cssi/SciSciGPT-SciSciNet huggingface dataset:
BATCAVE: calling somatic mutations with a tumor- and site-specific prior.

Detecting somatic mutations withins tumors is key to understanding treatment resistance, patient prognosis and tumor evolution. Mutations at low allelic frequency, those present in only a small portion of tumor cells, are particularly difficult to detect. Many algorithms have been developed to detect such mutations, but none models a key aspect of tumor biology. Namely, every tumor has its own profile of mutation types that it tends to generate. We present BATCAVE (Bayesian Analysis Tools for Context-Aware Variant Evaluation), an algorithm that first learns the individual tumor mutational profile and mutation rate then uses them in a prior for evaluating potential mutations. We also present an R implementation of the algorithm, built on the popular caller MuTect. Using simulations, we show that adding the BATCAVE algorithm to MuTect improves variant detection. It also improves the calibration of posterior probabilities, enabling more principled tradeoff between precision and recall. We also show that BATCAVE performs well on real data. Our implementation is computationally inexpensive and straightforward to incorporate into existing MuTect pipelines. More broadly, the algorithm can be added to other variant callers, and it can be extended to include additional biological features that affect mutation generation.
Reconstruction of pediatric skull base defects: A retrospective analysis emphasizing the very young

Abstract Introduction Pathology of the pediatric skull base is rare and ranges from congenital defects to malignancy and traumatic defects. Pediatric patients, particularly those ≤6 years of age, present a unique set of anatomic challenges for the skull base surgeon. The goal of this study was to retrospectively review our experience with reconstruction of pediatric skull base defects with particular emphasis on those ≤6 years of age. Methods A retrospective chart review was conducted of patients ≤20 years of age who underwent endoscopic endonasal and combined endoscopic and open approaches to address cranial base pathology from 2007 to 2018. Patients were divided into two groups; those ≤6 years of age (group A) and those >6 years of age and ≤20 years of age (group B) and reconstructive techniques and outcomes were analyzed. Results Intraoperative communication with the subarachnoid space (CSF leak) was created in 50% (3/6) of patients ≤ 6 years of age compared to 32.7% (16/49) in patients > 6 years and ≤20 years of age (P 6 years and ≤20 years of age and most commonly included a nasoseptal flap (22.4%, 11/49). A lumbar drain was utilized in 50% (3/6) of patients ≤ 6 years of age compared to 18.4% (9/49) in patients > 6 and ≤20 years of age (P 6 and ≤20 years of age (P Conclusions Pediatric patients, particularly those ≤6 years of age, present a unique set of anatomic and behavioral challenges for the skull base surgeon as highlighted in this study.
Discontinuous Galerkin via Interpolation: The Direct Flux Reconstruction Method

The discontinuous Galerkin (DG) method is based on the idea of projection using integration. The recent direct flux reconstruction (DFR) method by Romero et al. (J Sci Comput 67(1):351–374, 2016) is derived via interpolation and results in a scheme identical to DG (on hexahedral meshes). The DFR method is further studied and developed here. Two proofs for its equivalence with the DG scheme considerably simpler than the original proof are presented. The first proof employs the $$ 2K - 1 $$ degree of precision by a $$ K $$-point Gauss quadrature. The second shows the equivalence of DG, FR, and DFR by using the property that the derivative of the degree $$ K + 1 $$ Lobatto polynomial vanishes at the $$ K $$ Gauss points. Fourier analysis for these schemes are presented using an approach more geometric compared with existing analytic approaches. The effects of nonuniform mesh and those of high-order mesh transformation (a precursor for curved meshes in two and three spatial dimensions) on stability and accuracy are examined. These nonstandard analyses are obtained via an in-depth study of the behavior of eigenvalues and eigenvectors.
Computational and experimental performance of CRISPR homing gene drive strategies with multiplexed gRNAs

The rapid evolution of resistance alleles poses a major obstacle for genetic manipulation of populations with CRISPR homing gene drives. One proposed solution is using multiple guide RNAs (gRNAs), allowing a drive to function even if some resistant target sites are present. Here, we develop a model of homing mechanisms parameterized by experimental studies. Our model incorporates several factors affecting drives with multiple gRNAs, including timing of cleavage, reduction in homology-directed repair efficiency due to imperfect homology, Cas9 activity saturation, gRNA activity level variance, and incomplete homology-directed repair. We find that homing drives have an optimal number of gRNAs, usually between two and eight, depending on the specific drive type and performance parameters. These results contradict the notion that resistance rates can be reduced to arbitrarily low levels by gRNA multiplexing and highlight the need for combined approaches to counter resistance evolution in CRISPR homing drives.
Left–right pairs and complex forests of infinite rooted binary trees

Let &#x1d49f;0 := {x + iy|x,y ∈ ℝ,x,y > 0}, and let (L,R) be a pair of Mobius transformations corresponding to SL2(ℕ0) matrices such that L(&#x1d49f;0) and R(&#x1d49f;0) are disjoint. Given such a pair (called a left–righ...
Prospects, Risks, and Vulnerabilities in Emerging and Developing Economies : Lessons from the Past Decade

Growth in emerging markets and developing economies (EMDEs) has generally disappointed since the 2009 global recession, with sizable forecast downgrades in most years. EMDEs continue to face downside risks to growth outlook over the next couple of years. These include heightened global policy uncertainty, trade tensions, spillovers from weaker-than-expected growth in major economies, and disorderly financial market developments. These risks are accompanied by region-specific risks, including geopolitical tensions, armed conflict, and severe weather events. If risks materialize, their impact on EMDEs depends on the magnitude of spillovers and domestic vulnerabilities. Since the 2009 global recession, external, corporate sector and sovereign vulnerabilities have risen in most EMDEs, leaving them less well-prepared for future shocks. Low-income countries, in particular, face elevated vulnerabilities, with about 40 percent of them currently in debt distress. Over the longer run, EMDEs also face weakening potential growth, reflecting decelerations in capital accumulation and productivity growth, as well as demographic headwinds. These constraints are likely to hamper growth in the next decade unless they are mitigated by ambitious and credible reform agendas.
Markov-Chain Monte Carlo approximation of the Ideal Observer using generative adversarial networks

The Ideal Observer (IO) performance has been advocated when optimizing medical imaging systems for signal detection tasks. However, analytical computation of the IO test statistic is generally intractable. To approximate the IO test statistic, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been developed. However, current applications of MCMC techniques have been limited to several object models such as a lumpy object model and a binary texture model, and it remains unclear how MCMC methods can be implemented with other more sophisticated object models. Deep learning methods that employ generative adversarial networks (GANs) hold great promise to learn stochastic object models (SOMs) from image data. In this study, we described a method to approximate the IO by applying MCMC techniques to SOMs learned by use of GANs. The proposed method can be employed with arbitrary object models that can be learned by use of GANs, thereby the domain of applicability of MCMC techniques for approximating the IO performance is extended. In this study, both signal-known-exactly (SKE) and signal-known-statistically (SKS) binary signal detection tasks are considered. The IO performance computed by the proposed method is compared to that computed by the conventional MCMC method. The advantages of the proposed method are discussed.
Presence or Absence of Ras Dimerization Shows Distinct Kinetic Signature in Ras-Raf Interaction.

Abstract Initiations of cell signaling pathways often occur through the formation of multiprotein complexes that form through protein-protein interactions. Therefore, detecting their presence is central to understanding the function of a cell signaling pathway, aberration of which often leads to fatal diseases, including cancers. However, the multiprotein complexes are often difficult to detect using microscopes due to their small sizes. Therefore, currently, their presence can be only detected through indirect means. In this article, we propose to investigate the presence or absence of protein complexes through some easily measurable kinetic parameters, such as activation rates. As a proof of concept, we investigate the Ras-Raf system, a well-characterized cell signaling system. It has been hypothesized that Ras dimerization is necessary to create activated Raf dimers. Although there are circumstantial evidences supporting the Ras dimerization hypothesis, direct proof of Ras dimerization is still inconclusive. In the absence of conclusive direct experimental proof, this hypothesis can only be examined through indirect evidences of Ras dimerization. In this article, using a multiscale simulation technique, we provide multiple criteria that distinguishes an activation mechanism involving Ras dimerization from another mechanism that does not involve Ras dimerization. The provided criteria will be useful in the investigation of not only Ras-Raf interaction but also other two-protein interactions.
Learning Precisely Timed Feedforward Control of the Sensor-Denied Inverted Pendulum

Time delays due to signal latency, computational complexity, and sensor-denied environments, pose a critical challenge in both engineered and biological control systems. In this letter, we investigate biologically inspired strategies to develop predictive control laws for engineered systems with time delays of similar order to their system dynamics. We demonstrate this approach on the nonlinear pendulum with partially denied observations, so that it is only possible to measure the state of the system near the upright position. Given a large disturbance that overwhelms the local feedback controller, it is necessary to add or remove energy from the pendulum so that it returns to the upright position after one full revolution. The partial observation near the upright position introduces a significant delay between observations and the region where actuation is most effective. Thus, we develop a learning algorithm that integrates sensor information into a triggered feedforward control signal to overcome this delay with minimal computation, training data, and set of control decisions. This simple controller can serve as a model for many biological systems, and can be implemented in engineered systems with time delays.
No Increase in Infections or Readmissions in Al Amyloid Patients Who Undergo Heart Transplant

Purpose After initial efforts at heart transplantation (HT) in patients with light chain amyloidosis were fraught with poor outcomes, emerging experience with modern chemotherapy regimens has led to a major improvement in outcomes in select centers. Given the systemic nature of the disease and the added immunosuppression in the form of chemotherapy, this study sought to analyze if AL patients undergoing HT had a higher burden of readmissions and infections non-amyloid heart transplant patients. Methods One-year all-cause readmissions and infections requiring admissions were analyzed retrospectively between the 2 groups. Results A total of 253 patients underwent heart transplant in our institution from 2009 to 2017, of these, 15 patients had AL-amyloidosis with substantial cardiac involvement. There was no difference in readmissions or infections 1 year from transplant (Table 1). 2/15 patients died within 1 month from transplant due to multi-organ failure and cerebrovascular accident, respectively. All patients underwent chemotherapy pre-transplant. 9/13 patients continued chemotherapy after heart transplantation with a median duration of 7.2 months (range 1.5-42 months), 1 was transferred to another institution, 2 remained in remission. Chemotherapy included bortezomib (88.9%), lenalidomide (11.1%), and melphalan (11.1%). Conclusion Despite chemotherapy pre and post HT in most AL patients, there was not a significant difference in readmissions or infections in the first year post -HT compared to non-amyloid patients undergoing HT. This adds to the growing evidence that carefully selected patients with AL amyloid can safely undergo HT with ongoing chemotherapy.
Lunar orbit determination in the presence of unmodeled accelerations

A technique for estimating the state of an artificial satellite in the presence of unmodeled accelerations is presented. The unmodeled acceleration is approximated by a first-order Gauss-Markov sequence which can be separated into a timewise correlated component and a purely random component. Using this approximation, a sequential procedure for estimating the position, velocity, and the unmodeled acceleration is developed. The method is evaluated by reducing range-rate observations obtained by tracking the Apollo 10 and 11 spacecraft during the lunar orbit phase of the mission. Numerical results are presented which show that the observation residual pattern lies within the observation noise standard deviation. The values of the estimated components of the unmodeled acceleration are repeatable from orbit to orbit within a given mission and from mission to mission when the same ground track is covered. Finally, the variation in the radial component of the unmodeled acceleration shows a high correlation with the reported location of the lunar surface mascons.
Solute-vacancy clustering in aluminum

Abstract We present an extensive first-principles database of solute-vacancy, homoatomic, heteroatomic solute-solute, and solute-solute-vacancy binding energies of relevant alloying elements in aluminum. We particularly focus on the systems with major alloying elements in aluminum, i.e., Cu, Mg, and Si. The computed binding energies of solute-vacancy, solute-solute pairs, and solute-solute-vacancy triplets agree with available experiments and theoretical results in literature. We consider physical factors such as solute size and formation energies of intermetallic compounds to correlate with binding energies. Systematic studies of the homoatomic solute-solute-vacancy and heteroatomic (Cu, Mg, or Si)-solute-vacancy complexes reveal the overarching effect of the vacancy in stabilizing solute-solute pairs. The binding energy database presented here elucidates the interaction between solute cluster and vacancy in aluminum, and it is expected to provide insight into the design of advanced Al alloys with tailored properties.
Complete Leibniz algebras

Abstract Leibniz algebras are certain generalizations of Lie algebras. It is natural to generalize concepts in Lie algebras to Leibniz algebras and investigate whether the corresponding results still hold. In this paper we introduce the notion of complete Leibniz algebras as a generalization of complete Lie algebras. Then we study properties of complete Leibniz algebras and their holomorphs.
Proteasome inhibitor-induced modulation reveals the spliceosome as a specific therapeutic vulnerability in multiple myeloma

Enhancing the efficacy of proteasome inhibitors (PI) is a central goal in myeloma therapy. We proposed that signaling-level responses after PI may reveal new mechanisms of action that can be therapeutically exploited. Unbiased phosphoproteomics after treatment with the PI carfilzomib surprisingly demonstrates the most prominent phosphorylation changes on splicing related proteins. Spliceosome modulation is invisible to RNA or protein abundance alone. Transcriptome analysis after PI demonstrates broad-scale intron retention, suggestive of spliceosome interference, as well as specific alternative splicing of protein homeostasis machinery components. These findings lead us to evaluate direct spliceosome inhibition in myeloma, which synergizes with carfilzomib and shows potent anti-tumor activity. Functional genomics and exome sequencing further support the spliceosome as a specific vulnerability in myeloma. Our results propose splicing interference as an unrecognized modality of PI mechanism, reveal additional modes of spliceosome modulation, and suggest spliceosome targeting as a promising therapeutic strategy in myeloma.
Developmental loss of MeCP2 from VIP interneurons impairs cortical function and behavior

Rett Syndrome is a devastating neurodevelopmental disorder resulting from mutations in the gene MECP2. Mutations of Mecp2 that are restricted to GABAergic cell types largely replicate the behavioral phenotypes associated with mouse models of Rett Syndrome, suggesting a pathophysiological role for inhibitory interneurons. Recent work has suggested that vasoactive intestinal peptide-expressing (VIP) interneurons may play a critical role in the proper development and function of cortical circuits, making them a potential key point of vulnerability in neurodevelopmental disorders. However, little is known about the role of VIP interneurons in Rett Syndrome. Here we find that loss of MeCP2 specifically from VIP interneurons replicates key neural and behavioral phenotypes observed following global Mecp2 loss of function.
Cases Relating to Chapter 13 – Documentary Evidence

Publisher Summary This chapter presents several case studies related to documentary evidence. These cases include the case of Wilkerson versus State, State versus Huehn, and McKeehan versus State. In the first case, the appellant Jeffery Scott Wilkerson seeks the reversal of his conviction for delivering a Schedule VI controlled substance—marijuana. On appeal, appellant argues that the trial court erred: (1) by denying his motion for a directed verdict because there was insufficient evidence to support the conviction; (2) in admitting into evidence a tape recording between a confidential informant and appellant that was not properly authenticated; (3) in failing to suppress evidence where the application for search warrant failed to set forth particular facts bearing on the informant's reliability; (4) by denying his motion to suppress where the search warrant did not indicate that it was based upon either recorded testimony or sworn affidavits. In the second cases, the defendant, Daniel Huehn, appeals the judgment of conviction entered on a jury verdict finding him guilty of theft. In the third case, the defendant, Ronald McKeehan, was found guilty after jury trial of robbery with a firearm, grand theft, aggravated assault with a firearm, and kidnapping with intent to commit a felony.
Force Limits Measured on a Space Shuttle Flight

The random vibration forces between a payload and the sidewall of the space shuttle have been measured in flight and compared with the force specifications used in ground vibration tests. The flight data are in agreement with a semi-empirical method, which is widely used to predict vibration test force limits. The flight measurements are less than one-half of the random vibration loads specified in the shuttle payload design guide. These data are consistent with published data [1]
Sequence Similarity Searching Using the BLAST Family of Programs

Database sequence similarity searching is carried out thousands of times each day by researchers worldwide. This unit describes the BLAST (Basic Local Alignment Search Tool) family of sequence similarity search programs. It compares basic and advanced searches, discusses the BLAST algorithm, and presents the variety of databases that can be searched. Examples of various BLAST searches (e.g., BLASTP, BLASTX, BLASTN) are included. The unit also includes a discussion of different search strategies, such as filtering repeats and low-complexity regions, and tips for analyzing genomic DNA, mRNA, and short sequences. Two unit appendices detail the BLAST parameters and sequence identifier syntax.
Problems with different time scales

In this section we discuss a very simple problem. Consider the scalar initial value problem Here e > 0 is a small constant and a = a 1 + i a 2 , a 1 , a 2 real, is a complex number with | a | = 1. We can write down the solution of (1.1) explicity. It is where is the forced solution and is a solution of the homogeneous equation y S varies on the time scale ‘1’ while y F varies on the much faster scale 1/e. We say that y S , y F vary on the slow and fast scale, respectively. We use also the phrase: y S and y F are the slow and the fast part of the solution, respectively.
Earnings Forecasts and Price Efficiency after Earnings Realizations: Reduction in Information Asymmetry through Learning from Price

When information asymmetry is a major market friction, earnings forecasts can lead to higher price efficiency even after the information in forecasts completely dissipates upon earnings realizations. We show this in an experimental market that features information asymmetry (i.e., some traders possess differential private information). Earnings forecasts reduce information asymmetry and lead to prices that reflect a greater amount of private information. Traders can learn more about others' information from prices. This information learned from past prices continues to reduce information asymmetry and improve price efficiency even after earnings realizations. We contribute to the disclosure literature by showing the evidence that the learning‐from‐price effect amplifies the impact of public disclosure on price efficiency. Previsions de benefices et efficacite en matiere de prix apres la materialisation des benefices : Reduction de l'asymetrie de l'information grâce a l'apprentissage a partir des prix Lorsque l'asymetrie de l'information est une source de tension importante sur le marche, les previsions de benefices peuvent entrainer une hausse de l'efficacite en matiere de prix meme apres que l'information fournie dans les previsions est rendue entierement caduque par la materialisation des benefices. Nous montrons ce phenomene dans le cadre d'un marche experimental caracterise par une asymetrie de l'information (c'est‐a‐dire ou certains courtiers possedent de l'information differentielle confidentielle). Les previsions de benefices reduisent l'asymetrie de l'information et menent a des prix qui refletent une information confidentielle plus abondante. Les courtiers peuvent en apprendre davantage sur l'information que possedent les autres a partir des prix. L'information tiree des anciens prix continue de reduire l'asymetrie de l'information et d'accroitre l'efficacite en matiere de prix meme apres la materialisation des benefices. Nous contribuons a la litterature sur la divulgation de l'information en presentant la preuve que l'effet de l'apprentissage a partir des prix amplifie l'impact de la divulgation publique sur l'efficacite en matiere de prix.
A test for differential ascertainment in case‐control studies with application to child maltreatment

We propose a method to test for the presence of differential ascertainment in case-control studies, when data are collected by multiple sources. We show that, when differential ascertainment is present, the use of only the observed cases leads to severe bias in the computation of the odds ratio. We can alleviate the effect of such bias using the estimates that our method of testing for differential ascertainment naturally provides. We apply it to a dataset obtained from the National Violent Death Reporting System, with the goal of checking for the presence of differential ascertainment by race in the count of deaths caused by child maltreatment.
Improved Bounds for Induced Poset Saturation

Given a finite poset $\mathcal{P}$, a family $\mathcal{F}$ of elements in the Boolean lattice is induced-$\mathcal{P}$-saturated if $\mathcal{F}$ contains no copy of $\mathcal{P}$ as an induced subposet but every proper superset of $\mathcal{F}$ contains a copy of $\mathcal{P}$ as an induced subposet. The minimum size of an induced-$\mathcal{P}$-saturated family in the $n$-dimensional Boolean lattice, denoted $\mathrm{sat}^*(n,\mathcal{P})$, was first studied by Ferrara et al. (2017). Our work focuses on strengthening lower bounds. For the 4-point poset known as the diamond, we prove $\mathrm{sat}^*(n,\Diamond)\geq\sqrt{n}$, improving upon a logarithmic lower bound. For the antichain with $k+1$ elements, we prove $$\mathrm{sat}^*(n,\mathcal{A}_{k+1})\geq \left(1-\frac{1}{\log_2k}\right)\frac{kn}{\log_2 k}$$ for $n$ sufficiently large, improving upon a lower bound of $3n-1$ for $k\geq 3$.
Concept Bottleneck Models

We seek to learn models that we can interact with using high-level concepts: if the model did not think there was a bone spur in the x-ray, would it still predict severe arthritis? State-of-the-art models today do not typically support the manipulation of concepts like “the existence of bone spurs”, as they are trained end-to-end to go directly from raw input (e.g., pixels) to output (e.g., arthritis severity). We revisit the classic idea of first predicting concepts that are provided at training time, and then using these concepts to predict the label. By construction, we can intervene on these concept bottleneck models by editing their predicted concept values and propagating these changes to the final prediction. On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models, while enabling interpretation in terms of high-level clinical concepts (“bone spurs”) or bird attributes (“wing color”). These models also allow for richer human-model interaction: accuracy improves significantly if we can correct model mistakes on concepts at test time.
Rescuing logic encryption in post-SAT era by locking & obfuscation

The active participation of external entities in the manufacturing flow has produced numerous hardware security issues in which piracy and overproduction are likely to be the most ubiquitous and expensive ones. The main approach to prevent unauthorized products from functioning is logic encryption that inserts key-controlled gates to the original circuit in a way that the valid behavior of the circuit only happens when the correct key is applied. The challenge for the security designer is to ensure neither the correct key nor the original circuit can be revealed by different analyses of the encrypted circuit. However, in state-of-the-art logic encryption works, a lot of performance is sold to guarantee security against powerful logic and structural attacks. This contradicts the primary reason of logic encryption that is to protect a precious design from being pirated and overproduced. In this paper, we propose a bilateral logic encryption platform that maintains high degree of security with small circuit modification. The robustness against exact and approximate attacks is also demonstrated.
Can we do resect and discard with artificial intelligence-assisted colon polyp “optical biopsy?”

Abstract Resect and discard refers to a paradigm for the management of colorectal adenomas 1-5 mm in size. In this paradigm, histology of colorectal polyps is predicted endoscopically based on surface features. Lesions that are ≤5 mm in size and predicted to be adenomas are resected endoscopically and discarded rather than submitted to pathology. Adenomas in this size range have an extremely low risk of cancer, and the cost savings of the resect and discard paradigm would be substantial. Artificial intelligence programs can improve the overall prediction for histology based on endoscopic imaging, and reduce operator dependence in endoscopic predictions. Although meta-analyses have concluded that the accuracy of endoscopic prediction is sufficiently high to institute the resect and discard paradigm in clinical practice, actual implementation has faced several obstacles. These include lack of financial incentives for endoscopists, perceived increased medical-legal risk compared with the current management paradigm of submitting all polyps to pathology, and local rules for tissue handling.
Fear, lockdown, and diversion: Comparing drivers of pandemic economic decline 2020

Abstract The collapse of economic activity in 2020 from COVID-19 has been immense. An important question is how much of that collapse resulted from government-imposed restrictions versus people voluntarily choosing to stay home to avoid infection. This paper examines the drivers of the economic slowdown using cellular phone records data on customer visits to more than 2.25 million individual businesses across 110 different industries. Comparing consumer behavior over the crisis within the same commuting zones but across state and county boundaries with different policy regimes suggests that legal shutdown orders account for only a modest share of the massive changes to consumer behavior (and that tracking county-level policy conditions is significantly more accurate than using state-level policies alone). While overall consumer traffic fell by 60 percentage points, legal restrictions explain only 7 percentage points of this. Individual choices were far more important and seem tied to fears of infection. Traffic started dropping before the legal orders were in place; was highly influenced by the number of COVID deaths reported in the county; and showed a clear shift by consumers away from busier, more crowded stores toward smaller, less busy stores in the same industry. States that repealed their shutdown orders saw symmetric, modest recoveries in consumer visits, further supporting the small estimated effect of policy. Although the shutdown orders had little aggregate impact, they did have a significant effect in reallocating consumer visits away from “nonessential” to “essential” businesses and from restaurants and bars toward groceries and other food sellers.
Scattering and Sparse Partitions, and Their Applications.

A partition $\mathcal{P}$ of a weighted graph $G$ is $(\sigma,\tau,\Delta)$-sparse if every cluster has diameter at most $\Delta$, and every ball of radius $\Delta/\sigma$ intersects at most $\tau$ clusters. Similarly, $\mathcal{P}$ is $(\sigma,\tau,\Delta)$-scattering if instead for balls we require that every shortest path of length at most $\Delta/\sigma$ intersects at most $\tau$ clusters. Given a graph $G$ that admits a $(\sigma,\tau,\Delta)$-sparse partition for all $\Delta>0$, Jia et al. [STOC05] constructed a solution for the Universal Steiner Tree problem (and also Universal TSP) with stretch $O(\tau\sigma^2\log_\tau n)$. Given a graph $G$ that admits a $(\sigma,\tau,\Delta)$-scattering partition for all $\Delta>0$, we construct a solution for the Steiner Point Removal problem with stretch $O(\tau^3\sigma^3)$. We then construct sparse and scattering partitions for various different graph families, receiving many new results for the Universal Steiner Tree and Steiner Point Removal problems.
Structure of Collapsing Solutions of Generalized Ricci Flow

We derive modified Perelman-type monotonicity formulas for solutions to the generalized Ricci flow equation with symmetry on principal bundles, which lead to rigidity and classification results for nonsingular solutions.
A Simplified Calculation of the Kurtosis for RFI Detection

Microwave radiometers detecting geophysical parameters are susceptible to radio-frequency interference (RFI) from anthropogenic sources. The kurtosis statistic can be a powerful means of identifying some types of low-power RFI, as thermal noise has a distinct kurtosis value of three, while thermal noise contaminated even with low-power nonthermal RFI often has other values of kurtosis. This paper presents a method for calculating the kurtosis and brightness that significantly reduces the radiometer data download requirements.
Some Key Issues Confronting Inflationary Cosmology

Does inflation provide a compelling explanation for why the universe is so large, so flat, and so old, and a predictive theory of density perturbations? In this brief contribution (based on the role of the author as moderator of the discussion session on inflation), a list of some of the key issues confronting inflationary cosmology will be given, with the hope of focusing the debate on inflation and drawing more attention to some of the potential problems of the inflationary theory.
Ocular hypertension drives remodeling of AMPA receptors in select populations of retinal ganglion cells

AMPA-type glutamate receptors in the CNS are normally impermeable to Ca2+, but the aberrant expression of Ca2+-permeable AMPA receptors (CP-AMPARs) occurs in pathological conditions such as ischemia or epilepsy, or degenerative diseases such as ALS. Here, we show that select populations of retinal ganglion cells (RGCs) similarly express high levels of CP-AMPARs in a mouse model of glaucoma. CP-AMPAR expression increased dramatically in both On sustained alpha and Off transient alpha RGCs, and this increase was prevented by genomic editing of the GluA2 subunit. On sustained alpha RGCs with elevated CP-AMPAR levels displayed profound synaptic depression, which was reduced by selectively blocking CP-AMPARs, buffering Ca2+ with BAPTA, or with the CB1 antagonist AM251, suggesting that depression was mediated by a retrograde transmitter which might be triggered by the influx of Ca2+ through CP-AMPARs. Thus, glaucoma may alter the composition of AMPARs and depress excitatory synaptic input in select populations of RGCs.
Spatiotemporal Bundle Adjustment for Dynamic 3D Human Reconstruction in the Wild.

Bundle adjustment jointly optimizes camera intrinsics and extrinsics and 3D point triangulation to reconstruct a static scene. The triangulation constraint, however, is invalid for moving points captured in multiple unsynchronized videos and bundle adjustment is not designed to estimate the temporal alignment between cameras. We present a spatiotemporal bundle adjustment framework that jointly optimizes four coupled sub-problems: estimating camera intrinsics and extrinsics, triangulating static 3D points, as well as sub-frame temporal alignment between cameras and computing 3D trajectories of dynamic points. Key to our joint optimization is the careful integration of physics-based motion priors within the reconstruction pipeline, validated on a large motion capture corpus of human subjects. We devise an incremental reconstruction and alignment algorithm to strictly enforce the motion prior during the spatiotemporal bundle adjustment. This algorithm is further made more efficient by a divide and conquer scheme while still maintaining high accuracy. We apply this algorithm to reconstruct 3D motion trajectories of human bodies in dynamic events captured by multiple uncalibrated and unsynchronized video cameras in the wild. To make the reconstruction visually more interpretable, we fit a statistical 3D human body model to the asynchronous video streams…. Please refer to the pdf for the full version.
Cleavage-furrow formation without F-actin in Chlamydomonas.

It is widely believed that cleavage-furrow formation during cytokinesis is driven by the contraction of a ring containing F-actin and type-II myosin. However, even in cells that have such rings, they are not always essential for furrow formation. Moreover, many taxonomically diverse eukaryotic cells divide by furrowing but have no type-II myosin, making it unlikely that an actomyosin ring drives furrowing. To explore this issue further, we have used one such organism, the green alga Chlamydomonas reinhardtii. We found that although F-actin is associated with the furrow region, none of the three myosins (of types VIII and XI) is localized there. Moreover, when F-actin was eliminated through a combination of a mutation and a drug, furrows still formed and the cells divided, although somewhat less efficiently than normal. Unexpectedly, division of the large Chlamydomonas chloroplast was delayed in the cells lacking F-actin; as this organelle lies directly in the path of the cleavage furrow, this delay may explain, at least in part, the delay in cytokinesis itself. Earlier studies had shown an association of microtubules with the cleavage furrow, and we used a fluorescently tagged EB1 protein to show that microtubules are still associated with the furrows in the absence of F-actin, consistent with the possibility that the microtubules are important for furrow formation. We suggest that the actomyosin ring evolved as one way to improve the efficiency of a core process for furrow formation that was already present in ancestral eukaryotes.
Occurrence Rates from Direct Imaging Surveys

The occurrence rate of young giant planets from direct imaging surveys is a fundamental tracer of the efficiency with which planets form and migrate at wide orbital distances. These measurements have progressively converged to a value of about 1% for the most massive planets ($\approx$5-13 $M_\mathrm{Jup}$) averaged over all stellar masses at separations spanning a few tens to a few hundreds of AU. The subtler statistical properties of this population are beginning to emerge with ever-increasing sample sizes: there is tentative evidence that planets on wide orbits are more frequent around stars that possess debris disks; brown dwarf companions exist at comparable (or perhaps slightly higher) rates as their counterparts in the planetary-mass regime; and the substellar companion mass function appears to be smooth and may extend down to the opacity limit for fragmentation. Within a few years, the conclusion of second-generation direct imaging surveys will enable more definitive interpretations with the ultimate goal of identifying the dominant origin of this population and uncovering its relationship to planets at smaller separations.
Predicting the Activation of the Androgen Receptor by Mixtures of Ligands Using Generalized Concentration Addition

Concentration/dose addition is widely used for compounds that act by similar mechanisms. But it cannot make predictions for mixtures of full and partial agonists for effect levels above that of the least efficacious component. As partial agonists are common, we developed generalized concentration addition, which has been successfully applied to systems in which ligands compete for a single binding site. Here, we applied a pharmacodynamic model for a homodimer receptor system with 2 binding sites, the androgen receptor, that acts according to the classic homodimer activation model: Each cytoplasmic monomer protein binds ligand, undergoes a conformational change that relieves inhibition of dimerization, and binds to DNA response elements as a dimer. We generated individual dose-response data for full (dihydroxytestosterone, BMS564929) and partial (TFM-4AS-1) agonists and a competitive antagonist (MDV3100) using reporter data generated in the MDA-kb2 cell line. We used the Schild method to estimate the binding affinity of MDV3100. Data for individual compounds fit the homodimer pharmacodynamic model well. In the presence of a full agonist, the partial agonist had agonistic effects at low effect levels and antagonistic effects at high levels, as predicted by pharmacological theory. The generalized concentration addition model fits the empirical mixtures data-full/full agonist, full/partial agonist, and full agonist/antagonist-as well or better than relative potency factors or effect summation. The ability of generalized concentration addition to predict the activity of mixtures of different types of androgen receptor ligands is important as a number of environmental compounds act as partial androgen receptor agonists or antagonists.
Enhancement of risk informed validation framework for external hazard scenario

Abstract In recent years, the U.S. Nuclear Regulatory Commission (USNRC) and the International Atomic Energy Agency (IAEA) have developed methodologies to assess the vulnerabilities of nuclear plants against site specific extreme hazards. In many cases, advanced simulation tools are being considered to simulate multi-physics, multi-scale phenomena and to evaluate vulnerability of nuclear facilities. The credibility of advanced simulation tools is assessed based on a formal verification, validation, and uncertainty quantification procedure. One of the key limitations in validation is the lack of relevant experimental data at system-level. This limitation leads to a decrease in the confidence of system-level risk predictions. Therefore, a robust validation framework is needed to formalize the confidence in predictive capability of advanced simulation results. This study enhances the existing risk informed validation methodology, originally proposed by Kwag et al. [1] and Bodda et al. [2], by developing additional attributes and a new set of validation indicies for a complete and wider applicability of the framework. In this manuscript, the methodology to identify the critical path that leads to the system-level failure is illustrated. The overall validation is checked for completeness and consistency by comparing the critical path for both the system-level simulation and experimental models. The applicability of the code for an intended application is represented in terms of various maturity levels and helps in the process of decision making.
Undocumented Activism and Minor Politics: Inside the Cramped Political Spaces of Deportation Defense Campaigns

Undocumented activism is on the rise. In response to the expansion of immigrant policing, detention, and deportation, a form of anti-deportation activism called ‘deportation defense campaigns’ have become a staple of immigrant rights movements in North America and Europe. DDCs seek to prevent individual deportations through multi-pronged grassroots organizing strategies and provide a path to temporary or permanent legalization. Yet in the process, campaigns must address questions about when and how to challenge dominant discourses and institutions while also achieving short-term goals. By drawing on the notion of the “minor” in critical geographic scholarship, we view DDCs through lens of “minor politics.” We argue that the disruptive and conformist potential of undocumented activism should be understood not as a static evaluative framework, but as strategies that condition, and are conditioned by, the contexts in which undocumented activism unfolds. Using ethnographic methods, we examine two DDCs to show how the campaigns strategically navigated the cramped political spaces of undocumented organizing in the months following the new Trump administration’s surge of anti-immigrant policies. We conclude that DDCs unfold under historically and geographically specific conditions that not only shape what counts as disruptive and conformist, but may call into question any easy division between the two altogether.
Descending chains of semistar operations

A class of integer-valued functions defined on the set of ideals of an integral domain R is investigated. We show that this class of functions, which we call ideal valuations, are in one-to-one cor...
Silhouette: Efficient Protected Shadow Stacks for Embedded Systems

Microcontroller-based embedded systems are increasingly used for applications that can have serious and immediate consequences if compromised---including automobile control systems, smart locks, drones, and implantable medical devices. Due to resource and execution-time constraints, C is the primary language used for programming these devices. Unfortunately, C is neither type-safe nor memory-safe, and control-flow hijacking remains a prevalent threat. This paper presents Silhouette: a compiler-based defense that efficiently guarantees the integrity of return addresses, significantly reducing the attack surface for control-flow hijacking. Silhouette combines an incorruptible shadow stack for return addresses with checks on forward control flow and memory protection to ensure that all functions return to the correct dynamic caller. To protect its shadow stack, Silhouette uses store hardening, an efficient intra-address space isolation technique targeting various ARM architectures that leverages special store instructions found on ARM processors. We implemented Silhouette for the ARMv7-M architecture, but our techniques are applicable to other common embedded ARM architectures. Our evaluation shows that Silhouette incurs a geometric mean of 1.3% and 3.4% performance overhead on two benchmark suites. Furthermore, we prototyped Silhouette-Invert, an alternative implementation of Silhouette, which incurs just 0.3% and 1.9% performance overhead, at the cost of a minor hardware change.
Weighted Maximum Independent Set of Geometric Objects in Turnstile Streams.

We study the Maximum Independent Set problem for geometric objects given in the data stream model. A set of geometric objects is said to be independent if the objects are pairwise disjoint. We consider geometric objects in one and two dimensions, i.e., intervals and disks. Let α be the cardinality of the largest independent set. Our goal is to estimate α in a small amount of space, given that the input is received as a one-pass stream. We also consider a generalization of this problem by assigning weights to each object and estimating β, the largest value of a weighted independent set. We initialize the study of this problem in the turnstile streaming model (insertions and deletions) and provide the first algorithms for estimating α and β. For unit-length intervals, we obtain a (2+e)-approximation to α and β in poly(log(n)/e) space. We also show a matching lower bound. Combined with the 3/2-approximation for insertion-only streams by Cabello and Perez-Lanterno [Cabello and Perez-Lantero, 2017], our result implies a separation between the insertion-only and turnstile model. For unit-radius disks, we obtain a (8√3/π)-approximation to α and β in poly(log(n)/e) space, which is closely related to the hexagonal circle packing constant. Finally, we provide algorithms for estimating α for arbitrary-length intervals under a bounded intersection assumption and study the parameterized space complexity of estimating α and β, where the parameter is the ratio of maximum to minimum interval length.
Nanophotonic materials for space applications

Space exemplifies the ultimate test-bed environment for any materials technology. The harsh conditions of space, with extreme temperature changes, lack of gravity and atmosphere, intense solar and cosmic radiation, and mechanical stresses of launch and deployment, represent a multifaceted set of challenges. The materials we engineer must not only meet these challenges, but they need to do so while keeping overall mass to a minimum and guaranteeing performance over long periods of time with no opportunity for repair. Nanophotonic materials—materials that embody structural variations on a scale comparable to the wavelength of light—offer opportunities for addressing some of these difficulties. Here, we examine how advances in nanophotonics and nanofabrication are enabling ultrathin and lightweight structures with unparalleled ability to shape light–matter interactions over a broad electromagnetic spectrum. From solar panels that can be fabricated in space to applications of light for propulsion, the next generation of lightweight and multifunctional photonic materials stands to both impact existing technologies and pave the way for new space technologies.