cssi/SciSciGPT-SciSciNet huggingface dataset:
General and Selective Olefination of Aldehydes and Ketones Catalyzed by a Cobalt(II) Porphyrin Complex

The Co(II) porphyrin complex Co(TPP) is an efficient and versatile catalyst for selective olefination of carbonyl compounds, including aldehydes and activated and unactivated ketones, with ethyl diazoacetate (EDA) in the presence of triphenylphosphine. The catalytic reactions of aldehydes and activated ketones can be carried out in a one-pot fashion with the use of a stoichiometric amount of diazo reagents, affording olefins in high yields and high E selectivity. In the case of unactivated ketones, a substoichiometric amount of benzoic acid is required to promote the catalytic reactions.
Predicting three-dimensional genome organization with chromatin states.

We introduce a computational model to simulate chromatin structure and dynamics. Starting from one-dimensional genomics and epigenomics data that are available for hundreds of cell types, this model enables de novo prediction of chromatin structures at five-kilo-base resolution. Simulated chromatin structures recapitulate known features of genome organization, including the formation of chromatin loops, topologically associating domains (TADs) and compartments, and are in quantitative agreement with chromosome conformation capture experiments and super-resolution microscopy measurements. Detailed characterization of the predicted structural ensemble reveals the dynamical flexibility of chromatin loops and the presence of cross-talk among neighboring TADs. Analysis of the model’s energy function uncovers distinct mechanisms for chromatin folding at various length scales and suggests a need to go beyond simple A/B compartment types to predict specific contacts between regulatory elements using polymer simulations.
A tandem hetero Diels–Alder/Mannich approach for the synthesis of a versatile hexahydro-2H-pyrano[3,2-c]pyridin-4(3H)-one core

Abstract The synthesis of a highly functionalizable hexahydro-2H-pyrano[3,2-c]pyridin-4(3H)-one core is described and sequentially involves a hetero-Diels Alder (HDA) reaction and an intramolecular Mannich reaction resulting in selective formation of the trans-fused ring geometry.
Computational Causal Modeling of the Dynamic Biomarker Cascade in Alzheimer's Disease.

Background. Alzheimer’s disease (AD) is a major public health concern, and there is an urgent need to better understand its complex biology and develop effective therapies. AD progression can be tracked in patients through validated imaging and spinal fluid biomarkers of pathology and neuronal loss. We still, however, lack a coherent quantitative model that explains how these biomarkers interact and evolve over time. Such a model could potentially help identify the major drivers of disease in individual patients and simulate response to therapy prior to entry in clinical trials. A current theory of AD biomarker progression, known as the dynamic biomarker cascade model, hypothesizes AD biomarkers evolve in a sequential but temporally overlapping manner. A computational model incorporating assumptions about the underlying biology of this theory and its variations would be useful to test and refine its accuracy with longitudinal biomarker data from clinical trials. Methods. We implemented a causal model to simulate time-dependent biomarker data under the descriptive assumptions of the dynamic biomarker cascade theory. We modeled pathologic biomarkers (beta-amyloid and tau), neuronal loss biomarkers, and cognitive impairment as nonlinear first-order ordinary differential equations (ODEs) to include amyloid-dependent and nondependent neurodegenerative cascades. We tested the feasibility of the model by adjusting its parameters to simulate three specific natural history scenarios in early-onset autosomal dominant AD and late-onset AD and determine whether computed biomarker trajectories agreed with current assumptions of AD biomarker progression. We also simulated the effects of antiamyloid therapy in late-onset AD. Results. The computational model of early-onset AD demonstrated the initial appearance of amyloid, followed by biomarkers of tau and neurodegeneration and the onset of cognitive decline based on cognitive reserve, as predicted by the prior literature. Similarly, the late-onset AD computational models demonstrated the first appearance of amyloid or nonamyloid-related tauopathy, depending on the magnitude of comorbid pathology, and also closely matched the biomarker cascades predicted by the prior literature. Forward simulation of antiamyloid therapy in symptomatic late-onset AD failed to demonstrate any slowing in progression of cognitive decline, consistent with prior failed clinical trials in symptomatic patients. Conclusions. We have developed and computationally implemented a mathematical causal model of the dynamic biomarker cascade theory in AD. We demonstrate the feasibility of this model by simulating biomarker evolution and cognitive decline in early- and late-onset natural history scenarios, as well as in a treatment scenario targeted at core AD pathology. Models resulting from this causal approach can be further developed and refined using patient data from longitudinal biomarker studies and may in the future play a key role in personalizing approaches to treatment.
Structures and reactions of P2N2: A hybrid of elemental N2 and P4?

A systematic study of all possible tetraatomic P2N2 structures has been made at the B3LYP/6-311+G(2df) and CASPT2(12e,12o)/ANO-L levels. A closed-shell butterfly C2v structure (A) is predicted to have the lowest energy on the tetraatomic P2N2 potential energy surface. The energies for dissociation of P2N2 to two P⋮N or to N2 and P2 have been examined. A closed-shell quasi-tetrahedral C2v structure of P2N2 (B) and a linear open-shell singlet P−N⋮N−P (E*) have been found to be kinetically stable to fragmentation or rearrangement at the CASPT2/ANO-L level. Several transition states have significant biradical character which reflects the orbital crossings that are found along reaction paths of least motion. The fragmentation reaction of P−N⋮N−P to 2P⋮N involves a trans-bent transition state that has a strong similarity to a known transition metal reaction.
Comparison of the Reactions of Branched Alcohols and Aldehydes on Rh(111)

Alcohols adsorbed on the Rh(111) surface have been suggested to decompose via unstable surface oxametallacycle intermediates rather than via aldehydes. The chemistry of alcohols and aldehydes containing multiple methyl groups at the β-position was examined in this study to determine whether metallacycle formation could be blocked. Temperature-programmed desorption (TPD) and high-resolution electron energy loss spectroscopy (HREELS) studies demonstrated that complete substitution of β-hydrogens with methyl groups did lead to common alcohol and aldehyde decomposition pathways. 2,2-Dimethyl-1-propanol and 2,2-dimethyl-1-propanal decarbonylated to deposit isobutene on the surface; the sequence of subsequent dehydrogenation steps was the same, whether adsorbed isobutene was generated from these oxygenates, from t-butanol, or by isobutene exposure. In contrast, partial substitution at the β-position did not produce a common path for alcohol and aldehyde decarbonylation. 2-Methyl-1-propanol decomposition resulte...
Controlled conversion of phenylacetic acids to phenylacetonitriles or benzonitriles using bis(2-methoxyethyl)aminosulfur trifluoride

A mild, efficient, and practical method for the one-step synthesis of benzonitriles from phenylacetic acids using bis(2-methoxyethyl)aminosulfur trifluoride is described. The reaction was easily extended to the synthesis of the corresponding phenylacetonitriles by inclusion of triethylphosphine.
Skeletal descriptions of shape provide unique perceptual information for object recognition.

With seemingly little effort, humans can both identify an object across large changes in orientation and extend category membership to novel exemplars. Although researchers argue that object shape is crucial in these cases, there are open questions as to how shape is represented for object recognition. Here we tested whether the human visual system incorporates a three-dimensional skeletal descriptor of shape to determine an object’s identity. Skeletal models not only provide a compact description of an object’s global shape structure, but also provide a quantitative metric by which to compare the visual similarity between shapes. Our results showed that a model of skeletal similarity explained the greatest amount of variance in participants’ object dissimilarity judgments when compared with other computational models of visual similarity (Experiment 1). Moreover, parametric changes to an object’s skeleton led to proportional changes in perceived similarity, even when controlling for another model of structure (Experiment 2). Importantly, participants preferentially categorized objects by their skeletons across changes to local shape contours and non-accidental properties (Experiment 3). Our findings highlight the importance of skeletal structure in vision, not only as a shape descriptor, but also as a diagnostic cue of object identity.
Center-of-Gravity-Based Approach for Modeling Dynamics of Multisection Continuum Arms

Multisection continuum arms offer complementary characteristics to those of traditional rigid-bodied robots. Inspired by biological appendages, such as elephant trunks and octopus arms, these robots trade rigidity for compliance and accuracy for safety and, therefore, exhibit strong potential for applications in human-occupied spaces. Prior work has demonstrated their superiority in operation in congested spaces and manipulation of irregularly shaped objects. However, they are yet to be widely applied outside laboratory spaces. One key reason is that, due to compliance, they are difficult to control. Sophisticated and numerically efficient dynamic models are a necessity to implement dynamic control. In this paper, we propose a novel numerically stable center-of-gravity-based dynamic model for variable-length multisection continuum arms. The model can accommodate continuum robots having any number of sections with varying physical dimensions. The dynamic algorithm is of $\mathcal {O}\left(n^{2}\right)$ complexity, runs at 9.5 kHz, simulates six to eight times faster than real time for a three-section continuum robot, and, therefore, is ideally suited for real-time control implementations. The model accuracy is validated numerically against an integral-dynamic model proposed by the authors and experimentally for a three-section pneumatically actuated variable-length multisection continuum arm. This is the first sub-real-time dynamic model based on a smooth continuous deformation model for variable-length multisection continuum arms.
Salicylate increases fitness cost associated with MarA-mediated antibiotic resistance

Abstract Antibiotic resistance is generally associated with a fitness deficit resulting from the burden of producing and maintaining resistance machinery. This additional cost suggests that resistant bacteria will be outcompeted by susceptible bacteria in conditions without antibiotics. However, in practice, this process is slow in part because of regulation that minimizes expression of these genes in the absence of antibiotics. This suggests that if it were possible to turn on their expression, the cost would increase, thereby accelerating removal of resistant strains. Experimental and theoretical studies have shown that environmental chemicals can change the fitness cost associated with resistance and therefore have a significant impact on population dynamics. The multiple antibiotic resistance activator (MarA) is a clinically important regulator in Escherichia coli that activates downstream genes to increase resistance against multiple classes of antibiotics. Salicylate is an inducer of MarA that can be found in the environment and derepresses marA’s expression. In this study, we sought to unravel the interplay between salicylate and the fitness cost of MarA-mediated antibiotic resistance. Using salicylate as an inducer of MarA, we found that a wide spectrum of concentrations can increase burden in resistant strains compared to susceptible strains. Induction resulted in rapid exclusion of resistant bacteria from mixed populations of antibiotic-resistant and susceptible cells. A mathematical model captures the process and predicts its effect in various environmental conditions. Our work provides a quantitative understanding of salicylate exposure on the fitness of different MarA variants and suggests that salicylate can lead to selection against MarA-mediated resistant strains. More generally, our findings show that natural inducers may serve to bias population membership and could impact antibiotic resistance and other important phenotypes.
Event Organization 101: Understanding Latent Factors of Event Popularity.

The problem of understanding people's participation in real-world events has been a subject of active research and can offer valuable insights for human behavior analysis and event-related recommendation/advertisement. In this work, we study the latent factors for determining event popularity using large-scale datasets collected from the popular Meetup.com EBSN in three major cities around the world. We have conducted modeling analysis of four contextual factors (spatial, group, temporal, and semantic), and also developed a group-based social influence propagation network to model group-specific influences on events. By combining the Contextual features And Social Influence Network, our integrated prediction framework CASINO can capture the diverse influential factors of event participation and can be used by event organizers to predict/improve the popularity of their events. Evaluations demonstrate that our CASINO framework achieves high prediction accuracy with contributions from all the latent features we capture.
On The Reliability Function of Discrete Memoryless Multiple-Access Channel with Feedback

We derive a lower and upper bounds on the reliability function of discrete memoryless multiple-access channel (MAC) with noiseless feedback and variable-length codes (VLCs). For the upper-bound, we use proof techniques of Burnashev for the point-to-point case. Also, we adopt the techniques used to prove the converse for the feedback-capacity of MAC. For the lower-bound on the error exponent, we present a coding scheme consisting of a data and a confirmation stage. In the data stage, any arbitrary feedback capacity-achieving code is used. In the confirmation stage, each transmitter sends one bit of information to the receiver using a pair of codebooks of size two, one for each transmitter. The codewords at this stage are selected randomly according to an appropriately optimized joint probability distribution. The bounds increase linearly with respect to a specific Euclidean distance measure defined between the transmission rate pair and the capacity boundary. The lower and upper bounds match for a class of MACs.
Repairable replication-based storage systems using resolvable designs

We consider the design of regenerating codes for distributed storage systems at the minimum bandwidth regeneration (MBR) point. The codes allow for a repair process that is exact and uncoded, but table-based. These codes were introduced in prior work and consist of an outer MDS code followed by an inner fractional repetition (FR) code where copies of the coded symbols are placed on the storage nodes. The main challenge in this domain is the design of the inner FR code. In our work, we consider generalizations of FR codes, by establishing their connection with a family of combinatorial structures known as resolvable designs. Our constructions based on affine geometries, Hadamard designs and mutually orthogonal Latin squares allow the design of systems where a new node can be exactly regenerated by downloading β ≥ 1 packets from a subset of the surviving nodes (prior work only considered the case of β = 1). Our techniques allow the design of systems over a large range of parameters. Specifically, the repetition degree of a symbol, which dictates the resilience of the system can be varied over a large range in a simple manner. Moreover, the actual table needed for the repair can also be implemented in a rather straightforward way. Furthermore, we answer an open question posed in prior work by demonstrating the existence of codes with parameters that are not covered by Steiner systems.
Optical and near-IR field luminosity functions

We present preliminary measurements of the b_J and K-band luminosity functions (LFs) of field galaxies obtained from optical and K-band imaging of a sample of galaxies selected from the Stromlo-APM Redshift Survey. The b_J LF is consistent with that previously published from photographic data. The K-band LF has been estimated over a range of 12 magnitudes and is reasonably well fit by a Schechter function with faint-end slope alpha = -1.2.
Efficient Statistics, in High Dimensions, from Truncated Samples

We provide an efficient algorithm for the classical problem, going back to Galton, Pearson, and Fisher, of estimating, with arbitrary accuracy the parameters of a multivariate normal distribution from truncated samples. Truncated samples from a d-variate normal N(mu, Sigma) means a samples is only revealed if it falls in some subset S of the d-dimensional Euclidean space; otherwise the samples are hidden and their count in proportion to the revealed samples is also hidden. We show that the mean mu and covariance matrix Sigma can be estimated with arbitrary accuracy in polynomial-time, as long as we have oracle access to S, and S has non-trivial measure under the unknown d-variate normal distribution. Additionally we show that without oracle access to S, any non-trivial estimation is impossible.
Investigating student understanding of bipolar junction transistor circuits

Many upper-division physics students do not developed a robust conceptual understanding of the functionality of bipolar junction transistor circuits in a typical electronics course.
Higher order spectral shift

Abstract We construct higher order spectral shift functions, extending the perturbation theory results of M.G. Krein [M.G. Krein, On a trace formula in perturbation theory, Mat. Sb. 33 (1953) 597–626 (in Russian)] and L.S. Koplienko [L.S. Koplienko, Trace formula for perturbations of nonnuclear type, Sibirsk. Mat. Zh. 25 (1984) 62–71 (in Russian); translation in: Trace formula for nontrace-class perturbations, Siberian Math. J. 25 (1984) 735–743] on representations for the remainders of the first and second order Taylor-type approximations of operator functions. The higher order spectral shift functions represent the remainders of higher order Taylor-type approximations; they can be expressed recursively via the lower order (in particular, Krein's and Koplienko's) ones. We also obtain higher order spectral averaging formulas generalizing the Birman–Solomyak spectral averaging formula. The results are obtained in the semi-finite von Neumann algebra setting, with the perturbation taken in the Hilbert–Schmidt class of the algebra.
Recovering the initial condition of parabolic equations from lateral Cauchy data via the quasi-reversibility method

We consider the problem of computing the initial condition for a general parabolic equation from the Cauchy lateral data. The stability of this problem is well-known to be logarithmic. In this pape...
The role of molecular properties on the dimerization of aromatic compounds

Abstract Recent results have shown the presence and importance of oxygen chemistry during the growth of aromatic compounds, leading to the formation of oxygenated structures that have been identified in various environments. Since the formation of polycyclic aromatic compounds (PAC) bridge the formation of gas-phase species with particle inception, in this work we report a detailed analysis of the effects of molecular characteristics on physical growth of PAC via dimerization. We have included oxygen content, mass, type of bonds (rigid versus rotatable), and shape as main properties of the molecules and studied their effect on the propensity of these structures to form homo-molecular and hetero-molecular dimers. Using enhanced sampling molecular dynamics techniques, we have computed the free energy of dimerization in the temperature range 500–1680 K. Initial structures used in this study were obtained from experimental data. The results show that the effects of shape, presence of oxygen, mass, and internal bonds are tightly intertwined, and that their relative importance changes with temperature. In general, mass and the presence of rotatable bonds are the most influential factor to predict dimerization propensity among the one considered. The results provide knowledge on the inception step and the role that particle characteristics play during inception. In addition, our study highlights the fact that current models that use stabilomers as monomers for physical aggregation are overestimating the importance of their dimerization during particle nucleation.
Covariant Isolation from an Abelian Gauge Field of Its Nondynamical Potential, Which, When Fed Back, Can Transform into a “Confining Yukawa”

For Abelian gauge theory a properly relativistic gauge is developed by supplementing the Lorentz condition with causal determination of the time component of the four-vector potential by retarded Coulomb transformation of the charge density. This causal Lorentz gauge agrees with the Coulomb gauge for static charge densities, but allows the four-vector potential to have a longitudinal component that is determined by the time derivative of the four-vector potential’s time component. Just as in Coulomb gauge, the two transverse components of the four-vector potential are its sole dynamical part. The four-vector potential in this gauge covariantly separates into a dynamical transverse four-vector potential and a nondynamical timelike/longitudinal four-vector potential, where each of these two satisfies the Lorentz condition. In fact, analogous partition of the conserved four-current shows each to satisfy a Lorentz-condition Maxwell-equation system with its own conserved fourcurrent. Because of this complete separation, either of these four-vector potentials can be tinkered with without affecting its counterpart. Since it satisfies the Lorentz condition, the nondynamical four-vector potential times a constant with dimension of inverse length squared is itself a conserved four-current, and so can be fed back into its own source current, which transforms its time component into an extended Yukawa, with both exponentially decaying and exponentially growing components. The latter might be the mechanism of quark-gluon confinement: in non-Abelian color gauge theory the Yukawa mixture ratio ought to be tied to color, with palpable consequences for “colorful” hot quark-gluon plasmas.
Community detection in hypergraphs, spiked tensor models, and Sum-of-Squares

We study the problem of community detection in hypergraphs under a stochastic block model. Similarly to how the stochastic block model in graphs suggests studying spiked random matrices, our model motivates investigating statistical and computational limits of exact recovery in certain spiked tensor models. In contrast with the matrix case, the spiked model naturally arising from community detection in hypergraphs is different from the one arising in the so-called tensor Principal Component Analysis model. We investigate the effectiveness of algorithms in the Sum-of-Squares hierarchy on these models. Interestingly, our results suggest that these two apparently similar models might exhibit very different computational to statistical gaps.
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections

We present a new algorithm to generate minimal, stable, and symbolic corrections to an input that will cause a neural network with ReLU activations to change its output. We argue that such a correction is a useful way to provide feedback to a user when the network's output is different from a desired output. Our algorithm generates such a correction by solving a series of linear constraint satisfaction problems. The technique is evaluated on three neural network models: one predicting whether an applicant will pay a mortgage, one predicting whether a first-order theorem can be proved efficiently by a solver using certain heuristics, and the final one judging whether a drawing is an accurate rendition of a canonical drawing of a cat.
Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting

Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (i.e., compresses and paraphrases) to generate a concise overall summary. We use a novel sentence-level policy gradient method to bridge the non-differentiable computation between these two neural networks in a hierarchical way, while maintaining language fluency. Empirically, we achieve the new state-of-the-art on all metrics (including human evaluation) on the CNN/Daily Mail dataset, as well as significantly higher abstractiveness scores. Moreover, by first operating at the sentence-level and then the word-level, we enable parallel decoding of our neural generative model that results in substantially faster (10-20x) inference speed as well as 4x faster training convergence than previous long-paragraph encoder-decoder models. We also demonstrate the generalization of our model on the test-only DUC-2002 dataset, where we achieve higher scores than a state-of-the-art model.
Vulnerability of fixed-time control of signalized intersections to cyber-tampering

Recent experimental studies have shown that traffic management systems are vulnerable to cyber-attacks on sensor data. This paper studies the vulnerability of fixed-time control of signalized intersections when sensors measuring traffic flow information are compromised and perturbed by an adversary. The problems are formulated by considering three malicious objectives: 1) worst-case network accumulation, which aims to destabilize the overall network as much as possible; 2) worst-case lane accumulation, which aims to cause worst-case accumulation on some target lanes; and 3) risk-averse target accumulation, which aims to reach a target accumulation by making the minimum perturbation to sensor data. The problems are solved using bilevel programming optimization methods. Finally, a case study of a real network is used to illustrate the results.
The Cobordism hypothesis

In this expository paper we introduce extended topological quantum field theories and the cobordism hypothesis.
On the geography of simply connected nonspin symplectic 4-manifolds with nonnegative signature

Abstract In [8] , [5] , the first author and his collaborators constructed the irreducible symplectic 4-manifolds that are homeomorphic but not diffeomorphic to ( 2 n − 1 ) CP 2 # ( 2 n − 1 ) CP ‾ 2 for each integer n ≥ 25 , and the families of simply connected irreducible nonspin symplectic 4-manifolds with positive signature that are interesting with respect to the symplectic geography problem. In this paper, we improve the main results in [8] , [5] . In particular, we construct (i) an infinitely many irreducible symplectic and nonsymplectic 4-manifolds that are homeomorphic but not diffeomorphic to ( 2 n − 1 ) CP 2 # ( 2 n − 1 ) CP ‾ 2 for each integer n ≥ 12 , and (ii) the families of simply connected irreducible nonspin symplectic 4-manifolds that have the smallest Euler characteristics among the all known simply connected 4-manifolds with positive signature and with more than one smooth structure. Our construction uses the complex surfaces of Hirzebruch and Bauer–Catanese on Bogomolov–Miyaoka–Yau line with c 1 2 = 9 χ h = 45 , along with the exotic symplectic 4-manifolds constructed in [2] , [6] , [4] , [7] , [11] .
Quantitative Comparison of Open-Source Data for Fine-Grain Mapping of Land Use

This paper performs a quantitative comparison of open-source data available on the Internet for the fine-grain mapping of land use. Three points of interest (POI) data sources--Google Places, Bing Maps, and the Yellow Pages--and one volunteered geographic information data source--Open Street Map (OSM)--are compared with each other at the parcel level for San Francisco with respect to a proposed fine-grain land-use taxonomy. The sources are also compared to coarse-grain authoritative data which we consider to be the ground truth. Results show limited agreement among the data sources as well as limited accuracy with respect to the authoritative data even at coarse class granularity. We conclude that POI and OSM data do not appear to be sufficient alone for fine-grain land-use mapping.
Filtergraph: An Interactive Web Application for Visualization of Astronomy Datasets

Abstract Filtergraph is a web application being developed and maintained by the Vanderbilt Initiative in Data-intensive Astrophysics (VIDA) to flexibly and rapidly visualize a large variety of astronomy datasets of various formats and sizes. The user loads a flat-file dataset into Filtergraph which automatically generates an interactive data portal that can be easily shared with others. From this portal, the user can immediately generate scatter plots of up to five dimensions as well as histograms and tables based on the dataset. Key features of the portal include intuitive controls with auto-completed variable names, the ability to filter the data in real time through user-specified criteria, the ability to select data by dragging on the screen, and the ability to perform arithmetic operations on the data in real time. To enable seamless data visualization and exploration, changes are quickly rendered on screen and visualizations can be exported as high quality graphics files. The application is optimized for speed in the context of large datasets: for instance, a plot generated from a stellar database of 3.1 million entries renders in less than 2 s on a standard web server platform. This web application has been created using the Web2py web framework based on the Python programming language. Filtergraph is free to use at http://filtergraph.vanderbilt.edu/ .
An $O(n^{0.4732})$ upper bound on the complexity of the GKS communication game

We give an 5·n30 5 upper bund on the complexity of the communication game introduced by G. Gilmer, M. Koucký and M. Saks [3] to study the Sensitivity Conjecture [4], improving on their √ 999 1000 √ n bound. We also determine the exact complexity of the game up to n ≤ 9. 1 The O(n) upper bound The GKS communication game, defined by G. Gilmer, M. Koucký and M. Saks [3] is played by two cooperating players, Alice and Bob, against an all-powerful adversary, Merlin. The game has a single parameter n. Merlin has a permutation π = π1π2 . . . πn of [n] and a bit b. Alice has a strategy S : {partial permutations on [n]} → {0, 1} and Bob has a strategy T : {0, 1}n → 2[n]. In Phase 1. Alice assigns zeroes and ones to all but one entries of an array A[1..n] and Merlin sets the remaining entry to 0 or 1 according to the Alice-Merlin protocol described below. In Phase 2 Bob has to guess which entry was set by Merlin by merely looking at Afinal, where Afinal is the setting of A when Phase 1 is finished. Bob’s guess, T (Afinal) ⊆ [n], is a subset of entries of A that must include the entry Merlin has set. This has to hold for every strategy π, b of Merlin. Alice-Merlin protocol: For 1 ≤ i ≤ n− 1 Alice sets A[πi] = S(π1 . . . πi) In the end Merlin sets A[πn] = b. Definition 1. A (k, n) strategy for the GKS game with parameter n is a pair S, T as above such that in addition |T (σ)| ≤ k for every σ ∈ {0, 1}n. k(n) = min k There is a (k, n) strategy for the GKS game with parameter n The relevance of the GKS communication game is that k(n) gives a lower bound on the sensitivity, s(f) = maxx |{i | f(x ⊕ ei) 6= f(x)}| for any Boolean function f : {0, 1}n → {0, 1} with Fourier degree deg(f) = n. See [3]. See [5, 1, 2] for more background. In turn, any lower bound k(n) ∈ Ω(nα) for some α > 0 would positively resolve the long-standing Sensitivity Conjecture [4] which says that the sensitivity and the Fourier degree are polynomially related.
The chromatic number of the plane is at least 5 - a new proof

We present an alternate proof of the fact that given any 4-coloring of the plane there exist two points one unit apart which are identically colored.
On the number of hypercubic bipartitions of an integer

Abstract For n ≤ 2 k we study the maximum number of edges of an induced subgraph on n vertices of the k -dimensional hypercube Q k . In the process we revisit a well-known divide-and-conquer maximin recurrence f ( n ) = max ( min ( n 1 , n 2 ) + f ( n 1 ) + f ( n 2 ) ) where the maximum is taken over all proper bipartitions n = n 1 + n 2 . We first use known results to present a characterization of those bipartitions n = n 1 + n 2 that yield the maximum f ( n ) = min ( n 1 , n 2 ) + f ( n 1 ) + f ( n 2 ) . Then we use this characterization to present the main result of this article, namely, for a given n ∈ N , the determination of the number h ( n ) of these bipartitions that yield the said maximum f ( n ) . We present recursive formulae for h ( n ) , a generating function h ( x ) , and an explicit formula for h ( n ) in terms of a special representation of n .
Adaptive local window for level set segmentation of CT and MRI liver lesions

We propose a novel method, the adaptive local window, for improving level set segmentation technique. The window is estimated separately for each contour point, over iterations of the segmentation process, and for each individual object. Our method considers the object scale, the spatial texture, and the changes of the energy functional over iterations. Global and local statistics are considered by calculating several gray level co-occurrence matrices. We demonstrate the capabilities of the method in the domain of medical imaging for segmenting 233 images with liver lesions. To illustrate the strength of our method, those lesions were screened by either Computed Tomography or Magnetic Resonance Imaging. Moreover, we analyzed images using three different energy models. We compared our method to a global level set segmentation, to a local framework that uses predefined fixed-size square windows and to a local region-scalable fitting model. The results indicate that our proposed method outperforms the other methods in terms of agreement with the manual marking and dependence on contour initialization or the energy model used. In case of complex lesions, such as low contrast lesions, heterogeneous lesions, or lesions with a noisy background, our method shows significantly better segmentation with an improvement of 0.25 ± 0.13 in Dice similarity coefficient, compared with state of the art fixed-size local windows (Wilcoxon, p < 0.001).
Bounds for higher topological complexity of real projective space implied by BP

We use Brown–Peterson cohomology to obtain lower bounds for the higher topological complexity, $${\text {TC}}_k(\hbox {RP}^{2m})$$ , of real projective spaces, which are often much stronger than those implied by ordinary mod-2 cohomology.
The Information Bottleneck and Geometric Clustering

The information bottleneck (IB) approach to clustering takes a joint distribution P ( X , Y ) and maps the data X to cluster labels T , which retain maximal information about Y (Tishby, Pereira, & Bialek, 1999 ). This objective results in an algorithm that clusters data points based on the similarity of their conditional distributions P ( Y ∣ X ) . This is in contrast to classic geometric clustering algorithms such as k -means and gaussian mixture models (GMMs), which take a set of observed data points { x i } i = 1 : N and cluster them based on their geometric (typically Euclidean) distance from one another. Here, we show how to use the deterministic information bottleneck (DIB) (Strouse & Schwab, 2017 ), a variant of IB, to perform geometric clustering by choosing cluster labels that preserve information about data point location on a smoothed data set. We also introduce a novel intuitive method to choose the number of clusters via kinks in the information curve. We apply this approach to a variety of simple clustering problems, showing that DIB with our model selection procedure recovers the generative cluster labels. We also show that, in particular limits of our model parameters, clustering with DIB and IB is equivalent to k -means and EM fitting of a GMM with hard and soft assignments, respectively. Thus, clustering with (D)IB generalizes and provides an information-theoretic perspective on these classic algorithms.
Downwinding for preserving strong stability in explicit integrating factor Runge–Kutta methods

Strong stability preserving (SSP) Runge-Kutta methods are desirable when evolving in time problems that have discontinuities or sharp gradients and require nonlinear non-inner-product stability properties to be satisfied. Unlike the case for L2 linear stability, implicit methods do not significantly alleviate the time-step restriction when the SSP property is needed. For this reason, when handling problems with a linear component that is stiff and a nonlinear component that is not, SSP integrating factor Runge--Kutta methods may offer an attractive alternative to traditional time-stepping methods. The strong stability properties of integrating factor Runge--Kutta methods where the transformed problem is evolved with an explicit SSP Runge--Kutta method with non-decreasing abscissas was recently established. In this work, we consider the use of downwinded spatial operators to preserve the strong stability properties of integrating factor Runge--Kutta methods where the Runge--Kutta method has some decreasing abscissas. We present the SSP theory for this approach and present numerical evidence to show that such an approach is feasible and performs as expected. However, we also show that in some cases the integrating factor approach with explicit SSP Runge--Kutta methods with non-decreasing abscissas performs nearly as well, if not better, than with explicit SSP Runge--Kutta methods with downwinding. In conclusion, while the downwinding approach can be rigorously shown to guarantee the SSP property for a larger time-step, in practice using the integrating factor approach by including downwinding as needed with optimal explicit SSP Runge--Kutta methods does not necessarily provide significant benefit over using explicit SSP Runge--Kutta methods with non-decreasing abscissas.
ConfidentCare: A Clinical Decision Support System for Personalized Breast Cancer Screening

Breast cancer screening policies attempt to achieve timely diagnosis by regularly screening healthy women via various imaging tests. Various clinical decisions are needed to manage the screening process: selecting initial screening tests, interpreting test results, and deciding if further diagnostic tests are required. Current screening policies are guided by clinical practice guidelines (CPGs), which represent a “one-size-fits-all” approach, designed to work well (on average) for a population, and can only offer coarse expert-based patient stratification that is not rigorously validated through data. Since the risks and benefits of screening tests are functions of each patient's features, personalized screening policies tailored to the features of individuals are desirable. To address this issue, we developed ConfidentCare : a computer-aided clinical decision support system that learns a personalized screening policy from electronic health record (EHR) data. By a “personalized screening policy,” we mean a clustering of women's features, and a set of customized screening guidelines for each cluster. ConfidentCare operates by computing clusters of patients with similar features, then learning the “best” screening procedure for each cluster using a supervised learning algorithm. The algorithm ensures that the learned screening policy satisfies a predefined accuracy requirement with a high level of confidence for every cluster. By applying ConfidentCare to real-world data, we show that it outperforms the current CPGs in terms of cost efficiency and false positive rates: a reduction of 31 $\%$ in the false positive rate can be achieved.
Modular Inflatable Composites for Space Telescopes

There is an ever-growing need to construct large space telescopes and structures for observation of exo-planets, asteroids in the main-belt and NEOs. Space observation capabilities could potentially be revolutionized by structures spanning several meters in size. Of particular interest are star-shades for imaging distant and high-resolution large aperture telescopes. Such structures efficient load bearing features and controllable precision surfaces. A promising approach to achieve high compaction for large surface areas is by incorporating compliant materials or gossamers. For most applications, gossamer structures require supporting stiffening structures to improve accuracy but this comes at the cost of reduced packing efficiency. Structural design strategies that maximize the use of gossamers are required to fully harness their potential. Our present work focuses on such a strategy using inflatable membranes. We introduce a methodology to investigate large structural assemblies of modular inflatable units stiffened pneumatically using inflation gas. Our work shows that such units assembled into composite structures can yield desirable characteristics. Further, modifying tension and geometry of such units could be used for localized tuning of the structure thereby reducing the need for rigid support structures. Our studies focus on two separate structural requirements. The first is efficient load bearing and distribution. Such structures do not need high precision surfaces but the ability to efficiently and reliably transmit large loads. Applications include deployable drag devices for atmospheric maneuvering. The second are structures with precision surfaces for optical imaging and high gain communications. A structural analysis strategy using discrete finite elements has been developed to simulate the assembled behavior of modular units. Our analysis leads to an understanding of the extent to which inflatables can be used to create large space structures.
Determinants of target prioritization and regulatory hierarchy for the bacterial small RNA SgrS

Small RNA (sRNA) regulators promote efficient responses to stress, but the mechanisms for prioritizing target mRNA regulation remain poorly understood. This study examines mechanisms underlying hierarchical regulation by the sRNA SgrS, found in enteric bacteria and produced under conditions of metabolic stress. SgrS posttranscriptionally coordinates a nine-gene regulon to restore growth and homeostasis. An in vivo reporter system quantified SgrS-dependent regulation of target genes and established that SgrS exhibits a clear target preference. Regulation of some targets is efficient even at low SgrS levels, whereas higher SgrS concentrations are required to regulate other targets. In vivo and in vitro analyses revealed that RNA structure and the number and position of base pairing sites relative to the start of translation impact the efficiency of regulation of SgrS targets. The RNA chaperone Hfq uses distinct modes of binding to different SgrS mRNA targets, which differentially influences positive and negative regulation. The RNA degradosome plays a larger role in regulation of some SgrS targets compared to others. Collectively, our results suggest that sRNA selection of target mRNAs and regulatory hierarchy are influenced by several molecular features and that the combination of these features precisely tunes the efficiency of regulation of multi-target sRNA regulons.
Complexity Analysis and Efficient Measurement Selection Primitives for High-Rate Graph SLAM

Sparsity has been widely recognized as crucial for efficient optimization in graph-based SLAM. Because the sparsity and structure of the SLAM graph reflect the set of incorporated measurements, many methods for sparsification have been proposed in hopes of reducing computation. These methods often focus narrowly on reducing edge count without regard for structure at a global level. Such structurally-naive techniques can fail to produce significant computational savings, even after aggressive pruning. In contrast, simple heuristics such as measurement decimation and keyframing are known empirically to produce significant computation reductions. To demonstrate why, we propose a quantitative metric called elimination complexity (EC) that bridges the existing analytic gap between graph structure and computation. EC quantifies the complexity of the primary computational bottleneck: the factorization step of a Gauss-Newton iteration. Using this metric, we show rigorously that decimation and keyframing impose favorable global structures and therefore achieve computation reductions on the order of r2/9 and r3, respectively, where r is the pruning rate. We additionally present numerical results showing EC provides a good approximation of computation in both batch and incremental (iSAM2) optimization and demonstrate that pruning methods promoting globally-efficient structure outperform those that do not.
Near-Optimal Compressed Sensing of a Class of Sparse Low-Rank Matrices Via Sparse Power Factorization

Compressed sensing of simultaneously sparse and low-rank matrices enables recovery of sparse signals from a few linear measurements of their bilinear form. One important question is how many measurements are needed for a stable reconstruction in the presence of measurement noise. Unlike conventional compressed sensing for sparse vectors, where convex relaxation via the $\ell _{1}$ -norm achieves near-optimal performance, for compressed sensing of sparse low-rank matrices, it has been shown recently that convex programmings using the nuclear norm and the mixed norm are highly suboptimal even in the noise-free scenario. We propose an alternating minimization algorithm called sparse power factorization (SPF) for compressed sensing of sparse rank-one matrices. For a class of signals whose sparse representation coefficients are fast-decaying, SPF achieves stable recovery of the rank-one matrix formed by their outer product and requires number of measurements within a logarithmic factor of the information-theoretic fundamental limit. For the recovery of general sparse low-rank matrices, we propose subspace-concatenated SPF (SCSPF), which has analogous near-optimal performance guarantees to SPF in the rank-one case. Numerical results show that SPF and SCSPF empirically outperform convex programmings using the best known combinations of mixed norm and nuclear norm.