cssi/SciSciGPT-SciSciNet huggingface dataset:
The dangers of extremes

While extreme black hole spacetimes with smooth horizons are known at the level of mathematics, we argue that the horizons of physical extreme black holes are effectively singular. Test particles encounter a singularity the moment they cross the horizon, and only objects with significant back-reaction can fall across a smooth (now non-extreme) horizon. As a result, classical interior solutions for extreme black holes are theoretical fictions that need not be reproduced by any quantum mechanical model. This observation suggests that significant quantum effects might be visible outside extreme or nearly extreme black holes. It also suggests that the microphysics of such black holes may be very different from that of their Schwarzschild cousins.
Enhanced Inhibition of Human Anti-Gal Antibody Binding to Mammalian Cells by Synthetic α-Gal Epitope Polymers

Neoglycopolymers of polyacrylamide backbone conjugated with varying densities of Galα1−3Galβ1−4Glcβ trisaccharide epitopes (α-Gal epitopes) were designed and synthesized to study the inhibition of the binding of human natural anti-Gal antibodies to either α-Gal-containing glycoproteins or α-Gal antigens on the surface of mammalian cells. An inhibition ELISA using mouse laminin and a flow cytometry assay using pig kidney cells (PK15) were established to determine the binding affinity of the synthesized polymers. In comparison to the α-Gal monomer (Galα1−3Galβ1−4GlcNHAcβ), the α-Gal polymers dramatically enhanced the inhibition of human anti-Gal antibodies (IgG, IgM, and IgA) binding to mouse laminin or mammalian cells. Increases of 7.8 × 103- and 5.0 × 104 -fold in inhibitory potential of polymer 7C to IgA and IgM (with IC50s of 7.0 and 5.6 nM respectively) were observed over the monomer in inhibition ELISA. The results also indicated that binding enhancement of α-Gal polymers is greater for anti-Gal IgA a...
Fracking and indoor radon: Spurious correlation or cause for concern?

Abstract Unconventional gas development (fracking) is controversial in large part because of environmental and health concerns. We consider the concern that fracking leads to more carcinogenic radon gas in nearby buildings. Our empirical approach estimates treatment effects where treatment is continuous (number of wells) and varies in intensity (distance to the wells) and in duration of exposure (the time since wells were drilled). The approach allows any potential effect of fracking to vary non-linearly with the distance between the well and test site and, holding distance constant, the time between drilling and testing. Our main model gives a precisely estimated zero effect of wells on radon concentrations in nearby buildings. It also reveals that energy firms drilled wells in places with higher pre-existing radon levels, which, if ignored, makes it appear that wells within 2 km increase indoor radon but wells 3 km away do not. This explains the finding of a prior study showing a link between drilling and indoor radon.
Nuclear Androgen Receptor Regulates Testes Organization and Oocyte Maturation in Zebrafish

Androgens act through the nuclear androgen receptor (AR) to regulate gonad differentiation and development. In mice, AR is necessary for spermatogenesis, testis development, and formation of external genitalia in males and oocyte maturation in females. However, the extent to which these phenotypes are conserved in nonmammalian vertebrates is not well understood. Here, we generate zebrafish with a mutation in the ar gene (aruab105/105) and examine the role of AR in sexual determination and gonad development. We found that zebrafish AR regulates male sexual determination, because the majority of aruab105/105 mutant embryos developed ovaries and displayed female secondary sexual characteristics. The small percentage of mutants that developed testes displayed female secondary sexual characteristics, exhibited structurally disorganized testes, and were unable to release or produce normal levels of sperm, demonstrating that AR is necessary for zebrafish testis development and fertility. In females, we found that AR regulates oocyte maturation and fecundity. The aruab105/105 mutant females developed ovaries filled primarily with immature stage I oocytes and few mature stage III oocytes. Two genes whose expression is enriched in wild-type ovaries compared with testes (cyp19a1a, foxl2a) were upregulated in ar mutant testes, and two genes enriched in testes (amh, dmrt1) were upregulated in ar mutant ovaries. These findings demonstrate that AR regulates sexual determination, testis development, and oocyte maturation and suggest that AR regulates sexually dimorphic gene expression. The ar mutant we developed will be useful for modeling human endocrine function in zebrafish.
Solvent-Free, Base-Free Microwave-Mediated Iridium-Catalyzed N-Alkylation of Amides with Alcohols

Solvent-free, base-free microwave-mediated (Cp*IrCl2)2-catalyzed conditions for the N-alkylation of amides with alcohols have been developed. A series of primary and secondary alcohols have been shown to produce high yields of N-alkyl arylamides and N-alkyl alkylamides.
Crystal Chemistry of LuPd2O4 and Other Spinel-Related NdCu2O4−LaPd2O4-Type Compounds

Lutetium palladium oxide (LuPd2O4) has been prepared in a multianvil apparatus at 60 kbar pressure and 1000 °C. It crystallizes in the tetragonal LaPd2O4-type structure with space group I41/a, a = ...
An inconvenient cost: The effects of climate change on municipal bonds

Abstract Counties more likely to be affected by climate change pay more in underwriting fees and initial yields to issue long-term municipal bonds compared to counties unlikely to be affected by climate change. This difference disappears when comparing short-term municipal bonds, implying the market prices climate change risks for long-term securities only. Higher issuance costs for climate risk counties are driven by bonds with lower credit ratings. Investor attention is a driving factor, as the difference in issuance costs on bonds issued by climate and nonclimate affected counties increases after the release of the 2006 Stern Review on climate change.
Geographic range size is predicted by plant mating system

Species' geographic ranges vary enormously, and even closest relatives may differ in range size by several orders of magnitude. With data from hundreds of species spanning 20 genera in 15 families, we show that plant species that autonomously reproduce via self-pollination consistently have larger geographic ranges than their close relatives that generally require two parents for reproduction. Further analyses strongly implicate autonomous self-fertilisation in causing this relationship, as it is not driven by traits such as polyploidy or annual life history whose evolution is sometimes correlated with selfing. Furthermore, we find that selfers occur at higher maximum latitudes and that disparity in range size between selfers and outcrossers increases with time since their evolutionary divergence. Together, these results show that autonomous reproduction--a critical biological trait that eliminates mate limitation and thus potentially increases the probability of establishment--increases range size.
A general method for the rapid reduction of alkenes and alkynes using sodium borohydride, acetic acid, and palladium

Abstract Alkenes and alkynes are rapidly reduced to the corresponding alkanes using sodium borohydride and acetic acid in the presence of a small amount of palladium catalyst. The heterogeneous reaction is conducted in open air at room temperature. Reactions typically afford conversions to the alkane product of 98% or more within 15 min. The best solvent system was determined to be isopropyl alcohol, though reduction also takes place in solvents such as tetrahydrofuran, chloroform and, with some substrates, even in water. The method described is a convenient alternative to hydrogenations that require an external supply of hydrogen gas.
Synthesis and Structural Characterization of the First Quaternary Plutonium Thiophosphates: K3Pu(PS4)2 and APuP2S7 (A = K, Rb, Cs)

The first quaternary plutonium metal thiophosphates have been synthesized by the reactive flux method and characterized by single-crystal X-ray diffraction: K3Pu(PS4)2 (I), KPuP2S7 (II), RbPuP2S7 ...
3-[2-(N-Phenylacetamide)]-1,5-benzodiazepines: Orally Active, Binding Selective CCK-A Agonists

A series of modifications were made to the C-3 substituent of the 1,5-benzodiazepine CCK-A agonist 1. Replacement of the inner urea NH and addition of a methyl group to generate a C-3 quaternary carbon resulted in acetamide 6, which showed CCK-A receptor binding selectivity and sub-micromolar agonist activity in vitro. Benzodiazepine 6 was active in an in vivo mouse gallbladder emptying assay and represents a novel orally active, binding selective CCK-A agonist.
Fingerprinting Orientation Distribution Functions in diffusion MRI detects smaller crossing angles

Diffusion tractography is routinely used to study white matter architecture and brain connectivity in vivo. A key step for successful tractography of neuronal tracts is the correct identification of tract directions in each voxel. Here we propose a fingerprinting-based methodology to identify these fiber directions in Orientation Distribution Functions, dubbed ODF-Fingerprinting (ODF-FP). In ODF-FP, fiber configurations are selected based on the similarity between measured ODFs and elements in a pre-computed library. In noisy ODFs, the library matching algorithm penalizes the more complex fiber configurations. ODF simulations and analysis of bootstrapped partial and whole-brain in vivo datasets show that the ODF-FP approach improves the detection of fiber pairs with small crossing angles while maintaining fiber direction precision, which leads to better tractography results. Rather than focusing on the ODF maxima, the ODF-FP approach uses the whole ODF shape to infer fiber directions to improve the detection of fiber bundles with small crossing angle. The resulting fiber directions aid tractography algorithms in accurately displaying neuronal tracts and calculating brain connectivity.
Porphyrins with exocyclic rings. Part 12. Synthesis of meso,β-butano- and meso,β-pentanoporphyrins from cycloalka[b]pyrroles

Abstract Pyrroles 5 with b-face fused seven- or eight-membered carbocyclic rings were prepared in good yields by Knorr-type condensations with cycloheptanone or cyclooctanone, respectively. Treatment with lead tetraacetate in dichloromethane afforded the labile ω-acetates 8 and subsequent acid catalyzed condensation with α-unsubstituted pyrrole-2-carboxylates yielded the related pyrrolylcycloalka[b]pyrroles 29 and 30 . The α-unsubstituted pyrroles 14 and 23 were prepared by condensing α,β-unsubstituted ketones, or their β-acetoxycarbonyl precursors, with benzyl N-tosylglycinate in the presence of DBU, followed by dehydration with POCl 3 -pyridine and base catalyzed elimination of p-toluenesulfinic acid. Dipyrrole dibenzyl esters 29 and 30 were hydrogenolysed over 10% Pd-C to give the corresponding dicarboxylic acids. Acid catalyzed condensation with diformyldipyrrylmethane 33 under modified MacDonald “2 + 2” conditions afforded a series of four cycloalkanoporphyrins 4a-d with seven- or eight-membered exocyclic rings. Although the meso,β-butanoporphyrins 4a and 4b were isolated in good yields, poorer results were obtained for the related meso,β-pentanoporphyrins 4c and 4d . It is proposed that the eight-membered carbocyclic ring distorts the geometry of the open-chain tetrapyrrolic intermediates and this deleterious influence results in the lower yields observed.
EFFICIENT CHIRAL POISONING OF RACEMIC TITANIUM CATALYSTS FOR THE ASYMMETRIC CHLORAL-ENE REACTION

A Ti(O-i-Pr)2Cl2racemic BINOL catalyst can be poisoned with an inactive enantiopure catalyst, diisopropyl D-tartrateTi(O-i-Pr)2Cl2, to yield an effective catalyst for the asymmetric ene reaction with chloral. In some cases the ee's are greater than those observed with catalysts prepared from enantiopure BINOL.
Coordination Intercalation of NbAsO5 with Alkali-Metal Chloride. Synthesis, Structure, and Spectroscopy of Two New Layered Niobium(V) Arsenate Compounds, ANbAsO5Cl (A = Rb, Cs)

Via a direct reaction of NbAsO5 and ACl (A = Rb, Cs), a new family of layered niobium(V) arsenate compounds, ANbAsO5Cl, was synthesized. Single crystals of these new phases were grown by employing ...
Syntheses and Structures of LiAuS and Li3AuS2

The compounds LiAuS and Li3AuS2 were synthesized by the reactions of Au and S with the Li2S as the reactive flux. The compound LiAuS crystallizes with 16 formula units in the orthorhombic space group Fddd in a cell at T = 153 K. An earlier report of a monoclinic cell for this compound is incorrect. The compound Li3AuS2, which is isostructural with Na3AuS2, crystallizes with four formula units in the orthorhombic space group Ibam in a cell at T = 153 K. LiAuS contains infinite zigzag chains. Li3AuS2 is composed of [AuS23-] clusters. In both compounds, each Au atom is linearly coordinated to two S atoms. Both compounds exhibit short Au+···Au+ interactions: 3.1632(4) A in LiAuS and 3.1383(6) A in Li3AuS2.
Development-induced Displacement and Homelessness in Seattle, Washington

As big technological companies expand across the Seattle Metropolitan area, real estate construction is exploding in downtown Seattle where homelessness due to development-induced displacement is concerning. To this end, the internal displacement of vulnerable populations, including Indigenous peoples, homeless persons, and refugees is mainly due to large scale development of big tech companies. The indifference of using the term ―internal displacement‖ to describe the situation of development-induced displacement makes it difficult to identify, name and raise the agenda of development-induced displaced homeless people in the area. Thus, the goal of this paper is to present a general overview and assessment of the state of development-induced displacement of homeless peoples in Seattle due to big companies‘ expansion, so as to initiate and stimulate critical dialogue on furthering research in the area.
Pseudomonas syringae Increases Water Availability in Leaf Microenvironments via Production of Hygroscopic Syringafactin.

ABSTRACT The epiphytic bacterium Pseudomonas syringae strain B728a produces the biosurfactant syringafactin, which is hygroscopic. The water-absorbing potential of syringafactin is high. Syringafactin attracts 250% of its weight in water at high relative humidities but is less hygroscopic at lower relative humidities. This finding suggests that the benefit of syringafactin to the producing cells is strongly context dependent. The contribution of syringafactin to the water availability around cells on different matrices was assessed by examining the water stress exhibited by biosensor strains expressing gfp via the water-stress-activated proU promoter. Wild-type cells exhibited significantly less green fluorescent protein (GFP) fluorescence than a syringafactin-deficient strain on dry filters in atmospheres of high water saturation, as well as on leaf surfaces, indicating greater water availability. When infiltrated into the leaf apoplast, wild-type cells also subsequently exhibited less GFP fluorescence than the syringafactin-deficient strain. These results suggest that the apoplast is a dry but humid environment and that, just as on dry but humid leaf surfaces, syringafactin increases liquid water availability and reduces the water stress experienced by P. syringae. IMPORTANCE Many microorganisms, including the plant pathogen Pseudomonas syringae, produce amphiphilic compounds known as biosurfactants. While biosurfactants are known to disperse hydrophobic compounds and to reduce water tension, they have other properties that can benefit the cells that produce them. Leaf-colonizing bacteria experience frequent water stress, since liquid water is present only transiently on or in leaf sites that they colonize. The demonstration that syringafactin, a biosurfactant produced by P. syringae, is sufficiently hygroscopic to increase water availability to cells, thus relieving water stress, reveals that P. syringae can modify its local habitat both on leaf surfaces and in the leaf apoplast. Such habitat modification may be a common role for biosurfactants produced by other bacterial species that colonize habitats (such as soil) that are not always water saturated.
Disorder Atlas: Web-based software for the proteome-based interpretation of intrinsic disorder predictions

Intrinsically disordered proteins lack a stable three-dimensional structure under physiological conditions. While this property has gained considerable interest within the past two decades, disorder poses substantial challenges to experimental characterization efforts. In effect, numerous computational tools have been developed to predict disorder from primary sequences, however, interpreting the output of these algorithms remains a challenge. To begin to bridge this gap, we present Disorder Atlas, web-based software that facilitates the interpretation of intrinsic disorder predictions using proteome-based descriptive statistics. This service is also equipped to facilitate large-scale systematic exploratory searches for proteins encompassing disorder features of interest, and further allows users to browse the prevalence of multiple disorder features at the proteome level. As a result, Disorder Atlas provides a user-friendly tool that places algorithm-generated disorder predictions in the context of the proteome, thereby providing an instrument to compare the results of a query protein against predictions made for an entire population. Disorder Atlas currently supports ten eukaryotic proteomes and is freely available for non-commercial users at http://www.disorderatlas.org.
Recursive strategy for decomposing Betti tables of complete intersections

In the spirit of Boij–Soderberg theory, we introduce a recursive decomposition algorithm for the Betti diagram of a complete intersection using the diagram of a complete intersection defined by a s...
Morse theory for the uniform energy

In this paper we develop a Morse theory for the uniform energy. We use the one-sided directional derivative of the distance function to study the minimizing properties of variations through closed geodesics. This derivative is then used to define a one-sided directional derivative for the uniform energy which allows us to identify gradient-like vectors at those points where the function is not differentiable. These vectors are used to restart the standard negative gradient flow of the Morse energy at its critical points. We illustrate this procedure on the flat torus and demonstrate that the restarted flow improves the minimizing properties of the associated closed geodesics.
Gaussian Process Regression for Arctic Coastal Erosion Forecasting

Arctic coastal morphology is governed by multiple factors, many of which are affected by climatological changes. As the season length for shore-fast ice decreases and temperatures warm permafrost soils, coastlines are more susceptible to erosion from storm waves. Such coastal erosion is a concern since a large proportion of the population centers and infrastructure in the Arctic are located near the coasts. Stakeholders and decision makers increasingly need models capable of scenario-based predictions to assess and mitigate the effects of coastal morphology on infrastructure and land use. Our research uses Gaussian process (GP) models to forecast Arctic coastal erosion along the Beaufort Sea near Drew Point, AK, USA. GP regression is a data-driven modeling methodology capable of extracting patterns and trends from data-sparse environments such as remote Arctic coastlines. To train our model, we use annual coastline positions and near-shore summer temperature averages from existing data sets and extend these data by extracting additional coastlines from satellite imagery. We combine our calibrated models with future climate models to generate a range of plausible future erosion scenarios. Our results show that the GP methodology substantially improves yearly predictions compared to linear and nonlinear least squares methods and is capable of generating detailed forecasts suitable for the use by decision makers.
A Geometric Multigrid Preconditioning Strategy for DPG System Matrices

Abstract The discontinuous Petrov–Galerkin (DPG) methodology of Demkowicz and Gopalakrishnan (2010, 2011) guarantees the optimality of the solution in an energy norm, and provides several features facilitating adaptive schemes. A key question that has not yet been answered in general – though there are some results for Poisson, e.g.– is how best to precondition the DPG system matrix, so that iterative solvers may be used to allow solution of large-scale problems. In this paper, we detail a strategy for preconditioning the DPG system matrix using geometric multigrid which we have implemented as part of Camellia (Roberts, 2014, 2016), and demonstrate through numerical experiments its effectiveness in the context of several variational formulations. We observe that in some of our experiments, the behavior of the preconditioner is closely tied to the discrete test space enrichment. We include experiments involving adaptive meshes with hanging nodes for lid-driven cavity flow, demonstrating that the preconditioners can be applied in the context of challenging problems. We also include a scalability study demonstrating that the approach – and our implementation – scales well to many MPI ranks.
An Iterative Global Structure-Assisted Labeled Network Aligner

Integrating data from heterogeneous sources is often modeled as merging graphs. Given two or more "compatible'', but not-isomorphic graphs, the first step is to identify a graph alignment, where a potentially partial mapping of vertices between two graphs is computed. A significant portion of the literature on this problem only takes the global structure of the input graphs into account. Only more recent ones additionally use vertex and edge attributes to achieve a more accurate alignment. However, these methods are not designed to scale to map large graphs arising in many modern applications. We propose a new iterative graph aligner, gsaNA, that uses the global structure of the graphs to significantly reduce the problem size and align large graphs with a minimal loss of information. Concretely, we show that our proposed technique is highly flexible, can be used to achieve higher recall, and it is orders of magnitudes faster than the current state of the art techniques.
Forecasting the onset and course of mental illness with Twitter data

We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners’ average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (Nusers = 174, Ntweets = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models

We marry ideas from deep neural networks and approximate Bayesian inference to derive a generalised class of deep, directed generative models, endowed with a new algorithm for scalable inference and learning. Our algorithm introduces a recognition model to represent an approximate posterior distribution and uses this for optimisation of a variational lower bound. We develop stochastic backpropagation - rules for gradient backpropagation through stochastic variables - and derive an algorithm that allows for joint optimisation of the parameters of both the generative and recognition models. We demonstrate on several real-world data sets that by using stochastic backpropagation and variational inference, we obtain models that are able to generate realistic samples of data, allow for accurate imputations of missing data, and provide a useful tool for high-dimensional data visualisation.
Proper Distinguishing Colorings with Few Colors for Graphs with Girth at Least 5

The distinguishing chromatic number, $\chi_D(G)$, of a graph $G$ is the smallest number of colors in a proper coloring, $\varphi$, of $G$, such that the only automorphism of $G$ that preserves all colors of $\varphi$ is the identity map. Collins and Trenk conjectured that if $G$ is connected with girth at least 5 and $G\ne C_6$, then $\chi_D(G)\leqslant \Delta+1$. We prove this conjecture.
Abelian-by-central Galois groups of fields II: Definability of inertia/decomposition groups

This paper explores some first-order properties of commuting-liftable pairs in pro-l abelian-by-central Galois groups of fields. The main focus of the paper is to prove that minimized inertia and decomposition groups of many valuations are first-order definable using a predicate for the collection of commuting-liftable pairs. For higher-dimensional function fields over algebraically closed fields, we show that the minimized inertia and decomposition groups of quasi-divisorial valuations are uniformly first-order definable in this language.
SoundSignaling: Realtime, Stylistic Modification of a Personal Music Corpus for Information Delivery

Drawing inspiration from the notion of cognitive incongruence associated with Stroop's famous experiment, from musical principles, and from the observation that music consumption on an individual basis is becoming increasingly ubiquitous, we present the SoundSignaling system -- a software platform designed to make real-time, stylistically relevant modifications to a personal corpus of music as a means of conveying information or notifications. In this work, we discuss in detail the system's technical implementation and its motivation from a musical perspective, and validate these design choices through a crowd-sourced signal identification experiment consisting of 200 independent tasks performed by 50 online participants. We then qualitatively discuss the potential implications of such a system from the standpoint of switch cost, cognitive load, and listening behavior by considering the anecdotal outcomes of a small-scale, in-the-wild experiment consisting of over 180 hours of usage from 6 participants. Through this work, we suggest a re-evaluation of the age-old paradigm of binary audio notifications in favor of a system designed to operate upon the relatively unexplored medium of a user's musical preferences.
Estimating the Number of Clusters Using Cross-Validation

Many clustering methods, including k-means, require the user to specify the number of clusters as an input parameter. A variety of methods have been devised to choose the number of clusters automat...
Parsing Coordination For Spoken Language Understanding

Typical spoken language understanding systems provide narrow semantic parses using a domain-specific ontology. The parses contain intents and slots that are directly consumed by downstream domain applications. In this work we discuss expanding such systems to handle compound entities and intents by introducing a domain-agnostic shallow parser that handles linguistic coordination. We show that our model for parsing coordination learns domain-independent and slot-independent features and is able to segment conjunct boundaries of many different phrasal categories. We also show that using adversarial training can be effective for improving generalization across different slot types for coordination parsing.
Effect of bonus payments in cost sharing mechanism design for renewable energy aggregation

The participation of renewable energy sources in energy markets is challenging, mainly because of the uncertainty associated with the renewables. Aggregation of renewable energy suppliers is shown to be very effective in decreasing this uncertainty. In the present paper, we propose a cost sharing mechanism that entices the suppliers of wind, solar and other renewable resources to form or join an aggregate. In particular, we consider the effect of a bonus for surplus in supply, which is neglected in previous work. We introduce a specific proportional cost sharing mechanism, which satisfies the desired properties of such mechanisms that are introduced in the literature, e.g., budget balancedness, ex-post individual rationality and fairness. In addition, we show that the proposed mechanism results in a stable market outcome. Finally, the results of the paper are illustrated by numerical examples.
Binary Classifier Calibration Using an Ensemble of Near Isotonic Regression Models

Learning accurate probabilistic models from data is crucial in many practical tasks in data mining. In this paper we present a new non-parametric calibration method called Ensemble of Near Isotonic Regression (ENIR). The method can be considered as an extension of BBQ, a recently proposed calibration method, as well as the commonly used calibration method based on isotonic regression (IsoRegC). ENIR is designed to address the key limitation of IsoRegC which is the monotonicity assumption of the predictions. Similar to BBQ, the method post-processes the output of a binary classifier to obtain calibrated probabilities. Thus it can be used with many existing classification models to generate accurate probabilistic predictions. We demonstrate the performance of ENIR on synthetic and real datasets for commonly applied binary classification models. Experimental results show that the method outperforms several common binary classifier calibration methods. In particular on the real data, ENIR commonly performs statistically significantly better than the other methods, and never worse. It is able to improve the calibration power of classifiers, while retaining their discrimination power. The method is also computationally tractable for large scale datasets, as it is O(N log N) time, where N is the number of samples.
Variation on a theme of Nathan Fine. New weighted partition identities

Abstract We utilize false theta function results of Nathan Fine to discover four new partition identities involving weights. These relations connect Gollnitz–Gordon type partitions and partitions with distinct odd parts, partitions into distinct parts and ordinary partitions, and partitions with distinct odd parts where the smallest positive integer that is not a part of the partition is odd and ordinary partitions subject to some initial conditions, respectively. Some of our weights involve new partition statistics, one is defined as the number of different odd parts of a partition larger than or equal to a given value and another one is defined as the number of different even parts larger than the first integer that is not a part of the partition.
Fusible HSTs and the Randomized k-Server Conjecture

We exhibit a poly(log k)-competitive randomized algorithm for the k-server problem on any metric space. The best previous result independent of the geometry of the underlying metric space is the 2k–1 competitive ratio established for the deterministic work function algorithm by Koutsoupias and Papadimitriou (1995). Even for the special case when the underlying metric space is the real line, the best known competitive ratio was k. Since deterministic algorithms can do no better than k on any metric space with at least k+1 points, this establishes that for every metric space on which the problem is non-trivial, randomized algorithms give an exponential improvement over deterministic algorithms. Our algorithm maintains an approximation of the underlying metric space by a distribution over HSTs. The granularity and accuracy of the approximation is adjusted dynamically according to the aggregate behavior of the HST algorithms. In short: We try to obtain more accurate approximations at the locations and scales where the gactionh is happening. Thus a crucial component of our approach is the O((log k)^2)-competitive randomized algorithm for HSTs obtained in our previous work with Bubeck, Cohen, Lee, and Ma.dry, and its "multiscale information theory" perspective.
Wandering Montel theorems for Hilbert space valued holomorphic functions

We prove a Montel theorem for Hilbert space valued functions, and a non-commutative version of this theorem, by composing with unitaries to achieve convergence.
Excess-Risk of Distributed Stochastic Learners

This paper studies the learning ability of consensus and diffusion distributed learners from continuous streams of data arising from different but related statistical distributions. Four distinctive features for diffusion learners are revealed in relation to other decentralized schemes even under left-stochastic combination policies. First, closed-form expressions for the evolution of their excess-risk are derived for strongly convex risk functions under a diminishing step-size rule. Second, using these results, it is shown that the diffusion strategy improves the asymptotic convergence rate of the excess-risk relative to non-cooperative schemes. Third, it is shown that when the in-network cooperation rules are designed optimally, the performance of the diffusion implementation can outperform that of naive centralized processing. Finally, the arguments further show that diffusion outperforms consensus strategies asymptotically and that the asymptotic excess-risk expression is invariant to the particular network topology. The framework adopted in this paper studies convergence in the stronger mean-square-error sense, rather than in distribution, and develops tools that enable a close examination of the differences between distributed strategies in terms of asymptotic behavior, as well as in terms of convergence rates.
Relaying in diffusion-based molecular communication

This paper is eligible for the student paper award. Molecular communication between biological entities is a new paradigm in communications. Recently, we studied molecular communication between two nodes formed from synthetic bacteria. Due to high randomness in behavior of bacteria, we used a population of them in each node. The reliability of such communication systems depends on both the maximum concentration of molecules that a transmitter node is able to produce at the receiver node as well as the number of bacteria in each nodes. This maximum concentration of molecules falls with distance which makes the communication to the far nodes nearly impossible. In order to alleviate this problem, in this paper, we propose to use a molecular relaying node. The relay node can resend the message either by the different or the same type of molecules as the original signal from the transmitter. We study two scenarios of relaying. In the first scenario, the relay node simply senses the received concentration and forwards it to the receiver. We show that this sense and forward scenario, depending on the type of molecules used for relaying, results in either increasing the range of concentration of molecules at the receiver or increasing the effective number of bacteria in the receiver node. For both cases of sense and forward relaying, we obtain the resulting improvement in channel capacity. We conclude that multi-type molecular relaying outperforms the single-type relaying. In the second scenario, we study the decode and forward relaying for the M-ary signaling scheme. We show that this relaying strategy increases the reliability of M-ary communication significantly.
Statistically consistent divide-and-conquer pipelines for phylogeny estimation using NJMerge

Divide-and-conquer methods, which divide the species set into overlapping subsets, construct a tree on each subset, and then combine the subset trees using a supertree method, provide a key algorithmic framework for boosting the scalability of phylogeny estimation methods to large datasets. Yet the use of supertree methods, which typically attempt to solve NP-hard optimization problems, limits the scalability of such approaches. In this paper, we introduce a divide-and-conquer approach that does not require supertree estimation: we divide the species set into pairwise disjoint subsets, construct a tree on each subset using a base method, and then combine the subset trees using a distance matrix. For this merger step, we present a new method, called NJMerge, which is a polynomial-time extension of Neighbor Joining (NJ); thus, NJMerge can be viewed either as a method for improving traditional NJ or as a method for scaling the base method to larger datasets. We prove that NJMerge can be used to create divide-and-conquer pipelines that are statistically consistent under some models of evolution. We also report the results of an extensive simulation study evaluating NJMerge on multi-locus datasets with up to 1000 species. We found that NJMerge sometimes improved the accuracy of traditional NJ and substantially reduced the running time of three popular species tree methods (ASTRAL-III, SVDquartets, and “concatenation” using RAxML) without sacrificing accuracy. Finally, although NJMerge can fail to return a tree, in our experiments, NJMerge failed on only 11 out of 2560 test cases. Theoretical and empirical results suggest that NJMerge is a valuable technique for large-scale phylogeny estimation, especially when computational resources are limited. NJMerge is freely available on Github ( http://github.com/ekmolloy/njmerge ).
Learning mixtures of submodular shells with application to document summarization

We introduce a method to learn a mixture of submodular "shells" in a large-margin setting. A submodular shell is an abstract sub-modular function that can be instantiated with a ground set and a set of parameters to produce a submodular function. A mixture of such shells can then also be so instantiated to produce a more complex submodular function. What our algorithm learns are the mixture weights over such shells. We provide a risk bound guarantee when learning in a large-margin structured-prediction setting using a projected subgradient method when only approximate submodular optimization is possible (such as with submodular function maximization). We apply this method to the problem of multi-document summarization and produce the best results reported so far on the widely used NIST DUC-05 through DUC-07 document summarization corpora.
Embedded formative assessment in the undergraduate engineering classroom

This paper first provides an overview of the pedagogical role of formative assessment in the undergraduate engineering classroom. In the last decade, technology-facilitated implementation of the collection and analysis of student responses has reduced the clerical burden on educators, making the practice more widespread. We discuss some of the reasons why this practice may not have yet reached its full potential in undergraduate engineering classrooms, as well as some available solutions.
Supercoiled tangles and stick numbers of 2-Bridge links

Utilizing both twisting and writhing, we construct integral tangles with few sticks, leading to an efficient method for constructing polygonal 2-bridge links. Let L be a two-bridge link with crossing number c, stick number s, and n tangles. It is shown that $s \le \frac{2}{3}c + 2n + 3$. We also show that if c > 12n + 3, then minimal stick representatives do not admit minimal crossing projections.
CWL-Airflow: a lightweight pipeline manager supporting Common Workflow Language.

BACKGROUND Massive growth in the amount of research data and computational analysis has led to increased use of pipeline managers in biomedical computational research. However, each of the >100 such managers uses its own way to describe pipelines, leading to difficulty porting workflows to different environments and therefore poor reproducibility of computational studies. For this reason, the Common Workflow Language (CWL) was recently introduced as a specification for platform-independent workflow description, and work began to transition existing pipelines and workflow managers to CWL. FINDINGS Herein, we present CWL-Airflow, a package that adds support for CWL to the Apache Airflow pipeline manager. CWL-Airflow uses CWL version 1.0 specification and can run workflows on stand-alone MacOS/Linux servers, on clusters, or on a variety of cloud platforms. A sample CWL pipeline for processing of chromatin immunoprecipitation sequencing data is provided. CONCLUSIONS CWL-Airflow will provide users with the features of a fully fledged pipeline manager and the ability to execute CWL workflows anywhere Airflow can run-from a laptop to a cluster or cloud environment. CWL-Airflow is available under Apache License, version 2.0 (Apache-2.0), and can be downloaded from https://barski-lab.github.io/cwl-airflow, https://scicrunch.org/resolver/RRID:SCR_017196.
Mind Switches in Futurama and Stargate

SummaryWe generalize two mind-switching problems that arise in connection with the popular sci-fi television series Futurama and Stargate SG-1. Optimal solutions to these problems are found by answering the following question about a permutation σ expressed as a product of nontrivial disjoint cycles. “When writing σ as a product of distinct transpositions, none occurring as factors in the disjoint cycle representation of σ, what is the smallest number of transpositions that can be used?”
Small-dimensional projective representations of symmetric and alternating groups

We classify the irreducible projective representations of symmetric and alternating groups of minimal possible and second minimal possible dimensions, and get a lower bound for the third minimal dimension. On the way we obtain some new results on branching which might be of independent interest.
Dominant K-theory and integrable highest weight representations of Kac–Moody groups

We give a topological interpretation of the highest weight representations of Kac–Moody groups. Given the unitary form G of a Kac–Moody group (over C), we define a version of equivariant K-theory, KG on the category of proper G-CW complexes. We then study Kac–Moody groups of compact type in detail (see Section 2 for definitions). In particular, we show that the Grothendieck group of integrable highest weight representations of a Kac–Moody group G of compact type, maps isomorphically onto K˜G∗(EG), where EG is the classifying space of proper G-actions. For the affine case, this agrees very well with recent results of Freed–Hopkins–Teleman. We also explicitly compute KG∗(EG) for Kac–Moody groups of extended compact type, which includes the Kac–Moody group E10.
Interference Alignment via Improved Subspace Conditioning

For the K user, single input single output (SISO), frequency selective interference channel, a new low complexity transmit beamforming design that improves the achievable sum rate is presented. Jointly employing the interference alignment (IA) scheme presented by Cadambe and Jafar in [1] and linear minimum mean square error (MMSE) decoding at the transmitters and receivers, respectively, the new IA precoding design improves the average sum rate while preserving the achievable degrees of freedom of the Cadambe and Jafar scheme, K/2.
Evaluating Predictive Models of Student Success: Closing the Methodological Gap.

Model evaluation – the process of making inferences about the performance of predictive models – is a critical component of predictive model-ing research in learning analytics. In this work, we present an overview of the state-of-the-practice of model evaluation in learning analytics, which overwhelmingly uses only na ive methods for model evaluation or, less commonly, statistical tests which are not appropriate for predictive model evaluation. We then provide an overview of more appropriate methods for model evaluation, presenting both frequentist and a preferred Bayesian method. Finally, we apply three methods – the na ive average commonly used in learning analytics, frequentist null hypothesis significance test(NHST), and hierarchical Bayesian model evaluation – to a large set ofMOOC data. We compare 96 different predictive modeling techniques,including different feature sets, statistical modeling algorithms, and tuning hyperparameters for each, using this case study to demonstrate the different experimental conclusions these evaluation techniques provide.
Fundamental limits of covert communication over MIMO AWGN channel

Fundamental limits of covert communication have been studied for different models of scalar channels. It was shown that, over n independent channel uses, O(√n) bits can transmitted reliably over a public channel while achieving an arbitrarily low probability of detection (LPD) by other stations. This result is well known as the square-root law and even to achieve this diminishing rate of covert communication, all existing studies utilized some form of secret shared between the transmitter and the receiver. In this paper, we establish the limits of LPD communication over the MIMO AWGN channel. In particular, using relative entropy as our LPD metric, we study the maximum codebook size for which the transmitter can guarantee reliability and LPD conditions are met. We first show that, the optimal codebook generating input distribution under δ-PD constraint is the zero-mean Gaussian distribution. Then, assuming channel state information (CSI) on only the main channel at the transmitter, we derive the optimal input covariance matrix, hence, establishing scaling laws of the codebbok size. We evaluate the codebook scaling rates in the limiting regimes for the number of channel uses (asymptotic block length) and the number of antennas (massive MIMO). We show that, in the asymptotic block-length regime, square-root law still holds for the MIMO AWGN. Meanwhile, in massive MIMO limit, the codebook size, while it scales linearly with √n, it scales exponentially with the number of transmitting antennas. The practical implication of our result is that MIMO has the potential to provide a substantial increase in the file sizes that can be covertly communicated subject to a reasonably low delay.
Intercell Interference-Aware Scheduling for Delay Sensitive Applications in C-RAN

Cloud radio access network (C-RAN) architecture is a new mobile network architecture that enables cooperative baseband processing and information sharing among multiple cells and achieves high adaptability to nonuniform traffic by centralizing the baseband processing resources in a virtualized baseband unit (BBU) pool. In this work, we formulate the utility of each user using a convex delay cost function, and design a two-step scheduling algorithm with good delay performance for the C-RAN architecture. In the first step, all users in multiple cells are grouped into small user groups, according to their interference levels and estimated utilities. In the second step, channels are matched to the user groups to maximize the system utility. The performance of our algorithm is further studied via simulations, and the advantages of C-RAN architecture is verified.