
A Routing and Link Scheduling Strategy for Smart Grid NAN Communications

As large scale deployment of smart devices in the power grid continues, research efforts need to increasingly focus on efficient communication of generated information. This paper describes a strategy for static routing and scheduling of messages in a multi-hop wireless Smart Grid Neighborhood Area Network (NAN) with multiple source nodes and a common set of destinations or gateways. The problem is formulated as a Mixed Integer Linear Program (MILP) and solved using commercial optimization solver CPLEX. Feasibility of the scheme is demonstrated using different network models, constraints, message injection rates, and initial conditions. It is shown that the proposed approach can be used to generate an optimal link schedule for collecting user-generated bids in a transactive energy market in the least possible time. It is also shown that the methodology is applicable to multiple destination nodes and that their location affects message delivery time.
Pseudo-Anosov homeomorphisms not arising from branched covers

In this paper we provide a negative answer to a question of Farb about the relation between the algebraic degree of the stretch factor of a pseudo-Anosov homeomorphism and the genus of the surface on which it is defined.
Kinetic generalities in catalysis

Chemical kinetics makes it possible to discuss catalysis without chemistry. The general kinetic rules lead to generalities concerning catalytic mechanisms including rate constants, their impact on the turnover frequency of the cycle, product inhibition and catalyst development.
Cutting lemma and Zarankiewicz's problem in distal structures

We establish a cutting lemma for definable families of sets in distal structures, as well as the optimality of the distal cell decomposition for definable families of sets on the plane in o-minimal expansions of fields. Using it, we generalize the results in Fox et al. (J Eur Math Soc 19(6):1785–1810, 2017 ) on the semialgebraic planar Zarankiewicz problem to arbitrary o-minimal structures, in particular obtaining an o-minimal generalization of the Szemeredi–Trotter theorem.
Distributed Fictitious Play in Potential Games with Time Varying Communication Networks

We propose a distributed algorithm for multiagent systems that aim to optimize a common objective when agents differ in their estimates of the objective-relevant state of the environment. Each agent keeps an estimate of the environment and a model of the behavior of other agents. The model of other agents’ behavior assumes agents choose their actions randomly based on a stationary distribution determined by the empirical frequencies of past actions. At each step, each agent takes the action that maximizes its expectation of the common objective computed with respect to its estimate of the environment and its model of others. We propose a weighted averaging rule with non-doubly stochastic weights for agents to estimate the empirical frequency of past actions of all other agents by exchanging their estimates with their neighbors over a time-varying communication network. Under this averaging rule, we show agents’ estimates converge to the actual empirical frequencies fast enough. This implies convergence of actions to a Nash equilibrium of the game with identical payoffs given by the expectation of the common objective with respect to an asymptotically agreed estimate of the state of the environment.
A quasi-local Penrose inequality for the quasi-local energy with static references

The positive mass theorem is one of the fundamental results in general relativity. It states that, assuming the dominant energy condition, the total mass of an asymptotically flat spacetime is non-negative. The Penrose inequality provides a lower bound on mass by the area of the black hole and is closely related to the cosmic censorship conjecture in general relativity. In [14], Lu and Miao proved a quasi-local Penrose inequality for the quasi-local energy with reference in the Schwarzschild manifold. In this article, we prove a quasi-local Penrose inequality for the quasi-local energy with reference in any spherically symmetric static spacetime.
Synchronization of boundary coupled Hindmarsh–Rose neuron network

Abstract In this work, we present a new mathematical model of a boundary coupled neuron network described by the partly diffusive Hindmarsh–Rose equations. We prove the global absorbing property of the solution semiflow and then the main result on the asymptotic synchronization of this neuron network at a uniform exponential rate provided that the boundary coupling strength and the stimulating signal exceed a quantified threshold in terms of the parameters.
Nonminimal Lorentz-Violating Extensions of Gauge Field Theories

A general method is presented to build all gauge-invariant terms in gauge field theories, including quantum electrodynamics and quantum chromodynamics. It is applied to two experiments, light-by-light scattering and deep inelastic scattering, to extract first bounds on certain nonminimal coefficients for Lorentz violation.
Online Community Detection by Spectral Cusum

We present an online community change detection algorithm called spectral CUSUM to detect the emergence of a community using a subspace projection procedure based on a Gaus-sian model setting. Theoretical analysis is provided to char-acterize the average run length (ARL) and expected detection delay (EDD), as well as the asymptotic optimality. Simulation and real data examples demonstrate the good performance of the proposed method.
Age of Information with Finite Horizon and Partial Updates

A resource-constrained system monitors a source of information by requesting a finite number of updates subject to random transmission delays. An a priori fixed update request policy is shown to minimize a polynomial penalty function of the age of information over arbitrary time horizons. Partial updates, compressed updates with reduced transmission and information content, in the presented model are shown to incur an age penalty independent of the compression. Finite horizons are shown to have better performance in terms of second order statistic relative to infinite horizons.
Admission thrombelastography does not guide dose adjustment of enoxaparin in trauma patients.

Abstract Background Enoxaparin is used as chemoprophylaxis to reduce incidence of venous thromboembolism and its complications following trauma. Serum anti-Xa monitoring is used to assess efficacy but requires several doses to be administered. Thrombelastography assesses hypercoagulability and may have utility identifying high-risk patients for venous thromboembolism. The objective was to evaluate whether thrombelastography parameters could identify trauma patients requiring enoxaparin dose adjustment earlier than serum anti-Xa concentrations. Methods A single-center, retrospective medical record review evaluated patients admitted to a regional level I trauma center that received an admission thrombelastography and a dose of enoxaparin with a serum trough anti-Xa concentration drawn. Patients were divided into standard-dose or dose-adjusted enoxaparin. Venous thromboembolism incidence between groups and risk factors for enoxaparin dose adjustment and venous thromboembolism development were evaluated. Results A total of 204 patients were included. Differences observed between groups included age (standard-dose enoxaparin, 48.5 [29.3–72] vs dose-adjusted enoxaparin, 38.5 [25–55.7] years; P = .005), admission creatinine clearance (standard-dose enoxaparin, 92.9 [67.4–113.4] vs dose-adjusted enoxaparin, 102.1 [83.8–129.2] mL/min; P = .017), and time to venous thromboembolism prophylaxis initiation (standard-dose enoxaparin, 23.8 [11.2–36.4] vs dose-adjusted enoxaparin, 34.5 [18.3–52.7] hours; P = .004). No differences in thrombelastography parameters or venous thromboembolism incidence were observed. No independent risk factors for enoxaparin dose adjustment were identified; however, risk assessment profile score > 10 was an independent risk factor for venous thromboembolism development. Conclusion No relationship between admission thrombelastography and need for enoxaparin dose adjustment in trauma patients was observed. As thrombelastography continues growing in clinical use, it is prudent to investigate other potential applications. Currently, thrombelastography should not be used to guide enoxaparin dosing.
Spectral Jaccard Similarity: A New Approach to Estimating Pairwise Sequence Alignments

We present Spectral Jaccard Similarity, a technique that combines min-hashing and spectral methods in order to efficiently estimate pairwise alignment between genomic reads.
Online Ranking with Top-1 Feedback

We consider a setting where a system learns to rank a fixed set of $m$ items. The goal is produce good item rankings for users with diverse interests who interact online with the system for $T$ rounds. We consider a novel top-$1$ feedback model: at the end of each round, the relevance score for only the top ranked object is revealed. However, the performance of the system is judged on the entire ranked list. We provide a comprehensive set of results regarding learnability under this challenging setting. For PairwiseLoss and DCG, two popular ranking measures, we prove that the minimax regret is $\Theta(T^{2/3})$. Moreover, the minimax regret is achievable using an efficient strategy that only spends $O(m \log m)$ time per round. The same efficient strategy achieves $O(T^{2/3})$ regret for Precision@$k$. Surprisingly, we show that for normalized versions of these ranking measures, i.e., AUC, NDCG \& MAP, no online ranking algorithm can have sublinear regret.
Does Child Labor Decline with Improving Economic Status

Between 1993 and 1997, child labor in Vietnam declined by nearly 30 percent while the country’s GDP grew by nearly 9 percent per year on average. Using a simple, nonparametric decomposition, I investigate the relationship between improvements in per capita expenditure and child labor with a panel data set that spans this episode of growth in Vietnam. Improvements in per capita expenditure can explain 80 percent of the decline in child labor that occurs in households whose expenditures improve enough to move out of poverty. This finding suggests a previously undocumented role for economic growth in the amelioration of child labor.