cssi/SciSciGPT-SciSciNet huggingface dataset:
Detection with multimodal dependent data using low-dimensional random projections

Performing likelihood ratio based detection with high dimensional multimodal data is a challenging problem since the computation of the joint probability density functions (pdfs) in the presence of intermodal dependence is difficult. While some computationally expensive approaches have been proposed for dependent multimodal data fusion (e.g., based on copula theory), a commonly used tractable approach is to compute the joint pdf as the product of marginal pdfs ignoring dependence. However, this method leads to poor performance when the data is strongly dependent. In this paper, we consider the problem of detection when dependence among multimodal data is modeled in a compressed domain where compression is obtained using low dimensional random projections. We employ a Gaussian approximation while modeling inter-modal dependence in the compressed domain which is computationally more efficient. We show that, under certain conditions, detection with multimodal dependent data in the compressed domain with a small number of compressed measurements yields enhanced performance compared to detection with high dimensional data via either the product approach or other suboptimal fusion approaches proposed in the literature.
3D Terrain Segmentation in the Swir Spectrum

We focus on the automatic 3D terrain segmentation problem using hyperspectral shortwave IR (HS-SWIR) imagery and 3D Digital Elevation Models (DEM). The datasets were independently collected, and metadata for the HS-SWIR dataset are unavailable. We explore an overall slope of the SWIR spectrum that correlates with the presence of moisture in soil to propose a band ratio test to be used as a proxy for soil moisture content to distinguish two broad classes of objects: live vegetation from impermeable manmade surface. We show that image based localization techniques combined with the Optimal Randomized RANdom Sample Consensus (RANSAC) algorithm achieve precise spatial matches between HS-SWIR data of a portion of downtown Los Angeles (LA (USA)) and the Visible image of a georegistered 3D DEM, covering a wider-area of LA. Our spectral-elevation rule based approach yields an overall accuracy of 97.7%, segmenting the object classes into buildings, houses, trees, grass, and roads/parking lots.
GAN Dissection: Visualizing and Understanding Generative Adversarial Networks

Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, they have not been well visualized or understood. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models. In this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to object concepts using a segmentation-based network dissection method. Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. We examine the contextual relationship between these units and their surroundings by inserting the discovered object concepts into new images. We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in a scene. We provide open source interpretation tools to help researchers and practitioners better understand their GAN models.
Learning linear structural equation models in polynomial time and sample complexity

The problem of learning structural equation models (SEMs) from data is a fundamental problem in causal inference. We develop a new algorithm --- which is computationally and statistically efficient and works in the high-dimensional regime --- for learning linear SEMs from purely observational data with arbitrary noise distribution. We consider three aspects of the problem: identifiability, computational efficiency, and statistical efficiency. We show that when data is generated from a linear SEM over $p$ nodes and maximum degree $d$, our algorithm recovers the directed acyclic graph (DAG) structure of the SEM under an identifiability condition that is more general than those considered in the literature, and without faithfulness assumptions. In the population setting, our algorithm recovers the DAG structure in $\mathcal{O}(p(d^2 + \log p))$ operations. In the finite sample setting, if the estimated precision matrix is sparse, our algorithm has a smoothed complexity of $\widetilde{\mathcal{O}}(p^3 + pd^7)$, while if the estimated precision matrix is dense, our algorithm has a smoothed complexity of $\widetilde{\mathcal{O}}(p^5)$. For sub-Gaussian noise, we show that our algorithm has a sample complexity of $\mathcal{O}(\frac{d^8}{\varepsilon^2} \log (\frac{p}{\sqrt{\delta}}))$ to achieve $\varepsilon$ element-wise additive error with respect to the true autoregression matrix with probability at most $1 - \delta$, while for noise with bounded $(4m)$-th moment, with $m$ being a positive integer, our algorithm has a sample complexity of $\mathcal{O}(\frac{d^8}{\varepsilon^2} (\frac{p^2}{\delta})^{1/m})$.
Power Diagram Detection with Applications to Information Elicitation

Power diagrams, a type of weighted Voronoi diagram, have many applications throughout operations research. We study the problem of power diagram detection: determining whether a given finite partition of $${\mathbb {R}}^d$$ takes the form of a power diagram. This detection problem is particularly prevalent in the field of information elicitation, where one wishes to design contracts to incentivize self-minded agents to provide honest information. We devise a simple linear program to decide whether a polyhedral cell complex can be described as a power diagram. For positive instances, a representation of the cell complex as a power diagram is returned. Further, we discuss applications to property elicitation, peer prediction, and mechanism design, where this question arises. Our model can efficiently decide the question for complexes of $${\mathbb {R}}^d$$ or of a convex subset thereof. The approach is based on the use of an alternative representation of power diagrams and invariance of a power diagram under uniform scaling of the parameters in this representation.
A method to construct 1‐rotational factorizations of complete graphs and solutions to the oberwolfach problem

The concept of a $1$-rotational factorization of a complete graph under a finite group $G$ was studied in detail by Buratti and Rinaldi. They found that if $G$ admits a $1$-rotational $2$-factorization, then the involutions of $G$ are pairwise conjugate. We extend their result by showing that if a finite group $G$ admits a $1$-rotational $k=2^nm$-factorization where $n\geq 1$, and $m$ is odd, then $G$ has at most $m(2^n-1)$ conjugacy classes containing involutions. Also, we show that if $G$ has exactly $m(2^n-1)$ conjugacy classes containing involutions, then the product of a central involution with an involution in one conjugacy class yields an involution in a different conjugacy class. We then demonstrate a method of constructing a $1$-rotational $2n$-factorization under $G \times \mathbb{Z}_n$ given a $1$-rotational $2$-factorization under a finite group $G$. This construction, given a $1$-rotational solution to the Oberwolfach problem $OP(a_{\infty},a_1, a_2 \cdots, a_n)$, allows us to find a solution to $OP(2a_{\infty}-1,^2a_1, ^2a_2\cdots, ^2a_n)$ when the $a_i$'s are even ($i \neq \infty$), and $OP(p(a_{\infty}-1)+1, ^pa_1, ^pa_2 \cdots, ^pa_n)$ when $p$ is an odd prime, with no restrictions on the $a_i$'s.
Properties of Hamiltonian variational integrators

Author(s): Schmitt, Jeremy M. | Advisor(s): Leok, Melvin | Abstract: This dissertation explores Hamiltonian variational integrators. Variational integrators are a common class of symplectic integrators, which have primarily been analyzed and constructed by discretizing Hamilton's principle. Hamiltonian variational integrators are derived by discretizing Hamilton's phase space principle and have not been studied as thoroughly. In this dissertation new error analysis theorems and other related results extend the theory of Hamiltonian variational integrators. It is shown that these two formulations of variational integrators are not always numerically equivalent, even when they analytically represent the same map. Numerical simulations show there can be important differences between these two formulations of variational integrators, particularly for averaging methodsNext, a new class of variational integrators is developed based on the Taylor method combined with an augmented shooting method. A symmetric and more computationally efficient version is also developed, as well as a comparison of Lagrangian and Hamiltonian formulations of the integrator. Error analysis results are presented, in addition a proof is given of a sufficient condition for the equivalence of a Hamiltonian and Lagrangian variational integrator. Numerical simulations are presented, as well as a discussion on the role of automatic differentiation in the implementation of Taylor variational integrators.The last topic focuses on an adaptive framework for symplectic integrators. The Poincare transformation is used to construct an extended Hamiltonian system, which allows for variable step sizes. However, it is shown that the resulting Hamiltonian is often degenerate, and the only plausible framework for variational integrators is to use Hamiltonian variational integrators. Furthermore, the degeneracy of the Hamiltonian is discussed with regards to error analysis and the invertibility of the discrete Legendre transforms. A few monitor functions are considered, and numerical simulations demonstrate the significant gains in efficiency when using adaptive variational integrators.
A generalized Calderón formula for open-arc diffraction problems: theoretical considerations

We deal with the general problem of scattering by open arcs in two-dimensional space. We show that this problem can be solved by means of certain second-kind integral equations of the form NS[φ] = ƒ, where N and S are first-kind integral operators whose composition gives rise to a generalized Calderon formula of the form NS = J^τ_0 + K in a weighted, periodized Sobolev space. (Here J^τ_0 is a continuous and continuously invertible operator and K is a compact operator.) The NS formulation provides, for the first time, a second-kind integral equation for the open-arc scattering problem with Neumann boundary conditions. Numerical experiments show that, for both the Dirichlet and Neumann boundary conditions, our second-kind integral equations have spectra that are bounded away from zero and infinity as k → ∞; to the authors’ knowledge these are the first integral equations for these problems that possess this desirable property. This situation is in stark contrast with that arising from the related classical open-surface hypersingular and single-layer operators N and S, whose composition NS maps, for example, the function ϕ = 1 into a function that is not even square integrable. Our proofs rely on three main elements: algebraic manipulations enabled by the presence of integral weights; use of the classical result of continuity of the Cesaro operator; and explicit characterization of the point spectrum of J^τ_0, which, interestingly, can be decomposed into the union of a countable set and an open set, both of which are tightly clustered around -1/4. As shown in a separate contribution, the new approach can be used to construct simple, spectrally accurate numerical solvers and, when used in conjunction with Krylov-subspace iterative solvers such as the generalized minimal residual method, it gives rise to a dramatic reduction in the number of iterations compared with those required by other approaches.
Characterizing the deep uncertainties surrounding coastal flood hazard projections: A case study for Norfolk, VA

Coastal planners and decision makers design risk management strategies based on hazard projections. However, projections can differ drastically. What causes this divergence and which projection(s) should a decision maker adopt to create plans and adaptation efforts for improving coastal resiliency? Using Norfolk, Virginia, as a case study, we start to address these questions by characterizing and quantifying the drivers of differences between published sea-level rise and storm surge projections, and how these differences can impact efforts to improve coastal resilience. We find that assumptions about the complex behavior of ice sheets are the primary drivers of flood hazard diversity. Adopting a single hazard projection neglects key uncertainties and can lead to overconfident projections and downwards biased hazard estimates. These results highlight key avenues to improve the usefulness of hazard projections to inform decision-making such as (i) representing complex ice sheet behavior, (ii) covering decision-relevant timescales beyond this century, (iii) resolving storm surges with a low chance of occurring (e.g., a 0.2% chance per year), (iv) considering that storm surge projections may deviate from the historical record, and (v) communicating the considerable deep uncertainty.
AdaLinUCB: Opportunistic Learning for Contextual Bandits

In this paper, we propose and study opportunistic contextual bandits - a special case of contextual bandits where the exploration cost varies under different environmental conditions, such as network load or return variation in recommendations. When the exploration cost is low, so is the actual regret of pulling a sub-optimal arm (e.g., trying a suboptimal recommendation). Therefore, intuitively, we could explore more when the exploration cost is relatively low and exploit more when the exploration cost is relatively high. Inspired by this intuition, for opportunistic contextual bandits with Linear payoffs, we propose an Adaptive Upper-Confidence-Bound algorithm (AdaLinUCB) to adaptively balance the exploration-exploitation trade-off for opportunistic learning. We prove that AdaLinUCB achieves O((log T)^2) problem-dependent regret upper bound, which has a smaller coefficient than that of the traditional LinUCB algorithm. Moreover, based on both synthetic and real-world dataset, we show that AdaLinUCB significantly outperforms other contextual bandit algorithms, under large exploration cost fluctuations.
An SYK-like model without disorder

Making use of known facts about "tensor models," it is possible to construct a quantum system without quenched disorder that has the same large n limit for its correlation functions and thermodynamics as the SYK model. This might be useful in further probes of this approach to holographic duality.
Performance and near-wake characterization of a tidal current turbine in elevated levels of free stream turbulence

Abstract Tidal turbines are deployed in sites which have elevated levels of free stream turbulence (FST). Accounting for elevated FST on their operation become vital from a design standpoint. Detailed experimental measurements of the dynamic near-wake of a tidal turbine model in elevated FST environments is presented; an active grid turbulence generator developed by our group was used to seed in the elevated FST and evaluate the influence of turbulence intensity (Ti) and inflow integral length scale (L) on the near-wake of the turbine. Three inflow conditions are tested: a quasi-laminar flow with Ti ~ 2.2% and two elevated Ti (~12–14%) cases, one with L ~ 0.4D (D is the turbine diameter) and the other where L ~ D. Elevated Ti cases were found to increase the standard deviation of rotor torque by 4.5 times the value in quasi-laminar flow. Energy recovery was also found to be accelerated; at X/D = 4, the percentage of inflow energy recovered was 37% and was twice the corresponding value in quasi-laminar flow. Elevated FST was observed to disrupt the rotational character of the wake; the drop in swirl number ranged between 12% at X/D = 0.5 and 71% at X/D = 4. Elevated Ti also resulted in L that were considerably larger (>2 times) than the quasi-laminar flow case. An increase in inflow integral length scale (from 0.4D to D) was observed to result in enhanced wake Ti, wake structures and anisotropy; however, no noticeable influence was found on the rate of wake recovery.
Increased mistranslation protects E. coli from protein misfolding stress due to activation of a RpoS-dependent heat shock response.

The misincorporation of an incorrect amino acid into a polypeptide during protein synthesis is considered a detrimental phenomenon. A mistranslated protein is often misfolded and degraded or nonfunctional and results in an increased cost to quality control machinery. Despite these costs, errors during protein synthesis are common in bacteria. Here, we report that mistranslation in Escherichia coli increase the protein level of the heat shock sigma factor RpoH and protect cells against heat stress. Surprisingly, this increase in RpoH due to mistranslation is dependent on the presence of the general stress response sigma factor RpoS. This report provides evidence for a protective function of mistranslation and suggests a novel regulatory role of RpoS in the heat shock response.
Automatically Inferring Gender Associations from Language

In this paper, we pose the question: do people talk about women and men in different ways? We introduce two datasets and a novel integration of approaches for automatically inferring gender associations from language, discovering coherent word clusters, and labeling the clusters for the semantic concepts they represent. The datasets allow us to compare how people write about women and men in two different settings – one set draws from celebrity news and the other from student reviews of computer science professors. We demonstrate that there are large-scale differences in the ways that people talk about women and men and that these differences vary across domains. Human evaluations show that our methods significantly outperform strong baselines.
Universal Lowest-Twist in CFTs from Holography

We probe the conformal block structure of a scalar four-point function in d ≥ 2 conformal field theories by including higher-order derivative terms in a bulk gravitational action. We consider a heavy-light four-point function as the boundary correlator at large central charge. Such a four-point function can be computed, on the gravity side, as a two-point function of the light operator in a black hole geometry created by the heavy operator. We consider analytically solving the corresponding scalar field equation in a near-boundary expansion and find that the multi-stress tensor conformal blocks are insensitive to the horizon boundary condition. The main result of this paper is that the lowest-twist operator product expansion (OPE) coefficients of the multi-stress tensor conformal blocks are universal: they are fixed by the dimension of the light operators and the ratio between the dimension of the heavy operator and the central charge CT. Neither supersymmetry nor unitary is assumed. Higher-twist coefficients, on the other hand, generally are not protected. A recursion relation allows us to efficiently compute universal lowest-twist coefficients. The universality result hints at the potential existence of a higher-dimensional Virasoro-like symmetry near the lightcone. While we largely focus on the planar black hole limit in this paper, we include some preliminary analysis of the spherical black hole case in an appendix.
Learning Nearest Neighbor Graphs from Noisy Distance Samples

We consider the problem of learning the nearest neighbor graph of a dataset of n items. The metric is unknown, but we can query an oracle to obtain a noisy estimate of the distance between any pair of items. This framework applies to problem domains where one wants to learn people's preferences from responses commonly modeled as noisy distance judgments. In this paper, we propose an active algorithm to find the graph with high probability and analyze its query complexity. In contrast to existing work that forces Euclidean structure, our method is valid for general metrics, assuming only symmetry and the triangle inequality. Furthermore, we demonstrate efficiency of our method empirically and theoretically, needing only O(n\log(n)\Delta^{-2}) queries in favorable settings, where \Delta^{-2} accounts for the effect of noise. Using crowd-sourced data collected for a subset of the UT~Zappos50K dataset, we apply our algorithm to learn which shoes people believe are most similar and show that it beats both an active baseline and ordinal embedding.
Considerations in the employment of blind computer professionals

According to sections 503 and 504 of the Rehabilitation Act of 1973, employers are required to hire qualified handicapped persons and, where necessary, to provide reasonable accommodations relating to accessibility to their actual job station and to work environs—including rest rooms, recreation areas, eating facilities, etc. Further, companies holding federal contracts are required to have an Affirmative Action Program to seek out qualified handicapped individuals as potential employees.
Segmentation of Cortical Spreading Depression Wavefronts Through Local Similarity Metric

In this paper, we present a novel region-based segmentation method for cortical spreading depressions in 2-photon microscopy images. Fluorescent microscopy has become an important tool in neuroscience, but segmentation approaches are challenged by the opaque properties and structures of brain tissue. These challenges are made more extreme when segmenting events such as cortical spreading depressions, where low signal-to-noise ratios and intensity inhomogeneity dominate images. The method we propose uses a local intensity similarity measure that takes advantage of normalized Euclidean and geodesic distance maps of the image. This method provides a smooth segmentation boundary which is robust to the noise and inhomogeneity within cortical spreading depression images. Experimental results yielded a DICE index of 0.9859, an increase of 6% over the current state-of-the-art, and a reduction of root mean square error by 79.9%.
Multi-resolution Hashing for Fast Pairwise Summations

A basic computational primitive in the analysis of massive datasets is summing simple functions over a large number of objects. Modern applications pose an additional challenge in that such functions often depend on a parameter vector y (query) that is unknown a priori. Given a set of points X and a pairwise function w(x,y), we study the problem of designing a data-structure that enables sub-linear time approximation of the summation of w(x,y) for all x in X for any query point y. By combining ideas from Harmonic Analysis (partitions of unity and approximation theory) with Hashing-Based-Estimators [Charikar, Siminelakis FOCS’17], we provide a general framework for designing such data structures through hashing that reaches far beyond what previous techniques allowed. A key design principle is constructing a collection of hash families, each inducing a different collision probability between points in the dataset, such that the pointwise supremum of the collision probabilities scales as the square root of the function w(x,y). This leads to a data-structure that approximates pairwise summations using a sub-linear number of samples from each hash family. Using this new framework along with Distance Sensitive Hashing [Aumuller, Christiani, Pagh, Silvestri PODS’18], we show that such a collection can be constructed and evaluated efficiently for log-convex functions of the inner product between two vectors. Our method leads to data structures with sub-linear query time that significantly improve upon random sampling and can be used for Kernel Density, Partition Function Estimation and sampling.
Dietary Phosphorus and FGF23: Is More Restriction Better?

Patients with ESKD undergoing hemodialysis are at high risk for cardiovascular events, which have been associated with abnormal phosphate homeostasis. Hyperphosphatemia and elevated serum fibroblast growth factor 23 (FGF23) cause vascular calcifications and left ventricular hypertrophy, respectively
What happens in acquisitions?: Evidence from brand ownership changes and advertising investment

Abstract We study advertising at the brand level in a sample of corporate acquisitions. New owners display an elevated propensity to sharply cut advertising in acquired brands. This behavior is most pronounced in private equity transactions. When a buyer's existing brands overlap with the acquired brands, aggregate advertising spending on the merged portfolio of brands tends to shift downward. Sharp advertising cuts are more likely to be observed when the old owner of the assets was investing at an elevated level and when the new owner has displayed past restraint in their investment spending activities. Combined buyer and seller abnormal returns are more positive in deals characterized by post-acquisition cuts in advertising, suggesting that these cuts often represent efficiency-enhancing cost savings.
Intracellular cholesterol trafficking is dependent upon NPC2 interaction with lysobisphosphatidic acid

Cholesterol is a type of fat that is essential for many processes in the body, such as repairing damaged cells and producing certain hormones. Normally, cholesterol enters cells from the bloodstream and is then moved to the parts of the cell that need it via a process known as ‘trafficking’. When cholesterol trafficking goes wrong, abnormally large amounts of cholesterol and other fats accumulate within the cell. Over time, these fatty deposits become toxic to cells and eventually damage the affected tissues. Niemann-Pick type C disease (NPC) is a severe genetic disorder affecting cholesterol trafficking. It is characterized by cholesterol build-up in multiple tissues, including the brain, which ultimately causes degeneration and death of nerve cells. Two proteins, NPC1 and NPC2, are involved in NPC disease. Both proteins normally help move cholesterol out of important trafficking compartments (known as the endosomal and lysosomal compartments) to other areas of the cell where it is needed. Patients with the disease can have mutations in either the gene for NPC1 or the gene for NPC2. This means that cells from NPC1 patients do not make enough functional NPC1 protein (but contain working NPC2), and vice versa. Previous studies had shown that giving cells with NPC1 mutations large amounts of the small molecule lysobisphosphatidic acid (LBPA for short) could compensate for the loss of NPC1, and stop the toxic build-up of cholesterol. McCauliff, Langan, Li et al. therefore wanted to explore exactly how LBPA was doing this. They had shown that LBPA dramatically increased the ability of purified NPC2 protein to transport cholesterol, and wondered if the effect of LBPA in the cells without NPC1 depended on NPC2. They predicted that boosting LBPA levels would not work in cells lacking NPC2. Biochemical experiments using purified protein showed that LBPA and NPC2 did indeed interact directly with each other. Systematically changing different building blocks of NPC2 revealed that a single region of the protein is sensitive to LBPA, and when this region was altered, LBPA could no longer interact with NPC2. Since LBPA is naturally produced by cells, they then stimulated cells grown in the laboratory to generate more LBPA using its precursor phosphatidylglycerol. They used cells from patients with mutations in either NPC1 or NPC2 and demonstrated that LBPA’s ability to reverse the accumulation of cholesterol was dependent on its interaction with NPC2. Thus, increasing LBPA levels in cells from patients with NPC1 mutations was beneficial, but had no effect on cells from patients with NPC2 mutations. These results shed new light not only on how cells transport cholesterol, but also on potential methods to combat disorders of cellular cholesterol trafficking. In the future, LBPA could be developed as a genetically tailored, patient-specific therapy for diseases like NPC.
ASSD: Attentive single shot multibox detector

Abstract This paper proposes a new deep neural network for object detection. The proposed network, termed ASSD, builds feature relations in the spatial space of the feature map. With the global relation information, ASSD learns to highlight useful regions on the feature maps while suppressing the irrelevant information, thereby providing reliable guidance for object detection. Compared to methods that rely on complicated CNN layers to refine the feature maps, ASSD is simple in design and is computationally efficient. Experimental results show that ASSD competes favorably with the state-of-the-arts, including SSD, DSSD, FSSD and RetinaNet. Code is available at: .
A new generation of effective core potentials from correlated calculations: 4s and 4p main group elements and first row additions.

Recently, we developed a new method for generating effective core potentials (ECPs) using valence energy isospectrality with explicitly correlated all-electron (AE) excitations and norm-conservation criteria. We apply this methodology to the 3rd-row main group elements, creating new correlation consistent ECPs (ccECPs) and also deriving additional ECPs to complete the ccECP table for H–Kr. For K and Ca, we develop Ne-core ECPs, and for the 4p main group elements, we construct [Ar]3d10-core potentials. Scalar relativistic effects are included in their construction. Our ccECPs reproduce AE spectra with significantly better accuracy than many existing pseudopotentials and show better overall consistency across multiple properties. The transferability of ccECPs is tested on monohydride and monoxide molecules over a range of molecular geometries. For the constructed ccECPs, we also provide optimized DZ-6Z valence Gaussian basis sets.
Constructing permutation arrays using partition and extension

We give new lower bounds for M(n, d), for various positive integers n and d with $$n>d$$, where M(n, d) is the largest number of permutations on n symbols with pairwise Hamming distance at least d. Large sets of permutations on n symbols with pairwise Hamming distance d are needed for constructing error correcting permutation codes, which have been proposed for power-line communications. Our technique, partition and extension, is universally applicable to constructing such sets for all n and all d, $$d<n$$. We describe three new techniques, sequential partition and extension, parallel partition and extension, and a modified Kronecker product operation, which extend the applicability of partition and extension in different ways. We describe how partition and extension gives improved lower bounds for $$M(n,n-1)$$ using mutually orthogonal Latin squares (MOLS). We present efficient algorithms for computing new partitions: an iterative greedy algorithm and an algorithm based on integer linear programming. These algorithms yield partitions of positions (or symbols) used as input to our partition and extension techniques. We report many new lower bounds for M(n, d) found using these techniques for n up to 600.
(“Oops! Had the silly thing in reverse”)—Optical injection attacks in through LED status indicators

It is possible to attack a computer remotely through the front panel LEDs. Following on previous results that showed information leakage at optical wavelengths, now it seems practicable to inject information into a system as well. It is shown to be definitely feasible under realistic conditions (by infosec standards) of target system compromise; experimental results suggest it further may be possible, through a slightly different mechanism, even under high security conditions that put extremely difficult constraints on the attacker. The problem is of recent origin; it could not have occurred before a confluence of unrelated technological developments made it possible. Arduino-type mi-crocontrollers are involved; this is an Internet of Things (IoT) vulnerability. Unlike some previous findings, the vulnerability here is moderate—at present—because it takes the infosec form of a classical covert channel. However, the architecture of several popular families of microcontrollers suggests that a Rowhammer-like directed energy optical attack that requires no malware might be possible. Phase I experiments yielded surprising and encouraging results; a covert channel is definitely practicable without exotic hardware, bandwidth approaching a Mbit/s, and the majority of discrete LEDs tested were found to be reversible on GPIO pins. Phase II experiments, not yet funded, will try to open the door remotely.
Exploit the Connectivity: Multi-Object Tracking with TrackletNet

Multi-object tracking (MOT) is an important topic and critical task related to both static and moving camera applications, such as traffic flow analysis, autonomous driving and robotic vision. However, due to unreliable detection, occlusion and fast camera motion, tracked targets can be easily lost, which makes MOT very challenging. Most recent works exploit spatial and temporal information for MOT, but how to combine appearance and temporal features is still not well addressed. In this paper, we propose an innovative and effective tracking method called TrackletNet Tracker (TNT) that combines temporal and appearance information together as a unified framework. First, we define a graph model which treats each tracklet as a vertex. The tracklets are generated by associating detection results frame by frame with the help of the appearance similarity and the spatial consistency. To compensate camera movement, epipolar constraints are taken into consideration in the association. Then, for every pair of two tracklets, the similarity, called the connectivity in the paper, is measured by our designed multi-scale TrackletNet. Afterwards, the tracklets are clustered into groups and each group represents a unique object ID. Our proposed TNT has the ability to handle most of the challenges in MOT, and achieves promising results on MOT16 and MOT17 benchmark datasets compared with other state-of-the-art methods.
PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings

For autonomous vehicles (AVs) to behave appropriately on roads populated by human-driven vehicles, they must be able to reason about the uncertain intentions and decisions of other drivers from rich perceptual information. Towards these capabilities, we present a probabilistic forecasting model of future interactions between a variable number of agents. We perform both standard forecasting and the novel task of conditional forecasting, which reasons about how all agents will likely respond to the goal of a controlled agent (here, the AV). We train models on real and simulated data to forecast vehicle trajectories given past positions and LIDAR. Our evaluation shows that our model is substantially more accurate in multi-agent driving scenarios compared to existing state-of-the-art. Beyond its general ability to perform conditional forecasting queries, we show that our model's predictions of all agents improve when conditioned on knowledge of the AV's goal, further illustrating its capability to model agent interactions.
Voice of the Customer: Creating Client Centered Cultures in Accounting Firms for Retaining Clients and Increasing Profitability

Client satisfaction and retention books line the shelves of library business sections and are frequently the subjects at conferences and online blogs. The relationship between client satisfaction and profitability is often assumed, yet not prioritized or measured. Instead, industries and companies are continuously developing new clients by selling their goods and services. The ability to compete simply on product or service no longer exists, and with the evolution of technology, customers are continuously inundated with noise. However, one could argue the greatest voice to take cues from is that of the client – meeting their needs and solving their problems. Using a case comparison method, we found that a combination of voice of the client (VoC) and “Kaizen” leads to higher levels of client satisfaction and retention levels in accounting firms.
Roadblocked monotonic paths and the enumeration of coalescent histories for non-matching caterpillar gene trees and species trees

Abstract Given a gene tree topology and a species tree topology, a coalescent history represents a possible mapping of the list of gene tree coalescences to associated branches of a species tree on which those coalescences take place. Enumerative properties of coalescent histories have been of interest in the analysis of relationships between gene trees and species trees. The simplest enumerative result identifies a bijection between coalescent histories for a matching caterpillar gene tree and species tree with monotonic paths that do not cross the diagonal of a square lattice, establishing that the associated number of coalescent histories for n-taxon matching caterpillar trees ( n ⩾ 2 ) is the Catalan number C n − 1 = 1 n ( 2 n − 2 n − 1 ) . Here, we show that a similar bijection applies for non-matching caterpillars, connecting coalescent histories for a non-matching caterpillar gene tree and species tree to a class of roadblocked monotonic paths. The result provides a simplified algorithm for enumerating coalescent histories in the non-matching caterpillar case. It enables a rapid proof of a known result that given a caterpillar species tree, no non-matching caterpillar gene tree has a number of coalescent histories exceeding that of the matching gene tree. Additional results on coalescent histories can be obtained by a bijection between permissible roadblocked monotonic paths and Dyck paths. We study the number of coalescent histories for non-matching caterpillar gene trees that differ from the species tree by nearest-neighbor-interchange and subtree-prune-and-regraft moves, characterizing the non-matching caterpillar with the largest number of coalescent histories. We discuss the implications of the results for the study of the combinatorics of gene trees and species trees.
Probing universalities in d > 2 CFTs: from black holes to shockwaves

Gravitational shockwaves are insensitive to higher-curvature corrections in the action. Recent work found that the OPE coefficients of lowest-twist multi-stress-tensor operators, computed holographically in a planar black hole background, are insensitive as well. In this paper, we analyze the relation between these two limits. We explicitly evaluate the two-point function on a shockwave background to all orders in a large central charge expansion. In the geodesic limit, we find that the ANEC exponentiates in the multi-stress-tensor sector. To compare with the black hole limit, we obtain a recursion relation for the lowest-twist products of two stress tensors in a spherical black hole background, letting us efficiently compute their OPE coefficients and prove their insensitivity to higher curvature terms. After resumming the lowest-twist stress-tensors and analytically continuing their contributions to the Regge limit, we find a perfect agreement with the shockwave computation. We also discuss the role of double-trace operators, global degenerate states, and multi-stress-tensor conformal blocks. These holographic results suggest the existence of a larger universal structure in higher-dimensional CFTs.
Waterfall Atrous Spatial Pooling Architecture for Efficient Semantic Segmentation.

We propose a new efficient architecture for semantic segmentation, based on a “Waterfall” Atrous Spatial Pooling architecture, that achieves a considerable accuracy increase while decreasing the number of network parameters and memory footprint. The proposed Waterfall architecture leverages the efficiency of progressive filtering in the cascade architecture while maintaining multiscale fields-of-view comparable to spatial pyramid configurations. Additionally, our method does not rely on a postprocessing stage with Conditional Random Fields, which further reduces complexity and required training time. We demonstrate that the Waterfall approach with a ResNet backbone is a robust and efficient architecture for semantic segmentation obtaining state-of-the-art results with significant reduction in the number of parameters for the Pascal VOC dataset and the Cityscapes dataset.
Absence of many-body localization in a single Landau level

The localization properties of the highly excited states of a system projected into a single Landau level are analyzed. An analysis of level spacing ratios for finite size systems shows a clear crossover from extended (GUE) to localized (Poisson) statistics, indicating a many-body localization transition. However, the location of this transition depends very strongly on system size, and appears to scale to infinite disorder in the thermodynamic limit. This result does not depend on the properties of the ground state (such as whether the ground state exhibits topological order), as expected for a transition of highly excited eigenstates. We therefore conclude that many-body localization does not exist in these systems. Our results demonstrate that a subthermodynamic number of single particle effectively extended states is sufficient to cause all many-body states to become extended.
Physical Processes in Protoplanetary Disks

This review, based on lectures given at the 45th Saas-Fee Advanced Course “From Protoplanetary Disks to Planet Formation”, introduces physical processes in protoplanetary disks relevant to accretion and the initial stages of planet formation. After a brief overview of the observational context, I introduce the elementary theory of disk structure and evolution, review the gas-phase physics of angular momentum transport through turbulence and disk winds, and discuss possible origins for the episodic accretion observed in Young Stellar Objects. Turning to solids, I review the evolution of single particles under aerodynamic forces, and describe the conditions necessary for the development of collective gas-particle instabilities. Observations show that disks can exhibit pronounced large-scale structure, and I discuss the types of structures that may form from gas and particle interactions at ice lines, vortices and zonal flows, prior to the formation of large planetary bodies. I conclude with disk dispersal.
Span Recovery for Deep Neural Networks with Applications to Input Obfuscation

The tremendous success of deep neural networks has motivated the need to better understand the fundamental properties of these networks, but many of the theoretical results proposed have only been for shallow networks. In this paper, we study an important primitive for understanding the meaningful input space of a deep network: \textit{span recovery}. For $k<n$, let $\AA \in \R^{k \times n}$ be the innermost weight matrix of an arbitrary feed forward neural network $M:\R^n \to \R$, so $M(x)$ can be written as $M(x) = \sigma(\AA x)$, for some network $\sigma:\R^k \to \R$. The goal is then to recover the row span of $\AA$ given only oracle access to the value of $M(x)$. We show that if $M$ is a multi-layered network with ReLU activation functions, then partial recovery is possible: namely, we can provably recover $k/2$ linearly independent vectors in the row span of $\AA$ using $\poly(n)$ non-adaptive queries to $M(x)$. Furthermore, if $M$ has differentiable activation functions, we demonstrate that \textit{full} span recovery is possible even when the output is first passed through a sign or $0/1$ thresholding function; in this case our algorithm is adaptive. Empirically, we confirm that full span recovery is not always possible, but only for unrealistically thin layers. For reasonably wide networks, we obtain full span recovery on both random networks and networks trained on MNIST data. Furthermore, we demonstrate the utility of span recovery as an attack by inducing neural networks to misclassify data obfuscated by controlled random noise as sensical inputs.
Signed Topological Measures on Locally Compact Spaces

In this paper we define and study signed deficient topological measures and signed topological measures (which generalize signed measures) on locally compact spaces. We prove that a signed deficient topological measure is τ-smooth on open sets and τ-smooth on compact sets. We show that the family of signed measures that are differences of two Radon measures is properly contained in the family of signed topological measures, which in turn is properly contained in the family of signed deficient topological measures. Extending known results for compact spaces, we prove that a signed topological measure is the difference of its positive and negative variations if at least one variation is finite; we also show that the total variation is the sum of the positive and negative variations. If the space is locally compact, connected, locally connected, and has the Alexandrov one-point compactification of genus 0, a signed topological measure of finite norm can be represented as a difference of two topological measures.
LEARNING EXECUTION THROUGH NEURAL CODE FUSION

As the performance of computer systems stagnates due to the end of Moore’s Law, there is a need for new models that can understand and optimize the execution of general purpose code. While there is a growing body of work on using Graph Neural Networks (GNNs) to learn static representations of source code, these representations do not understand how code executes at runtime. In this work, we propose a new approach using GNNs to learn fused representations of general source code and its execution. Our approach defines a multi-task GNN over low-level representations of source code and program state (i.e., assembly code and dynamic memory states), converting complex source code constructs and data structures into a simpler, more uniform format. We show that this leads to improved performance over similar methods that do not use execution and it opens the door to applying GNN models to new tasks that would not be feasible from static code alone. As an illustration of this, we apply the new model to challenging dynamic tasks (branch prediction and prefetching) from the SPEC CPU benchmark suite, outperforming the state-of-the-art by 26% and 45% respectively. Moreover, we use the learned fused graph embeddings to demonstrate transfer learning with high performance on an indirectly related algorithm classification task.
A Dual-Radix Approach to Steiner’s 1-Cycle Theorem

This article presents three algebraic proofs of Steiner’s 1-Cycle Theorem [14] within the context of the (accelerated) \(3x+1\) dynamical system. Furthermore, under an assumption of an exponential upper-bound on the iterates, the article demonstrates that the only 1-cycles in the (accelerated) \(3x-1\) dynamical system are (1) and (5, 7).
ROC-guided survival trees and ensembles.

Tree-based methods are popular nonparametric tools in studying time-to-event outcomes. In this article, we introduce a novel framework for survival trees and ensembles, where the trees partition the dynamic survivor population and can handle time-dependent covariates. Using the idea of randomized tests, we develop generalized time-dependent receiver operating characteristic (ROC) curves for evaluating the performance of survival trees. The tree-building algorithm is guided by decision-theoretic criteria based on ROC, targeting specifically for prediction accuracy. To address the instability issue of a single tree, we propose a novel ensemble procedure based on averaging martingale estimating equations, which is different from existing methods that average the predicted survival or cumulative hazard functions from individual trees. Extensive simulation studies are conducted to examine the performance of the proposed methods. We apply the methods to a study on AIDS for illustration.