cssi/SciSciGPT-SciSciNet huggingface dataset:
Stimulus-responsive self-assembly of protein-based fractals by computational design.

Fractal topologies, which are statistically self-similar over multiple length scales, are pervasive in nature. The recurrence of patterns in fractal-shaped branched objects, such as trees, lungs and sponges, results in a high surface area to volume ratio, which provides key functional advantages including molecular trapping and exchange. Mimicking these topologies in designed protein-based assemblies could provide access to functional biomaterials. Here we describe a computational design approach for the reversible self-assembly of proteins into tunable supramolecular fractal-like topologies in response to phosphorylation. Guided by atomic-resolution models, we develop fusions of Src homology 2 (SH2) domain or a phosphorylatable SH2-binding peptide, respectively, to two symmetric, homo-oligomeric proteins. Mixing the two designed components resulted in a variety of dendritic, hyperbranched and sponge-like topologies that are phosphorylation-dependent and self-similar over three decades (~10 nm–10 μm) of length scale, in agreement with models from multiscale computational simulations. Designed assemblies perform efficient phosphorylation-dependent capture and release of cargo proteins. Fractal topologies are ubiquitous, and synthetic fractal objects provide unique functional advantages by virtue of their high surface area:volume ratios. Now, a computationally guided bottom-up design approach for constructing protein-based fractal assemblies in response to phosphorylation has been developed. Designed assemblies are shown to perform reversible and efficient molecular capture.
A Simple Representation of Three-Dimensional Molecular Structure.

Statistical and machine learning approaches predict drug-to-target relationships from 2D small-molecule topology patterns. One might expect 3D information to improve these calculations. Here we apply the logic of the extended connectivity fingerprint (ECFP) to develop a rapid, alignment-invariant 3D representation of molecular conformers, the extended three-dimensional fingerprint (E3FP). By integrating E3FP with the similarity ensemble approach (SEA), we achieve higher precision-recall performance relative to SEA with ECFP on ChEMBL20 and equivalent receiver operating characteristic performance. We identify classes of molecules for which E3FP is a better predictor of similarity in bioactivity than is ECFP. Finally, we report novel drug-to-target binding predictions inaccessible by 2D fingerprints and confirm three of them experimentally with ligand efficiencies from 0.442–0.637 kcal/mol/heavy atom.
Chosen-Ciphertext Secure Proxy Re-Encryption.

In a proxy re-encryption (PRE) scheme, a proxy is given special information that allows it to translate a ciphertext under one key into a ciphertext of the same message under a dierent key. The proxy cannot, however, learn anything about the messages encrypted under either key. PRE schemes have many practical applications, including distributed storage, email, and DRM. Previously proposed re-encryption schemes achieved only semantic security; in contrast, applications often require security against chosen ciphertext attacks. We propose a denition of security against chosen ciphertext attacks for PRE schemes, and present a scheme that satises the denition. Our construction is ecient and based only on the Decisional Bilinear DieHellman assumption in the standard model. We also formally capture CCA security for PRE schemes via both a game-based denition and simulation-based denitions that guarantee universally composable security. We note that, simultaneously with our work, Green and Ateniese proposed a CCA-secure PRE, discussed herein.
AdaFDR: A Fast, Powerful and Covariate-Adaptive Approach to Multiple Hypothesis Testing.

Multiple hypothesis testing is an essential component of modern data science. Its goal is to maximize the number of discoveries while controlling the fraction of false discoveries. In many settings, in addition to the p-value, additional information/covariates for each hypothesis are available. For example, in eQTL studies, each hypothesis tests the correlation between a variant and the expression of a gene. We also have additional covariates such as the location, conservation and chromatin status of the variant, which could inform how likely the association is to be due to noise. However, popular multiple hypothesis testing approaches, such as Benjamini-Hochberg procedure (BH) and independent hypothesis weighting (IHW), either ignore these covariates or assume the covariate to be univariate. We introduce AdaFDR, a fast and flexible method that adaptively learns the optimal p-value threshold from covariates to significantly improve detection power. On eQTL analysis of the GTEx data, AdaFDR discovers 32% and 27% more associations than BH and IHW, respectively, at the same false discovery rate. We prove that AdaFDR controls false discovery proportion, and show that it makes substantially more discoveries while controlling FDR in extensive experiments. AdaFDR is computationally efficient and can process more than 100 million hypotheses within an hour and allows multi-dimensional covariates with both numeric and categorical values. It also provides exploratory plots for the user to interpret how each covariate affects the significance of hypotheses, making it broadly useful across many applications.
Highly Chemoselective and Stereoselective Synthesis of Z-Enol Silanes

Three-component nickel-catalyzed couplings of enals, alkynes, and silanes have been developed as a new entry to enol silanes. The enol silane and a trisubstituted alkene are both formed with >98:2 stereoselectivity, and the reaction tolerates a broad range of functionality including aldehydes, ketones, esters, free hydroxyls, and basic secondary amines. A mechanistic pathway involving the formation of a metallacycle that possesses an eta1 nickel O-enolate motif explains the high level of stereoselection.
Neutrinos: Key to New Physics

The Seesaw mechanism predicted tiny neutrino masses by postulating a new large scale in particle physics, using new theoretical ideas prompted by the Standard Model. It adds credence to a theoretical vista that is a quarter century old, and fits with the most endearing speculations of ultimate unification. By relating the measurement of static neutrino properties to near-Planck physics, it may even prove key to solving the riddles of flavor.
The first buckybowl aryne. Corannulyne: a nonplanar benzyne.

Treatment of bromocorannulene with NaNH2/tBuOK in THF in the presence of furans or amines leads to formation of addition products in good yields, presumably through formation of 1,2-didehydrocorannulene (corannulyne). HDFT calculations predict that corannulyne exists as a bowl-shaped entity and that the didehydrogenation energy of corannulene is slightly lower than that of benzene.
Highly general synthesis of [E]- and [Z]-3-alkylsubstituted allylboronates via one-carbon homologation of stereospecific 1-alken-1-ylboronates

Abstract Stereospecific 1-alken-1-ylboronates readily react with in situ generated chloromethyllithium, producing the corresponding allylboronate in good yields and in excellent stereochemical purities. These are very important synthetic intermediates and conversion of a representative derivative, [ E ]-2-(2-heptenyl)-1,3,2-dioxaborinane, 2 (R 1 = C 4 H 9 , R 2 = R 3 = H), into several compounds of synthetic interest is described.
Palladium-catalyzed intramolecular oxidative alkylation of unactivated olefins.

Reaction of 5,5-dimethyl-8-nonene-2,4-dione catalyzed by PdCl2(CH3CN)2 (5 mol %) in the presence of CuCl2 (2.5 equiv) at room temperature for 3 h formed 2-acetyl-3,6,6-trimethyl-2-cyclohexenone in ...
A mild and efficient one-step synthesis of quinolines.

[reaction: see text] The Friedlander synthesis of quinolines is an extensively employed protocol, yielding the desired heterocycle in a two-step reduction-condensation sequence. We have developed a mild, efficient, high-yielding single-step variant of this methodology, which employs SnCl(2) and ZnCl(2) to effect the reaction.
Highly Efficient and Practical Syntheses of Lavendamycin Methyl Ester and Related Novel Quinolindiones.

The novel 7-(N-formyl-, 7-(N-acetyl-, and 7-(N-isobutyrylamino)-2-methylquinoline-5,8-diones were synthesized in excellent overall yields in three steps via the nitration of the commercially available 8-hydroxy-2-methylquinoline followed by a reduction−acylation step and then oxidation. Acid hydrolysis of 7-(N-acetylamino)-2-methylquinoline-5,8-dione (14a) afforded the novel 7-aminoquinoline-5,8-dione 7 in excellent yields. Due to our efficient preparation of dione 14a, we now report a short and practical method for the total synthesis of the potent antitumor agent lavendamycin methyl ester (1b) with an excellent overall yield.
Nickel-catalyzed organozinc-promoted carbocyclizations of electron-deficient alkenes with tethered unsaturation

A nickel-catalyzed method for cyclizations of electron-deficient alkenes with tethered unsaturation in the presence of organozincs was developed. Considerable flexibility in the structure of each reactive component was observed. Enones, alkylidene malonates, unsaturated β-ketoesters, and nitroalkenes participated as the electron-deficient alkene; alkynes, enones, 1,3-dienes, and aldehydes participated as the tethered unsaturation; and a variety of sp2 and sp3-hybridized organozincs, including those that possess β-hydrogens, participated as the nucleophilic component. Substrate structure, organozinc structure, and ligand structure all played a significant role in determining product selectivities. Of particular synthetic significance was the opportunity to prepare either E or Z tri- or tetrasubstituted alkenes from a common alkyne. A discussion of probable mechanisms is provided.
Chemical synthesis of cross-linked purine nucleosides.

An efficient route to all of the possible cross-linked 2‘-deoxypurines 1−3 has been developed by means of the Pd-mediated C−N bond formation in the key step. Utilizing this protocol, the synthesis of the first unnatural protected purine trimeric adduct 4 has been accomplished.
The Drosophila Afadin and ZO-1 homologues Canoe and Polychaetoid act in parallel to maintain epithelial integrity when challenged by adherens junction remodeling

During morphogenesis, cells must change shape and move without disrupting tissue integrity. This requires cell-cell junctions to allow dynamic remodeling while resisting forces generated by the actomyosin cytoskeleton. Multiple proteins play roles in junctional-cytoskeletal linkage, but the mechanisms by which they act remain unclear. Drosophila Canoe maintains adherens junction-cytoskeletal linkage during gastrulation. Canoe's mammalian homologue Afadin plays similar roles in cultured cells, working in parallel with ZO-1 proteins, particularly at multicellular junctions. We take these insights back to the fly embryo, exploring how cells maintain epithelial integrity when challenged by adherens junction remodeling during germband extension and dorsal closure. We found that Canoe helps cells maintain junctional-cytoskeletal linkage when challenged by the junctional remodeling inherent in mitosis, cell intercalation, and neuroblast invagination or by forces generated by the actomyosin cable at the leading edge. However, even in the absence of Canoe, many cells retain epithelial integrity. This is explained by a parallel role played by the ZO-1 homologue Polychaetoid. In embryos lacking both Canoe and Polychaetoid, cell junctions fail early, with multicellular junctions especially sensitive, leading to widespread loss of epithelial integrity. Our data suggest that Canoe and Polychaetoid stabilize Bazooka/Par3 at cell-cell junctions, helping maintain balanced apical contractility and tissue integrity.
A Cryptographic Treatment of the Wiretap Channel

The wiretap channel is a setting where one aims to provide information-theoretic privacy of communicated data based solely on the assumption that the channel from sender to adversary is “noisier” than the channel from sender to receiver. It has been the subject of decades of work in the information and coding (I&C) community. This paper bridges the gap between this body of work and modern cryptography with contributions along two fronts, namely metrics (definitions) of security, and schemes. We explain that the metric currently in use is weak and insufficient to guarantee security of applications and propose two replacements. One, that we call mis-security, is a mutual-information based metric in the I&C style. The other, semantic security, adapts to this setting a cryptographic metric that, in the cryptography community, has been vetted by decades of evaluation and endorsed as the target for standards and implementations. We show that they are equivalent (any scheme secure under one is secure under the other), thereby connecting two fundamentally different ways of defining security and providing a strong, unified and well-founded target for designs. Moving on to schemes, results from the wiretap community are mostly nonconstructive, proving the existence of schemes without necessarily yielding ones that are explicit, let alone efficient, and only meeting their weak notion of security. We apply cryptographic methods based on extractors to produce explicit, polynomial-time and even practical encryption schemes that meet our new and stronger security target. 1 Department of Computer Science & Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. Email: mihir@cs.ucsd.edu. URL: http://www.cs.ucsd.edu/users/mihir. Supported in part by NSF grants CNS-0904380, CNS-1116800 and CCF-0915675 and DARPA contract HR011-09-C-0129. 2 Department of Computer Science & Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. Email: stessaro@cs.ucsd.edu. URL: http://www.cs.ucsd.edu/users/stessaro. Supported in part by Calit2 and NSF grant CNS-0716790. 3 Department of Electrical and Computer Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA. Email: avardy@ucsd.edu. URL: http://www.ece.ucsd.edu/∼avardy/. Supported in part by DARPA contract HR011-09-C-0129.
A convenient method for the syntheses of tetrahydrofuran moiety from furan by catalytic transfer of hydrogenation with ammonium formate

Abstract Pd–C/ammonium formate efficiently and selectively reduces hetero-aromatic furan ring to the corresponding tetrahydrofuran moiety. Under this reaction condition, carbon–carbon double bond and α,β-unsaturated ketones also reduced to the corresponding alkanes and saturated ketones.
Oxidative addition of heteroaromatic halides to Negishi reagent and subsequent cross-coupling reactions

Abstract Heteroarylzirconocene halides were prepared via the oxidative addition of heteroaryl halides to the Negishi reagent ‘Cp 2 ZrBu 2 ’. The palladium-catalyzed cross-coupling of the in situ generated organozirconium reagents with functionalized aryl and heteroaryl halides proceeded smoothly in the presence of CuCl to produce the cross-coupling products in high yields.
A new reaction involving Cp(CO)2Fe−K+ and carboxonium salts for synthesis of carbene precursors

Abstract The synthesis of electrophilic iron carbene precursors from Fp anion and carboxonium salts of acetals is described.
Polycyclic fused phenanthridines: An alternative approach from benzotriazoles

We describe an alternative approach to polycyclic phenanthridines (7) and some of their analogues using conditions much milder than those previously reported. The procedure includes the generation of a benzotriazole stabilized carbanion, oxidation of the resulting anion to a radical, and elimination of nitrogen followed by ring closure to produce phenanthridines.
Inducible Gene Switches with Memory in Human T Cells for Cellular Immunotherapy

Cell-based therapies that employ engineered T cells-including those modified to express chimeric antigen receptors (CARs)-to target cancer cells have demonstrated promising responses in clinical trials. However, engineered T cell responses must be regulated to prevent severe side effects such as cytokine storms and off-target responses. Here we present a class of recombinase-based gene circuits that will enable inducible, one-time state switching in adoptive T cell therapy using an FDA-approved drug, creating a generalizable platform that can be used to control when and how strongly a gene is expressed. These circuits exhibit memory such that induced T cells will maintain any changes made even when the drug inducer is removed. This memory feature avoids prolonged drug inducer exposure, thus reducing the complexity and potential side effect associated with the drug inducer. We have utilized these circuits to control the expression of an anti-Her2-CAR, demonstrating the ability of these circuits to regulate CAR expression and T cell activity. We envision this platform can be extended to regulate other genes involved in T cell behavior for applications in various adoptive T cell therapies.
Depth-based weighted jackknife empirical likelihood for non-smooth U-structure equations

In many applications, parameters of interest are estimated by solving some non-smooth estimating equations with U-statistic structure. Jackknife empirical likelihood (JEL) approach can solve this problem efficiently by reducing the computation complexity of the empirical likelihood (EL) method. However, as EL, JEL suffers the sensitivity problem to outliers. In this paper, we propose a weighted jackknife empirical likelihood (WJEL) to tackle the above limitation of JEL. The proposed WJEL tilts the JEL function by assigning smaller weights to outliers. The asymptotic of the WJEL ratio statistic is derived. It converges in distribution to a multiple of a chi-square random variable. The multiplying constant depends on the weighting scheme. The self-normalized version of WJEL ratio does not require to know the constant and hence yields the standard chi-square distribution in the limit. Robustness of the proposed method is illustrated by simulation studies and one real data application.
Detecting strong signals in gene perturbation experiments: An adaptive approach with power guarantee and FDR control.

The perturbation of a transcription factor should affect the expression levels of its direct targets. However, not all genes showing changes in expression are direct targets. To increase the chance...
Invariant derivations and differential forms for reflection groups

Classical invariant theory of a complex reflection group $W$ highlights three beautiful structures: -- the $W$-invariant polynomials constitute a polynomial algebra, over which -- the $W$-invariant differential forms with polynomial coefficients constitute an exterior algebra, and -- the relative invariants of any $W$-representation constitute a free module. When $W$ is a duality (or well-generated) group, we give an explicit description of the isotypic component within the differential forms of the irreducible reflection representation. This resolves a conjecture of Armstrong, Rhoades and the first author, and relates to Lie-theoretic conjectures and results of Bazlov, Broer, Joseph, Reeder, and Stembridge, and also Deconcini, Papi, and Procesi. We establish this result by examining the space of $W$-invariant differential derivations; these are derivations whose coefficients are not just polynomials, but differential forms with polynomial coefficients. For every complex reflection group $W$, we show that the space of invariant differential derivations is finitely generated as a module over the invariant differential forms by the basic derivations together with their exterior derivatives. When $W$ is a duality group, we show that the space of invariant differential derivations is free as a module over the exterior subalgebra of $W$-invariant forms generated by all but the top-degree exterior generator. (The basic invariant of highest degree is omitted.) Our arguments for duality groups are case-free, i.e., they do not rely on any reflection group classification.
A formula for the core of certain strongly stable ideals

Abstract The core of an ideal is the intersection of all of its reductions. The core is a natural object to study from the point of view of blow-up algebras and integral closure; and it also has geometric significance coming, for example, from its connection to adjoint and multiplier ideals. In general, the core is difficult to describe explicitly. In this paper, we investigate a particular family of strongly stable ideals. We prove that ideals in this family satisfy an Artin–Nagata property, yet fail to satisfy other, stronger standard depth conditions. We then show that there is a surprisingly simple explicit formula for the core of these ideals.
Random matrices have simple spectrum

Let Mn =(źij)1≤i,j≤n be a real symmetric random matrix in which the upper-triangular entries źij, i
Can You Trust This Prediction? Auditing Pointwise Reliability After Learning

To use machine learning in high stakes applications (e.g. medicine), we need tools for building confidence in the system and evaluating whether it is reliable. Methods to improve model reliability often require new learning algorithms (e.g. using Bayesian inference to obtain uncertainty estimates). An alternative is to audit a model after it is trained. In this paper, we describe resampling uncertainty estimation (RUE), an algorithm to audit the pointwise reliability of predictions. Intuitively, RUE estimates the amount that a prediction would change if the model had been fit on different training data. The algorithm uses the gradient and Hessian of the model’s loss function to create an ensemble of predictions. Experimentally, we show that RUE more effectively detects inaccurate predictions than existing tools for auditing reliability subsequent to training. We also show that RUE can create predictive distributions that are competitive with state-of-the-art methods like Monte Carlo dropout, probabilistic backpropagation, and deep ensembles, but does not depend on specific algorithms at train-time like these methods do.
LTE and Wi−Fi Coexistence in Unlicensed Spectrum with Application to Smart Grid: A Review

Long Term Evolution (LTE) is expanding its utilization in unlicensed band by deploying LTE Unlicensed (LTE-U) and Licensed Assisted Access LTE (LTE-LAA) technology. Smart Grid can take the advantages of unlicensed bands for achieving two-way communication between smart meters and utility data centers by using LTE-UILTE-LAA. However, both schemes must co-exist with the incumbent Wi-Fi system. In this paper, several co-existence schemes of Wi-Fi and LTE technology is comprehensively reviewed. The challenges of deploying LTE and Wi-Fi in the same band are clearly addressed based on the papers reviewed. Solution procedures and techniques to resolve the challenging issues are discussed in a short manner. The performance of various network architectures such as listen-before-talk (LBT) based LTE, carrier sense multiple access with collision avoidance (CSMA/CA) based Wi-Fi is briefly compared. Finally, an attempt is made to implement these proposed LTE-Wi-Fi models in smart grid technology.
Imprecise meanings as a cause of uncertainty in medical knowledge-based systems

There has been a considerable amount of work on uncertainty in knowledge-based systems. This work has generally been concerned with uncertainty arising from the strength of inferences and the weighting of evidence. In this paper, we discuss another type of uncertainty: that which is due to imprecision in the underlying primitives used to represent the knowledge of the system. In particular, a given word may denote many similar but not identical entities. Such words are said to be lexically imprecise. Lexical imprecision has caused widespread problems in many areas. Unless this phenomenon is recognized and appropriately handled, it can degrade the performance of knowledge-based systems. In particular, it can lead to difficulties with the user interface, and with the inferencing processes of these systems. Some techniques are suggested for coping with this phenomenon.
Optimal Crowdsourced Classification with a Reject Option in the Presence of Spammers

We explore the design of an effective crowdsourcing system for an $M$ -ary classification task. Crowd workers complete simple binary microtasks whose results are aggregated to give the final decision. We consider the scenario where the workers have a reject option so that they are allowed to skip microtasks when they are unable to or choose not to respond to binary microtasks. We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize crowd's classification performance.
Remote Control of Chemistry in Optical Cavities

Summary Manipulation of chemical reactivity often involves changing reagents or environmental conditions. Alternatively, strong coupling between light and matter offers ways to tunably hybridize their physicochemical properties and thereby change reaction dynamics without synthetic modifications to starting materials. Here, we theoretically design a polaritonic (hybrid photonic-molecular) device that supports ultrafast tuning of reaction yields even when the catalyst and reactant are spatially separated across several optical wavelengths. We demonstrate how photoexcitation of the “remote catalyst” in an optical microcavity can control the photochemistry of the reactant in another microcavity. Harnessing the delocalization that arises from strong cavity-molecule coupling of the spatially separated compounds, this intriguing phenomenon is shown for the infrared-induced cis → trans conformational isomerization of nitrous acid. Indeed, increasing the excited-state population of the remote catalyst can enhance isomerization efficiency by an order of magnitude. The theoretical proposal herein is generalizable to other reactions and thus introduces a versatile tool to control photochemistry.
Evaluating Reactor Antineutrino Signals for WATCHMAN

Increasing the distance from which an antineutrino detector is capable of monitoring the operation of a registered reactor, or discovering a clandestine reactor, strengthens the Non-Proliferation of Nuclear Weapons Treaty. This paper presents calculations of reactor antineutrino interactions from quasi-elastic neutrino-proton scattering and elastic neutrino-electron scattering in a water-based detector operated $\gtrsim10$ km from a commercial power reactor. It separately calculates signal from the proximal reactor and background from all other registered reactors. The main results are differential and integral interaction rates from the quasi-elastic and elastic processes. There are two underground facilities capable of hosting a detector ($\sim1$ kT H$_2$O) project nearby ($L\sim20$ km) an operating commercial reactor ($P_{th}\sim3$ GW). These reactor-site combinations, which are under consideration for project WATCHMAN, are Perry-Morton on the southern shore of Lake Erie in the United States and Hartlepool-Boulby on the western shore of the North Sea in England. The signal rate from the proximal reactor is about five times greater at the Morton site than at the Boulby site due to shorter reactor-site separation distance, larger reactor thermal power, and greater neutrino oscillation survival probability. Although the background rate from all other reactors is larger at Morton than at Boulby, it is a smaller fraction of the signal rate from the proximal reactor at Morton than at Boulby. Moreover, the Hartlepool power plant has two cores whereas the Perry plant has a single core. The Boulby site, therefore, offers an opportunity for remotely monitoring the on/off cycle of a reactor core under more stringent conditions than does the Morton site.
Analysis of Non-Pilot Interference on Link Adaptation and Latency in Cellular Networks

Modern wireless standards such as Long-Term Evolution (LTE) and 5G New Radio (5G NR) use pilot-aided SINR estimates to adapt the modulation and coding scheme (MCS) and transmission mode of data blocks, to fully utilize the channel capacity. However, when interference is localized exclusively on non-pilot resources, pilot-aided SINR estimates become inaccurate. We show that this leads to congestion due to retransmissions, and in the worst case, outage due to very high block error rate (BLER). We demonstrate this behavior through numerical as well as experimental results with the 4G LTE downlink, which show high BLER and significant throughput detriment in the presence of non-pilot interference (NPI). To provide useful insights on the impact of NPI on low-latency communications, we derive an approximate relation between the retransmission- induced latency and BLER. Our results show that NPI can severely compromise low-latency applications in vehicle-to-vehicle (V2V) communications and 5G NR. We identify robust link adaptation schemes as the key to reliable communications.
Rigidity of quasi-Einstein metrics ☆

Abstract We call a metric quasi-Einstein if the m -Bakry–Emery Ricci tensor is a constant multiple of the metric tensor. This is a generalization of Einstein metrics, it contains gradient Ricci solitons and is also closely related to the construction of the warped product Einstein metrics. We study properties of quasi-Einstein metrics and prove several rigidity results. We also give a splitting theorem for some Kahler quasi-Einstein metrics.
The dehn surgery characterization of the trefoil and the figure eight knot

We give a Dehn surgery characterization of the trefoil and the figure eight knots. These results are gotten by combining surgery formulas in Heegaard Floer homology from an earlier paper with the characterization of these knots in terms of their knot Floer homology given in a recent paper of Ghiggini.
New lower bounds for hypergraph Ramsey numbers

The Ramsey number $r_k(s,n)$ is the minimum $N$ such that for every red-blue coloring of the $k$-tuples of $\{1,\ldots, N\}$, there are $s$ integers such that every $k$-tuple among them is red, or $n$ integers such that every $k$-tuple among them is blue. We prove the following new lower bounds for 4-uniform hypergraph Ramsey numbers: $$r_4(5,n) > 2^{n^{c\log n}} \qquad \hbox{ and } \qquad r_4(6,n) > 2^{2^{cn^{1/5}}},$$ where $c$ is an absolute positive constant. This substantially improves the previous best bounds of $2^{n^{c\log\log n}}$ and $2^{n^{c\log n}}$, respectively. Using previously known upper bounds, our result implies that the growth rate of $r_4(6,n)$ is double exponential in a power of $n$. As a consequence, we obtain similar bounds for the $k$-uniform Ramsey numbers $r_k(k+1, n)$ and $r_k(k+2, n)$ where the exponent is replaced by an appropriate tower function. This almost solves the question of determining the tower growth rate for {\emph {all}} classical off-diagonal hypergraph Ramsey numbers, a question first posed by Erd\H os and Hajnal in 1972. The only problem that remains is to prove that $r_4(5,n)$ is double exponential in a power of $n$.
QUANTUM ERGODIC SEQUENCES AND EQUILIBRIUM MEASURES

We generalize the definition of a “quantum ergodic sequence” of sections of ample line bundles $$L \rightarrow M$$ from the case of positively curved Hermitian metrics h on L to general smooth metrics. A choice of smooth Hermitian metric h on L and a Bernstein–Markov measure $$\nu $$ on M induces an inner product on $$H^0(M, L^N)$$ . When $$||s_N||_{L^2} =1$$ , quantum ergodicity is the condition that $$|s_N(z)|^2 d\nu \rightarrow d\mu _{\varphi _{eq}} $$ weakly, where $$d\mu _{\varphi _{eq}} $$ is the equilibrium measure associated with $$(h, \nu )$$ . The main results are that normalized logarithms $$\frac{1}{N} \log |s_N|^2$$ of quantum ergodic sections tend to the equilibrium potential, and that random orthonormal bases of $$H^0(M, L^N)$$ are quantum ergodic.
TossingBot: Learning to Throw Arbitrary Objects With Residual Physics

We investigate whether a robot arm can learn to pick and throw arbitrary rigid objects into selected boxes quickly and accurately. Throwing has the potential to increase the physical reachability and picking speed of a robot arm. However, precisely throwing arbitrary objects in unstructured settings presents many challenges: from acquiring objects in grasps suitable for reliable throwing, to handling varying object-centric properties (e.g., mass distribution, friction, shape) and complex aerodynamics. In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (RGB-D images of arbitrary objects in a bin) through trial and error. Within this formulation, we investigate the synergies between grasping and throwing (i.e., learning grasps that enable more accurate throws) and between simulation and deep learning (i.e., using deep networks to predict residuals on top of control parameters predicted by a physics simulator). The resulting system, TossingBot , is able to grasp and successfully throw arbitrary objects into boxes located outside its maximum reach range at 500+ mean picks per hour (600+ grasps per hour with 85% throwing accuracy); and generalizes to new objects and target locations.
A finite difference method for estimating second order parameter sensitivities of discrete stochastic chemical reaction networks

We present an efficient finite difference method for the approximation of second derivatives, with respect to system parameters, of expectations for a class of discrete stochastic chemical reaction networks. The method uses a coupling of the perturbed processes that yields a much lower variance than existing methods, thereby drastically lowering the computational complexity required to solve a given problem. Further, the method is simple to implement and will also prove useful in any setting in which continuous time Markov chains are used to model dynamics, such as population processes. We expect the new method to be useful in the context of optimization algorithms that require knowledge of the Hessian.
New Recurrence Relations and Matrix Equations for Arithmetic Functions Generated by Lambert Series

We consider relations between the pairs of sequences, $(f, g_f)$, generated by Lambert series expansions, $L_f(q) = \sum_{n \geq 1} f(n) q^n / (1-q^n)$, in $q$. In particular, we prove new forms of recurrence relations and matrix equations defining these sequences for all $n \in \mathbb{Z}^{+}$. The key ingredient to the proof of these results is given by the statement of Euler's pentagonal number theorem expanding the series for the infinite $q$-Pochhammer product, $(q; q)_{\infty}$, and for the first $n$ terms of the partial products, $(q; q)_n$, forming the denominators of the rational $n^{th}$ partial sums of $L_f(q)$. Examples of the new results given in the article include applications to the Euler phi function, $\phi(n)$, the M\"obius function, $\mu(n)$, the sum of divisors functions, $\sigma_1(n)$ and $\sigma_{\alpha}(n)$, for $\alpha \geq 0$, and to Liouville's lambda function, $\lambda(n)$.
Volume and rigidity of hyperbolic polyhedral 3-manifolds

We investigate the rigidity of hyperbolic cone metrics on 3-manifolds which are isometric gluing of ideal and hyper-ideal tetrahedra in hyperbolic spaces. These metrics will be called ideal and hyper-ideal hyperbolic polyhedral metrics. It is shown that a hyper-ideal hyperbolic polyhedral metric is determined up to isometry by its curvature and a decorated ideal hyperbolic polyhedral metric is determined up to isometry and change of decorations by its curvature. The main tool used in the proof is the Fenchel dual of the volume function.
Learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings

We study a symmetric collaborative dialogue setting in which two agents, each with private knowledge, must strategically communicate to achieve a common goal. The open-ended dialogue state in this setting poses new challenges for existing dialogue systems. We collected a dataset of 11K human-human dialogues, which exhibits interesting lexical, semantic, and strategic elements. To model both structured knowledge and unstructured language, we propose a neural model with dynamic knowledge graph embeddings that evolve as the dialogue progresses. Automatic and human evaluations show that our model is both more effective at achieving the goal and more human-like than baseline neural and rule-based models.
Making Root Cause Analysis Feasible for Large Code Bases: A Solution Approach for a Climate Model

Large-scale simulation codes that model complicated science and engineering applications typically have huge and complex code bases. For such simulation codes, where bit-for-bit comparisons are too restrictive, finding the source of statistically significant discrepancies (e.g., from a previous version, alternative hardware or supporting software stack) in output is non-trivial at best. Although there are many tools for program comprehension through debugging or slicing, few (if any) scale to a model as large as the Community Earth System Model (CESM#8482;), which consists of more than 1.5 million lines of Fortran code. Currently for the CESM, we can easily determine whether a discrepancy exists in the output using a by now well-established statistical consistency testing tool. However, this tool provides no information as to the possible cause of the detected discrepancy, leaving developers in a seemingly impossible (and frustrating) situation. Therefore, our aim in this work is to provide the tools to enable developers to trace a problem detected through the CESM output to its source. To this end, our strategy is to reduce the search space for the root cause(s) to a tractable size via a series of techniques that include creating a directed graph of internal CESM variables, extracting a subgraph (using a form of hybrid program slicing), partitioning into communities, and ranking nodes by centrality. Runtime variable sampling then becomes feasible in this reduced search space. We demonstrate the utility of this process on multiple examples of CESM simulation output by illustrating how sampling can be performed as part of an efficient parallel iterative refinement procedure to locate error sources, including sensitivity to CPU instructions. By providing CESM developers with tools to identify and understand the reason for statistically distinct output, we have positively impacted the CESM software development cycle and, in particular, its focus on quality assurance.
$R$-linear convergence of limited memory steepest descent

The limited memory steepest descent method (LMSD) proposed by Fletcher is an extension of the Barzilai-Borwein "two-point step size" strategy for steepest descent methods for solving unconstrained optimization problems. It is known that the Barzilai-Borwein strategy yields a method with an R-linear rate of convergence when it is employed to minimize a strongly convex quadratic. This paper extends this analysis for LMSD, also for strongly convex quadratics. In particular, it is shown that the method is R-linearly convergent for any choice of the history length parameter. The results of numerical experiments are provided to illustrate behaviors of the method that are revealed through the theoretical analysis.
DTATG: An Automatic Title Generator based on Dependency Trees

We study automatic title generation for a given block of text and present a method called DTATG to generate titles. DTATG first extracts a small number of central sentences that convey the main meanings of the text and are in a suitable structure for conversion into a title. DTATG then constructs a dependency tree for each of these sentences and removes certain branches using a Dependency Tree Compression Model we devise. We also devise a title test to determine if a sentence can be used as a title. If a trimmed sentence passes the title test, then it becomes a title candidate. DTATG selects the title candidate with the highest ranking score as the final title. Our experiments showed that DTATG can generate adequate titles. We also showed that DTATG-generated titles have higher F1 scores than those generated by the previous methods.
Analytic solutions for D-optimal factorial designs under generalized linear models

We develop two analytic approaches to solve D-optimal approximate designs under generalized linear models. The first approach provides analytic D-optimal allocations for generalized linear models with two factors, which include as a special case the $2^{2}$ main-effects model considered by Yang, Mandal and Majumdar [19]. The second approach leads to explicit solutions for a class of generalized linear models with more than two factors. With the aid of the analytic solutions, we provide a necessary and sufficient condition under which a D-optimal design with two quantitative factors could be constructed on the boundary points only. It bridges the gap between D-optimal factorial designs and D-optimal designs with continuous factors.
Adversarial Network Compression

Neural network compression has recently received much attention due to the computational requirements of modern deep models. In this work, our objective is to transfer knowledge from a deep and accurate model to a smaller one. Our contributions are threefold: (i) we propose an adversarial network compression approach to train the small student network to mimic the large teacher, without the need for labels during training; (ii) we introduce a regularization scheme to prevent a trivially-strong discriminator without reducing the network capacity and (iii) our approach generalizes on different teacher-student models.
Lattice Agreement in Message Passing Systems.

This paper studies the lattice agreement problem and the generalized lattice agreement problem in distributed message passing systems. In the lattice agreement problem, given input values from a lattice, processes have to non-trivially decide output values that lie on a chain. We consider the lattice agreement problem in both synchronous and asynchronous systems. For synchronous lattice agreement, we present two algorithms which run in $\log f$ and $\min \{O(\log^2 h(L)), O(\log^2 f)\}$ rounds, respectively, where $h(L)$ denotes the height of the {\em input sublattice} $L$, $f < n$ is the number of crash failures the system can tolerate, and $n$ is the number of processes in the system. These algorithms have significant better round complexity than previously known algorithms. The algorithm by Attiya et al. \cite{attiya1995atomic} takes $\log n$ synchronous rounds, and the algorithm by Mavronicolasa \cite{mavronicolasabound} takes $\min \{O(h(L)), O(\sqrt{f})\}$ rounds. For asynchronous lattice agreement, we propose an algorithm which has time complexity of $2 \cdot \min \{h(L), f + 1\}$ message delays which improves on the previously known time complexity of $O(n)$ message delays. The generalized lattice agreement problem defined by Faleiro et al in \cite{faleiro2012generalized} is a generalization of the lattice agreement problem where it is applied for the replicated state machine. We propose an algorithm which guarantees liveness when a majority of the processes are correct in asynchronous systems. Our algorithm requires $\min \{O(h(L)), O(f)\}$ units of time in the worst case which is better than $O(n)$ units of time required by the algorithm of Faleiro et al. \cite{faleiro2012generalized}.
On the Number of 5‐Cycles in a Tournament

We find an exact formula for the number of directed 5-cycles in a tournament in terms of its edge score sequence. We use this formula to find both upper and lower bounds on the number of 5-cycles in any $n$-tournament. In particular, we show that the maximum number of 5-cycles is asymptotically equal to $\frac{3}{4}{n \choose 5}$, the expected number 5-cycles in a random tournament ($p=\frac{1}{2}$), with equality (up to order of magnitude) for almost all tournaments. Note that this means that almost all $n$-tournaments contain the maximum number of $5$-cycles.
Incidence stratifications on Hilbert schemes of smooth surfaces, and an application to Poisson structures

Given a smooth curve on a smooth surface, the Hilbert scheme of points on the surface is stratified according to the length of the intersection with the curve. The strata are highly singular. We show that this stratification admits a natural log-resolution, namely the stratified blowup. As a consequence, the induced Poisson structure on the Hilbert scheme of a Poisson surface has unobstructed deformations.
Phase Retrieval Using Alternating Minimization

Phase retrieval problems involve solving linear equations, but with missing sign (or phase, for complex numbers) information. More than four decades after it was first proposed, the seminal error reduction algorithm of Gerchberg and Saxton and Fienup is still the popular choice for solving many variants of this problem. The algorithm is based on alternating minimization; i.e., it alternates between estimating the missing phase information, and the candidate solution. Despite its wide usage in practice, no global convergence guarantees for this algorithm are known. In this paper, we show that a (resampling) variant of this approach converges geometrically to the solution of one such problem—finding a vector $\bf x$ from ${\bf y}, {\bf A}$ , where ${\bf y} = \vert {\bf A}^T{\bf x}\vert$ and $\vert{\bf z}\vert$ denotes a vector of element-wise magnitudes of ${\bf z}$ —under the assumption that $ {\bf A}$ is Gaussian. Empirically, we demonstrate that alternating minimization performs similar to recently proposed convex techniques for this problem (which are based on “lifting” to a convex matrix problem) in sample complexity and robustness to noise. However, it is much more efficient and can scale to large problems. Analytically, for a resampling version of alternating minimization, we show geometric convergence to the solution, and sample complexity that is off by log factors from obvious lower bounds. We also establish close to optimal scaling for the case when the unknown vector is sparse. Our work represents the first theoretical guarantee for alternating minimization (albeit with resampling) for any variant of phase retrieval problems in the non-convex setting.
Power-Law Graph Cuts

Algorithms based on spectral graph cut objectives such as normalized cuts, ratio cuts and ratio association have become popular in recent years because they are widely applicable and simple to implement via standard eigenvector computations. Despite strong performance for a number of clustering tasks, spectral graph cut algorithms still suer from several limitations: rst, they require the number of clusters to be known in advance, but this information is often unknown a priori; second, they tend to produce clusters with uniform sizes. In some cases, the true clusters exhibit a known size distribution; in image segmentation, for instance, humansegmented images tend to yield segment sizes that follow a power-law distribution. In this paper, we propose a general framework of power-law graph cut algorithms that produce clusters whose sizes are power-law distributed, and also does not x the number of clusters upfront. To achieve our goals, we treat the Pitman-Yor exchangeable partition probability function (EPPF) as a regularizer to graph cut objectives. Because the resulting objectives cannot be solved by relaxing via eigenvectors, we derive a simple iterative algorithm to locally optimize the objectives. Moreover, we show that our proposed algorithm can be viewed as performing MAP inference on a particular Pitman-Yor mixture model. Our experiments on various data sets show the eectiveness of our algorithms.