cssi/SciSciGPT-SciSciNet huggingface dataset:
The effect of mold precession on channel and macro-segregation in ammonium chloride-water analog castings

Analog castings of two geometrical forms have been studied to observe the effect of continuously changing the direction of gravitational force on patterns of segregation. Molds of slab form, cooled on one side, and of cylindrical form, cooled at the base, were rotated axially at rates from 0 to 10 rpm with the axis of rotation tilted from 0 to 30 deg to the vertical: the development of “A” channel segregation in the former and of “freckel” channels in the latter were recorded photographically, while changes in bulk liquid concentration were followed over time periods up to one hour. It was observed that changing the direction of gravitation, slowly and continuously, markedly retarded or eliminated the formation of segregation channels and that consequent macrosegregation was also retarded and reduced. The application of the principle to foundry practice is discussed.
Visual Localization for Autonomous Driving: Mapping the Accurate Location in the City Maze

Accurate localization is a foundational capacity, required for autonomous vehicles to accomplish other tasks such as navigation or path planning. It is a common practice for vehicles to use GPS to acquire location information. However, the application of GPS can result in severe challenges when vehicles run within the inner city where different kinds of structures may shadow the GPS signal and lead to inaccurate location results. To address the localization challenges of urban settings, we propose a novel feature voting technique for visual localization. Different from the conventional front-view-based method, our approach employs views from three directions (front, left, and right) and thus significantly improves the robustness of location prediction. In our work, we craft the proposed feature voting method into three state-of-the-art visual localization networks and modify their architectures properly so that they can be applied for vehicular operation. Extensive field test results indicate that our approach can predict location robustly even in challenging inner-city settings. Our research sheds light on using the visual localization approach to help autonomous vehicles to find accurate location information in a city maze, within a desirable time constraint. The source code is available at github.com/HappyDonkey13/Visual- Localization-for- Autonomous-Driving.
Some Geometric Applications of Anti-Chains.

We present an algorithmic framework for computing anti-chains of maximum size in geometric posets. Specifically, posets in which the entities are geometric objects, where comparability of two entities is implicitly defined but can be efficiently tested. Computing the largest anti-chain in a poset can be done in polynomial time via maximum-matching in a bipartite graph, and this leads to several efficient algorithms for the following problems, each running in (roughly) $O(n^{3/2})$ time: (A) Computing the largest Pareto-optimal subset of a set of $n$ points in $\mathbb{R}^d$. (B) Given a set of disks in the plane, computing the largest subset of disks such that no disk contains another. This is quite surprising, as the independent version of this problem is computationally hard. (C) Given a set of axis-aligned rectangles, computing the largest subset of non-crossing rectangles.
Structure and hydrogen bonds of hydrophobic deep eutectic solvent-aqueous liquid–liquid interfaces

Hydrophobic deep eutectic solvents (DESs) emerge as candidates to extract organic substrates from aqueous solutions. The DES-aqueous liquid-liquid interface plays a vital role in the extraction ability of hydrophobic DES because the non-bulk structure of molecules at the interface could cause thermodynamic and kinetic barriers. One question is how the DES compositions affect the structural features of the DES-aqueous liquid-liquid interface. We investigate the density profile, dipole moment and hydrogen bonds of eight hydrophobic DES-aqueous liquid-liquid interfaces using molecular dynamics simulations. The eight DESs are composed of four organic compounds: decanoic acid, menthol, thymol, and lidocaine. The simulation results show the variations of dipole moment and hydrogen bond structure and dynamics at the liquid-liquid interfaces. Such variations could influence the extraction ability of DES through adjusting the partition and kinetics of organic substrates in the DES-aqueous biphasic systems.
Integrating spatial, phylogenetic, and threat assessment data from frogs and lizards to identify areas for conservation priorities in Peninsular Malaysia

Abstract Malaysia is recognized as a megadiverse country and biodiversity hotspot which necessitates sufficient levels of habitat protection and effective conservation management. However, conservation planning in Malaysia has hitherto relied largely on species distribution data without taking into account the rich evolutionary history of taxa. This represents the first study that integrates spatial and evolutionary approaches to identify important centers of diversity, endemism, and bioregionalization that can be earmarked for conservation priorities in Peninsular Malaysia. Using georeferenced species occurrences, comprehensive phylogenies, and threat assessments of frogs and lizards, we employed a spatial phylogenetics framework that incorporates various diversity metrics including weighted endemism, phylogenetic diversity, phylogenetic endemism, and evolutionary distinctiveness and global endangerment. Ten areas of high conservation value were identified via the intersection of these metrics—northern Perlis, Langkawi Geopark, southern Bintang range, Cameron Highlands, Fraser’s Hill, Benom-Krau complex, Selangor-Genting complex, Endau-Rompin National Park, Seribuat Archipelago (Tioman and Pemanggil Islands), and southern Johor. Of these, Cameron Highlands requires the highest conservation priority based on the high numbers of endangered and evolutionary distinct species coupled with severe environmental degradation and inadequately protected areas. Other areas, especially in the northwestern (states of Kedah and Penang) and northeastern regions (state of Kelantan) were not only identified as areas of high conservation value but also areas of biogeographic importance. Taken together, frogs and lizards demonstrate distinct east-west and north-south patterns of bioregionalization that are largely modulated by mountain ranges.
Empty Axis-Parallel Boxes

We show that, for every set of $n$ points in the $d$-dimensional unit cube, there is an empty axis-parallel box of volume at least $\Omega(d/n)$ as $n\to\infty$ and $d$ is fixed. In the opposite direction, we give a construction without an empty axis-parallel box of volume $O(d^2\log d/n)$. These improve on the previous best bounds of $\Omega(\log d/n)$ and $O(2^{7d}/n)$ respectively.
Exploiting Underlay Spectrum Sharing in Cell-Free Massive MIMO Systems

We investigate the coexistence of underlay spectrum sharing in cell-free massive multiple-input multiple-output (MIMO) systems. A primary system with geographically distributed primary access points (P-APs) serves a multitude of primary users (PUs), while a secondary system serves a large number of secondary users (SUs) in the same primary/licensed spectrum by exploiting the underlay spectrum sharing. To mitigate the secondary co-channel interference inflected at PUs, stringent secondary transmit power constraints are defined for the secondary access points (S-APs). A generalized pilots sharing scheme is used to locally estimate the uplink channels at P-APs/S-APs, and thereby, conjugate precoders are adopted to serve PUs/SUs in the same time-frequency resource element. Moreover, the effect of a user-centric AP clustering scheme is investigated by assigning a suitable set of APs to a particular user. The impact of estimated downlink (DL) channels at PUs/SUs via DL pilots beamformed by P-APs/S-APs is investigated. The achievable primary/secondary rates at PUs/SUs are derived for the statistical DL and estimated DL CSI cases. User-fairness for PUs/SUs is achieved by designing efficient transmit power control policies based on a multi-objective optimization problem formulation of joint underlay spectrum sharing and max-min criteria. The proposed orthogonal multiple-access based analytical framework is also extended to facilitate non-orthogonal multiple-access. Our analysis and numerical results manifest that the primary/secondary performance of underlay spectrum sharing can be boosted by virtue of the average reduction of transmit powers/path-losses, uniform coverage/service, and macro-diversity gains, which are inherent to distributed transmissions/receptions of cell-free massive MIMO.
Nonconvex interactions in mean-field spin glasses

We propose a conjecture for the limit of mean-field spin glasses with a bipartite structure, and show that the conjectured limit is an upper bound. The conjectured limit is described in terms of the solution of an infinite-dimensional Hamilton-Jacobi equation. A fundamental difficulty of the problem is that the nonlinearity in this equation is not convex. We also question the possibility to characterize this conjectured limit in terms of a saddle-point problem.
Lessons Learned from Accelerating Quicksilver on Programmable Integrated Unified Memory Architecture (PIUMA) and How That’s Different from CPU

Quicksilver represents key elements of the Mercury Monte Carlo Particle Transport simulation software developed at Lawrence Livermore National Laboratory (LLNL). Mercury is one of the applications used in the Department of Energy (DOE) for nuclear security and nuclear reactor simulations. Thus Quicksilver, as a Mercury proxy, influences DOE’s hardware procurement and co-design activities. Quicksilver has a complicated implementation and performance profile: its performance is dominated by latency-bound table look-ups and control flow divergence that limit SIMD/SIMT parallelization opportunities. Therefore, obtaining high performance for Quicksilver is quite challenging.
Edge: Enriching Knowledge Graph Embeddings with External Text

Knowledge graphs suffer from sparsity which degrades the quality of representations generated by various methods. While there is an abundance of textual information throughout the web and many existing knowledge bases, aligning information across these diverse data sources remains a challenge in the literature. Previous work has partially addressed this issue by enriching knowledge graph entities based on “hard” co-occurrence of words present in the entities of the knowledge graphs and external text, while we achieve “soft” augmentation by proposing a knowledge graph enrichment and embedding framework named Edge. Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level. To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original graph and augmented graph. To enhance the embedding learning on the augmented graph, we further regularize the locality relationship of target entity based on negative sampling. Experimental results on four benchmark datasets demonstrate the robustness and effectiveness of Edge in link prediction and node classification.
Assessment of S-values in stylized and voxel-based rat models for positron-emitting radionuclides

Purpose Positron emission tomography (PET) is a powerful tool in small animal research, enabling noninvasive quantitative imaging of biochemical processes in living subjects. However, the dosimetric characteristics of small animal PET imaging are usually overlooked, although the radiation dose may be significant. The variations of anatomical characteristics between the various computational models may result in differences in the dosimetric outcome.
Blind Estimation of Eigenvector Centrality from Graph Signals: Beyond Low-pass Filtering

This paper characterizes the difficulty of estimating a network’s eigenvector centrality only from data on the nodes, i.e., with no information about the topology of the network. We model this nodal data as graph signals generated by passing white noise through generic (not necessarily low-pass) graph filters. Leveraging the spectral properties of graph filters, we estimate the eigenvectors of the adjacency matrix of the underlying network. To this end, a simple selection algorithm is proposed, which chooses the correct eigenvector of the signal covariance matrix with minimal assumptions on the underlying graph filter. We then present a theoretical characterization of the asymptotic and non-asymptotic performance of this algorithm, thus providing a sample complexity bound for the centrality estimation and revealing key elements driving this complexity. Finally, we illustrate the developed insights through a set of numerical experiments on different random graph models.
Measuring Dependencies of Order Statistics: An Information Theoretic Perspective

This work considers a random sample X 1 ,X 2 ,…,X n drawn independently and identically distributed from some known parent distribution P X with X (1) ≤ X (2) ≤ … ≤ X (n) being the order statistics of the sample. Under the assumption of an invertible cumulative distribution function associated with the parent distribution P X , a distribution-free property is established showing that the f-divergence between the joint distribution of order statistics and the product distribution of order statistics does not depend on P X . Moreover, it is shown that the mutual information between two subsets of order statistics also satisfies a distribution-free property; that is, it does not depend on P X . Furthermore, the decoupling rates between X (r) and X (m) (i.e., rates at which the mutual information approaches zero) are characterized for various choices of (r,m). The work also considers discrete distributions, which do not satisfy the previously-stated invertibility assumption, and it is shown that no such distribution-free property holds: the mutual information between order statistics does depend on the parent distribution P X . Upper bounds on the decoupling rates in the discrete setting are also established.
Improving Paraphrase Detection with the Adversarial Paraphrasing Task

If two sentences have the same meaning, it should follow that they are equivalent in their inferential properties, i.e., each sentence should textually entail the other. However, many paraphrase datasets currently in widespread use rely on a sense of paraphrase based on word overlap and syntax. Can we teach them instead to identify paraphrases in a way that draws on the inferential properties of the sentences, and is not over-reliant on lexical and syntactic similarities of a sentence pair? We apply the adversarial paradigm to this question, and introduce a new adversarial method of dataset creation for paraphrase identification: the Adversarial Paraphrasing Task (APT), which asks participants to generate semantically equivalent (in the sense of mutually implicative) but lexically and syntactically disparate paraphrases. These sentence pairs can then be used both to test paraphrase identification models (which get barely random accuracy) and then improve their performance. To accelerate dataset generation, we explore automation of APT using T5, and show that the resulting dataset also improves accuracy. We discuss implications for paraphrase detection and release our dataset in the hope of making paraphrase detection models better able to detect sentence-level meaning equivalence.
Ratings-Driven Demand and Systematic Price Fluctuations

We show that mutual fund ratings generate correlated demand that creates systematic price fluctuations. Mutual fund investors chase fund performance via Morningstar ratings. Until June 2002, funds pursuing the same investment style had highly correlated ratings. Therefore, rating-chasing investors directed capital into winning styles, generating style-level price pressures, which reverted over time. In June 2002, Morningstar reformed its methodology of equalizing ratings across styles. Style-level correlated demand via mutual funds immediately became muted, significantly altering the time-series and cross-sectional variation in style returns.
The m6A landscape of polyadenylated nuclear (PAN) RNA and its related methylome in the context of KSHV replication.

Polyadenylated nuclear (PAN) RNA is a long non-coding transcript involved in Kaposi's sarcoma-associated herpesvirus (KSHV) lytic reactivation and regulation of cellular and viral gene expression. We have previously shown that PAN RNA has a dynamic secondary structure and protein binding profile that can be influenced by the epitranscriptomic modifications. N6-methyladenosine (m6A) is one of the most abundant chemical signatures found in viral RNA genomes and virus-encoded RNAs. Here, we combined an antibody-independent next-generation mapping with direct RNA sequencing to address the epitranscriptomic status of PAN RNA in KSHV infected cells. We showed that PAN m6A status is dynamic, reaching the highest number of modifications at the late lytic stages of KSHV infection. Using a newly developed method, termed Selenium-modified deoxythymidine triphosphate (SedTTP)- Reverse Transcription (RT) and Ligation Assisted PCR analysis of m6A (SLAP), we gained insight into the fraction of modification at identified sites. By applying comprehensive proteomic approaches, we identified writers and erasers that regulate the m6A status of PAN, and readers that can convey PAN m6A phenotypic effects. We verified the temporal and spatial subcellular availability of the methylome components for PAN modification by performing confocal microscopy analysis. Additionally, the RNA biochemical probing (SHAPE-MaP) outlined local and global structural alterations invoked by m6A in the context of full-length PAN RNA. This work represents the first comprehensive overview of the dynamic interplay that takes place between the cellular epitranscriptomic machinery and a specific viral RNA in the context of KSHV infected cells.
Financial Returns in Major League Soccer

Major League Soccer (MLS) is the top-tier professional soccer league serving the United States and Canada. This study examines factors hypothesized to impact consumer demand for professional sports...
Carrier-bed plays in the Denver and Powder River Basins

Carrier-bed plays are a new type of unconventional oil play that are currently being developed in North America. The reservoirs are generally low quality because of any of the following: thin beds (heterolithic strata), diagenesis, or burrowing (heterogeneous, mixing of sandstones or carbonates and mudstones). The carrier beds are pervasively hydrocarbon saturated and can occur over an areally extensive (greater than than 50 square miles, greater than 129 square kilometers) area. These low-quality reservoirs generally do not meet traditional petrophysical cutoffs and because of the clay content can create low resistivity, low contrast pays. The reservoirs may be composed of clastics or carbonates or a mixture of both. A sub-category of a carrier-bed play is a halo play. Halo plays are the low permeability flanking edges of a conventional oil accumulation (i.e., waste zone or the fringe). The low reservoir quality is generally stratigraphic in origin (facies change from high quality to low quality reservoirs). Carrier-bed and halo plays are being developed with horizontal drilling and multistage hydraulic fracturing. Traditional vertical drilling yields marginal to uneconomic wells but are a clue to the existence of a carrier-bed play. This paper reviews Upper Cretaceous carrier bed plays in the Denver (Codell SandstoneMember of the Carlile Shale) and Powder River (Turner Sandy Member of the Carlile Shale) basins.
A numerical study of gravity-driven instability in strongly coupled dusty plasma. Part 2. Hetero-interactions between a rising bubble and a falling droplet

In part 1 (V. S. Dharodi and A. Das, J. Plasma Phys. 87 (02), 905870216 (2021)), we simulated the individual dynamics of a bubble (a localized low-density region) and a droplet (a localized high-density region) in a strongly coupled dusty plasma. We observed that under the influence of gravity, the result of a pair of counter-rotating vorticity lobes causes the bubble to rise and droplet to fall. With an interest to understand the hetero- (bubble–droplet) interactions between them, we extend this study to their combined evolution through the following two arrangements. First, both are placed side-by-side in a row at the same height. We observe that the overall dynamics is governed by the competition between the net vertical motion induced by gravity and rotational motion induced by the pairing between two co-rotating inner vorticity lobes. In the second arrangement, the vertically aligned bubble (below) and droplet (above) after collision exchange their partners and subsequently start to move horizontally in opposite directions away from each other. This horizontal movement becomes slower with increasing coupling strength. For these arrangements, we consider varying the distance between the fixed-size bubble and droplet, and varying the coupling strength. To visualize the bubble–droplet interactions, a series of two-dimensional simulations have been conducted in the framework of an incompressible generalized hydrodynamic viscoelastic fluid model.
Urban fox squirrels exhibit tolerance to humans but respond to stimuli from natural predators

Animals in urban areas that experience frequent exposure to humans often behave differently than those in less urban areas, such as exhibiting less vigilance or anti-predator behavior. These behavioral shifts may be an adaptive response to urbanization, but it may be costly if animals in urban areas also exhibit reduced anti-predator behavior in the presence of natural predators. In trials with only a human observer as the stimulus, urban squirrels exhibited reduced vigilance and anti-predator behavior compared to those in less urban areas. Next, we exposed squirrels in multiple urban and less urban sites to acoustic playbacks of a control stimulus (non-predatory bird calls), a natural predator (hawk), and dogs and recorded their vigilance and three different anti-predator behaviors when a human approached them while either broadcasting one of these three playbacks or no playbacks at all. Squirrels at urban sites also did not differ in their behavioral responses to the playbacks from possible predators (hawks or dogs) when they were compared to those at less urban sites exposed to these playbacks. Urban squirrels also exhibited increased vigilance and anti-predator behavior when exposed to a human paired with hawk playbacks compared to the control playbacks. Together, our results indicate that urban squirrels did perceive and assess risk to the natural predator appropriately despite exhibiting increased tolerance to humans. These results provide little support for the hypothesis that increased tolerance to humans causes animals to lose their fear of natural predators.
When Curation Becomes Creation: Algorithms, microcontent, and the vanishing distinction between platforms and creators

Media platforms today benefit from: (1) discretion to organize content, (2) algorithms for curating user-posted content, and (3) absolution from liability. This favorable regulatory environment results from the current legal framework, which distinguishes between intermediaries and content providers. This distinction is ill-adapted to the modern social media landscape, where platforms deploy powerful data-driven algorithms to play an increasingly active role in shaping what people see, and where users supply disconnected bits of raw content as fodder. Today's platforms have license to monetize whatever content they like, moderate if and when it aligns with their corporate objectives, and curate their content however they wish.
Structural basis for target site selection in RNA-guided DNA transposition systems

CRISPR-associated transposition systems allow guide RNA-directed integration of a single DNA cargo in one orientation at a fixed distance from a programmable target sequence. We used cryo-electron microscopy (cryo-EM) to define the mechanism that underlies this process by characterizing the transposition regulator, TnsC, from a type V-K CRISPR-transposase system. In this scenario, polymerization of adenosine triphosphate-bound TnsC helical filaments could explain how polarity information is passed to the transposase. TniQ caps the TnsC filament, representing a universal mechanism for target information transfer in Tn7/Tn7-like elements. Transposase-driven disassembly establishes delivery of the element only to unused protospacers. Finally, TnsC transitions to define the fixed point of insertion, as revealed by structures with the transition state mimic ADP•AlF3 These mechanistic findings provide the underpinnings for engineering CRISPR-associated transposition systems for research and therapeutic applications.
Toward Robotic Weed Control: Detection of Nutsedge Weed in Bermudagrass Turf Using Inaccurate and Insufficient Training Data

To enable robotic weed control, we develop algorithms to detect nutsedge weed from bermudagrass turf. Due to the similarity between the weed and the background turf, manual data labeling is expensive and error-prone. Consequently, directly applying deep learning methods for object detection cannot generate satisfactory results. Building on an instance detection approach (i.e. Mask R-CNN), we combine synthetic data with raw data to train the network. We propose an algorithm to generate high fidelity synthetic data, adopting different levels of annotations to reduce labeling cost. Moreover, we construct a nutsedge skeleton-based probabilistic map (NSPM) as the neural network input to reduce the reliance on pixel-wise precise labeling. We also modify loss function from cross entropy to Kullback-Leibler divergence which accommodates uncertainty in the labeling process. We implement the proposed algorithm and compare it with both Faster R-CNN and Mask R-CNN. The results show that our design can effectively overcome the impact of imprecise and insufficient training sample issues and significantly outperform the Faster R-CNN counterpart with a false negative rate of only 0.4%. In particular, our approach also reduces labeling time by 95% while achieving better performance if comparing with the original Mask R-CNN approach.
Multiplier theorems via martingale transforms

Abstract We develop a new and general approach to prove multiplier theorems in various geometric settings. The main idea is to use martingale transforms and a Gundy-Varopoulos representation for multipliers defined via a suitable extension procedure. Along the way, we provide a probabilistic proof of a generalization of a result by Stinga and Torrea, which is of independent interest. Our methods here also recover the sharp L p bounds for second order Riesz transforms by a limiting argument.
Between-subject prediction reveals a shared representational geometry in the rodent hippocampus.

Summary The rodent hippocampus constructs statistically independent representations across environments (“global remapping”) and assigns individual neuron firing fields to locations within an environment in an apparently random fashion, processes thought to contribute to the role of the hippocampus in episodic memory. This random mapping implies that it should be challenging to predict hippocampal encoding of a given experience in one subject based on the encoding of that same experience in another subject. Contrary to this prediction, we find that by constructing a common representational space across rats in which neural activity is aligned using geometric operations (rotation, reflection, and translation; “hyperalignment”), we can predict data of “right” trials (R) on a T-maze in a target rat based on (1) the “left” trials (L) of the target rat and (2) the relationship between L and R trials from a different source rat. These cross-subject predictions relied on ensemble activity patterns, including both firing rate and field location, and outperformed a number of control mappings, such as those based on permuted data that broke the relationship between L and R activity for individual neurons and those based solely on within-subject prediction. This work constitutes proof of principle for successful cross-subject prediction of ensemble activity patterns in the hippocampus and provides new insights in understanding how different experiences are structured, enabling further work identifying what aspects of experience encoding are shared versus unique to an individual.
Tides in the High-eccentricity Migration of Hot Jupiters: Triggering Diffusive Growth by Nonlinear Mode Interactions

High eccentricity migration is a possible formation channel for hot Jupiters. However, in order for it to be consistent with the observed population of planets, tides must circularize the orbits in less than $\approx$ a Myr. A potential mechanism for such rapid circularization is the diffusive growth of the tidally driven planetary f-mode. Such growth occurs if the f-mode's phase at pericenter varies chaotically from one pericenter passage to the next. Previous studies focused on the variation of the orbital period due to tidal back-reaction on the orbit as the source of chaos. Here we show that nonlinear mode interactions can also be an important source. Specifically, we show that nonlinear interactions between a parent f-mode and daughter f-/p-modes induce an energy-dependent shift in the oscillation frequency of the parent. This frequency shift varies randomly from orbit to orbit because the parent's energy varies. As a result, the parent's phase at pericenter varies randomly, which we find can trigger it to grow diffusively. We show that the phase shift induced by nonlinear mode interactions in fact dominates the shift induced by tidal back-reaction and significantly lowers the one-kick energy threshold for diffusive growth by about a factor of 5 compared to the linear theory's prediction. Nonlinear interactions could thus enhance the formation rate of hot Jupiters through the high-eccentricity migration channel and potentially mitigate the discrepancy between the observed and predicted occurrence rates for close-in gas giants as compared to those further from the star.
Volatility of Sodium in Carbonaceous Chondrites at Temperatures Consistent with Low-Perihelia Asteroids

Solar system bodies with surface and sub-surface volatiles will show observational evidence of activity when they reach a temperature where those volatiles change from solid to gas and are released. This is most frequently seen in comets, where activity is driven by the sublimation of water, carbon dioxide, or carbon monoxide ices. However, some bodies (notably the asteroid (3200) Phaethon) show initiation of activity at very small heliocentric distances, long after they have reached the sublimation temperatures of these ices. We investigate whether the sodium present in the mineral matrix could act as the volatile element responsible for this activity. We conduct theoretical modeling which indicates that sodium has the potential to sublimate in the conditions that Phaethon experiences, depending on the mineral phase it is held in. To test this, we then exposed samples of the carbonaceous chondrite Allende to varying heating events similar to what would be experienced by low perihelion asteroids. We measured the change in sodium present in each sample, and find that the highest temperature samples show a significant loss of sodium from specific mineral phases over a single heating event, comparable to a day on the surface of Phaethon. Under specific thermal histories possible for Phaethon, this outgassing could be sufficient to explain this object's observed activity. This effect would also be expected to be observed for other low-perihelia asteroids as well, and may act as a critical step in the process of disrupting small low-albedo asteroids.
Employee Non-compete Agreements, Gender, and Entrepreneurship

I contribute to the literature on institutions, gender, and entrepreneurship by showing that macrolevel institutional policies that do not explicitly target women nonetheless discourage them from l...
Quasi-optimal adaptive hybridized mixed finite element methods for linear elasticity

For the planar Navier–Lame equation in mixed form with symmetric stress tensors, we prove the uniform quasi-optimal convergence of an adaptive method based on the hybridized mixed finite element proposed in Gong et al. [Numer. Math. 141 (2019) 569–604]. The main ingredients in the analysis consist of a discrete a posteriori upper bound and a quasi-orthogonality result for the stress field under the mixed boundary condition. Compared with existing adaptive methods, the proposed adaptive algorithm could be directly applied to the traction boundary condition and be easily implemented.
The Momentum Gap and Return Predictability

Momentum strategies have historically delivered large alphas, yet they also displayed significant time-variation that is not very well understood. I document that expected momentum profits vary negatively with the formation period return difference between past winners and losers, which I term the momentum gap. A one standard deviation increase in the momentum gap predicts a 1.29% decrease in the monthly momentum return after controlling for existing predictors. I find consistent results across 21 international stock markets. Following the simple real-time strategy of investing in momentum only when the momentum gap is below the 80th percentile generates monthly returns of 1.28%. ∗Cox School of Business, Southern Methodist University. This paper is based on my dissertation at Yale University. I would especially like to thank Nick Barberis, Will Goetzmann, Andrew Metrick, and Jake Thomas. I would also like to thank Sriya Anbil, Sandro Andrade, Brad Barber, Michael Brandt, Hui Chen, James Choi, Lauren Cohen, Kent Daniel, Assaf Eisdorfer, Harrison Hong, Sean Hundtofte, Stacey Jacobsen, Eric Kelley, Peter Kelly, Alok Kumar, Mattia Landoni, Charles Lee, Stefan Lewellen, Jim Linck, David McLean, Alan Moreira, Tyler Muir, Marina Niessner, Terry Odean, Mark Ready, Scott Richardson, Geert Rouwenhorst, Ravi Sastry, Clemens Sialm, Rick Sias, Sorin Sorescu, Matt Spiegel, Johan Sulaeman, Paul Tetlock, Sheridan Titman, Rex Thompson, Kumar Venkataraman, Jeff Wurgler, Lei Xie, Hongjun Yan, Frank Zhang, Harold Zhang, Guofu Zhou, as well as seminar participants at the WFA Meetings, the Lone Star Finance Conference, Southern Methodist University, Texas A&M University, the University of Arizona, the University of Connecticut, the University of Miami, the University of Wisconsin, Washington University at St. Louis, Yale University, Algert Global, Cubist Systematic Strategies, Menta Capital, Nipun Capital, QMS Capital Management, and Sensato Investors. I gratefully acknowledge financial support of the Whitebox Advisors Doctoral Fellowship.
Fast Tensor Disentangling Algorithm

Many recent tensor network algorithms apply unitary operators to parts of a tensor network in order to reduce entanglement. However, many of the previously used iterative algorithms to minimize entanglement can be slow. We introduce an approximate, fast, and simple algorithm to optimize disentangling unitary tensors. Our algorithm is asymptotically faster than previous iterative algorithms and often results in a residual entanglement entropy that is within 10 to 40% of the minimum. For certain input tensors, our algorithm returns an optimal solution. When disentangling order-4 tensors with equal bond dimensions, our algorithm achieves an entanglement spectrum where nearly half of the singular values are zero. We further validate our algorithm by showing that it can efficiently disentangle random 1D states of qubits.
New strategies to improve minimap2 alignment accuracy.

SUMMARY We present several recent improvements to minimap2, a versatile pairwise aligner for nucleotide sequences. Now minimap2 v2.22 can more accurately map long reads to highly repetitive regions and align through insertions or deletions up to 100kb by default, addressing major weakness in minimap2 v2.18 or earlier. AVAILABILITY AND IMPLEMENTATION https://github.com/lh3/minimap2.
Arithmetic-intensity-guided fault tolerance for neural network inference on GPUs

Neural networks (NNs) are increasingly employed in safety-critical domains and in environments prone to unreliability (e.g., soft errors), such as on spacecraft. Therefore, it is critical to impart fault tolerance to NN inference. Algorithm-based fault tolerance (ABFT) is emerging as an efficient approach for fault tolerance in NNs. We propose an adaptive approach to ABFT for NN inference that exploits untapped opportunities in emerging deployment scenarios. GPUs have high compute-to-memory-bandwidth ratios, while NN layers have a wide range of arithmetic intensities. This leaves some layers compute bound and others memory-bandwidth bound, but current approaches to ABFT do not consider these differences. We first investigate ABFT schemes best suited for each of these scenarios. We then propose intensity-guided ABFT, an adaptive, arithmetic-intensity-guided approach that selects the most efficient ABFT scheme for each NN layer. Intensity-guided ABFT reduces execution-time overhead by 1.09--5.3× across many NNs compared to traditional approaches to ABFT.
Soft Hybrid Aerial Vehicle via Bistable Mechanism

Unmanned aerial vehicles have been demonstrated successfully in a variety of tasks, including surveying and sampling tasks over large areas. These vehicles can take many forms. Quadrotors’ agility and ability to hover makes them well suited for navigating potentially tight spaces, while fixed wing aircraft are capable of efficient flight over long distances. Hybrid aerial vehicles (HAVs) attempt to achieve both of these benefits by exhibiting multiple modes; however, morphing HAVs typically require extra actuators which add mass, reducing both agility and efficiency. We propose a morphing HAV with folding wings that exhibits both a quadrotor and a fixed wing mode without requiring any extra actuation. This is achieved by leveraging the motion of a bistable mechanism at the center of the aircraft to drive folding of the wing using only the existing motors and the inertia of the system. We optimize both the bistable mechanism and the folding wing using a topology optimization approach. The resulting mechanisms were fabricated on a 3D printer and replaced the frame of an existing quadrotor. Our prototype successfully transitions between both modes and our experiments demonstrate that the behavior of the fabricated prototype is consistent with that of the simulation.
Partitions for semi-magic squares of size three

In the theory of Clebsch-Gordan coefficients, one may recognize the domain space as the set of weakly semi-magic squares of size three. Two partitions on this set are considered: a triangle-hexagon model based on top lines, and one based on the orbits under a finite group action. In addition to giving another proof of McMahon’s formula, we give a generating function that counts the so-called trivial zeros of Clebsch-Gordan coefficients and its associated quasi-polynomial.
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation.

The success of bidirectional encoders using masked language models, such as BERT, on numerous natural language processing tasks has prompted researchers to attempt to incorporate these pre-trained models into neural machine translation (NMT) systems. However, proposed methods for incorporating pre-trained models are non-trivial and mainly focus on BERT, which lacks a comparison of the impact that other pre-trained models may have on translation performance. In this paper, we demonstrate that simply using the output (contextualized embeddings) of a tailored and suitable bilingual pre-trained language model (dubbed BiBERT) as the input of the NMT encoder achieves state-of-the-art translation performance. Moreover, we also propose a stochastic layer selection approach and a concept of a dual-directional translation model to ensure the sufficient utilization of contextualized embeddings. In the case of without using back translation, our best models achieve BLEU scores of 30.45 for En→De and 38.61 for De→En on the IWSLT’14 dataset, and 31.26 for En→De and 34.94 for De→En on the WMT’14 dataset, which exceeds all published numbers.
Implicit Finite-Horizon Approximation and Efficient Optimal Algorithms for Stochastic Shortest Path

We introduce a generic template for developing regret minimization algorithms in the Stochastic Shortest Path (SSP) model, which achieves minimax optimal regret as long as certain properties are ensured. The key of our analysis is a new technique called implicit finite-horizon approximation, which approximates the SSP model by a finite-horizon counterpart only in the analysis without explicit implementation. Using this template, we develop two new algorithms: the first one is model-free (the first in the literature to our knowledge) and minimax optimal under strictly positive costs; the second one is model-based and minimax optimal even with zero-cost state-action pairs, matching the best existing result from [Tarbouriech et al., 2021b]. Importantly, both algorithms admit highly sparse updates, making them computationally more efficient than all existing algorithms. Moreover, both can be made completely parameter-free.
Generating self-contained and summary-centric question answer pairs via differentiable reward imitation learning

Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, summary-centric questions and length-constrained, article-summarizing answers. We begin by collecting a new dataset of news articles with questions as titles and pairing them with summaries of varying length. This dataset is used to learn a QA pair generation model producing summaries as answers that balance brevity with sufficiency jointly with their corresponding questions. We then reinforce the QA pair generation process with a differentiable reward function to mitigate exposure bias, a common problem in natural language generation. Both automatic metrics and human evaluation demonstrate these QA pairs successfully capture the central gists of the articles and achieve high answer accuracy.
Microstructure classification in the unsupervised context

Abstract Traditional microstructure classification requires human annotations provided by a subject matter expert. The requirement of human input is both costly and subjective and cannot keep up with the current volume of experimentally and computationally generated microstructure images. In this work, we develop a framework that is capable of reducing the cost of human annotation in this process by leveraging novel machine learning procedures for class discovery and label assignment. To reduce the penalty of a poor label assignment made by this automated process, labels are only assigned to high-confidence observations while ambiguous data are left unlabeled. Semi-supervised classification is then employed to leverage the high- and low-confidence label assignments, and a novel generalization of an established semi-supervised error estimation technique to the multi-class context is introduced to assess the resulting classifiers. Finally, it is shown that this framework can be used to produce highly accurate classifiers over microstructure image class taxonomies which are discovered solely through data-driven methods and which display consistent structural trends within and distinct morphological differences between classes.
Physical Exercise Recommendation and Success Prediction Using Interconnected Recurrent Neural Networks

Unhealthy behaviors, e.g., physical inactivity and unhealthful food choice, are the primary healthcare cost drivers in developed countries. Pervasive computational, sensing, and communication technology provided by smartphones and smart-watches have made it possible to support individuals in their everyday lives to develop healthier lifestyles. In this paper, we propose an exercise recommendation system that also predicts individual success rates. The system, consisting of two interconnected recurrent neural networks (RNNs), uses the history of workouts to recommend the next workout activity for each individual. The system then predicts the probability of successful completion of the predicted activity by the individual. The prediction accuracy of this interconnected-RNN model is assessed on previously published data from a four-week mobile health experiment and is shown to improve upon previous predictions from a computational cognitive model.
The Imprisoner's Dilemma: A Cost-Benefit Approach to Incarceration

Depriving an individual of life or liberty is one of the most intrusive powers that governments wield. Decisions about imprisonment capture the public imagination. The stories are told daily in newspapers and on TV, dramatized in literature and on film, and debated by scholars. The United States has created an ever-increasing amount of material for discussion as the state incarceration rate quadrupled between 1980 and 2000. While the decision to incarcerate an individual is given focused attention by a judge, prosecutor, and (occasionally) a jury, the overall incarceration rate is not.In this article, I apply a cost-benefit approach to incarceration with the goal of informing public policy. An excessive rate of incarceration not only deprives individuals of freedom, but also costs the taxpayers large amounts of money. Too little imprisonment harms society in a different way – through costs to victims and even non-victims who must increase precautions to avoid crime. Striking the right balance of costs and benefits is what good law and public policy strive for.Changes to the inmate population may be made in several different ways. One insight that I stress in this article is that the precise form of a proposed incarceration policy change is crucial to properly evaluating the impact of the change. Therefore, I analyze several potential policy changes and their implications for sentencing and imprisonment. The calculations are informed by recent empirical work on the various ways in which imprisonment impacts overall welfare. I find that the benefits of limited one-time prisoner releases, as well as the reclassification of some crimes exceed the costs.
Distributed Compression of Graphical Data

In contrast to time series, graphical data is data indexed by the vertices and edges of a graph. Modern applications such as the internet, social networks, genomics and proteomics generate graphical data, often at large scale. The large scale argues for the need to compress such data for storage and subsequent processing. Since this data might have several components available in different locations, it is also important to study distributed compression of graphical data. In this paper, we derive a rate region for this problem which is a counterpart of the Slepian–Wolf theorem. We characterize the rate region when the statistical description of the distributed graphical data can be modeled as being one of two types – as a member of a sequence of marked sparse Erdős–Renyi ensembles or as a member of a sequence of marked configuration model ensembles. Our results are in terms of a generalization of the notion of entropy introduced by Bordenave and Caputo in the study of local weak limits of sparse graphs. Furthermore, we give a generalization of this result for Erdős–Renyi and configuration model ensembles with more than two sources.