cssi/SciSciGPT-SciSciNet huggingface dataset:
Counting Permutations Modulo Pattern-Replacement Equivalences for Three-Letter Patterns

We study a family of equivalence relations on $S_n$, the group of permutations on $n$ letters, created in a manner similar to that of the Knuth relation and the forgotten relation. For our purposes, two permutations are in the same equivalence class if one can be reached from the other through a series of pattern-replacements using patterns whose order permutations are in the same part of a predetermined partition of $S_c$. When the partition is of $S_3$ and has one nontrivial part and that part is of size greater than two, we provide formulas for the number of classes created in all cases left unresolved by past authros. When the partition is of $S_3$ and has two nontrivial parts, each of size two (as do the Knuth and forgotten relations), we enumerate the classes for 13 of the 14 unresolved cases. In two of these cases, enumerations arise which are the same as those yielded by the Knuth and forgotten relations. The reasons for this phenomenon are still largely a mystery.
Solving multistage influence diagrams using branch-and-bound search

A branch-and-bound approach to solving influence diagrams has been previously proposed in the literature, but appears to have never been implemented and evaluated - apparently due to the difficulties of computing effective bounds for the branch-and-bound search. In this paper, we describe how to efficiently compute effective bounds, and we develop a practical implementation of depth-first branch-and-bound search for influence diagram evaluation that outperforms existing methods for solving influence diagrams with multiple stages.
Volume and topology of bounded and closed hyperbolic 3-manifolds

Let N be a compact, orientable hyperbolic 3-manifold with connected, totally geodesic boundary of genus 2. If N has Heegaard genus at least 5, then its volume is greater than 6.89. The proof of this result uses the following dichotomy: either N has a long return path (defined by Kojima-Miyamoto), or N has an embedded, codimension-0 submanifold X with incompressible boundary $T \sqcup \partial N$, where T is the frontier of X in N, which is not a book of I-bundles. As an application of this result, we show that if M is a closed, orientable hyperbolic 3-manifold such that H_1(M;Z_2) has dimension at least 5, and if the image in H^2(M;Z_2) of the cup product map has image of dimension at most 1, then M has volume greater than 3.44.
What Actions are Needed for Understanding Human Actions in Videos

What is the right way to reason about human activities? What directions forward are most promising? In this work, we analyze the current state of human activity understanding in videos. The goal of this paper is to examine datasets, evaluation metrics, algorithms, and potential future directions. We look at the qualitative attributes that define activities such as pose variability, brevity, and density. The experiments consider multiple state-of-the-art algorithms and multiple datasets. The results demonstrate that while there is inherent ambiguity in the temporal extent of activities, current datasets still permit effective benchmarking. We discover that fine-grained understanding of objects and pose when combined with temporal reasoning is likely to yield substantial improvements in algorithmic accuracy. We present the many kinds of information that will be needed to achieve substantial gains in activity understanding: objects, verbs, intent, and sequential reasoning. The software and additional information will be made available to provide other researchers detailed diagnostics to understand their own algorithms.
Homological mirror symmetry of elementary birational cobordisms

The derived category of coherent sheaves $${\mathcal {T}}_B$$ associated to a birational cobordism which is either a weighted projective space, a stacky Atiyah flip, or a stacky blow-up of a point has a conjectural mirror Fukaya–Seidel category $${\mathcal {T}}_A$$ . The potential W defining $${\mathcal {T}}_{A}$$ has base $${\mathbb {C}}^*$$ and exhibits a great deal of symmetry. This paper investigates the structure of the Fukaya–Seidel category for the mirror potentials. A proof of homological mirror symmetry $${\mathcal {T}}_A \cong {\mathcal {T}}_B$$ for these birational cobordisms is then given.
BRAIDED CHORD DIAGRAMS

The notion of a braided chord diagram is introduced and studied. An equivalence relation is given which identifies all braidings of a fixed chord diagram. It is shown that finite-type invariants are stratified by braid index for knots which can be represented as closed 3-braids. Partial results are obtained about spanning sets for the algebra of chord diagrams of braid index 3.
Relations such as Hypernymy: Identifying and Exploiting Hearst Patterns in Distributional Vectors for Lexical Entailment

We consider the task of predicting lexical entailment using distributional vectors. We perform a novel qualitative analysis of one existing model which was previously shown to only measure the prototypicality of word pairs. We find that the model strongly learns to identify hypernyms using Hearst patterns, which are well known to be predictive of lexical relations. We present a novel model which exploits this behavior as a method of feature extraction in an iterative procedure similar to Principal Component Analysis. Our model combines the extracted features with the strengths of other proposed models in the literature, and matches or outperforms prior work on multiple data sets.
Capacitated Dynamic Programming: Faster Knapsack and Graph Algorithms.

One of the most fundamental problems in Theoretical Computer Science is the Knapsack problem. Given a set of n items with different weights and values, it asks to pick the most valuable subset whose total weight is below a capacity threshold T. Despite its wide applicability in various areas in computer science, operations research, and finance, the best known running time for the problem is O(T n). The main result of our work is an improvement of this algorithm for the special case where the number of distinct weights D is small, running in time O(T D). Previously, better algorithms for knapsack were known only for cases with weights and values bounded by M and V respectively, running in time O(nMV). In comparison, our algorithm implies a bound of O(nM^2) without any dependence on V. Additionally, for the unbounded knapsack problem, we provide an algorithm running in time O(M^2 log T). We also initiate a systematic study of general capacitated dynamic programming, of which knapsack is core problem. This problem asks to compute the maximum weight path of length k in an edge- or node-weighted directed acyclic graph. In a graph with m edges, these problems are solvable by dynamic programming in time O(km), and we explore under which conditions the dependence on k can be eliminated. We identify large classes of graphs where this is possible and apply our results to obtain linear time algorithms for the problem of k-sparse Delta-separated sequences. The main technical innovation behind our results is identifying and exploiting concavity that appears in relaxations and subproblems of the tasks we consider.
Assigned Versus Chosen Relative Performance Information: The Effect of Feedback Frequency on Performance

ABSTRACT Technological advancements have greatly improved the ability to provide relative performance information (RPI), including information systems capable of providing feedback on demand. We us...
Lower Bounds for Small Ramsey Numbers on Hypergraphs.

The Ramsey number \(r_k(p, q)\) is the smallest integer N that satisfies for every red-blue coloring on k-subsets of [N], there exist p integers such that any k-subset of them is red, or q integers such that any k-subset of them is blue. In this paper, we study the lower bounds for small Ramsey numbers on hypergraphs by constructing counter-examples and recurrence relations. We present a new algorithm to prove lower bounds for \(r_k(k+1, k+1)\). In particular, our algorithm is able to prove \(r_5(6,6) \ge 72\), where there is no lower bound on 5-hypergraphs before this work. We also provide several recurrence relations to calculate lower bounds based on lower bound values on smaller p and q. Combining both of them, we achieve new lower bounds for \(r_k(p, q)\) on arbitrary p, q, and \(k \ge 4\).
Acting Is Seeing: Navigating Tight Space Using Flapping Wings

Wings of flying animals can not only generate lift and control torques but also can sense their surroundings. Such dual functions of sensing and actuation coupled in one element are particularly useful for small sized bio-inspired robotic flyers, whose weight, size, and power are under stringent constraint. In this work, we present the first flapping-wing robot using its flapping wings for environmental perception and navigation in tight space, without the need for any visual feedback. As the test platform, we introduce the Purdu Hummingbird, a flapping-wing robot with 17cm wingspan and 12 grams weight, with a pair of 30-40Hz flapping wings driven by only two actuators. By interpreting the wing loading feedback and its variations, the vehicle can detect the presence of environmental changes such as grounds, walls, stairs, obstacles and wind gust. The instantaneous wing loading can be obtained through the measurements and interpretation of the current feedback by the motors that actuate the wings. The effectiveness of the proposed approach is experimentally demonstrated on several challenging flight tasks without vision: terrain following, wall following and going through a narrow corridor. To ensure flight stability, a robust controller was designed for handling unforeseen disturbances during the flight. Sensing and navigating one’s environment through actuator loading is a promising method for mobile robots, and it can serve as an alternative or complementary method to visual perception.
Linking artificial and human neural representations of language.

What information from an act of sentence understanding is robustly represented in the human brain? We investigate this question by comparing sentence encoding models on a brain decoding task, where the sentence that an experimental participant has seen must be predicted from the fMRI signal evoked by the sentence. We take a pre-trained BERT architecture as a baseline sentence encoding model and fine-tune it on a variety of natural language understanding (NLU) tasks, asking which lead to improvements in brain-decoding performance. We find that none of the sentence encoding tasks tested yield significant increases in brain decoding performance. Through further task ablations and representational analyses, we find that tasks which produce syntax-light representations yield significant improvements in brain decoding performance. Our results constrain the space of NLU models that could best account for human neural representations of language, but also suggest limits on the possibility of decoding fine-grained syntactic information from fMRI human neuroimaging.
Toward Modularization of Neural Network Autonomous Driving Policy Using Parallel Attribute Networks

Neural network autonomous driving policies are widely explored. However, no matter using imitation learning or reinforcement learning, the network policies are generally hard to train, and the learned knowledge encoded in neural network policies are hard to transfer. We propose to modularize the complicated driving policies in terms of the driving attributes, and present the parallel attribute networks (PAN), which can learn to fullfill the requirements of the attributes in the driving tasks separately, and later assemble their knowledge together. Concretely, we first train a policy network that accomplish the base lane tracking attribute. The modules for the add-on attributes such as avoiding obstacles and obeying traffic rules are then trained to map the corresponding state to a satisfactory set of the vehicle action space. Finally the reference action given by the base policy is projected into the satisfactory sets so as to satisfy the requirements of all the attributes. Using the PAN, many complicated tasks that are hard to train from scratch can be easily trained; also unseen driving tasks can be solved in a zero-shot manner by assembling the pretrained attribute modules. We have validated the capability of our model on a class of autonomous driving problems with attributes of obstacle avoidance, traffic light and speed limit in simulation. Experimental results based on an obstacle avoidance task are also presented.
Executives’ Legal Records and the Deterrent Effect of Corporate Governance

We study whether the effectiveness of corporate governance mechanisms varies depending on the characteristics of the executives subject to these mechanisms, namely their “psychological type,” as proxied by their history of legal infractions. In particular, we examine insider trading, where we can compare the trading behavior of different types of executives in the same firm. We find that “recordholder” executives, that is, those with prior legal infractions, earn significantly higher profits from purchases and sales than nonrecordholder executives. Furthermore, the profitability of both purchases and sales is significantly increasing in the severity of the infraction. Governance mechanisms, such as blackout policies, lower profits of executives with only traffic infractions; however, profits for executives with serious infractions appear insensitive to blackout policies. Insiders with serious infractions are also more likely to trade during blackout periods and before large information events and are more likely to report their trades to the SEC after the filing deadline. Collectively, our evidence suggests that while governance mechanisms can discipline executives with minor offenses, they appear largely ineffective for those with more serious infractions. Dossiers juridiques des dirigeants et effet dissuasif de la gouvernance d'entreprise Les auteurs se demandent si l'efficacite des mecanismes de gouvernance d'entreprise varie en fonction des caracteristiques des dirigeants qui y sont soumis — plus precisement selon leur « type psychologique » tel qu'en temoigne leur historique d'infractions a la loi. Ils se penchent en particulier sur les delits d'inities, qui leur permettent de comparer le comportement de negociation de differents types de dirigeants d'une meme societe. Ils constatent que les dirigeants qui ont un dossier juridique, soit ceux qui ont commis des infractions dans le passe, tirent de leurs operations d'achat et de vente des profits sensiblement plus eleves que les dirigeants qui n'en ont pas. En outre, la rentabilite des operations d'achat aussi bien que de vente augmente sensiblement avec la gravite de l'infraction. Les mecanismes de gouvernance, tels que les politiques d'interdiction, diminuent les profits des dirigeants qui n'ont commis que des infractions aux reglements de la circulation; les politiques d'interdiction ne semblent cependant pas avoir d'incidence sur les profits des dirigeants ayant commis des infractions graves. Les inities qui ont commis des infractions graves sont egalement davantage susceptibles de negocier durant les periodes d'interdiction et avant les livraisons d'information importantes et de declarer leurs operations a la SEC apres l’echeance. Dans l'ensemble, les donnees recueillies par les auteurs semblent indiquer que, meme si les mecanismes de gouvernance peuvent avoir un effet dissuasif sur les dirigeants ayant commis des infractions legeres, ils semblent en grande partie inefficaces dans le cas des dirigeants coupables d'infractions plus lourdes.
Lattice Boltzmann models for micro-tomographic pore-spaces

Abstract The lattice Boltzmann method (LBM) is a popular numerical framework to investigate single and multiphase flow though porous media. For estimation of absolute permeability based on micro-tomographic images of a porous medium, the single-relaxation time (SRT) collision model is the most widely-used, although the multiple-relaxation-time (MRT) collision model also has recently acquired wider usage, especially for industrial applications. However, the SRT collision model and a sub-optimal choice of the MRT collision parameters can both lead to permeability predictions that depend on the relaxation parameter, τ. This parametric dependence is nonphysical for Stokes flow in porous media and also leads to much larger number of iterations required for convergence. In this paper, we performed a systematic numerical evaluation of the different sets of relaxation parameters in the D3Q19-MRT model for modeling Stokes flow in 3D microtomographic pore-spaces using the bounceback scheme. These sets of parameters are evaluated from the point of view of accuracy, convergence rate, and an ability to generate parameter-independent permeability solutions. Instead of tuning all six independent relaxation rates that are available in the MRT model, the sets that were analyzed have relaxation rates that depend on one or two independent parameters, namely τ and Λ. We tested elementary porous media at different image resolutions and a random packing of spheres at relatively high resolution. We observe that sets of certain specific relaxation parameters (Sets B, D, or E as listed in Table 2), and τ in the range τ ∈ [1.0, 1.3] can result in best overall accuracy, convergence rate, and parameter-independent permeability predictions.
Telecom photon interface of solid-state quantum nodes

Solid-state spins such as nitrogen-vacancy (NV) center are promising platforms for large-scale quantum networks. Despite the optical interface of NV center system, however, the significant attenuation of its zero-phonon-line photon in optical fiber prevents the network extended to long distances. Therefore a telecom-wavelength photon interface would be essential to reduce the photon loss in transporting quantum information. Here we propose an efficient scheme for coupling telecom photon to NV center ensembles mediated by rare-earth doped crystal. Specifically, we proposed protocols for high fidelity quantum state transfer and entanglement generation with parameters within reach of current technologies. Such an interface would bring new insights into future implementations of long-range quantum network with NV centers in diamond acting as quantum nodes.
Self-triggered Control for Safety Critical Systems Using Control Barrier Functions

We propose a real-time control strategy that combines self-triggered control with Control Lyapunov Functions (CLF) and Control Barrier Functions (CBF). Similar to related works proposing CLF-CBF-based controllers, the computation of the controller is achieved by solving a Quadratic Program (QP). However, we propose a Zeroth-Order Hold (ZOH) implementation of the controller that overcomes the main limitations of traditional approaches based on periodic controllers, i.e., unnecessary controller updates and potential violations of the safety constraints. Central to our approach is the novel notion of safe period, which enforces a strong safety guarantee for implementing ZOH control. In addition, we prove that the system does not exhibit a Zeno behavior as it approaches the desired equilibrium.
Neurostructural Heterogeneity in Youths With Internalizing Symptoms.

Abstract Background Internalizing disorders such as anxiety and depression are common psychiatric disorders that frequently begin in youth and exhibit marked heterogeneity in treatment response and clinical course. Given that symptom-based classification approaches do not align with underlying neurobiology, an alternative approach is to identify neurobiologically informed subtypes based on brain imaging data. Methods We used a recently developed semisupervised machine learning method (HYDRA [heterogeneity through discriminative analysis]) to delineate patterns of neurobiological heterogeneity within youths with internalizing symptoms using structural data collected at 3T from a sample of 1141 youths. Results Using volume and cortical thickness, cross-validation methods indicated 2 highly stable subtypes of internalizing youths (adjusted Rand index = 0.66; permutation-based false discovery rate p Conclusions We identified 2 subtypes of internalizing youths differentiated by abnormalities in brain structure, function, and white matter integrity, with one of the subtypes showing poorer functioning across multiple domains. Identification of biologically grounded internalizing subtypes may assist in targeting early interventions and assessing longitudinal prognosis.
Autonomous Discovery in the Chemical Sciences Part II: Outlook.

This two-part Review examines how automation has contributed to different aspects of discovery in the chemical sciences. In this second part, we reflect on a selection of exemplary studies. It is increasingly important to articulate what the role of automation and computation has been in the scientific process and how that has or has not accelerated discovery. One can argue that even the best automated systems have yet to "discover" despite being incredibly useful as laboratory assistants. We must carefully consider how they have been and can be applied to future problems of chemical discovery in order to effectively design and interact with future autonomous platforms. The majority of this Review defines a large set of open research directions, including improving our ability to work with complex data, build empirical models, automate both physical and computational experiments for validation, select experiments, and evaluate whether we are making progress towards the ultimate goal of autonomous discovery. Addressing these practical and methodological challenges will greatly advance the extent to which autonomous systems can make meaningful discoveries.
Medical device surveillance with electronic health records

Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements—one of the most common implantable devices—as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8–53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t = 5.14; p < 0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.
Practical Design Space Exploration

Multi-objective optimization is a crucial matter in computer systems design space exploration because real-world applications often rely on a trade-off between several objectives. Derivatives are usually not available or impractical to compute and the feasibility of an experiment can not always be determined in advance. These problems are particularly difficult when the feasible region is relatively small, and it may be prohibitive to even find a feasible experiment, let alone an optimal one. We introduce a new methodology and corresponding software framework, HyperMapper 2.0, which handles multi-objective optimization, unknown feasibility constraints, and categorical/ordinal variables. This new methodology also supports injection of the user prior knowledge in the search when available. All of these features are common requirements in computer systems but rarely exposed in existing design space exploration systems. The proposed methodology follows a white-box model which is simple to understand and interpret (unlike, for example, neural networks) and can be used by the user to better understand the results of the automatic search. We apply and evaluate the new methodology to the automatic static tuning of hardware accelerators within the recently introduced Spatial programming language, with minimization of design run-time and compute logic under the constraint of the design fitting in a target field-programmable gate array chip. Our results show that HyperMapper 2.0 provides better Pareto fronts compared to state-of-the-art baselines, with better or competitive hypervolume indicator and with 8x improvement in sampling budget for most of the benchmarks explored.
Intuition versus Analytical Thinking and Impairment Testing

We examine the use of intuition versus analytical thinking in auditor risk assessment using a task that requires auditors to assess a group of impairment indicators. We expect that auditor intuition, rooted in the subconscious, more likely reacts to impairment indicator risk than does auditor analytical thinking. Results from two different experiments support this expectation for less‐experienced audit seniors. These seniors are more likely to assess step‐zero impairment indicators as signaling potential impairment when prompted to think intuitively versus analytically. In contrast, a third experiment finds that experienced seniors are more likely to assess step‐zero impairment indicators as signaling potential impairment when prompted to think analytically versus intuitively. This is consistent with the more experienced but still non‐expert seniors possessing developed analytical thinking, but struggling to effectively use their intuition. Our results inform theory by suggesting under what conditions auditor intuition and analytical thinking produce differential risk sensitivity. Furthermore, our results inform practice, given regulators' stated focus on auditor skepticism and impairment assessments. L'intuition par rapport au raisonnement analytique dans le cadre des tests de depreciation Nous examinons le recours a l'intuition comparativement au raisonnement analytique lorsque les auditeurs evaluent les risques, en mettant en oeuvre une tâche au cours de laquelle ils doivent evaluer une serie d'indicateurs de depreciation. Nous nous attendons a ce que l'intuition des auditeurs, ancree dans leur subconscient, soit plus susceptible que leur raisonnement analytique de reagir a un risque associe a un indicateur de depreciation. Les resultats de deux experiences distinctes soutiennent cette prediction dans le cas des charges de mission moins experimentes. Ceux‐ci sont plus susceptibles de conclure que certains indicateurs de depreciation a l’etape zero du test revelent une depreciation possible lorsqu'ils sont invites a reflechir de facon intuitive plutot qu'analytique. A l'inverse, une troisieme experience etablit que les charges de mission experimentes sont plus susceptibles d'estimer que ce type d'indicateurs a l’etape zero revelent une depreciation possible lorsqu'ils sont invites a reflechir de facon analytique plutot qu'intuitive. Ce resultat est coherent avec celui des charges de mission plus experimentes, mais qui ne sont pas encore des experts de la depreciation, qui possedent de fortes capacites de reflexion analytique, mais peinent a recourir efficacement a leur intuition. Nos resultats enrichissent la theorie en indiquant dans quelles conditions l'intuition ainsi que le raisonnement analytique des auditeurs produisent des niveaux de sensibilite differents aux risques. En outre, nos resultats renseignent la pratique comptable, etant donne l'accent mis par les organismes de reglementation sur le scepticisme des auditeurs et l’evaluation de la depreciation.
Improved sanitation increases long-term cognitive test scores

Abstract Poor sanitation has large negative impacts on environmental quality, health, and well-being. Sanitation infrastructure is particularly lacking in India, where in 2011, 66% of households did not own a toilet. Inadequate sanitation is a large contributor to diarrheal-related diseases, which cause 300,000 deaths in Indian children each year. We exploit an experimental sanitation campaign in rural Odisha, India to examine the relationship between sanitation improvements in early childhood and long-term cognitive development. We build on literature linking child health improvements to cognitive development and labor market outcomes and show that improvements in sanitation coverage can have large human capital returns. Using treatment assignment as an instrument for village latrine coverage, we find that children who belonged to a village with higher latrine coverage scored significantly higher on a cognitive test measuring analytic ability ten years later. We find that this effect is much stronger among girls than boys.
Structural pathway for allosteric activation of the autophagic PI 3-kinase complex I

Autophagy induction by starvation and stress involves the enzymatic activation of the class III phosphatidylinositol (PI) 3-kinase complex I (PI3KC3-C1). The inactive basal state of PI3KC3-C1 is maintained by inhibitory contacts between the VPS15 protein kinase and VPS34 lipid kinase domains that restrict the conformation of the VPS34 activation loop. Here, the proautophagic MIT domain-containing protein NRBF2 was used to map the structural changes leading to activation. Cryoelectron microscopy was used to visualize a 2-step PI3KC3-C1 activation pathway driven by NRFB2 MIT domain binding. Binding of a single NRBF2 MIT domain bends the helical solenoid of the VPS15 scaffold, displaces the protein kinase domain of VPS15, and releases the VPS34 kinase domain from the inhibited conformation. Binding of a second MIT stabilizes the VPS34 lipid kinase domain in an active conformation that has an unrestricted activation loop and is poised for access to membranes.
Mechanically resolved imaging of bacteria using expansion microscopy.

Imaging dense and diverse microbial communities has broad applications in basic microbiology and medicine, but remains a grand challenge due to the fact that many species adopt similar morphologies. While prior studies have relied on techniques involving spectral labeling, we have developed an expansion microscopy method (μExM) in which bacterial cells are physically expanded prior to imaging. We find that expansion patterns depend on the structural and mechanical properties of the cell wall, which vary across species and conditions. We use this phenomenon as a quantitative and sensitive phenotypic imaging contrast orthogonal to spectral separation to resolve bacterial cells of different species or in distinct physiological states. Focusing on host–microbe interactions that are difficult to quantify through fluorescence alone, we demonstrate the ability of μExM to distinguish species through an in vitro defined community of human gut commensals and in vivo imaging of a model gut microbiota, and to sensitively detect cell-envelope damage caused by antibiotics or previously unrecognized cell-to-cell phenotypic heterogeneity among pathogenic bacteria as they infect macrophages.
Markov perfect equilibria in non-stationary mean-field games

In this paper, we consider both finite and infinite horizon discounted dynamic mean-field games where there is a large population of homogeneous players sequentially making strategic decisions and each player is affected by other players through an aggregate population state. Each player has a private type that only she observes. Such games have been studied in the literature under simplifying assumption that population state dynamics are stationary. In this paper, we consider non-stationary population state dynamics and present a novel backward recursive algorithm to compute Markov perfect equilibrium (MPE) that depend on both, a player's private type, and current (dynamic) population state. Using this algorithm, we study a security problem in cyberphysical system where infected nodes put negative externality on the system, and each node makes a decision to get vaccinated. We numerically compute MPE of the game.
SwiftOrtho: A fast, memory-efficient, multiple genome orthology classifier

Background Gene homology type classification is required for many types of genome analyses, including comparative genomics, phylogenetics, and protein function annotation. Consequently, a large variety of tools have been developed to perform homology classification across genomes of different species. However, when applied to large genomic data sets, these tools require high memory and CPU usage, typically available only in computational clusters. Findings Here we present a new graph-based orthology analysis tool, SwiftOrtho, which is optimized for speed and memory usage when applied to large-scale data. SwiftOrtho uses long k-mers to speed up homology search, while using a reduced amino acid alphabet and spaced seeds to compensate for the loss of sensitivity due to long k-mers. In addition, it uses an affinity propagation algorithm to reduce the memory usage when clustering large-scale orthology relationships into orthologous groups. In our tests, SwiftOrtho was the only tool that completed orthology analysis of proteins from 1,760 bacterial genomes on a computer with only 4 GB RAM. Using various standard orthology data sets, we also show that SwiftOrtho has a high accuracy. Conclusions SwiftOrtho enables the accurate comparative genomic analyses of thousands of genomes using low-memory computers. SwiftOrtho is available at https://github.com/Rinoahu/SwiftOrtho.
On Monomial Ideals and Their Socles

For a finite subset M ⊂ [x1,…, xd] of monomials, we describe how to constructively obtain a monomial ideal $I\subseteq R = K[x_{1},\ldots ,x_{d}]$ such that the set of monomials in Soc(I) ∖ I is precisely M, or such that $\overline {M}\subseteq R/I$ is a K-basis for the the socle of R/I. For a given M we obtain a natural class of monomials ideals I with this property. This is done by using solely the lattice structure of the monoid [x1,…, xd]. We then present some duality results by using anti-isomorphisms between upsets and downsets of the lattice $({\mathbb {Z}}^{d},\preceq )$ . Finally, we define and analyze zero-dimensional monomial ideals of R of type k, where type 1 are exactly the Artinian Gorenstein ideals, and describe the structure of such ideals that correspond to order-generic antichains in ${\mathbb {Z}}^{d}$ .
All-FIT: allele-frequency-based imputation of tumor purity from high-depth sequencing data

Summary Clinical sequencing aims to identify somatic mutations in cancer cells for accurate diagnosis and treatment. However, most widely used clinical assays lack patient-matched control DNA and additional analysis is needed to distinguish somatic and unfiltered germline variants. Such computational analyses require accurate assessment of tumor cell content in individual specimens. Histological estimates often do not corroborate with results from computational methods that are primarily designed for normal-tumor matched data and can be confounded by genomic heterogeneity and presence of sub-clonal mutations. Allele-frequency-based imputation of tumor (All-FIT) is an iterative weighted least square method to estimate specimen tumor purity based on the allele frequencies of variants detected in high-depth, targeted, clinical sequencing data. Using simulated and clinical data, we demonstrate All-FIT's accuracy and improved performance against leading computational approaches, highlighting the importance of interpreting purity estimates based on expected biology of tumors. Availability and implementation Freely available at http://software.khiabanian-lab.org. Supplementary information Supplementary data are available at Bioinformatics online.
Heterogeneous Angular Spectrum Method for Trans-Skull Imaging and Focusing

Ultrasound, alone or in concert with circulating microbubble contrast agents, has emerged as a promising modality for therapy and imaging of brain diseases. While this has become possible due to advancements in aberration correction methods, a range of applications, including adaptive focusing and tracking of the microbubble dynamics through the human skull, may benefit from even more computationally efficient methods to account for skull aberrations. Here, we derive a general method for the angular spectrum approach (ASA) in a heterogeneous medium, based on a numerical marching scheme to approximate the full implicit solution. We then demonstrate its functionality with simulations for (human) skull-related aberration correction and trans-skull passive acoustic mapping. Our simulations show that the general solution provides accurate trans-skull focusing as compared to the uncorrected case (error in focal point location of 1.0 ± 0.4 mm vs 2.2 ± 0.7 mm) for clinically relevant frequencies (0.25–1.5MHz), apertures (50–100 mm), and targets, with peak focal pressures approximately 30 ± 17% of the free field case, with the effects of skull attenuation and amplitude shading included. In the case of source localization, our method leads to an average of 75% error reduction (from 2.9 ± 1.8 mm to 0.7 ± 0.5 mm) and 40–60% increase in peak intensity, evaluated over the range of frequencies (0.4–1.2 MHz), apertures (50–100 mm), and point source locations (40 mm by 50 mm grid) as compared to the homogeneous medium ASA. Overall, total computation times for both focusing and point source localization of the order milliseconds (166 ± 37 ms, compared with 44 ± 4 ms for the homogeneous ASA formulation) can be attained with this approach. Collectively our findings indicate that the proposed phase correction method based on the ASA could provide a computationally efficient and accurate method for trans-skull transmit focusing and imaging of point scatterers, potentially opening new possibilities for treatment and diagnosis of brain diseases.
Detecting, Categorizing, and Correcting Coverage Anomalies of RNA-Seq Quantification

Because of incomplete reference transcriptomes, incomplete sequencing bias models, or other modeling defects, algorithms to infer isoform expression from RNA sequencing (RNA-seq) sometimes do not accurately model expression. We present a computational method to detect instances where a quantification algorithm could not completely explain the input reads. Our approach identifies regions where the read coverage significantly deviates from expectation. We call these regions "expression anomalies." We further present a method to attribute their cause to either the incompleteness of the reference transcriptome or algorithmic mistakes. We detect anomalies for 30 GEUVADIS and 16 Human Body Map samples. By correcting anomalies when possible, we reduce the number of falsely predicted instances of differential expression. Anomalies that cannot be corrected are suspected to indicate the existence of isoforms unannotated by the reference. We detected 88 common anomalies of this type and find that they tend to have a lower-than-expected coverage toward their 3' ends.
Choosing between simple and complex remedies in socio-economic rights cases

Recent work by Kent Roach argues that the best way for judges to protect significant violations of rights is through a two-track approach that combines simple and complex, dialogical remedies. He i...
Private Information Retrieval with Private Coded Side Information: The Multi-Server Case

In this paper, we consider the multi-server setting of Private Information Retrieval with Private Coded Side Information (PIR-PCSI) problem. In this problem, there is a database of K messages whose copies are replicated across N servers, and there is a user who knows a random linear combination of a random subset of M messages in the database as side information. The user wishes to download one message from the servers, while protecting the identities of both the demand message and the messages contributing to the side information. We assume that the servers know the number of messages contributing to the user’s side information in advance, whereas the indices of these messages and their coefficients in the side information are not known to any of the servers a priori.Our goal is to characterize (or derive a lower bound on) the capacity, i.e., the maximum achievable download rate, for the following two settings. In the first setting, the set of messages contributing to the linear combination available to the user as side information, does not include the message demanded by the user. For this setting, we show that the capacity is equal to $(1+1/N+\cdots+1/N^{K-M-1})^{-1}$. In the second setting, the demand message contributes to the linear combination available to the user as side information, i.e., the demand message is one of the messages that form the user’s side information. For this setting, we show that the capacity is lower-bounded by $(1+1/N+\cdots+1/N^{K-M})^{-1}$. The proposed achievability schemes and proof techniques leverage ideas from both our recent methods proposed for the single-server PIR-PCSI problem as well as the techniques proposed by Sun and Jafar for multi-server private computation problem.
Longitudinal bioluminescent imaging of HIV-1 infection during antiretroviral therapy and treatment interruption in humanized mice

Non-invasive bioluminescent imaging (NIBLI) of HIV-1 infection dynamics allows for real-time monitoring of viral spread and the localization of infected cell populations in living animals. In this report, we describe full-length replication-competent GFP and Nanoluciferase (Nluc) expressing HIV-1 reporter viruses from two clinical transmitted / founder (T/F) strains: TRJO.c and Q23.BG505. By infecting humanized mice with these HIV-1 T/F reporter viruses, we were able to directly monitor longitudinal viral spread at whole-animal resolution via NIBLI at a sensitivity of as few as 30-50 infected cells. Bioluminescent signal strongly correlated with HIV-1 infection and responded proportionally to virus suppression in vivo in animals treated daily with a combination antiretroviral therapy (cART) regimen. Longitudinal NIBLI following cART withdrawal visualized tissue-sites that harbored virus during infection recrudescence. Notably, we observed rebounding infection in the same lymphoid tissues where infection was first observed prior to ART treatment. Our work demonstrates the utility of our system for studying in vivo viral infection dynamics and identifying infected tissue regions for subsequent analyses.
A Baseline for Few-Shot Image Classification

Fine-tuning a deep network trained with the standard cross-entropy loss is a strong baseline for few-shot learning. When fine-tuned transductively, this outperforms the current state-of-the-art on standard datasets such as Mini-Imagenet, Tiered-Imagenet, CIFAR-FS and FC-100 with the same hyper-parameters. The simplicity of this approach enables us to demonstrate the first few-shot learning results on the Imagenet-21k dataset. We find that using a large number of meta-training classes results in high few-shot accuracies even for a large number of few-shot classes. We do not advocate our approach as the solution for few-shot learning, but simply use the results to highlight limitations of current benchmarks and few-shot protocols. We perform extensive studies on benchmark datasets to propose a metric that quantifies the "hardness" of a few-shot episode. This metric can be used to report the performance of few-shot algorithms in a more systematic way.
What role does serious mental illness play in mass shootings, and how should we address it?

Research Summary A popular explanation for mass shootings is that the assailant “must’ve been mentally ill.” A popular policy solution is exceptionalist — enter more gun-disqualifying psychiatric records into the background check system to keep guns away from identified people with mental illness. We synthesized research on the connection between mental illness and common violence, gun violence, homicide, and mass violence. We focused on serious mental illnesses like schizophrenia, bipolar disorder and major depression, which are not the same as emotional distress from life circumstances and problematic personality traits. We found there is an association between serious mental illness and violence — but it is weaker than the public imagines or the media portrays; and rarely causal. Serious mental illness plays a limited role — it is neither a necessary nor sufficient condition for mass violence. Policy Implications Exceptionalist policies that assume serious mental illness is the cause of mass shootings will do little to prevent them — and they subject millions of nonviolent people with mental illness to stigma and unwarranted social control. Mass violence is a multi-determined problem. Because major risk factors for violence are shared, improvements in policies that keep guns out of the hands of dangerous people without mental illness, will also go far in preventing incidents involving those with mental illness. Chiefly, these steps include sharpening the criteria for gun-disqualification and temporarily removing guns from individuals at imminent risk for violence. The implementation of threat assessment teams and funding for crisis services for people with- and without mental illness may also be helpful.
Finger Paint and Physics: A Simple Demonstration of Circular Motion and Conservation of Energy

It can be a challenge to come up with simple demonstrations of circular motion and conservation of energy. One such demonstration consists of a large exercise ball, off of which a small solid ball is rolled. The small ball is coated in finger paint so, after an initial push, it rolls nearly without slipping and creates a visible track.
Wartime Health Shocks and the Postwar Socioeconomic Status and Mortality of Union Army Veterans and their Children

We investigate when and how health shocks reverberate across the life cycle and down to descendants in a manual labor economy by examining the association of war wounds with the socioeconomic status and older age mortality of US Civil War (1861-5) veterans and of their adult children. Younger veterans who had been severely wounded in the war left the farm sector, becoming laborers. Consistent with human capital and job matching models, older severely wounded men were unlikely to switch sectors and their wealth declined by 37-46%. War wounds were correlated with children's socioeconomic and mortality outcomes in ways dependent on sex and paternal age group.
UcoSLAM: Simultaneous localization and mapping by fusion of keypoints and squared planar markers

Abstract Simultaneous Localization and Mapping is the process of simultaneously creating a map of the environment while navigating in it. Most of the SLAM approaches use natural features (e.g. keypoints) that are unstable over time, repetitive in many cases or their number insufficient for a robust tracking (e.g. in indoor buildings). Other researchers, on the other hand, have proposed the use of artificial landmarks, such as squared fiducial markers, placed in the environment to help tracking and relocalization. This paper proposes a novel SLAM approach by fusing natural and artificial landmarks in order to achieve long-term robust tracking in many scenarios. Our method has been compared to the start-of-the-art methods ORB-SLAM2 [1], LDSO [2] and SPM-SLAM [3] in the public datasets Kitti [4], Euroc-MAV [5], TUM [6] and SPM [3], obtaining better precision, robustness and speed. Our tests also show that the combination of markers and keypoints achieves better accuracy than each one of them independently.