cssi/SciSciGPT-SciSciNet huggingface dataset:
Mitotic Gene Conversion Tracts Associated with Repair of a Defined Double-Strand Break in Saccharomyces cerevisiae

Mitotic recombination between homologous chromosomes leads to the uncovering of recessive alleles through loss of heterozygosity. In the current study, a defined double-strand break was used to initiate reciprocal loss of heterozygosity between diverged homologs of chromosome IV in Saccharomyces cerevisiae . These events resulted from the repair of two broken chromatids, one of which was repaired as a crossover and the other as a noncrossover. Associated gene conversion tracts resulting from the donor-directed repair of mismatches formed during strand exchange (heteroduplex DNA) were mapped using microarrays. Gene conversion tracts associated with individual crossover and noncrossover events were similar in size and position, with half of the tracts being unidirectional and mapping to only a one side of the initiating break. Among crossover events, this likely reflected gene conversion on only one side of the break, with restoration-type repair occurring on the other side. For noncrossover events, an ectopic system was used to directly compare gene conversion tracts produced in a wild-type strain to heteroduplex DNA tracts generated in the absence of the Mlh1 mismatch-repair protein. There was a strong bias for unidirectional tracts in the absence, but not in the presence, of Mlh1. This suggests that mismatch repair acts on heteroduplex DNA that is only transiently present in noncrossover intermediates of the synthesis-dependent strand-annealing pathway. Although the molecular features of events associated with loss of heterozygosity generally agreed with those predicted by current recombination models, there were unexpected complexities in associated gene conversion tracts.
Cyclocondensation of amino-propargyl silanes

Amino-propargyl silanes condense with carbonyl compounds to form imines and subsequently cyclize to form allenylidene tetrahydroquinolines. The cyclocondensations are catalyzed by a variety of Bronsted acids, among which phosphoric acids provide the highest yields. Subsequent intramolecular and intermolecular additions to the allene moiety provide complex polycyclic amines.
Synthesis and Characterization of Supported Metal Cluster Catalysts with Well-Defined Structures

Nearly uniform supported metal clusters are the focus of this summary, which addresses their synthesis, characterization, and catalytic properties. Supported clusters represented as Ir 4 and Ir 6 were prepared on MgO powder by thermal decarbonylation of [HIr 4 (CO) 11 ] − and [Ir 6 (CO) 15 ] 2− , respectively. The most useful characterization technique for supported clusters is extended X-ray absorption fine structure spectroscopy, but the structure data are limited by the precision of the coordination numbers (about ±20%). Ir 4 and Ir 6 clusters on supports catalyze hydrogenation of cyclohexene and of toluene, and they are catalytically distinct from larger iridium particles, although the reactions are structure insensitive (proceeding at about the same rate per exposed metal atom) when catalyzed by metal particles larger than about 1 nm. Thus the concept of structure insensitivity does not extend to supported metal clusters, which are regarded as quasi molecular.
A chiral C3 triisopropylamine and its silatrane derivatives

Abstract The first chiral C 3 triisopropylamine has been prepared with the stereogenic centers adjacent to the central nitrogen atom. This triol was converted to two new C 3 4,6,11-trimethylsilatranes. An X-ray structure revealed that the methyl substituents adopt sterically congested pseudoaxial orientations.
Synthesis of a Family of {[(VSe2)n]1.06(TaSe2)m}z Compounds.

The first sixteen members of the {[(VSe2)n]1.06(TaSe2)m}z family of compounds where n and m were varied from 1 to 4 were synthesized by annealing preconfigured reactants. The factor of 1.06 originates from the ab-plane lattice mismatch of VSe2 and TaSe2, reflecting the non-epitaxial structural relationship between the components. Each individual compound was addressed by sequentially depositing n layers of alternating elemental V and Se followed by m layers of alternating elemental Ta and Se in which the thickness of each pair of elemental layers was calibrated to yield a single-crystalline layer of the metal diselenide on annealing at low temperature for a short time. The structural changes during the transformation from initial reactant to crystalline superstructure are followed using X-ray diffraction. The structure of these nanoscale superstructures consist of an integral number of VSe2 and TaSe2 layers with minimal cation mixing between the layers. Electrical measurements show that all of the members...
Semantically Conditioned Dialog Response Generation via Hierarchical Disentangled Self-Attention

Semantically controlled neural response generation on limited-domain has achieved great performance. However, moving towards multi-domain large-scale scenarios are shown to be difficult because the possible combinations of semantic inputs grow exponentially with the number of domains. To alleviate such scalability issue, we exploit the structure of dialog acts to build a multi-layer hierarchical graph, where each act is represented as a root-to-leaf route on the graph. Then, we incorporate such graph structure prior as an inductive bias to build a hierarchical disentangled self-attention network, where we disentangle attention heads to model designated nodes on the dialog act graph. By activating different (disentangled) heads at each layer, combinatorially many dialog act semantics can be modeled to control the neural response generation. On the large-scale Multi-Domain-WOZ dataset, our model can yield a significant improvement over the baselines on various automatic and human evaluation metrics.
A direct synthesis of 5,6-dihydroindolo[2,1-a]isoquinolines that exhibit immunosuppressive activity.

Dihydroindolo[2,1-a]isoquinolines were synthesized from tetrahydroisoquinolines and α-fluoroaldehydes by a novel two-step procedure. These compounds exhibited significant immunosuppressive activity against IL-2, IL-10 and IFN-γ.
The Ag2O−V2O5−HF(aq) System and Crystal Structure of α-Ag3VO4

Reactions between the three components Ag2O, V2O5, and HF(aq) were investigated under hydrothermal conditions, and the recovered phases were, in increasing Ag:V content, Ag2V4O11, β-AgVO3, Ag4V2O6F...
Comparative Analyses of Chromatin Landscape in White Adipose Tissue Suggest Humans May Have Less Beigeing Potential than Other Primates

Humans carry a much larger percentage of body fat than other primates. Despite the central role of adipose tissue in metabolism, little is known about the evolution of white adipose tissue in primates. Phenotypic divergence is often caused by genetic divergence in cis-regulatory regions. We examined the cis-regulatory landscape of fat during human origins by performing comparative analyses of chromatin accessibility in human and chimpanzee adipose tissue using rhesus macaque as an outgroup. We find that many regions that have decreased accessibility in humans are enriched for promoter and enhancer sequences, are depleted for signatures of negative selection, are located near genes involved with lipid metabolism, and contain a short sequence motif involved in the beigeing of fat, the process in which lipid-storing white adipocytes are transdifferentiated into thermogenic beige adipocytes. The collective closing of many putative regulatory regions associated with beigeing of fat suggests a mechanism that increases body fat in humans.
Plasticity in auditory categorization is supported by differential engagement of the auditory-linguistic network

To construct our perceptual world, the brain categorizes variable sensory cues into behaviorally-relevant groupings. Categorical representations are apparent within a distributed fronto-temporo-parietal brain network but how this neural circuitry is shaped by experience remains undefined. Here, we asked whether speech and music categories might be formed within different auditory-linguistic brain regions depending on listeners' auditory expertise. We recorded EEG in highly skilled (musicians) vs. less experienced (nonmusicians) perceivers as they rapidly categorized speech and musical sounds. Musicians showed perceptual enhancements across domains, yet source EEG data revealed a double dissociation in the neurobiological mechanisms supporting categorization between groups. Whereas musicians coded categories in primary auditory cortex (PAC), nonmusicians recruited non-auditory regions (e.g., inferior frontal gyrus, IFG) to generate category-level information. Functional connectivity confirmed nonmusicians' increased left IFG involvement reflects stronger routing of signal from PAC directed to IFG, presumably because sensory coding is insufficient to construct categories in less experienced listeners. Our findings establish auditory experience modulates specific engagement and inter-regional communication in the auditory-linguistic network supporting categorical perception. Whereas early canonical PAC representations are sufficient to generate categories in highly trained ears, less experienced perceivers broadcast information downstream to higher-order linguistic brain areas (IFG) to construct abstract sound labels.
CHARACTER IS A SACRED BOND

Law clings to rules to stabilize a preferred normative reality. But rules never suffice. Character is the dark matter of law. Ethos anthropos daimon. “Character is fate.” This paradoxically reversi...
Short-range dependent processes subordinated to the Gaussian may not be strong mixing

Abstract There are all kinds of weak dependence. For example, strong mixing. Short-range dependence (SRD) is also a form of weak dependence. It occurs in the context of processes that are subordinated to the Gaussian. Is a SRD process strong mixing if the underlying Gaussian process is long-range dependent? We show that this is not necessarily the case.
DPP-PMRF: Rethinking Optimization for a Probabilistic Graphical Model Using Data-Parallel Primitives

We present a new parallel algorithm for probabilistic graphical model optimization. The algorithm relies on data-parallel primitives (DPPs), which provide portable performance over hardware architecture. We evaluate results on CPUs and GPUs for an image segmentation problem. Compared to a serial baseline, we observe runtime speedups of up to 13X (CPU) and 44X (GPU). We also compare our performance to a reference, OpenMP-based algorithm, and find speedups of up to 7X (CPU).
Characterizing elastic turbulence in channel flows at low Reynolds number

Flow instabilities have long been observed in viscoelastic fluids, even at low Reynolds numbers. Particle tracking methods are used to compare the turbulentlike features of polymeric solutions flowing in straight microchannels with those in curved geometries.
Perinormality -- a generalization of Krull domains

We introduce a new class of integral domains, the perinormal domains, which fall strictly between Krull domains and weakly normal domains. We establish basic properties of the class, and in the case of universally catenary domains we give equivalent characterizations of perinormality. (Later on, we point out some subtleties that occur only in the non-Noetherian context.) We also introduce and explore briefly the related concept of global perinormality, including a relationship with divisor class groups. Throughout, we provide illuminating examples from algebra, geometry, and number theory.
Quasi-planar optics: computing light propagation and scattering in planar waveguide arrays

We have developed a new mode matching technique capable of accurate numerical computation of wave coupling in arrays of planar structures. The algorithm is illustrated on several examples of plasmonic and volumetric waveguides.
Entropic Dynamics: Quantum Mechanics from Entropy and Information Geometry

Entropic Dynamics (ED) is a framework in which Quantum Mechanics (QM) is derived as an application of entropic methods of inference. The magnitude of the wave function is manifestly epistemic: its square is a probability distribution. The epistemic nature of the phase of the wave function is also clear: it controls the flow of probability. The dynamics is driven by entropy subject to constraints that capture the relevant physical information. The central concern is to identify those constraints and how they are updated. After reviewing previous work I describe how considerations from information geometry allow us to derive a phase space geometry that combines Riemannian, symplectic, and complex structures. The ED that preserves these structures is QM. The full equivalence between ED and QM is achieved by taking account of how gauge symmetry and charge quantization are intimately related to quantum phases and the single-valuedness of wave functions.
Milnor fibers and links of local complete intersections

There are essentially no previously-known results which show how Milnor fibers, real links, and complex links "detect" the dimension of the singular locus of a local complete intersection. In this paper, we show how a good understanding of the derived category and the perverse t-structure quickly yields such results for local complete intersections with singularities of arbitrary dimension.
Singular Ricci flows I

We establish several quantitative results about singular Ricci flows, including estimates on the curvature and volume, and the set of singular times.
Chirality and principal graph obstructions

Abstract Determining which bipartite graphs can be principal graphs of subfactors is an important and difficult question in subfactor theory. Using only planar algebra techniques, we prove a triple point obstruction which generalizes all known initial triple point obstructions to possible principal graphs. We also prove a similar quadruple point obstruction with the same technique. Using our obstructions, we eliminate some infinite families of possible principal graphs with initial triple and quadruple points which were a major hurdle in extending subfactor classification results above index 5.
Minimizing the probability of lifetime drawdown under constant consumption

Abstract We assume that an individual invests in a financial market with one riskless and one risky asset, with the latter’s price following geometric Brownian motion as in the Black–Scholes model. Under a constant rate of consumption, we find the optimal investment strategy for the individual who wishes to minimize the probability that her wealth drops below some fixed proportion of her maximum wealth to date, the so-called probability of lifetime drawdown. If maximum wealth is less than a particular value, m ∗ , then the individual optimally invests in such a way that maximum wealth never increases above its current value. By contrast, if maximum wealth is greater than m ∗ but less than the safe level, then the individual optimally allows the maximum to increase to the safe level.
Inferring strategies from limited reconnaissance in real-time strategy games

In typical real-time strategy (RTS) games, enemy units are visible only when they are within sight range of a friendly unit. Knowledge of an opponent's disposition is limited to what can be observed through scouting. Information is costly, since units dedicated to scouting are unavailable for other purposes, and the enemy will resist scouting attempts. It is important to infer as much as possible about the opponent's current and future strategy from the available observations. We present a dynamic Bayes net model of strategies in the RTS game Starcraft that combines a generative model of how strategies relate to observable quantities with a principled framework for incorporating evidence gained via scouting. We demonstrate the model's ability to infer unobserved aspects of the game from realistic observations.
Learning Deep Models: Critical Points and Local Openness

With the increasing interest in deeper understanding of the loss surface of many non-convex deep models, this paper presents a unifying framework to establish the local/global optima equivalence of the optimization problems arising from training of such non-convex models. Using the local openness property of the underlying training models, we provide simple sufficient conditions under which any local optimum of the resulting optimization problem is globally optimal. We first completely characterize the local openness of the symmetric and non-symmetric matrix multiplication mapping in its range. Then we use our characterization to: 1) provide a simple proof for the classical result of Burer-Monteiro and extend it to non-continuous loss functions. 2) show that every local optimum of two layer linear networks is globally optimal. Unlike many existing results in the literature, our result requires no assumption on the target data matrix Y, and input data matrix X. 3) Develop almost complete characterization of the local/global optima equivalence of multi-layer linear neural networks. We provide various counterexamples to show the necessity of each of our assumptions. 4) Show global/local optima equivalence of non-linear deep models having certain pyramidal structure. Unlike some existing works, our result requires no assumption on the differentiability of the activation functions and can go beyond "full-rank" cases.
Example Selection For Dictionary Learning.

In unsupervised learning, an unbiased uniform sampling strategy is typically used, in order that the learned features faithfully encode the statistical structure of the training data. In this work, we explore whether active example selection strategies - algorithms that select which examples to use, based on the current estimate of the features - can accelerate learning. Specifically, we investigate effects of heuristic and saliency-inspired selection algorithms on the dictionary learning task with sparse activations. We show that some selection algorithms do improve the speed of learning, and we speculate on why they might work.
Deep Predictive Coding Networks

The quality of data representation in deep learning methods is directly related to the prior model imposed on the representations; however, generally used fixed priors are not capable of adjusting to the context in the data. To address this issue, we propose deep predictive coding networks, a hierarchical generative model that empirically alters priors on the latent representations in a dynamic and contextsensitive manner. This model captures the temporal dependencies in time-varying signals and uses top-down information to modulate the representation in lower layers. The centerpiece of our model is a novel procedure to infer sparse states of a dynamic network which is used for feature extraction. We also extend this feature extraction block to introduce a pooling function that captures locally invariant representations. When applied on a natural video data, we show that our method is able to learn high-level visual features. We also demonstrate the role of the topdown connections by showing the robustness of the proposed model to structured noise.
Sequence Length Requirement of Distance-Based Phylogeny Reconstruction: Breaking the Polynomial Barrier

We introduce a new distance-based phylogeny reconstruction technique which provably achieves, at sufficiently short branch lengths, a sequence length requirement growing slower than any polynomial. The technique is based on a new averaging procedure that implicitly reconstructs ancestral sequences.In the same token, we extend previous results on phase transitions in phylogeny reconstruction to general time-reversible models. More precisely, we show that in the so-called Kesten-Stigum zone---roughly, a region of the parameter space where ancestral sequences are well approximated by ``linear combinations'' of observed sequences---sequences of length eradiclog n suffice for reconstruction. Here n is the number of extant species. We improve this result to poly(log n) the ultrametric case. Surprisingly, this last result suggests that a UPGMA-type algorithm may in some sense be "optimal'' under a molecular clock. Our results challenge---to some extent---the conventional wisdom that estimates of evolutionary distances alone carry significantly less information about phylogenies than full sequence datasets.
Cosmall Roots and Curve Neighborhoods

A previous result in a paper by Buch and Mihalcea relates the curve neighborhood of any Schubert variety to the curve neighborhood of a point and introduces the idea of P-cosmall roots. In our paper, we prove a conjecture given in that same paper about P-cosmall roots. We go on to provide explicit combinatorial conditions for a root to be P-cosmall.
On the Regimes in Millimeter Wave Networks: Noise-Limited or Interference-Limited?

Given the overcrowding in the 300 MHz-3 GHz spectrum, millimeter wave (mmWave) spectrum is a promising candidate for the future generations of wireless networks. With the unique propagation characteristics at mmWave frequencies, one of the fundamental questions to address is whether mmWave networks are noise or interference-limited. The regime in which the network operates significantly impacts the MAC layer design, resource allocation procedure and also interference management techniques. In this paper, we first derive the statistical characteristic of the cumulative interference in finite-sized mmWave networks considering configuration randomness across spatial and spectral domains while including the effect of blockages. Subsequently, using the derived interference model we set up a likelihood ratio test (LRT) (that is dependent on various network parameters) in order to detect the regime of the network from an arbitrarily located user standpoint. Unlike traditional networks, in mmWave networks, different likelihood of experiencing an interference-limited regime can be observed at different locations.
The regular semisimple locus of the affine quotient of the cotangent bundle of the Grothendieck–Springer resolution

Abstract Let G = G L n ( C ) , the general linear group over the complex numbers, and let B be the set of invertible upper triangular matrices in G . Let b = Lie ( B ) . For μ : T ∗ ( b × C n ) → b ∗ , where b ∗ ≅ g ∕ u and u being strictly upper triangular matrices in g = Lie ( G ) , we prove that the Hamiltonian reduction μ − 1 ( 0 ) r s s ∕ ∕ B of the extended regular semisimple locus b r s s of the Borel subalgebra is smooth, affine, reduced, and scheme-theoretically isomorphic to a dense open locus of C 2 n . We also show that the B -invariant functions on the regular semisimple locus of the Hamiltonian reduction of b × C n arise as the trace of a certain product of matrices.
Revisiting Frequency Moment Estimation in Random Order Streams.

We revisit one of the classic problems in the data stream literature, namely, that of estimating the frequency moments $F_p$ for $0 < p < 2$ of an underlying $n$-dimensional vector presented as a sequence of additive updates in a stream. It is well-known that using $p$-stable distributions one can approximate any of these moments up to a multiplicative $(1+\epsilon)$-factor using $O(\epsilon^{-2} \log n)$ bits of space, and this space bound is optimal up to a constant factor in the turnstile streaming model. We show that surprisingly, if one instead considers the popular random-order model of insertion-only streams, in which the updates to the underlying vector arrive in a random order, then one can beat this space bound and achieve $\tilde{O}(\epsilon^{-2} + \log n)$ bits of space, where the $\tilde{O}$ hides poly$(\log(1/\epsilon) + \log \log n)$ factors. If $\epsilon^{-2} \approx \log n$, this represents a roughly quadratic improvement in the space achievable in turnstile streams. Our algorithm is in fact deterministic, and we show our space bound is optimal up to poly$(\log(1/\epsilon) + \log \log n)$ factors for deterministic algorithms in the random order model. We also obtain a similar improvement in space for $p = 2$ whenever $F_2 \gtrsim \log n\cdot F_1$.
Does Antibiotic Resistance Evolve in Hospitals

Nosocomial outbreaks of bacteria are well documented. Based on these incidents, and the heavy usage of antibiotics in hospitals, it has been assumed that antibiotic resistance evolves in hospital environments. To test this assumption, we studied resistance phenotypes of bacteria collected from patient isolates at a community hospital over a 2.5-year period. A graphical model analysis shows no association between resistance and patient information other than time of arrival. This allows us to focus on time-course data. We introduce a hospital transmission model, based on negative binomial delay. Our main contribution is a statistical hypothesis test called the Nosocomial Evolution of Resistance Detector (NERD). It calculates the significance of resistance trends occurring in a hospital. It can inform hospital staff about the effects of various practices and interventions, can help detect clonal outbreaks, and is available as an R package. We applied the NERD method to each of the 16 antibiotics in the study via 16 hypothesis tests. For 13 of the antibiotics, we found that the hospital environment had no significant effect on the evolution of resistance; the hospital is merely a piece of the larger picture. The p-values obtained for the other three antibiotics (cefepime, ceftazidime, and gentamicin) indicate that particular care should be taken in hospital practices with these antibiotics. One of the three, ceftazidime, was significant after accounting for multiple hypotheses, indicating a trend of decreased resistance for this drug.
Cloud-Based MPC with Encrypted Data

This paper explores the privacy of cloud out-sourced Model Predictive Control (MPC) for a linear system with input constraints. A client sends her private states to the cloud who performs the MPC computation and returns the control inputs. In order to guarantee that the cloud can perform this computation without obtaining anything about the client's private data, we employ a partially homomorphic cryptosystem. We propose protocols for two cloud-MPC architectures: a client-server architecture and a two-server architecture. In the first case, a control input for the system is privately computed by the cloud server with the assistance of the client. In the second case, the control input is privately computed by two independent, non-colluding servers, with no additional requirements from the client. We prove that the proposed protocols preserve the privacy of the client's data and of the resulting control input. Furthermore, we compute bounds on the errors introduced by encryption. We discuss the trade-off between communication, MPC performance and privacy.
Algebraic bounds for heterogeneous site percolation on directed and undirected graphs

Abstract We analyze site percolation on directed and undirected graphs with site-dependent open-site probabilities. We construct upper bounds on cluster susceptibilities, vertex connectivity functions, and the expected number of simple open cycles through a chosen arc; separate bounds are given on finite and infinite (di)graphs. These produce lower bounds for percolation and uniqueness transitions in infinite (di)graphs, and for the formation of a giant component in finite (di)graphs. The bounds are formulated in terms of appropriately weighted adjacency and non-backtracking (Hashimoto) matrices. It turns out to be the uniqueness criterion that is most closely associated with an asymptotically vanishing probability of forming a giant strongly-connected component on a large finite (di)graph.
Confinement in high-temperature lattice gauge theories

There has been substantial progress in understanding a class of SU(N) gauge theories that are confining at high temperatures. This class includes theories with center-symmetric Polyakov loop deformations or with periodic adjoint fermions. The crucial role of monopoles in lattice gauge theories of this type can be understood analytically. The basic mechanisms occur in the two-dimensional O(3) spin model, deformed by appropriate mass term to give an XY model. Vortices of the XY model are constituents of O(3) instantons just as SU(N) magnetic monopoles are constituents of KvBLL instantons. Similar methods applied to an SU(2) lattice gauge theory yield an effective U(1) description in which monopoles are responsible for confinement.
Time in Fundamental Physics

Abstract The first three sections of this paper contain a broad brush summary of the profound changes in the notion of time in fundamental physics that were brought about by three revolutions: the foundations of mechanics distilled by Newton in his Principia , the discovery of special relativity by Einstein and its reformulation by Minkowski, and, finally, the fusion of geometry and gravity in Einstein׳s general relativity. The fourth section discusses two aspects of yet another deep revision that waits in the wings as we attempt to unify general relativity with quantum physics.
Conditional dependence via shannon capacity: axioms, estimators and applications

We consider axiomatically the problem of estimating the strength of a conditional dependence relationship PY|X from a random variables X to a random variable Y. This has applications in determining the strength of a known causal relationship, where the strength depends only on the conditional distribution of the effect given the cause (and not on the driving distribution of the cause). Shannon capacity, appropriately regularized, emerges as a natural measure under these axioms. We examine the problem of calculating Shannon capacity from the observed samples and propose a novel fixed-k nearest neighbor estimator, and demonstrate its consistency. Finally, we demonstrate an application to single-cell flow-cytometry, where the proposed estimators significantly reduce sample complexity.
Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation

Enabling robots to autonomously navigate complex environments is essential for real-world deployment. Prior methods approach this problem by having the robot maintain an internal map of the world, and then use a localization and planning method to navigate through the internal map. However, these approaches often include a variety of assumptions, are computationally intensive, and do not learn from failures. In contrast, learning-based methods improve as the robot acts in the environment, but are difficult to deploy in the real-world due to their high sample complexity. To address the need to learn complex policies with few samples, we propose a generalized computation graph that subsumes value-based model-free methods and model-based methods, with specific instantiations interpolating between model-free and model-based. We then instantiate this graph to form a navigation model that learns from raw images and is sample efficient. Our simulated car experiments explore the design decisions of our navigation model, and show our approach outperforms single-step and $N$ -step double Q-learning. We also evaluate our approach on a real-world RC car and show it can learn to navigate through a complex indoor environment with a few hours of fully autonomous, self-supervised training. Videos of the experiments and code can be found at github.com/gkahn13/gcg
Probabilistic Formulations of Regression with Mixed Guidance

Regression problems assume every instanceisannotated(labeled)witharealvalue, aformof annotation we call strong guidance. In order for these annotations to be accurate, they must be the result of a precise experiment or measurement. However, in some cases additional weak guidance might be given by imprecise measurements, a domain expert or even crowd sourcing. Current formulations of regression are unable to use both types of guidance. We propose a regression framework that can also incorporate weak guidance based on relative orderings, bounds, neighboring and similarity relations. Consider learning to predict ages from portrait images, these new types of guidanceallowweakerformsofguidancesuchasstating a person is in their 20s or two people are similar in age. These types of annotations can be easier to generate than strong guidance. We introduce a probabilistic formulation for these forms of weak guidance and show that the resulting optimization problems are convex. Our experimental results show the benefits of these formulations on several data sets.
Robust 3D Human Motion Reconstruction via Dynamic Template Construction

In multi-view human body capture systems, the recovered 3D geometry or even the acquired imagery data can be heavily corrupted due to occlusions, noise, limited fieldof- view, etc. Direct estimation of 3D pose, body shape or motion on these low-quality data has been traditionally challenging.In this paper, we present a graph-based non-rigid shape registration framework that can simultaneously recover 3D human body geometry and estimate pose/motion at high fidelity.Our approach first generates a global full-body template by registering all poses in the acquired motion sequence.We then construct a deformable graph by utilizing the rigid components in the global template. We directly warp the global template graph back to each motion frame in order to fill in missing geometry. Specifically,we combine local rigidity and temporal coherence constraints to maintain geometry and motion consistencies. Comprehensive experiments on various scenes show that our method is accurate and robust even in the presence of drastic motions.
TASI Lectures on Collider Physics

These lectures provide an introduction to the physics of particle colliders. Topics covered include a quantitative examination of the design and operational parameters of Large Hadron Collider, kinematics and observables at colliders, such as rapidity and transverse mass, properties of distributions, such as Jacobian peaks, jets, and an exploration of the decay modes and signatures of important composite and elementary and particles in the Standard Model, from pions to the Higgs boson. The goal of these lectures is to bridge the gap between theory and experiment in particle physics using intuitive arguments and estimates rather than precision calculations.
Connectivity Preserving Multivalued Functions in Digital Topology

We study connectivity preserving multivalued functions (Kovalevsky in A new concept for digital geometry, shape in picture, 1994) between digital images. This notion generalizes that of continuous multivalued functions (Escribano et al. in Discrete geometry for computer imagery, lecture notes in computer science, 2008; Escribano et al. in J Math Imaging Vis 42:76---91, 2012) studied mostly in the setting of the digital plane $${\mathbb {Z}}^2$$Z2. We show that connectivity preserving multivalued functions, like continuous multivalued functions, are appropriate models for digital morphological operations. Connectivity preservation, unlike continuity, is preserved by compositions, and generalizes easily to higher dimensions and arbitrary adjacency relations.
Supervising Unsupervised Learning

We introduce a framework to transfer knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithms. We demonstrate the versatility of our framework via rigorous agnostic bounds on a variety of unsupervised problems. In the context of clustering, our approach helps choose the number of clusters and the clustering algorithm, remove the outliers, and provably circumvent Kleinberg's impossibility result. Experiments across hundreds of problems demonstrate improvements in performance on unsupervised data with simple algorithms despite the fact our problems come from heterogeneous domains. Additionally, our framework lets us leverage deep networks to learn common features across many small datasets, and perform zero shot learning.
The two membranes problem for different operators

Abstract We study the two membranes problem for different operators, possibly nonlocal. We prove a general result about the Holder continuity of the solutions and we develop a viscosity solution approach to this problem. Then we obtain C 1 , γ regularity of the solutions provided that the orders of the two operators are different. In the special case when one operator coincides with the fractional Laplacian, we obtain the optimal regularity and a characterization of the free boundary.
Pseudo Mask Augmented Object Detection

In this work, we present a novel and effective framework to facilitate object detection with the instance-level segmentation information that is only supervised by bounding box annotation. Starting from the joint object detection and instance segmentation network, we propose to recursively estimate the pseudo ground-truth object masks from the instance-level object segmentation network training, and then enhance the detection network with top-down segmentation feedbacks. The pseudo ground truth mask and network parameters are optimized alternatively to mutually benefit each other. To obtain the promising pseudo masks in each iteration, we embed a graphical inference that incorporates the low-level image appearance consistency and the bounding box annotations to refine the segmentation masks predicted by the segmentation network. Our approach progressively improves the object detection performance by incorporating the detailed pixel-wise information learned from the weakly-supervised segmentation network. Extensive evaluation on the detection task in PASCAL VOC 2007 and 2012 [12] verifies that the proposed approach is effective.
Sparse domination of Hilbert transforms along curves

We obtain sharp sparse bounds for Hilbert transforms along curves in $\mathbb{R}^n$, and derive as corollaries weighted norm inequalities for such operators. The curves that we consider include monomial curves and arbitrary $C^n$ curves with nonvanishing torsion.
Low-frequency radio bursts and space weather

Low-frequency radio phenomena are due to the presence of nonthermal electrons in the interplanetary (IP) medium. Understanding these phenomena is important in characterizing the space environment near Earth and other destinations in the solar system. Substantial progress has been made in the past two decades, because of the continuous and uniform data sets available from space-based radio and white-light instrumentation. This paper highlights some recent results obtained on IP radio phenomena. In particular, the source of type IV radio bursts, the behavior of type III storms, shock propagation in the IP medium, and the solar-cycle variation of type II radio bursts are considered. All these phenomena are closely related to solar eruptions and active region evolution. The results presented were obtained by combining data from the Wind and SOHO missions.
Improved lower bounds on the number of edges in list critical and online list critical graphs

Abstract We prove that every k-list-critical graph ( k ≥ 7 ) on n ≥ k + 2 vertices has at least 1 2 ( k − 1 + k − 3 ( k − c ) ( k − 1 ) + k − 3 ) n edges where c = ( k − 3 ) ( 1 2 − 1 ( k − 1 ) ( k − 2 ) ) . This improves the bound established by Kostochka and Stiebitz [11] . The same bound holds for online k-list-critical graphs, improving the bound established by Riasat and Schauz [16] . Both bounds follow from a more general result stating that either a graph has many edges or it has an Alon-Tarsi orientable induced subgraph satisfying a certain degree condition.
Human-Machine Inference Networks for Smart Decision Making: Opportunities and Challenges

The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines complementary cognitive strengths of humans and machines in an intelligent manner to tackle various inference tasks and achieves higher performance than either humans or machines by themselves. While inference performance optimization techniques for human-only or sensor-only networks are quite mature, HuMaINs require novel signal processing and machine learning solutions. In this paper, we present an overview of the HuMaINs architecture with a focus on three main issues that include architecture design, inference algorithms including security/privacy challenges, and application areas/use cases.
On the Determination of the Plancherel measure for Lebedev-Whittaker transforms on GL(n)

We give a simple derivation of the Plancherel measure for Lebedev-Whittaker transforms on GL(n).
A generalized Serre’s condition

Throughout, let R be a commutative Noetherian ring. A ring R satisfies Serre’s condition (Sl) if for all p∈SpecR, depthRp≥min{l,dimRp}. Serre’s condition has been a topic of expanding interest. In ...
Nonlocal maximum principles for active scalars

Abstract Active scalars appear in many problems of fluid dynamics. The most common examples of active scalar equations are 2D Euler, Burgers, and 2D surface quasi-geostrophic equations. Many questions about regularity and properties of solutions of these equations remain open. We develop the idea of nonlocal maximum principle introduced in Kiselev, Nazarov and Volberg (2007) [19] , formulating a more general criterion and providing new applications. The most interesting application is finite time regularization of weak solutions in the supercritical regime.
Global multivariate point pattern models for rain type occurrence.

Abstract We seek statistical methods to study the occurrence of multiple rain types observed by satellite on a global scale. The main scientific interests are to relate rainfall occurrence with various atmospheric state variables and to study the dependence between the occurrences of multiple types of rainfall (e.g. short-lived and intense vs. long-lived and weak; the heights of the rain clouds are also considered). Commonly in point process model literature, the spatial domain is assumed to be a small, and thus planar domain. We consider the log-Gaussian Cox Process (LGCP) models on the surface of a sphere and take advantage of cross-covariance models for spatial processes on a global scale to model the stochastic intensity function of the LGCP models. We present analysis results for rainfall observations from the TRMM satellite and atmospheric state variables from MERRA-2 reanalysis data over the tropical Eastern and Western Pacific Ocean, as well as over the entire tropical and subtropical ocean regions. Statistical inference is done through Monte Carlo likelihood approximation for LGCP models. We employ covariance approximation to deal with massive data.
Image reconstruction in fluorescence molecular tomography with sparsity-initialized maximum-likelihood expectation maximization

We present a reconstruction method involving maximum-likelihood expectation maximization (MLEM) to model Poisson noise as applied to fluorescence molecular tomography (FMT). MLEM is initialized with the output from a sparse reconstruction-based approach, which performs truncated singular value decomposition-based preconditioning followed by fast iterative shrinkage-thresholding algorithm (FISTA) to enforce sparsity. The motivation for this approach is that sparsity information could be accounted for within the initialization, while MLEM would accurately model Poisson noise in the FMT system. Simulation experiments show the proposed method significantly improves images qualitatively and quantitatively. The method results in over 20 times faster convergence compared to uniformly initialized MLEM and improves robustness to noise compared to pure sparse reconstruction. We also theoretically justify the ability of the proposed approach to reduce noise in the background region compared to pure sparse reconstruction. Overall, these results provide strong evidence to model Poisson noise in FMT reconstruction and for application of the proposed reconstruction framework to FMT imaging.
Differentially Private Online Learning

In this paper, we consider the problem of preserving privacy in the context of online learning. Online learning involves learning from data in real-time, due to which the learned model as well as its predictions are continuously changing. This makes preserving privacy of each data point significantly more challenging as its effect on the learned model can be easily tracked by observing changes in the subsequent predictions. Furthermore, with more and more online systems (e.g. search engines like Bing, Google etc.) trying to learn their customers’ behavior by leveraging their access to sensitive customer data (through cookies etc.), the problem of privacy preserving online learning has become critical. We study the problem in the framework of online convex programming (OCP)—a popular online learning setting with several theoretical and practical implications—while using differential privacy as the formal measure of privacy. For this problem, we provide a generic framework that can be used to convert any given OCP algorithm into a private OCP algorithm with provable privacy as well as regret guarantees (utility), provided that the given OCP algorithm satisfies the following two criteria: 1) linearly decreasing sensitivity, i.e., the effect of the new data points on the learned model decreases linearly, 2) sub-linear regret. We then illustrate our approach by converting two popular OCP algorithms into corresponding differentially private algorithms while guaranteeing ~ O( p T ) regret for strongly convex functions. Next, we consider the practically important class of online linear regression problems, for which we generalize the approach by Dwork et al. (2010a) to provide a differentially private algorithm with just poly-log regret. Finally, we show that our online learning framework can be used to provide differentially private algorithms for the offline learning problem as well. For the offline learning problem, our approach guarantees better error bounds and is more practical than the existing state-of-the-art methods (Chaudhuri et al., 2011; Rubinstein et al., 2009).
Lee–Yang zeros for the DHL and 2D rational dynamics, I. Foliation of the physical cylinder

Abstract In a classical work of the 1950's, Lee and Yang proved that the zeros of the partition functions of a ferromagnetic Ising model always lie on the unit circle. Distribution of these zeros is physically important as it controls phase transitions in the model. We study this distribution for the Migdal–Kadanoff Diamond Hierarchical Lattice (DHL). In this case, it can be described in terms of the dynamics of an explicit rational function R in two variables (the renormalization transformation). We prove that R is partially hyperbolic on an invariant cylinder C . The Lee–Yang zeros are organized in a transverse measure for the central-stable foliation of R | C . Their distribution is absolutely continuous. Its density is C ∞ (and non-vanishing) below the critical temperature. Above the critical temperature, it is C ∞ on a open dense subset, but it vanishes on the complementary set of positive measure.
Implicit particle filters for data assimilation

Implicit particle filters for data assimilation update the particles by first choosing probabilities and then looking for particle locations that assume them, guiding the particles one by one to the high probability domain. We provide a detailed description of these filters, with illustrative examples, together with new, more general, methods for solving the algebraic equations and with a new algorithm for parameter identification.
Algorithms for Routing an Unmanned Aerial Vehicle in the Presence of Refueling Depots

We consider a single Unmanned Aerial Vehicle (UAV) routing problem where there are multiple depots and the vehicle is allowed to refuel at any depot. The objective of the problem is to find a path for the UAV such that each target is visited at least once by the vehicle, the fuel constraint is never violated along the path for the UAV, and the total fuel required by the UAV is a minimum. We develop an approximation algorithm for the problem, and propose fast construction and improvement heuristics to solve the same. Computational results show that solutions whose costs are on an average within 1.4% of the optimum can be obtained relatively fast for the problem involving five depots and 25 targets.