cssi/SciSciGPT-SciSciNet huggingface dataset:
Changes in the genetic requirements for microbial interactions with increasing community complexity

Microbial community structure and function rely on complex interactions whose underlying molecular mechanisms are poorly understood. To investigate these interactions in a simple microbiome, we introduced E. coli into an experimental community based on a cheese rind and identified the differences in E. coli's genetic requirements for growth in interactive and non-interactive contexts using Random Barcode Transposon Sequencing (RB-TnSeq) and RNASeq. Genetic requirements varied among pairwise growth conditions and between pairwise and community conditions. Our analysis points to mechanisms by which growth conditions change as a result of increasing community complexity and suggests that growth within a community relies on a combination of pairwise and higher-order interactions. Our work provides a framework for using the model organism E. coli as a readout to investigate microbial interactions regardless of the genetic tractability of members of the studied ecosystem.
The Effect of Previous Life Cycle Phase on the Growth Kinetics, Morphology, and Antibiotic Resistance of Salmonella Typhimurium DT104 in Brain Heart Infusion and Ground Chicken Extract.

Growth models are predominately used in the food industry to estimate the potential growth of selected microorganisms under environmental conditions. The growth kinetics, cellular morphology, and antibiotic resistance were studied throughout the life cycle of Salmonella Typhimurium. The effect of the previous life cycle phase [late log phase (LLP), early stationary phase (ESP), late stationary phase (LSP), and early death phase (EDP)] of Salmonella after reinoculation in brain heart infusion broth (BHI), ground chicken extract (GCE), and BHI at pH 5, 7, and 9 and salt concentrations 2, 3, and 4% was investigated. The growth media and previous life cycle phase had significant effects on the lag time (λ), specific growth rate (μmax), and maximum population density (Ymax). At 2 and 4% salt concentration, the LLP had the significantly (p < 0.05) fastest μmax (1.07 and 0.69 log CFU/ml/h, respectively). As the cells transitioned from the late log phase (LLP) to the early death phase (EDP), the λ significantly (p < 0.05) increased. At pH 5 and 9, the EDP had a significantly (p < 0.05) lower Ymax than the LLP, ESP, and LSP. As the cells transitioned from a rod shape to a coccoid shape in the EDP, the cells were more susceptible to antibiotics. The cells regained their resistance as they transitioned back to a rod shape from the EDP to the log and stationary phase. Our results revealed that growth kinetics, cell’s length, shape, and antibiotic resistance were significantly affected by the previous life cycle phase. The results of this study also demonstrate that the previous life cycle should be considered when developing growth models of foodborne pathogens to better ensure the safety of poultry and poultry products.
Single-molecule magnets: a molecular approach to nanoscale magnetic materials

Abstract A brief survey is provided of single-molecule magnets (SMMs), or molecular nanomagnets. The [Mn12O12(O2CR)16(H2O)4] (Mn12; R = various) family of SMMs continues to be the one with the highest blocking temperatures, and the one on which the most detailed studies are being performed within the chemistry and physics communities. For this reason, methods have been developed for their controlled modification in various ways, and these are summarized. In addition, new SMMs continue to be sought to improve knowledge of this phenomenon, and several representative examples of new synthetic procedures and the resulting products are described.
Structures, Automerizations, and Isomerizations of C3H2 Isomers

A combination of experiment and theory provides insight into the structure and rearrangements of various C3H2 isomers. Photolysis of [13C]diazopropynes 6a−c under matrix isolation conditions afford...
Synthesis, spectroscopic and structural studies of spirocyclic pseudosilatranes

Abstract Spirocyclic pseudosilatranes have been prepared from the condensation of (HOCH 2 CH 2 ) 2 NMe with dihydrophenazasilines or by alcohol exchange with dialkoxyphenazasilines. The structures have been investigated in solution by 1 H, 13 C and 29 Si NMR spectroscopy and in the solid state by X-ray diffraction. The interatomic Si⋯N distance in the 1,3-dioxa-6-aza-2-silacyclooctane ring in two 5-methyl-2,8-dioxa-5-aza-1-silacyclooctane-1- spiro -(2,8-dibromo-5-alkyl-10-silaphenzasiline) derivatives is 2.968 and 2.986 A which indicates a weak nitrogensilicon interaction such as might occur in an intermediate stage of nucleophilic substitution at tetrahedral silicon. The solution data support an equilibrium between a boat—boat conformation (closed form with presumed pentacoordinate Si) and the chair—chair conformation (open form, tetrahedral Si). The spirocyclic pseudosilatranes and related acyclic pseudosilatranes are compared.
Electronic States of Al3P2, Al2P3, and Their Ions

Complete active-space self-consistent field (CASSCF) method followed by multireference singles + doubles configuration interaction (MRSDCI) calculations that included up to 4 million configurations were carried out on the electronic states of Al3P2, Al2P3 and their anions and cations. Our computed results explain the recently reported anion photoelectron spectra of Al2P3- and Al3P2- by Neumark and co-workers.1 We find that both the Al2P3- and Al2P3 species have D3h undistorted trigonal bipyramidal structures consistent with the sharp and intense ground-state peak in the observed spectra. But the neutral Al3P2 cluster was found to be Jahn−Teller distorted in contrast to the Al3P2- anion, which has a D3h undistorted trigonal bipyramidal structure, consistent with the observed extended vibrational progression of the X state of the spectra. Our computed electron affinities agree well with the onsets of the observed X peaks in both spectra. We assign the partially resolved vibrational structure in the observed...
Approaches to novel bicarbocyclic nucleosides

Abstract Approaches to the synthesis of a novel family of carbocyclic nucleosides of potential anti-HIV interest have been developed. The key carbocyclic intermediate 9 was coupled with desired heterocyclic bases to give the corresponding bicyclic nucleosides. The structure of compound 9 and the bicarbocyclic dideoxynucleosides were confirmed by extensive 1H and 13C NMR studies including COSY, NOESY, DEPT and selective INEPT experiments.
Enantiomeric synthesis of 3′-fluoro-apionucleosides using Claisen rearrangement

Abstracts Enantiomeric synthesis of 3′-fluoro-apionucleosides was accomplished from 1,2- O -isopropylidene d -glyceraldehyde. The key intermediate, γ,δ-unsaturated tert -fluoro ethyl ester 7 from the fluoro allylic alcohol derivative 5 was achieved via Claisen rearrangement reaction with a 90.4% enantioselectivity. The condensation of the intermediate 10 with silylated N 4 -benzoylcytosine and 6-chloropurine followed by deprotection gave the desired pyrimidine and purine apionucleosides, respectively.
From cytoskeletal dynamics to organ asymmetry: a nonlinear, regulative pathway underlies left-right patterning.

Consistent left–right (LR) asymmetry is a fundamental aspect of the bodyplan across phyla, and errors of laterality form an important class of human birth defects. Its molecular underpinning was fi...
The homologation of carbonyl compounds by single carbon insertion reactions

This short review describes the methods of single carbon insertion reactions into carbonyl compounds.
Control of functional group proximity and direction by conformational networks: synthesis and stereodynamics of persubstituted arenes

Abstract The cooperative nonbonded interactions present in hexaethylbenzene result in an arrangement of alkyl groups such that the 1,3,5 and 2,4,6 substituents point to opposite faces of the benzene ring. Correspondingly, derivatives of hexaethylbenzene have their functional groups convergent ( meta as in 1,3,5-trisubstituted-2,4,6-triethylbenzene) or divergent ( ortho , para as in 1,2-disubstituted-3,4,5,6-tetraethylbenzenes or 1,4-disubstituted-2,3,5,6-tetraethylbenzenes) due to this cooperative conformational network. To illustrate this structural feature and probe its dynamics, 1,4-di-X-2,3,5,6-tetraethylbenzenes have been synthesized. The dynamic stereochemistry of the disubstituted compounds has been studied by variable temperature 1 H NMR spectroscopy. Using the same strategy, the 1,3,5-tris(CH 2 Y)-2,4,6-triethylbenzenes have also been prepared. The steric bulk of the substituent in the disubstituted compounds has been found to influence the barrier height. The trends found are applicable for the use of these compounds as angular building blocks for the design of ligands, polymers, and supramolecular architectures.
Emergent community agglomeration from data set geometry.

In the statistical learning language, samples are snapshots of random vectors drawn from some unknown distribution. Such vectors usually reside in a high-dimensional Euclidean space, and thus the ``curse of dimensionality'' often undermines the power of learning methods, including community detection and clustering algorithms, that rely on Euclidean geometry. This paper presents the idea of effective dissimilarity transformation (EDT) on empirical dissimilarity hyperspheres and studies its effects using synthetic and gene expression data sets. Iterating the EDT turns a static data distribution into a dynamical process purely driven by the empirical data set geometry and adaptively ameliorates the curse of dimensionality, partly through changing the topology of a Euclidean feature space ${\mathbb{R}}^{n}$ into a compact hypersphere ${S}^{n}$. The EDT often improves the performance of hierarchical clustering via the automatic grouping information emerging from global interactions of data points. The EDT is not restricted to hierarchical clustering, and other learning methods based on pairwise dissimilarity should also benefit from the many desirable properties of EDT.
One‐step synthesis of novel 2,4‐diaminopyrimidine antifolates from bridged alicyclic ketones and cyanoguanidine

A convenient one-step reaction with cyanoguanidine was used to convert alicyclic ketones to previously undescribed 2,4-diamino-5,6,7,8-tetrahydroquinazolines with a one-, two-, or three-carbon bridge in the carbocyclic ring. Although the yields of the desired products were modest, the principal advantage of this one-step process was that it provided easy access to a variety of novel bridged heterocyclic ring systems whose synthesis from sterically hindered ketones by other methods would have required multiple steps with an even lower overall yield. The products were tested as inhibitors of dihydrofolate reductases from Pneumocystis carinii, Toxoplasma gondii, and rat liver with a view to examining the effect of a space-filling bridge on binding. The most potent and selective compound in the group was 4,6-diamino-3,5-diazatricyclo[7.2.1.02,7]dodeca-2,4,6-triene (13), whose potency and selectivity approached those of trimethoprim, a drug commonly used to treat P. carinii and T. gondii infection. 3,5-Diamino-4,6-diazatricyclo[6.2.1.02,7]-undeca-2,4,6-triene (14), the analog of 13 with a one-carbon rather than a two-carbon bridge showed similar potency and selectivity against the T. gondii enzyme, but was a weak and nonselective inhibitor of P. carinii dihydrofolate reductase. The other compounds tested were likewise weak and nonselective.
Towards a state-space geometry of neural responses to natural scenes: A steady-state approach.

Abstract Our understanding of information processing by the mammalian visual system has come through a variety of techniques ranging from psychophysics and fMRI to single unit recording and EEG. Each technique provides unique insights into the processing framework of the early visual system. Here, we focus on the nature of the information that is carried by steady state visual evoked potentials (SSVEPs). To study the information provided by SSVEPs, we presented human participants with a population of natural scenes and measured the relative SSVEP response. Rather than focus on particular features of this signal, we focused on the full state-space of possible responses and investigated how the evoked responses are mapped onto this space. Our results show that it is possible to map the relatively high-dimensional signal carried by SSVEPs onto a 2-dimensional space with little loss. We also show that a simple biologically plausible model can account for a high proportion of the explainable variance (~73%) in that space. Finally, we describe a technique for measuring the mutual information that is available about images from SSVEPs. The techniques introduced here represent a new approach to understanding the nature of the information carried by SSVEPs. Crucially, this approach is general and can provide a means of comparing results across different neural recording methods. Altogether, our study sheds light on the encoding principles of early vision and provides a much needed reference point for understanding subsequent transformations of the early visual response space to deeper knowledge structures that link different visual environments.
Computation Offloading in Multi-Access Edge Computing Networks: A Multi-Task Learning Approach

Multi-access edge computing (MEC) has already shown the potential in enabling mobile devices to bear the computation-intensive applications by offloading some tasks to a nearby access point (AP) integrated with a MEC server (MES). However, due to the varying network conditions and limited computation resources of the MES, the offloading decisions taken by a mobile device and the computational resources allocated by the MES may not be efficiently achieved with the lowest cost. In this paper, we propose a dynamic offloading framework for the MEC network, in which the uplink non-orthogonal multiple access (NOMA) is used to enable multiple devices to upload their tasks via the same frequency band. We formulate the offloading decision problem as a multiclass classification problem and formulate the MES computational resource allocation problem as a regression problem. Then a multi-task learning based feedforward neural network (MTFNN) model is designed to jointly optimize the offloading decision and computational resource allocation. Numerical results illustrate that the proposed MTFNN outperforms the conventional optimization method in terms of inference accuracy and computation complexity.
Algebras from surfaces without punctures

Abstract We introduce a new class of finite-dimensional gentle algebras, the surface algebras, which are constructed from an unpunctured Riemann surface with boundary and marked points by introducing cuts in internal triangles of an arbitrary triangulation of the surface. We show that surface algebras are endomorphism algebras of partial cluster-tilting objects in generalized cluster categories, we compute the invariant of Avella-Alaminos and Geiss for surface algebras and we provide a geometric model for the module category of surface algebras.
Pipe-SGD: a decentralized pipelined SGD framework for distributed deep net training

Distributed training of deep nets is an important technique to address some of the present day computing challenges like memory consumption and computational demands. Classical distributed approaches, synchronous or asynchronous, are based on the parameter server architecture, i.e., worker nodes compute gradients which are communicated to the parameter server while updated parameters are returned. Recently, distributed training with AllReduce operations gained popularity as well. While many of those operations seem appealing, little is reported about wall-clock training time improvements. In this paper, we carefully analyze the AllReduce based setup, propose timing models which include network latency, bandwidth, cluster size and compute time, and demonstrate that a pipelined training with a width of two combines the best of both synchronous and asynchronous training. Specifically, for a setup consisting of a four-node GPU cluster we show wall-clock time training improvements of up to 5.4 × compared to conventional approaches.
Constant Approximation for Capacitated k-Median with (1+epsilon)-Capacity Violation

We study the Capacitated k-Median problem for which existing constant-factor approximation algorithms are all pseudo-approximations that violate either the capacities or the upper bound k on the number of open facilities. Using the natural LP relaxation for the problem, one can only hope to get the violation factor down to 2. Li [SODA'16] introduced a novel LP to go beyond the limit of 2 and gave a constant-factor approximation algorithm that opens (1 + epsilon)*k facilities. We use the configuration LP of Li [SODA'16] to give a constant-factor approximation for the Capacitated k-Median problem in a seemingly harder configuration: we violate only the capacities by 1 + epsilon. This result settles the problem as far as pseudo-approximation algorithms are concerned.
HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization

Neural extractive summarization models usually employ a hierarchical encoder for document encoding and they are trained using sentence-level labels, which are created heuristically using rule-based methods. Training the hierarchical encoder with these inaccurate labels is challenging. Inspired by the recent work on pre-training transformer sentence encoders (Devlin et al., 2018), we propose Hibert (as shorthand for HIerachical Bidirectional Encoder Representations from Transformers) for document encoding and a method to pre-train it using unlabeled data. We apply the pre-trained Hibert to our summarization model and it outperforms its randomly initialized counterpart by 1.25 ROUGE on the CNN/Dailymail dataset and by 2.0 ROUGE on a version of New York Times dataset. We also achieve the state-of-the-art performance on these two datasets.
Constrained policy optimization

For many applications of reinforcement learning it can be more convenient to specify both a reward function and constraints, rather than trying to design behavior through the reward function. For example, systems that physically interact with or around humans should satisfy safety constraints. Recent advances in policy search algorithms (Mnih et al., 2016; Schulman et al., 2015; Lillicrap et al., 2016; Levine et al., 2016) have enabled new capabilities in high-dimensional control, but do not consider the constrained setting. We propose Constrained Policy Optimization (CPO), the first general-purpose policy search algorithm for constrained reinforcement learning with guarantees for near-constraint satisfaction at each iteration. Our method allows us to train neural network policies for high-dimensional control while making guarantees about policy behavior all throughout training. Our guarantees are based on a new theoretical result, which is of independent interest: we prove a bound relating the expected returns of two policies to an average divergence between them. We demonstrate the effectiveness of our approach on simulated robot locomotion tasks where the agent must satisfy constraints motivated by safety.
Probabilistic fasttext for multi-sense word embeddings

We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information. In particular, we represent each word with a Gaussian mixture density, where the mean of a mixture component is given by the sum of n-grams. This representation allows the model to share the “strength” across sub-word structures (e.g. Latin roots), producing accurate representations of rare, misspelt, or even unseen words. Moreover, each component of the mixture can capture a different word sense. Probabilistic FastText outperforms both FastText, which has no probabilistic model, and dictionary-level probabilistic embeddings, which do not incorporate subword structures, on several word-similarity benchmarks, including English RareWord and foreign language datasets. We also achieve state-of-art performance on benchmarks that measure ability to discern different meanings. Thus, our model is the first to achieve best of both the worlds: multi-sense representations while having enriched semantics on rare words.
Efficient Contextual Bandits in Non-stationary Worlds

Most contextual bandit algorithms minimize regret against the best fixed policy, a questionable benchmark for non-stationary environments that are ubiquitous in applications. In this work, we develop several efficient contextual bandit algorithms for non-stationary environments by equipping existing methods for i.i.d. problems with sophisticated statistical tests so as to dynamically adapt to a change in distribution. We analyze various standard notions of regret suited to non-stationary environments for these algorithms, including interval regret, switching regret, and dynamic regret. When competing with the best policy at each time, one of our algorithms achieves regret \(O(\sqrt{ST})\) if there are \(T\) rounds with \(S\) stationary periods, or more generally \(O(\Delta^{1/3}T^{2/3})\) where \(\Delta\) is some non-stationarity measure. These results almost match the optimal guarantees achieved by an inefficient baseline that is a variant of the classic Exp4 algorithm. The dynamic regret result is also the first one for efficient and fully adversarial contextual bandit. Furthermore, while the results above require tuning a parameter based on the unknown quantity \(S\) or \(\Delta\), we also develop a parameter free algorithm achieving regret \(\min\{S^{1/4}T^{3/4},\Delta^{1/5}T^{4/5}\}\). This improves and generalizes the best existing result \(\Delta^{0.18}T^{0.82}\) by Karnin and Anava (2016) which only holds for the two-armed bandit problem.
An efficient minibatch acceptance test for metropolis-hastings

We present a novel Metropolis-Hastings method for large datasets that uses small expected-size minibatches of data. Previous work on reducing the cost of Metropolis-Hastings tests yields only constant factor reductions versus using the full dataset for each sample. Here we present a method that can be tuned to provide arbitrarily small batch sizes, by adjusting either proposal step size or temperature. Our test uses the noise-tolerant Barker acceptance test with a novel additive correction variable. The resulting test has similar cost to a normal SGD update. Our experiments demonstrate several order-of-magnitude speedups over previous work.
A new highly parallel non-Hermitian eigensolver

Calculating portions of eigenvalues and eigenvectors of matrices or matrix pencils has many applications. An approach to this calculation for Hermitian problems based on a density matrix has been proposed in 2009 and a software package called FEAST has been developed. The density-matrix approach allows FEAST's implementation to exploit a key strength of modern computer architectures, namely, multiple levels of parallelism. Consequently, the software package has been well received and subsequently commercialized. A detailed theoretical analysis of Hermitian FEAST has also been established very recently. This paper generalizes the FEAST algorithm and theory, for the first time, to tackle non-Hermitian problems. Fundamentally, the new algorithm is basic subspace iteration or Bauer bi-iteration, except applied with a novel accelerator based on Cauchy integrals. The resulting algorithm retains the multi-level parallelism of Hermitian FEAST, making it a valuable new tool for large-scale computational science and engineering problems on leading-edge computing platforms.
Null boundary controllability of a 1-dimensional heat equation with an internal point mass

We consider a linear hybrid system composed by two rods of equal length connected by a point mass. We show that the system is null controllable with either a Dirichlet or Neumann boundary control at one end. The results are based on spectral analysis together with the moment method.
Rooted forests that avoid sets of permutations

Abstract We say that an unordered rooted labeled forest avoids the pattern π ∈ S n if the sequence obtained from the labels along the path from the root to any vertex does not contain a subsequence that is in the same relative order as π . We enumerate several classes of forests that avoid certain sets of permutations, including the set of unimodal forests, via bijections with set partitions with certain properties. We also define and investigate an analog of Wilf equivalence for forests.
Modular Architecture for StarCraft II with Deep Reinforcement Learning

We present a novel modular architecture for StarCraft II AI. The architecture splits responsibilities between multiple modules that each control one aspect of the game, such as buildorder selection or tactics. A centralized scheduler reviews macros suggested by all modules and decides their order of execution. An updater keeps track of environment changes and instantiates macros into series of executable actions. Modules in this framework can be optimized independently or jointly via human design, planning, or reinforcement learning. We present the first result of applying deep reinforcement learning techniques to training a modular agent with selfplay, achieving 92% or 86% win rates against the ”Harder” (level 5) built-in Blizzard bot in Zerg vs. Zerg matches, with or without fog-of-war.
Exact multiplicity of solutions for some semilinear Dirichlet problems

The classical result of Ambrosetti and Prodi [1], in the form of Berger and Podolak [3], gives the exact number of solutions for the problem Δu+g(u)=μφ1(x)+e(x)inD,u=0on∂D, depending on the real pa...
Homological Mirror Symmetry for the Quartic Surface

Introduction $A_{\infty}$-categories Deformation theory Group actions Coherent sheaves Symplectic terminology Monodromy and negativity Fukaya categories Computations in Fukaya categories The algebras $Q_4$ and $Q_{64}$ Counting polygons References
$K$ -User Symmetric $M\times N$ MIMO Interference Channel Under Finite Precision CSIT: A GDoF Perspective

Generalized degrees of freedom (GDoF) are characterized for the symmetric $K$ -user multiple-input multiple-output interference channel under the assumption that the channel-state information at the transmitters (CSITs) is limited to finite precision. In this symmetric setting, each transmitter is equipped with $M$ antennas, each receiver is equipped with $N$ antennas, each desired channel (i.e., a channel between a transmit antenna and a receive antenna belonging to the same user) has strength $\sim P$ , while each undesired channel has strength $\sim P^\alpha $ , where $P$ is a nominal SNR parameter. The result generalizes a previous GDoF characterization for the SISO setting $(M=N=1)$ and is enabled by a significant extension of the aligned image sets bound that is broadly useful. GDoF per user take the form of a $W$ -curve with respect to $\alpha $ for fixed values of $M$ and $N$ . Under finite precision CSIT, in spite of the presence of multiple antennas, all the benefits of interference alignment are lost.
Structural properties of index coding capacity using fractional graph theory

The capacity region of the index coding problem is characterized through the notion of confusion graph and its fractional chromatic number. Based on this multiletter characterization, several structural properties of the capacity region are established, some of which are already noted by Tahmasbi, Shahrasbi, and Gohari, but proved here with simple and more direct graph-theoretic arguments. In particular, the capacity region of a given index coding problem is shown to be simple functionals of the capacity regions of smaller subproblems when the interaction between the subproblems is none, one-way, or complete.
Simple examples of distinct Liouville type symplectic structures

We provide simple examples of open manifolds which support inequivalent symplectic structures that are Liouville (exact, and with an infinite end modeled on the convex part of a symplectization).
Formal design of robot Integrated Task and Motion Planning

Integrated Task and Motion Planning (ITMP) for mobile robots becomes a new trend. Most existing methods for ITMP either restrict to static environments or lack performance guarantees. This motivates us to use formal design methods for mobile robots ITMP in a dynamic environment with moving obstacles. Our basic idea is to synthesize a global integrated task and motion plan through composing simple local moves and actions, and to achieve its performance guarantee through modular and incremental verifications. The design consists of two steps. First, reactive motion controllers are designed and verified locally. Then, a global plan is built upon these certified controllers by concatenating them together. In particular, we model the controllers and verify their safety through formulating them as Differential Dynamic Logic (dL) formula. Furthermore, these proven safe controllers are abstracted in Counter Linear Temporal Logic over Constraint System CLTLB(D) and composed based on an encoding to Satisfiability Modulo Theories (SMT) that takes into account the geometric constraints. Since dL allows compositional verification, the sequential composition of the safe motion primitives also preserves safety properties. Illustrative examples are presented to show the effectiveness of the method.
Provably manipulation-resistant reputation systems

Reputation and reliability play a central role in a wide range of applications, from online marketplaces to review aggregators to ridesharing services. Many reputation systems are vulnerable to manipulation, and protected only by keeping algorithms secret, avoiding high-stakes applications, or using side information to identify malicious users. The current situation is reminiscent of pre-modern cryptography, characterized by a patchwork of ad hoc techniques with minimal formal understanding of their security. We propose a reputation system which provably achieves a very strong correctness guarantee under extremely pessimistic assumptions—it works even given a supermajority of malicious users, converges to optimal behavior after a constant number of interactions per user, does not require repeated interactions, and accommodates time-varying quality of resources. Our formal model is simple but general. In each period, a user is given an opportunity to interact with a resource, and must accept or reject the proposed interaction. If they accept, they receive a payoff in [−1, 1]. Ideally all users would behave honestly, pooling their data and quickly learning which resources are worth interacting with. Our protocol essentially matches this performance when all users are honest, while guaranteeing that adding malicious users or users with varying tastes does very little damage. We also extend our results to a more challenging setting where users interact with each other rather than with static resources, and where the two parties to an interaction may receive different payoffs.
Optimal geometric estimates for fractional Sobolev capacities

Abstract This note discovers new sharp inequalities relating the fractional Sobolev capacity of a set to its standard volume and fractional perimeter, respectively, and consequently proves that the sharp fractional Sobolev inequality is equivalent to either the sharp fractional isocapacitary inequality or the sharp fractional isoperimetric inequality.
Capacity of Gaussian Many-Access Channels

Classical multiuser information theory studies the fundamental limits of models with a fixed (often small) number of users as the coding blocklength goes to infinity. This paper proposes a new paradigm, referred to as many-user information theory , where the number of users is allowed to grow with the blocklength. This paradigm is motivated by emerging systems with a massive number of users in an area, such as the Internet of Things. The focus of this paper is the many-access channel model, which consists of a single receiver and many transmitters, whose number increases unboundedly with the blocklength. Moreover, an unknown subset of transmitters may transmit in a given block and need to be identified as well as decoded by the receiver. A new notion of capacity is introduced and characterized for the Gaussian many-access channel with random user activities. The capacity can be achieved by first detecting the set of active users and then decoding their messages. The minimum cost of identifying the active users is also quantified.
The power of deeper networks for expressing natural functions

It is well-known that neural networks are universal approximators, but that deeper networks tend in practice to be more powerful than shallower ones. We shed light on this by proving that the total number of neurons $m$ required to approximate natural classes of multivariate polynomials of $n$ variables grows only linearly with $n$ for deep neural networks, but grows exponentially when merely a single hidden layer is allowed. We also provide evidence that when the number of hidden layers is increased from $1$ to $k$, the neuron requirement grows exponentially not with $n$ but with $n^{1/k}$, suggesting that the minimum number of layers required for practical expressibility grows only logarithmically with $n$.
Segregation-induced phase transformations in grain boundaries

Phase transformations in metallic grain boundaries (GBs) present significant fundamental interest in the context of thermodynamics of low-dimensional physical systems. We report on atomistic computer simulations of the Cu-Ag system that provide direct evidence that GB phase transformations in a single-component GB can continue to exist in a binary alloy. This gives rise to segregation-induced phase transformations with varying chemical composition at a fixed temperature. Furthermore, for such transformations we propose an approach to calculations of free-energy differences between different GB phases by thermodynamic integration along a segregation isotherm. This opens the possibility of developing quantitative thermodynamics of GB phases, their transformations to each other, and critical phenomena in the future.
Bounds between contraction coefficients

In this paper, we delineate how the contraction coefficient of the strong data processing inequality for KL divergence can be used to learn likelihood models. We then present an alternative formulation that forces the input KL divergence to vanish, and achieves a contraction coefficient equivalent to the squared maximal correlation using a linear algebraic solution. To analyze the performance loss in using this simple but suboptimal procedure, we bound these coefficients in the discrete and finite regime, and prove their equivalence in the Gaussian regime.
Incorporating Behavioral Constraints in Online AI Systems

AI systems that learn through reward feedback about the actions they take are increasingly deployed in domains that have significant impact on our daily life. However, in many cases the online rewards should not be the only guiding criteria, as there are additional constraints and/or priorities imposed by regulations, values, preferences, or ethical principles. We detail a novel online agent that learns a set of behavioral constraints by observation and uses these learned constraints as a guide when making decisions in an online setting while still being reactive to reward feedback. To define this agent, we propose to adopt a novel extension to the classical contextual multi-armed bandit setting and we provide a new algorithm called Behavior Constrained Thompson Sampling (BCTS) that allows for online learning while obeying exogenous constraints. Our agent learns a constrained policy that implements the observed behavioral constraints demonstrated by a teacher agent, and then uses this constrained policy to guide the reward-based online exploration and exploitation. We characterize the upper bound on the expected regret of the contextual bandit algorithm that underlies our agent and provide a case study with real world data in two application domains. Our experiments show that the designed agent is able to act within the set of behavior constraints without significantly degrading its overall reward performance.
Online convex optimization for cumulative constraints

We propose the algorithms for online convex optimization which lead to cumulative squared constraint violations of the form $\sum\limits_{t=1}^T\big([g(x_t)]_+\big)^2=O(T^{1-\beta})$, where $\beta\in(0,1)$. Previous literature has focused on long-term constraints of the form $\sum\limits_{t=1}^Tg(x_t)$. There, strictly feasible solutions can cancel out the effects of violated constraints. In contrast, the new form heavily penalizes large constraint violations and cancellation effects cannot occur. Furthermore, useful bounds on the single step constraint violation $[g(x_t)]_+$ are derived. For convex objectives, our regret bounds generalize existing bounds, and for strongly convex objectives we give improved regret bounds. In numerical experiments, we show that our algorithm closely follows the constraint boundary leading to low cumulative violation.
Data is Personal: Attitudes and Perceptions of Data Visualization in Rural Pennsylvania

Many of the guidelines that inform how designers create data visualizations originate in studies that unintentionally exclude populations that are most likely to be among the 'data poor'. In this paper, we explore which factors may drive attention and trust in rural populations with diverse economic and educational backgrounds - a segment that is largely underrepresented in the data visualization literature. In 42 semi-structured interviews in rural Pennsylvania (USA), we find that a complex set of factors intermix to inform attitudes and perceptions about data visualization - including educational background, political affiliation, and personal experience. The data and materials for this research can be found at https://osf.io/uxwts/
A t-motivic interpretation of shuffle relations for multizeta values

Abstract Thakur [Tha10] showed that, for r, s ∈ N , a product of two Carlitz zeta values ζ A ( r ) and ζ A ( s ) can be expressed as an F p -linear combination of ζ A ( r + s ) and double zeta values of weight r + s . Such an expression is called shuffle relation by Thakur. Fixing r, s ∈ N , we construct a t-module E ′ . To determine whether an ( r + s ) -tuple C in F q ( θ ) r + s gives a shuffle relation, we relate it to the F q [ t ] -torsion property of the point v C ∈ E ′ ( F q [ θ ] ) constructed with respect to the given ( r + s ) -tuple C . We also provide an effective criterion for deciding the F q [ t ] -torsion property of the point v C .
Reduction of total-cost and average-cost MDPs with weakly continuous transition probabilities to discounted MDPs

Abstract This note describes sufficient conditions under which total-cost and average-cost Markov decision processes (MDPs) with general state and action spaces, and with weakly continuous transition probabilities, can be reduced to discounted MDPs. For undiscounted problems, these reductions imply the validity of optimality equations and the existence of stationary optimal policies. The reductions also provide methods for computing optimal policies. The results are applied to a capacitated inventory control problem with fixed costs and lost sales.
Assembly Planning by Subassembly Decomposition Using Blocking Reduction

The sequence in which a complex product is assembled directly impacts the ease and efficiency of the assembly process, whether executed by a human or a robot. A sequence that gives the assembler the greatest freedom of movement is, therefore, desirable. Our main contribution is an expression of obstruction relationships between parts as a disassembly interference graph (DIG). We validate this heuristic by developing a disassembly sequence planner that partitions assemblies in a way that prioritizes access to parts, resulting in plans that are comparable in efficiency to two state-of-the-art assembly methods in terms of total plan length. Using DIG, our method generates successive subassembly decompositions, yielding a tree structure that makes parallization opportunities apparent. Our planner generates viable disassembly plans by minimizing our part blockage measure, and thereby demonstrates that this measure is a valuable addition to the assembly sequence planning toolkit.
Viziometrics: Analyzing Visual Information in the Scientific Literature

Scientific results are communicated visually in the literature through diagrams, visualizations, and photographs. These information-dense objects have been largely ignored in bibliometrics and scientometrics studies when compared to citations and text. In this paper, we use techniques from computer vision and machine learning to classify more than 8 million figures from PubMed into five figure types and study the resulting patterns of visual information as they relate to scholarly impact. We find that the distribution of figures and figure types in the literature has remained relatively constant over time, but can vary widely across field and topic. Remarkably, we find a significant correlation between scientific impact and the use of visual information, where higher impact papers tend to include more diagrams, and to a lesser extent more plots. To explore these results and other ways of extracting this visual information, we have built a visual browser to illustrate the concept and explore design alternatives for supporting viziometric analysis and organizing visual information. We use these results to articulate a new research agenda-viziometrics-to study the organization and presentation of visual information in the scientific literature.
Label optimal regret bounds for online local learning

We resolve an open question from Christiano (2014b) posed in COLT’14 regarding the optimal dependency of the regret achievable for online local learning on the size of the label set. In this framework, the algorithm is shown a pair of items at each step, chosen from a set of n items. The learner then predicts a label for each item, from a label set of size L and receives a real valued payoff. This is a natural framework which captures many interesting scenarios such as online gambling and online max cut. Christiano (2014a) designed an efficient online learning algorithm for this problem achieving a regret of O( p nL 3 T ), where T is the number of rounds. Information theoretically, one can achieve a regret of O( p n logLT ). One of the main open questions left in this framework concerns closing the above gap. In this work, we provide a complete answer to the question above via two main results. We show, via a tighter analysis, that the semi-definite programming based algorithm of Christiano (2014a) in fact achieves a regret ofO( p nLT ). Second, we show a matching computational lower bound. Namely, we show that a polynomial time algorithm for online local learning with lower regret would imply a polynomial time algorithm for the planted clique problem which is widely believed to be hard. We prove a similar hardness result under a related conjecture concerning planted dense subgraphs that we put forth. Unlike planted clique, the planted dense subgraph problem does not have any known quasi-polynomial time algorithms. Computational lower bounds for online learning are relatively rare, and we hope that the ideas developed in this work will lead to lower bounds for other online learning scenarios as well.
ON RACK POLYNOMIALS

We study rack polynomials and the link invariants they define. We show that constant action racks are classified by their generalized rack polynomials and show that nsata-quandles are not classified by their generalized quandle polynomials. We use subrack polynomials to define enhanced rack counting invariants, generalizing the quandle polynomial invariants.
Data Sets: Word Embeddings Learned from Tweets and General Data

A word embedding is a low-dimensional, dense and real-valued vector representation of a word. Word embeddings have been used in many NLP tasks. They are usually generated from a large text corpus. The embedding of a word captures both its syntactic and semantic aspects. Tweets are short, noisy and have unique lexical and semantic features that are different from other types of text. Therefore, it is necessary to have word embeddings learned specifically from tweets. In this paper, we present ten word embedding data sets. In addition to the data sets learned from just tweet data, we also built embedding sets from the general data and the combination of tweets and the general data. The general data consist of news articles, Wikipedia data and other web data. These ten embedding models were learned from about 400 million tweets and 7 billion words from the general data. In this paper, we also present two experiments demonstrating how to use the data sets in some NLP tasks, such as tweet sentiment analysis and tweet topic classification tasks.
Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time

MAP perturbation models have emerged as a powerful framework for inference in structured prediction. Such models provide a way to efficiently sample from the Gibbs distribution and facilitate predictions that are robust to random noise. In this paper, we propose a provably polynomial time randomized algorithm for learning the parameters of perturbed MAP predictors. Our approach is based on minimizing a novel Rademacher-based generalization bound on the expected loss of a perturbed MAP predictor, which can be computed in polynomial time. We obtain conditions under which our randomized learning algorithm can guarantee generalization to unseen examples.
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference

Many matching, tracking, sorting, and ranking problems require probabilistic reasoning about possible permutations, a set that grows factorially with dimension. Combinatorial optimization algorithms may enable efficient point estimation, but fully Bayesian inference poses a severe challenge in this high-dimensional, discrete space. To surmount this challenge, we start with the usual step of relaxing a discrete set (here, of permutation matrices) to its convex hull, which here is the Birkhoff polytope: the set of all doubly-stochastic matrices. We then introduce two novel transformations: first, an invertible and differentiable stick-breaking procedure that maps unconstrained space to the Birkhoff polytope; second, a map that rounds points toward the vertices of the polytope. Both transformations include a temperature parameter that, in the limit, concentrates the densities on permutation matrices. We then exploit these transformations and reparameterization gradients to introduce variational inference over permutation matrices, and we demonstrate its utility in a series of experiments.
On Approximations and Ergodicity Classes in Random Chains

We study the limiting behavior of a random dynamic system driven by a stochastic chain. Our interest is in the chains that are not necessarily ergodic but are decomposable into ergodic classes. To investigate the conditions under which the ergodic classes of a model can be identified, we introduce and study an l1 -approximation and infinite flow graph of the model. We show that the l1-approximations of random chains preserve certain limiting behavior. Using the l1-approximations, we show how the connectivity of the infinite flow graph is related to the structure of the ergodic groups of the model. Our main result of this paper provides conditions under which the ergodicity groups of the model can be identified by considering the connected components in the infinite flow graph. We provide two applications of our main result to random networks, namely broadcast over time-varying networks and networks with random link failure.