cssi/SciSciGPT-SciSciNet huggingface dataset:
The Natural Bite Angle of Chelating Diphosphines

The natural bite angles of chelating diphosphine ligands have been determined by molecular mechanics calculations using the MACROMODEL program with a modified AMBER force field. The natural bite angle (βn) is defined as the preferred chelation angle determined only by ligand backbone constraints and not by metal valence angles. Potential energy diagrams for diphosphine chelates were constructed to estimate chelate flexibility. Molecular mechanics calculations have been used to select diphosphine ligands with natural bite angles of 120° for diequatorial chelation in trigonal bipyramidal metal complexes.
A short diastereoselective synthesis of the (±)-rishirilide B core structure

Abstract A series of diastereoselective reactions are reported to form the core of (±)-rishirilide B. The key reactions include: (1) a regioselective tautomerization and etherification of an asymmetric cyclohexa-1,3-dione, (2) an alkoxide directed 1,2-addition of an organometallic reagent and carbamate cleavage and protonation sets two stereocenters, and (3) a novel RuO 4 cleavage of an α-hydroxy vinyl ether provides the α-hydroxy ester.
An expedient synthesis of (±)‐2‐amino‐3‐(3‐hydroxy‐5‐methylisoxazol‐4‐yl)propionic acid hydrobromide via a 3‐bromoisoxazole intermediate

The excitatory, amino acid ±2-amino-3-(3-hydroxy-5-methylisoxazol-4-yl)propionic acid hydrobromide was prepared in gram quantities in an 3.3% overall yield from methylbut 2-ynoate. The key step was the facile preparation of methyl 3-bromo-5-methylisoxazole-4-carboxylate.
Synthesis and characterization of (E) and (Z)-1,2-difluoroethenediylbisphosphonates

Abstract Cuprous halide mediated decomposition of (diethoxyphosphinyl)difluoromethylzinc bromide, (EtO)2P(O)CF2ZnBr, forms the title bisphosphonates in 50% isolated yield. The 19F and 31P NMR spectra of these isomeric bisphosphonates exhibit non-first order AA‘'XX’' patterns. Computer simulation permitted the determination of the JP-F, JF-F, and JP-P coupling constants for these compounds. Silylation and hydrolysis of the bisphosphonate mixture gave the silylated bisphosphonates and bisphosphonic acids respectively, as expected. Metathesis of the zinc reagent also yielded phenyl (diethoxyphosphinyl)difluoromethyl- mercury, (EtO)2P(O)CF2HgPh, in good yield. However, attempts to generate fluoro(diethoxyphosphinyl)carbene from this mercurial were unsuccessful.
multiPhATE: bioinformatics pipeline for functional annotation of phage isolates.

Summary To address the need for improved phage annotation tools that scale, we created an automated throughput annotation pipeline: multiple-genome Phage Annotation Toolkit and Evaluator (multiPhATE). multiPhATE is a throughput pipeline driver that invokes an annotation pipeline (PhATE) across a user-specified set of phage genomes. This tool incorporates a de novo phage gene calling algorithm and assigns putative functions to gene calls using protein-, virus- and phage-centric databases. multiPhATE's modular construction allows the user to implement all or any portion of the analyses by acquiring local instances of the desired databases and specifying the desired analyses in a configuration file. We demonstrate multiPhATE by annotating two newly sequenced Yersinia pestis phage genomes. Within multiPhATE, the PhATE processing pipeline can be readily implemented across multiple processors, making it adaptable for throughput sequencing projects. Software documentation assists the user in configuring the system. Availability and implementation multiPhATE was implemented in Python 3.7, and runs as a command-line code under Linux or Unix. multiPhATE is freely available under an open-source BSD3 license from https://github.com/carolzhou/multiPhATE. Instructions for acquiring the databases and third-party codes used by multiPhATE are included in the distribution README file. Users may report bugs by submitting to the github issues page associated with the multiPhATE distribution. Supplementary information Supplementary data are available at Bioinformatics online.
Regulation of cancer epigenomes with a histone-binding synthetic transcription factor

Chromatin proteins have expanded the mammalian synthetic biology toolbox by enabling control of active and silenced states at endogenous genes. Others have reported synthetic proteins that bind DNA and regulate genes by altering chromatin marks, such as histone modifications. Previously, we reported the first synthetic transcriptional activator, the “Polycomb-based transcription factor” (PcTF) that reads histone modifications through a protein–protein interaction between the polycomb chromodomain motif and trimethylated lysine 27 of histone H3 (H3K27me3). Here, we describe the genome-wide behavior of the polycomb-based transcription factor fusion protein. Transcriptome and chromatin profiling revealed several polycomb-based transcription factor-sensitive promoter regions marked by distal H3K27me3 and proximal fusion protein binding. These results illuminate a mechanism in which polycomb-based transcription factor interactions bridge epigenomic marks with the transcription initiation complex at target genes. In three cancer-derived human cell lines tested here, some target genes encode developmental regulators and tumor suppressors. Thus, the polycomb-based transcription factor represents a powerful new fusion protein-based method for cancer research and treatment where silencing marks are translated into direct gene activation. An engineered protein that manipulates DNA-protein packaging in cancer cells could offer a powerful new approach to treating tumors. Karmella Haynes and colleagues from Arizona State University in Tempe, USA, designed a protein that can recognize chemical tags on DNA-wound chromatin structures where gene activity is being silenced and then reactivate those epigenetically repressed genes. In previous work, Haynes’ team showed how this “Polycomb-based transcription factor” could stimulate gene expression at two silenced genes in one cancer cell line. Now, working in three different cancer cell types, the researchers demonstrate that treatment with this engineered protein increases the expression levels of many genes across the genome involved in developmental regulation and tumor suppression. If the engineered protein works specifically in cancer cells, while leaving healthy tissue alone, it could prove beneficial for patients.
Magic Electron Counts for Networks of Condensed Clusters: Vertex-Sharing Aluminum Octahedra

The aluminum substructure of the AeM2Al9 (Ae = Ba and M = Fe, Co, Ni; Ae = Sr and M = Co, Ni; Ae = Ca and M = Co) and CaNiAl9 compounds is a beautiful three-dimensional network of vertex-sharing aluminum octahedra. Bonding in this network is analyzed at the extended Huckel level by studying the effect of vertex sharing between isolated aluminum octahedral clusters in several model systems: a linear dimer of two clusters, a linear one-dimensional chain of clusters, a two-dimensional square sheet, and a three-dimensional cubic network of clusters. We find that the number of skeletal electrons per aluminum cluster optimal for Al−Al bonding is reduced from 14 for an isolated cluster to 12 for the cluster dimer and cluster chain, 10 for the two-dimensional cluster sheet, and 8 for the cubic network in which all cluster vertices are shared. Two effects are responsible for the reduction in the optimum electron count: First, vertex-sharing reduces the number of skeletal bonding orbitals per cluster (through res...
Synthesis and Structures of New Layered Ternary Manganese Tellurides: AMnTe2 (A = Li, Na)

New ternary manganese tellurides, AMnTe2 (A = Li, Na), were synthesized by solid state and cation exchange reactions. Both structures feature layers formed by condensation of MnTe4 tetrahedra. MnTe...
Synthesis of Sub-20-nm-Sized Bismuth 1-D Structures Using Gallium−Bismuth Systems

Bismuth (Bi) nanowires are interesting one-dimensional systems due to the significant quantum confinement effects exhibited as a function of the wire diameters, and synthesizing Bi nanowires with sizes below 20 nm is of fundamental importance in understanding quantum effects. Here, we report a bulk synthesis method to synthesize ultrafine Bi nanowires and a new morphology of bismuth nanostructures, tapered whiskers. These tapered whiskers are about 10−20 μm in length and have diameters of 5−10 nm at the tip and 250−500 nm at the base. The synthesis method is based upon the multiple nucleation and basal growth of nanometer scale nuclei from molten gallium (Ga) melts that result from the low solubility of Bi in Ga and the low eutectic temperature of the Ga−Bi binary system. Adopting different methods of supplying bismuth and using variations in simple heating and cooling, we have synthesized a variety of bismuth nanostructures.
A novel synthesis of quinolinium salts via benzotriazole methodology

A novel method for the synthesis of 1,4- and 1,3,4-substituted quinolinium salts and 1,2,4- and 1,2,3,4-substituted-1,2-dibydroquinolines from N-substituted-N(benzotriazol-1-ylmethyl)aniline derivatives is reported.
MULTIGRAM-SCALE STEREOSELECTIVE SYNTHESIS OF MESO-1,3-BUTADIENE BISEPOXIDE

meso-1,3-Butadiene bisepoxide, a potential building block for natural product synthesis and of interest as a toxic metabolite of butadiene, was synthesized in multi-gram scale in two steps from meso-erythritol in good overall yield.
Mind the gap: identifying what is missed when searching only the broad scope with clinical queries

Objective: The PubMed Clinical Study Category filters are subdivided into “Broad” and “Narrow” versions that are designed to maximize either sensitivity or specificity by using two different sets of keywords and Medical Subject Headings (MeSH). A searcher might assume that all items retrieved by Narrow would also be found by Broad, but there are occasions when some [Filter name] /Narrow citations are missed when using [Filter name] /Broad alone. This study quantifies the size of this effect. Methods: For each of the five Clinical Study Categories, PubMed was searched for citations matching the query Filter /Narrow NOT Filter /Broad. This number was compared with that for Filter /Broad to compute the number of Narrow citations missed per 1,000 Broad. This process was repeated for the MeSH terms for “Medicine” and “Diseases,” as well as for a set of individual test searches. Results: The Clinical Study Category filters for Etiology, Clinical Prediction Guides, Diagnosis, and Prognosis all showed notable numbers of Filter /Narrow citations that were missed when searching Filter /Broad alone. This was particularly true for Prognosis, where a searcher could easily miss one Prognosis/Narrow citation for every ten Prognosis/Broad citations retrieved. Conclusions: Users of the Clinical Study Category filters (except for Therapy) should consider combining Filter /Narrow together with Filter /Broad in their search strategy. This is particularly true when using Prognosis/Broad, as otherwise there is a substantial risk of missing potentially relevant citations.
Spatial-Temporal Assessment of Environmental Factors Related to Dengue Outbreaks in São Paulo, Brazil.

Dengue fever, a disease caused by a vector-borne flavivirus, is endemic to tropical countries, but its occurrence has been reported worldwide. This study aimed to understand important factors contributing to the spatial and temporal patterns of dengue occurrence in Sao Paulo, the largest municipality of Brazil. The temporal assessment of dengue occurrence covered the 2011-2016 time period and was based on climatological data, such as the El Nino indices and time series statistical tools such as the continuous wavelet transformation. The spatial assessment used Landsat 8 data for years 2014-2016 to estimate land surface temperature and normalized indices for vegetation, urban areas, and leaf water. Results from a cross correlation for the temporal analysis found a relationship between the sea surface temperature anomalies index and the number of reported dengue cases in Sao Paulo (r = 0.5) with a lag of +29 (weeks) between the climatic event and the response on the dengue incidence. This relationship, initially nonlinear, became linear after correcting for the lag period. For the spatial assessment, the linear stepwise regression model detected a low relationship between dengue incidence and minimum surface temperature (r = 0.357) and no relationship with other environmental parameters. The poor relationship might be due to confounding effects of socioeconomic factors as these seem to influence the spatial dynamics of dengue incidence. More testing is needed to validate these methods in other locations. Nevertheless, we presented possible tools to be used for the improvement of dengue control programs.
Super-resolution microscopy via ptychographic structured modulation of a diffuser

We report a new coherent imaging technique, termed ptychographic structured modulation (PSM), for quantitative super-resolution microscopy. In this technique, we place a thin diffuser (i.e., a scattering lens) in between the sample and the objective lens to modulate the complex light waves from the object. The otherwise inaccessible high-resolution object information can thus be encoded into the captured images. We then employ a ptychographic phase retrieval process to jointly recover the exit wavefront of the complex object and the unknown diffuser profile. Unlike the illumination-based super-resolution approach, the recovered image of our approach depends upon how the complex wavefront exits the sample—not enters it. Therefore, the sample thickness becomes irrelevant during reconstruction. After recovery, we can propagate the super-resolution complex wavefront to any position along the optical axis. We validate our approach using a resolution target, a quantitative phase target, a two-layer sample, and a thick polydimethylsiloxane sample. We demonstrate a 4.5-fold resolution gain over the diffraction limit. We also show that a four-fold resolution gain can be achieved with as few as ∼30 images. The reported approach may provide a quantitative super-resolution strategy for coherent light, x-ray, and electron imaging.
The absolute gradings on embedded contact homology and Seiberg–Witten Floer cohomology

Let Y be a closed connected contact 3–manifold. In [14], Taubes defines an isomorphism between the embedded contact homology (ECH) of Y and its Seiberg–Witten Floer cohomology. Both the ECH of Y and the Seiberg–Witten Floer cohomology of Y admit absolute gradings by homotopy classes of oriented 2–plane fields. We show that Taubes’ isomorphism preserves these gradings, which implies that the absolute grading on ECH is a topological invariant. To do this, we prove another result relating the expected dimension of any component of the Seiberg–Witten moduli space over a completed connected symplectic cobordism to the ECH index of a corresponding homology class.
Faster convex optimization: simulated annealing with an efficient universal barrier

This paper explores a surprising equivalence between two seemingly-distinct convex optimization methods. We show that simulated annealing, a well-studied random walk algorithms, is directly equivalent, in a certain sense, to the central path interior point algorithm for the the entropic universal barrier function. This connection exhibits several benefits. First, we are able improve the state of the art time complexity for convex optimization under the membership oracle model by devising a new temperature schedule for simulated annealing motivated by central path following interior point methods. Second, we get an efficient randomized interior point method with an efficiently computable universal barrier for any convex set described by a membership oracle. Previously, efficiently computable barriers were known only for particular convex sets.
Extractor-based time-space lower bounds for learning

A matrix M: A × X → {−1,1} corresponds to the following learning problem: An unknown element x ∈ X is chosen uniformly at random. A learner tries to learn x from a stream of samples, (a1, b1), (a2, b2) …, where for every i, ai ∈ A is chosen uniformly at random and bi = M(ai,x). Assume that k, l, r are such that any submatrix of M of at least 2−k · |A| rows and at least 2−l · |X| columns, has a bias of at most 2−r. We show that any learning algorithm for the learning problem corresponding to M requires either a memory of size at least Ω(k · l ), or at least 2Ω(r) samples. The result holds even if the learner has an exponentially small success probability (of 2−Ω(r)). In particular, this shows that for a large class of learning problems, any learning algorithm requires either a memory of size at least Ω((log|X|) · (log|A|)) or an exponential number of samples, achieving a tight Ω((log|X|) · (log|A|)) lower bound on the size of the memory, rather than a bound of Ω(min{(log|X|)2,(log|A|)2}) obtained in previous works by Raz [FOCS’17] and Moshkovitz and Moshkovitz [ITCS’18]. Moreover, our result implies all previous memory-samples lower bounds, as well as a number of new applications. Our proof builds on the work of Raz [FOCS’17] that gave a general technique for proving memory samples lower bounds.
Polynomial analogues of Ramanujan congruences for Han's hooklength formula

This article considers the eta power $\prod {(1-q^k)}^{b-1}$. It is proved that the coefficients of $\frac{q^n}{n!}$ in this expression, as polynomials in $b$, exhibit equidistribution of the coefficients in the nonzero residue classes mod 5 when $n=5j+4$. Other symmetries, as well as symmetries for other primes and prime powers, are proved, and some open questions are raised.
ex uno pluria: The Service-Infrastructure Cycle, Ossification, and the Fragmentation of the Internet

In this article I will first argue that a {\em Service-Infrastructure Cycle} is fundamental to networking evolution. Networks are built to accommodate certain services at an expected scale. New applications and/or a significant increase in scale require a rethinking of network mechanisms which results in new deployments. Four decades-worth of iterations of this process have yielded the Internet as we know it today, a common and shared global networking infrastructure that delivers almost all services. I will further argue, using brief historical case studies, that success of network mechanism deployments often hinges on whether or not mechanism evolution follows the iterations of this Cycle. Many have observed that this network, the Internet, has become ossified and unable to change in response to new demands. In other words, after decades of operation, the Service-Infrastructure Cycle has become stuck. However, novel service requirements and scale increases continue to exert significant pressure on this ossified infrastructure. The result, I will conjecture, will be a fragmentation, the beginnings of which are evident today, that will ultimately fundamentally change the character of the network infrastructure. By ushering in a ManyNets world, this fragmentation will lubricate the Service-Infrastructure Cycle so that it can continue to govern the evolution of networking. I conclude this article with a brief discussion of the possible implications of this emerging ManyNets world on networking research.
A general algorithm for compensation of trajectory errors: Application to radial imaging.

Purpose To reconstruct artifact-free images from measured k-space data, when the actual k-space trajectory deviates from the nominal trajectory due to gradient imperfections. Methods Trajectory errors arising from eddy currents and gradient delays introduce phase inconsistencies in several fast scanning MR pulse sequences, resulting in image artifacts. The proposed algorithm provides a novel framework to compensate for this phase distortion. The algorithm relies on the construction of a multi-block Hankel matrix, where each block is constructed from k-space segments with the same phase distortion. In the presence of spatially smooth phase distortions between the segments, the complete block-Hankel matrix is known to be highly low-rank. Since each k-space segment is only acquiring part of the k-space data, the reconstruction of the phase compensated image from their partially parallel measurements is posed as a structured low-rank matrix optimization problem, assuming the coil sensitivities to be known. Results The proposed formulation is tested on radial acquisitions in several settings including partial Fourier and golden-angle acquisitions. The experiments demonstrate the ability of the algorithm to successfully remove the artifacts arising from the trajectory errors, without the need for trajectory or phase calibration. The quality of the reconstruction was comparable to corrections achieved using the Trajectory Auto-Corrected Image Reconstruction (TrACR) for radial acquisitions. Conclusion The proposed method provides a general framework for the recovery of artifact-free images from radial trajectories without the need for trajectory calibration.
Lexicon Integrated CNN Models with Attention for Sentiment Analysis

With the advent of word embeddings, lex- icons are no longer fully utilized for sen- timent analysis although they still provide important features in the traditional setting. This paper introduces a novel approach to sentiment analysis that integrates lexicon embeddings and an attention mechanism into Convolutional Neural Networks. Our approach performs separate convolutions for word and lexicon embeddings and pro- vides a global view of the document using attention. Our models are experimented on both the SemEval’16 Task 4 dataset and the Stanford Sentiment Treebank and show comparative or better results against the existing state-of-the-art systems. Our analysis shows that lexicon embeddings al- low building high-performing models with much smaller word embeddings, and the attention mechanism effectively dims out noisy words for sentiment analysis.
A robust error estimator and a residual-free error indicator for reduced basis methods

Abstract The Reduced Basis Method (RBM) is a rigorous model reduction approach for solving parameterizedpartial differential equations. It identifies a low-dimensional subspace for approximation of the parametric solution manifold that is embedded in high-dimensional space. A reduced order model is subsequently constructed in this subspace. RBM relies on residual-based error indicators or a posteriori error bounds to guide construction of the reduced solution subspace, to serve as a stopping criteria, and to certify the resulting surrogate solutions. Unfortunately, it is well-known that the standard algorithm for residual norm computation suffers from premature stagnation at the level of the square root of machine precision. In this paper, we develop two alternatives to the standard offline phase of reduced basis algorithms. First, we design a robust strategy for computation of residual error indicators that allows RBM algorithms to enrich the solution subspace with accuracy beyond root machine precision. Secondly, we propose a new error indicator based on the Lebesgue function in interpolation theory. This error indicator does not require computation of residual norms, and instead only requires the ability to compute the RBM solution. This residual-free indicator is rigorous in that it bounds the error committed by the RBM approximation, but up to an uncomputable multiplicative constant. Because of this, the residual-free indicator is effective in choosing snapshots during the offline RBM phase, but cannot currently be used to certify error that the approximation commits. However, it circumvents the need for a posteriori analysis of numerical methods, and therefore can be effective on problems where such a rigorous estimate is hard to derive.
Exploiting channel memory for multi-user wireless scheduling without channel measurement: Capacity regions and algorithms

We study the fundamental network capacity of a multi-user wireless downlink under two assumptions: (1) Channels are not explicitly measured and thus instantaneous states are unknown, (2) Channels are modeled as ON/OFF Markov chains. This is an important network model to explore because channel probing may be costly or infeasible in some contexts. In this case, we can use channel memory with ACK/NACK feedback from previous transmissions to improve network throughput. Computing in closed form the capacity region of this network is difficult because it involves solving a high dimension partially observed Markov decision problem. Instead, in this paper we construct an inner and outer bound on the capacity region, showing that the bound is tight when the number of users is large and the traffic is symmetric. For the case of heterogeneous traffic and any number of users, we propose a simple queue-dependent policy that can stabilize the network with any data rates strictly within the inner capacity bound. The stability analysis uses a novel frame-based Lyapunov drift argument. The outer-bound analysis uses stochastic coupling and state aggregation to bound the performance of a restless bandit problem using a related multi-armed bandit system. Our results are useful in cognitive radio networks, opportunistic scheduling with delayed/uncertain channel state information, and restless bandit problems.
Regression Modeling and File Matching Using Possibly Erroneous Matching Variables

Many analyses require linking records from two databases comprising overlapping sets of individuals. In the absence of unique identifiers, the linkage procedure often involves matching on a set of ...
Damped Anderson Acceleration With Restarts and Monotonicity Control for Accelerating EM and EM-like Algorithms

The expectation-maximization (EM) algorithm is a well-known iterative method for computing maximum likelihood estimates in a variety of statistical problems. Despite its numerous advantages, a main...
Polynomiality for bin packing with a constant number of item types

We consider the bin packing problem with d different item sizes si and item multiplicities ai, where all numbers are given in binary encoding. This problem formulation is also known as the 1-dimensional cutting stock problem.In this work, we provide an algorithm which, for constant d, solves bin packing in polynomial time. This was an open problem for all d ≥ 3.In fact, for constant d our algorithm solves the following problem in polynomial time: given two d-dimensional polytopes P and Q, find the smallest number of integer points in P whose sum lies in Q.Our approach also applies to high multiplicity scheduling problems in which the number of copies of each job type is given in binary encoding and each type comes with certain parameters such as release dates, processing times and deadlines. We show that a variety of high multiplicity scheduling problems can be solved in polynomial time if the number of job types is constant.
Tangle functors from semicyclic representations

Let q be a 2Nth root of unity where N is odd. Let Uqs(sl 2) denote the quantum group with large center corresponding to the Lie algebra sl2ℂ with generators E,F,K, and K−1. A semicyclic representation of Uqs(sl 2) is an N-dimensional irreducible representation ρ : Uqs(sl 2) → MN(ℂ), so that ρ(EN) = aId with a≠0, ρ(FN) = 0 and ρ(KN) = Id. We construct a tangle functor for framed homogeneous tangles colored with semicyclic representations, and prove that for (1, 1)-tangles coming from knots, the invariant defined by the tangle functor coincides with Kashaev’s invariant.
A contribution to the Zarankiewicz problem

Abstract Given positive integers m , n , s , t , let z ( m , n , s , t ) be the maximum number of ones in a ( 0 , 1 ) matrix of size m × n that does not contain an all ones submatrix of size s × t . We show that if s ⩾ 2 and t ⩾ 2 , then for every k = 0 , … , s - 2 , z ( m , n , s , t ) ⩽ ( s - k - 1 ) 1 / t nm 1 - 1 / t + kn + ( t - 1 ) m 1 + k / t . This generic bound implies the known bounds of Kovari, Sos and Turan, and of Furedi. As a consequence, we also obtain the following results: Let G be a graph of n vertices and e ( G ) edges, and let μ be the spectral radius of its adjacency matrix. If G does not contain a complete bipartite subgraph K s , t , then the following bounds hold μ ⩽ ( s - t + 1 ) 1 / t n 1 - 1 / t + ( t - 1 ) n 1 - 2 / t + t - 2 , and e ( G ) 1 2 ( s - t + 1 ) 1 / t n 2 - 1 / t + 1 2 ( t - 1 ) n 2 - 2 / t + 1 2 ( t - 2 ) n .
Reactive Proximity Data Structures for Graphs

We consider data structures for graphs where we maintain a subset of the nodes called sites, and allow proximity queries, such as asking for the closest site to a query node, and update operations that enable or disable nodes as sites. We refer to a data structure that can efficiently react to such updates as reactive. We present novel reactive proximity data structures for graphs of polynomial expansion, i.e., the class of graphs with small separators, such as planar graphs and road networks. Our data structures can be used directly in several logistical problems and geographic information systems dealing with real-time data, such as emergency dispatching. We experimentally compare our data structure to Dijkstra’s algorithm in a system emulating random queries in a real road network.
The AAA + ATPase TorsinA polymerizes into hollow helical tubes with 8.5 subunits per turn

TorsinA is an ER-resident AAA + ATPase, whose deletion of glutamate E303 results in the genetic neuromuscular disease primary dystonia. TorsinA is an unusual AAA + ATPase that needs an external activator. Also, it likely does not thread a peptide substrate through a narrow central channel, in contrast to its closest structural homologs. Here, we examined the oligomerization of TorsinA to get closer to a molecular understanding of its still enigmatic function. We observe TorsinA to form helical filaments, which we analyzed by cryo-electron microscopy using helical reconstruction. The 4.4 A structure reveals long hollow tubes with a helical periodicity of 8.5 subunits per turn, and an inner channel of ~ 4 nm diameter. We further show that the protein is able to induce tubulation of membranes in vitro, an observation that may reflect an entirely new characteristic of AAA + ATPases. We discuss the implications of these observations for TorsinA function. Torsins are unusual AAA + ATPases of unknown function that reside in the endoplasmic reticulum of all animals. Here the authors report that TorsinA forms tubular helical filaments with an unusual periodicity and that filamentous TorsinA directly interacts with membranes to form tubular protrusions.
Optimal control of Connected Automated Vehicles at urban traffic intersections: A feasibility enforcement analysis

Earlier work has established a decentralized optimal control framework for coordinating on line a continuous flow of Connected Automated Vehicles (CAVs) entering a “control zone” and crossing two adjacent intersections in an urban area. A solution, when it exists, allows the vehicles to minimize their fuel consumption while crossing the intersections without the use of traffic lights, without creating congestion, and under the hard safety constraint of collision avoidance. We establish the conditions under which such solutions exist and show that they can be enforced through an appropriately designed “feasibility enforcement zone” that precedes the control zone. The proposed solution and overall control architecture are illustrated through simulation.
Active Learning for Out-of-Class Activities by Using Interactive Mobile Apps

Keeping students engaged with the course content outside the classroom is a challenging task. Since learning during undergraduate years occurs not only as student engagement in class, but also during out-of-class activities, we need to redesign and reinvent such activities for this and future generation of students. Although active learning has been used widely to improve in-class student learning and engagement, its usage outside the classroom is not widespread and researched. Active learning is often not utilized for out-of-class activities and traditional unsupervised activities are used mostly to keep students engaged in the content after they leave the classroom. Although there has been tremendous research performed to improve student learning and engagement in the classroom, there are a few pieces of researches on improving out-of-class learning and student engagement. This poster will present an approach to redesign the traditional out-of-class activities with the help of mobile apps, which are interactive and adaptive, and will provide personalization to satisfy student's needs outside the classroom so that optimal learning experience can be achieved.
Neyman-Pearson classification under high-dimensional settings

Most existing binary classification methods target on the optimization of the overall classification risk and may fail to serve some real-world applications such as cancer diagnosis, where users are more concerned with the risk of misclassifying one specific class than the other. Neyman-Pearson (NP) paradigm was introduced in this context as a novel statistical framework for handling asymmetric type I/II error priorities. It seeks classifiers with a minimal type II error and a constrained type I error under a user specified level. This article is the first attempt to construct classifiers with guaranteed theoretical performance under the NP paradigm in high-dimensional settings. Based on the fundamental Neyman-Pearson Lemma, we used a plug-in approach to construct NP-type classifiers for Naive Bayes models. The proposed classifiers satisfy the NP oracle inequalities, which are natural NP paradigm counterparts of the oracle inequalities in classical binary classification. Besides their desirable theoretical properties, we also demonstrated their numerical advantages in prioritized error control via both simulation and real data studies.
A Sample Complexity Measure with Applications to Learning Optimal Auctions

We introduce a new sample complexity measure, which we refer to as split-sample growth rate. For any hypothesis $H$ and for any sample $S$ of size $m$, the split-sample growth rate $\hat{\tau}_H(m)$ counts how many different hypotheses can empirical risk minimization output on any sub-sample of $S$ of size $m/2$. We show that the expected generalization error is upper bounded by $O\left(\sqrt{\frac{\log(\hat{\tau}_H(2m))}{m}}\right)$. Our result is enabled by a strengthening of the Rademacher complexity analysis of the expected generalization error. We show that this sample complexity measure, greatly simplifies the analysis of the sample complexity of optimal auction design, for many auction classes studied in the literature. Their sample complexity can be derived solely by noticing that in these auction classes, ERM on any sample or sub-sample will pick parameters that are equal to one of the points in the sample.
Estimation of Laplacian spectra of direct and strong product graphs

Calculating a product of multiple graphs has been studied in mathematics, engineering, computer science, and more recently in network science, particularly in the context of multilayer networks. One of the important questions to be addressed in this area is how to characterize spectral properties of a product graph using those of its factor graphs. While several such characterizations have already been obtained analytically (mostly for adjacency spectra), characterization of Laplacian spectra of direct product and strong product graphs has remained an open problem. Here we develop practical methods to estimate Laplacian spectra of direct and strong product graphs from spectral properties of their factor graphs using a few heuristic assumptions. Numerical experiments showed that the proposed methods produced reasonable estimation with percentage errors confined within a ? 10 % range for most eigenvalues.
Locally-orthogonal unstructured grid-generation for general circulation modelling on the sphere*

An algorithm for the generation of non-uniform, locally-orthogonal staggered unstructured spheroidal grids is described. This technique is designed to generate very high-quality staggered Voronoi/Delaunay meshes appropriate for general circulation modelling on the sphere, including applications to atmospheric simulation, ocean-modelling and numerical weather prediction. Using a recently developed Frontal-Delaunay refinement technique, a method for the construction of high-quality unstructured spheroidal Delaunay triangulations is introduced. A locally-orthogonal polygonal grid, derived from the associated Voronoi diagram, is computed as the staggered dual. It is shown that use of the Frontal-Delaunay refinement technique allows for the generation of very high-quality unstructured triangulations, satisfying a-priori bounds on element size and shape. Grid-quality is further improved through the application of hill-climbing type optimisation techniques. Overall, the algorithm is shown to produce grids with very high element quality and smooth grading characteristics, while imposing relatively low computational expense. A selection of uniform and non-uniform spheroidal grids appropriate for high-resolution, multi-scale general circulation modelling are presented. These grids are shown to satisfy the geometric constraints associated with contemporary unstructured C-grid type finite-volume models, including the Model for Prediction Across Scales (MPAS-O). The use of user-defined mesh-spacing functions to generate smoothly graded, non-uniform grids for multi-resolution type studies is discussed in detail.
Spatio-Temporal Data Mining: A Survey of Problems and Methods

Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains, including climate science, social sciences, neuroscience, epidemiology, transportation, mobile health, and Earth sciences. Spatio-temporal data differ from relational data for which computational approaches are developed in the data-mining community for multiple decades in that both spatial and temporal attributes are available in addition to the actual measurements/attributes. The presence of these attributes introduces additional challenges that needs to be dealt with. Approaches for mining spatio-temporal data have been studied for over a decade in the data-mining community. In this article, we present a broad survey of this relatively young field of spatio-temporal data mining. We discuss different types of spatio-temporal data and the relevant data-mining questions that arise in the context of analyzing each of these datasets. Based on the nature of the data-mining problem studied, we classify literature on spatio-temporal data mining into six major categories: clustering, predictive learning, change detection, frequent pattern mining, anomaly detection, and relationship mining. We discuss the various forms of spatio-temporal data-mining problems in each of these categories.
Paleohydrology on Mars constrained by mass balance and mineralogy of pre‐Amazonian sodium chloride lakes

Chloride-bearing deposits on Mars record high-elevation lakes during the waning stages of Mars’ wet era (mid-Noachian to late Hesperian). The water source pathways, seasonality, salinity, depth, lifetime, and paleoclimatic drivers of these widespread lakes are all unknown. Here we combine reaction-transport modeling, orbital spectroscopy, and new volume estimates from high-resolution digital terrain models, in order to constrain the hydrologic boundary conditions for forming the chlorides. Considering a T = 0 °C system, we find: (1) individual lakes were >100 m deep and lasted decades or longer; (2) if volcanic degassing was the source of chlorine, then the water-to-rock ratio or the total water volume were probably low, consistent with brief excursions above the melting point and/or arid climate; (3) if the chlorine source was igneous chlorapatite, then Cl-leaching events would require a (cumulative) time of >10 yr at the melting point; (4) Cl masses, divided by catchment area, give column densities 0.1 – 50 kg Cl/m2, and these column densities bracket the expected chlorapatite-Cl content for a seasonally-warm active layer. Deep groundwater was not required. Taken together, our results are consistent with Mars having a usually cold, horizontally segregated hydrosphere by the time chlorides formed.
Noncommutative Tori, Yang–Mills, and String Theory

Noncommutative tori are among historically the oldest and by now the most developed examples of noncommutative spaces. Noncommutative Yang-Mills theory can be obtained from string theory. This connection led to a cross-fertilization of research in physics and mathematics on Yang-Mills theory on noncommutative tori. One important result stemming from that work is the link between T-duality in string theory and Morita equivalence of associative algebras. In this article we give an overview of the basic results in differential geometry of noncommutative tori. Yang-Mills theory on noncommutative tori, the duality induced by Morita equivalence and its link with the T-duality are discussed. Noncommutative Nahm transform for instantons is introduced.
Higher order spt-functions

Abstract Andrewsʼ spt-function can be written as the difference between the second symmetrized crank and rank moment functions. Using the machinery of Bailey pairs a combinatorial interpretation is given for the difference between higher order symmetrized crank and rank moment functions. This implies an inequality between crank and rank moments that was only known previously for sufficiently large n and fixed order. This combinatorial interpretation is in terms of a weighted sum of partitions. A number of congruences for higher order spt-functions are derived.
Optimal estimation and completion of matrices with biclustering structures

Biclustering structures in data matrices were first formalized in a seminal paper by John Hartigan (Hartigan, 1972) where one seeks to cluster cases and variables simultaneously. Such structures are also prevalent in block modeling of networks. In this paper, we develop a theory for the estimation and completion of matrices with biclustering structures, where the data is a partially observed and noise contaminated matrix with a certain underlying biclustering structure. In particular, we show that a constrained least squares estimator achieves minimax rate-optimal performance in several of the most important scenarios. To this end, we derive unified high probability upper bounds for all sub-Gaussian data and also provide matching minimax lower bounds in both Gaussian and binary cases. Due to the close connection of graphon to stochastic block models, an immediate consequence of our general results is a minimax rate-optimal estimator for sparse graphons.
DocFace+: ID Document to Selfie Matching

Numerous activities in our daily life require us to verify who we are by showing our ID documents containing face images, such as passports and driver licenses, to human operators. However, this process is slow, labor intensive and unreliable. As such, an automated system for matching ID document photographs to live face images (selfies 1 ) in real time and with high accuracy is required. In this paper, we propose DocFace+ to meet this objective. We first show that gradient-based optimization methods converge slowly (due to the underfitting of classifier weights) when many classes have very few samples, a characteristic of existing ID-selfie datasets. To overcome this shortcoming, we propose a method, called dynamic weight imprinting, to update the classifier weights, which allows faster convergence and more generalizable representations. Next, a pair of sibling networks with partially shared parameters are trained to learn a unified face representation with domain-specific parameters. Cross-validation on an ID-selfie dataset shows that while a publicly available general face matcher (InsightFace) only achieves a true accept rate (TAR) of 88.78 ± 1.30% at a false accept rate of 0.01% on the problem, DocFace+ improves the TAR to 95.95 ± 0.54%. 1 Technically, the word “selfies” refers to self-captured photos from mobile phones. But here, we define “selfies” as any self-captured live face photos, including those from mobile phones and kiosks.
ON QUASI-MORPHISMS FROM KNOT AND BRAID INVARIANTS

We study quasi-morphisms on the groups Pn of pure braids on n strings and on the group of compactly supported area-preserving diffeomorphisms of an open two-dimensional disk. We show that it is possible to build quasi-morphisms on Pn by using knot invariants which satisfy some special properties. In particular, we study quasi-morphisms which come from knot Floer homology and Khovanov-type homology. We then discuss possible variations of the Gambaudo–Ghys construction, using the above quasi-morphisms on Pn to build quasi-morphisms on the group of diffeomorphisms of a 2-disk.
Hardness Results for the Subpower Membership Problem

The main result of this paper shows that if $\mathcal{M}$ is a consistent strong linear Maltsev condition which does not imply the existence of a cube term, then for any finite algebra $\mathbb{A}$ there exists a new finite algebra $\mathbb{A}_\mathcal{M}$ which satisfies the Maltsev condition $\mathcal{M}$, and whose subpower membership problem is at least as hard as the subpower membership problem for $\mathbb{A}$. We characterize consistent strong linear Maltsev conditions which do not imply the existence of a cube term, and show that there are finite algebras in varieties that are congruence distributive and congruence $k$-permutable ($k \geq 3$) whose subpower membership problem is EXPTIME-complete.
GPU Accelerated Self-Join for the Distance Similarity Metric

The self-join finds all objects in a dataset within a threshold of each other defined by a similarity metric. As such, the self-join is a building block for the field of databases and data mining, and is employed in Big Data applications. In this paper, we advance a GPU-efficient algorithm for the similarity self-join that uses the Euclidean distance metric. The search-and-refine strategy is an efficient approach for low dimensionality datasets, as index searches degrade with increasing dimension (i.e., the curse of dimensionality). Thus, we target the low dimensionality problem, and compare our GPU self-join to a search-and-refine implementation, and a state-of-the-art parallel algorithm. In low dimensionality, there are several unique challenges associated with efficiently solving the self-join problem on the GPU. Low dimensional data often results in higher data densities, causing a significant number of distance calculations and a large result set. As dimensionality increases, index searches become increasingly exhaustive, forming a performance bottleneck. We advance several techniques to overcome these challenges using the GPU. The techniques we propose include a GPU-efficient index that employs a bounded search, a batching scheme to accommodate large result set sizes, and a reduction in distance calculations through duplicate search removal. Our GPU self-join outperforms both search-and-refine and state-of-the-art algorithms.
Secure k-NN as a Service over Encrypted Data in Multi-User Setting

To securely leverage the advantages of Cloud Computing, a lot of research has happened in the area of "Secure Query Processing over Encrypted Data". As a concrete use case, many encryption schemes have been proposed for securely processing k Nearest Neighbors (SkNN) over encrypted data in the outsourced setting. Recently Zhu et al.[1] proposed a SkNN solution which claimed to satisfy following four properties: (1)Data Privacy, (2)Key Confidentiality, (3)Query Privacy, and (4)Query Controllability. However, in this paper, we present an attack which breaks the Query Controllability claim of their scheme. Further, we propose a new SkNN solution which satisfies all the four existing properties along with an additional essential property of Query Check Verification. We analyze the security of our proposed scheme and present the detailed experimental results to showcase the efficiency in real world scenario.