cssi/SciSciGPT-SciSciNet huggingface dataset:
Condensed matter physics of planets - Puzzles, progress and predictions

Attention is given to some of the major unresolved issues concerned with the physics of planetary interiors. The important advances in observations, and experimental and theoretical investigations are briefly reviewed, and some areas for further study are identified, including: the characteristics of atomic and electronic degrees of freedom at the high pressures and temperatures typical of a condensed planetary core; the behavior of water at megabar pressures; and the nature of the core-alloy in the earth and in the core mantle phase boundary. Consideration is also given to the behavior of carbon at high pressures and temperatures in the presence of oxygen and hydrogen; the behavior of the volatile ice assemblage in Titan at pressures of 2-40 kbar; and the electrical conductivities of matter under planetary core conditions.
Structural Guidance for Transformer Language Models

Transformer-based language models pre-trained on large amounts of text data have proven remarkably successful in learning generic transferable linguistic representations. Here we study whether structural guidance leads to more human-like systematic linguistic generalization in Transformer language models without resorting to pre-training on very large amounts of data. We explore two general ideas. The “Generative Parsing” idea jointly models the incremental parse and word sequence as part of the same sequence modeling task. The “Structural Scaffold” idea guides the language model’s representation via additional structure loss that separately predicts the incremental constituency parse. We train the proposed models along with a vanilla Transformer language model baseline on a 14 million-token and a 46 million-token subset of the BLLIP dataset, and evaluate models’ syntactic generalization performances on SG Test Suites and sized BLiMP. Experiment results across two benchmarks suggest converging evidence that generative structural supervisions can induce more robust and humanlike linguistic generalization in Transformer language models without the need for data intensive pre-training.
Efficiently Cooled Stellar Wind Bubbles in Turbulent Clouds. II. Validation of Theory with Hydrodynamic Simulations

In a companion paper, we develop a theory for the evolution of stellar wind driven bubbles in dense, turbulent clouds. This theory proposes that turbulent mixing at a fractal bubble-shell interface leads to highly efficient cooling, in which the vast majority of the input wind energy is radiated away. This energy loss renders the majority of the bubble evolution momentum-driven rather than energy-driven, with expansion velocities and pressures orders of magnitude lower than in the classical Weaver77 solution. In this paper, we validate our theory with three-dimensional, hydrodynamic simulations. We show that extreme cooling is not only possible, but is generic to star formation in turbulent clouds over more than three orders of magnitude in density. We quantify the few free parameters in our theory, and show that the momentum exceeds the wind input rate by only a factor ~ 1.2-4. We verify that the bubble/cloud interface is a fractal with dimension ~ 2.5-2.7. The measured turbulent amplitude (v_t ~ 200-400 km/s) in the hot gas near the interface is shown to be consistent with theoretical requirements for turbulent diffusion to efficiently mix and radiate away most of the wind energy. The fraction of energy remaining after cooling is only 1-\Theta ~ 0.1-0.01, decreasing with time, explaining observations that indicate low hot-gas content and weak dynamical effects of stellar winds.
Reinforcement Learning of Sequential Price Mechanisms

We introduce the use of reinforcement learning for indirect mechanisms, working with the existing class of sequential price mechanisms, which generalizes both serial dictatorship and posted price mechanisms and essentially characterizes all strongly obviously strategyproof mechanisms. Learning an optimal mechanism within this class forms a partially-observable Markov decision process. We provide rigorous conditions for when this class of mechanisms is more powerful than simpler static mechanisms, for sufficiency or insufficiency of observation statistics for learning, and for the necessity of complex (deep) policies. We show that our approach can learn optimal or near-optimal mechanisms in several experimental settings.
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning.

Incorporating second-order curvature information into machine learning optimization algorithms can be subtle, and doing so naively can lead to high per-iteration costs associated with forming the Hessian and performing the associated linear system solve. To address this, we introduce ADAHESSIAN, a new stochastic optimization algorithm. ADAHESSIAN directly incorporates approximate curvature information from the loss function, and it includes several novel performance-improving features, including: (i) a fast Hutchinson based method to approximate the curvature matrix with low computational overhead; (ii) a spatial averaging to reduce the variance of the second derivative; and (iii) a root-mean-square exponential moving average to smooth out variations of the second-derivative across different iterations. We perform extensive tests on NLP, CV, and recommendation system tasks, and ADAHESSIAN achieves state-of-the-art results. In particular, we find that ADAHESSIAN: (i) outperforms AdamW for transformers by0.13/0.33 BLEU score on IWSLT14/WMT14, 2.7/1.0 PPLon PTB/Wikitext-103; (ii) outperforms AdamW for Squeeze-Bert by 0.41 points on GLUE; (iii) achieves 1.45%/5.55%higher accuracy on ResNet32/ResNet18 on Cifar10/ImageNetas compared to Adam; and (iv) achieves 0.032% better score than Adagrad for DLRM on the Criteo Ad Kaggle dataset. The cost per iteration of ADAHESSIANis comparable to first-order methods, and ADAHESSIAN exhibits improved robustness towards variations in hyperparameter values. The code for ADAHESSIAN is open-sourced and publicly-available [1].
Pareto-Optimal Quantized ResNet Is Mostly 4-bit

Quantization has become a popular technique to compress neural networks and reduce compute cost, but most prior work focuses on studying quantization without changing the network size. Many real-world applications of neural networks have compute cost and memory budgets, which can be traded off with model quality by changing the number of parameters. In this work, we use ResNet as a case study to systematically investigate the effects of quantization on inference compute cost-quality tradeoff curves. Our results suggest that for each bfloat16 ResNet model, there are quantized models with lower cost and higher ac-curacy; in other words, the bfloat16 compute cost-quality tradeoff curve is Pareto-dominated by the 4-bit and 8-bit curves, with models primarily quantized to 4-bit yielding the best Pareto curve. Furthermore, we achieve state-of-the-art results on ImageNet for 4-bit ResNet-50 with quantization-aware training, obtaining a top-1 eval accuracy of 77.09%. We demonstrate the regularizing effect of quantization by measuring the generalization gap. The quantization method we used is optimized for practicality: It requires little tuning and is designed with hardware capabilities in mind. Our work motivates further research into optimal numeric formats for quantization, as well as the development of machine learning accelerators supporting these formats. As part of this work, we contribute a quantization library written in JAX, which is open-sourced at https://github.com/google-research/google-research/tree/master/aqt.
Model-Free Online Learning in Unknown Sequential Decision Making Problems and Games

Regret minimization has proved to be a versatile tool for tree-form sequential decision making and extensive-form games. In large two-player zero-sum imperfect-information games, modern extensions of counterfactual regret minimization (CFR) are currently the practical state of the art for computing a Nash equilibrium. Most regret-minimization algorithms for tree-form sequential decision making, including CFR, require (i) an exact model of the player’s decision nodes, observation nodes, and how they are linked, and (ii) full knowledge, at all times t, about the payoffs—even in parts of the decision space that are not encountered at time t. Recently, there has been growing interest towards relaxing some of those restrictions and making regret minimization applicable to settings for which reinforcement learning methods have traditionally been used—for example, those in which only black-box access to the environment is available. We give the first, to our knowledge, regret-minimization algorithm that guarantees sublinear regret with high probability even when requirement (i)—and thus also (ii)—is dropped. We formalize an online learning setting in which the strategy space is not known to the agent and gets revealed incrementally whenever the agent encounters new decision points. We give an efficient algorithm that achieves O(T^3/4) regret with high probability for that setting, even when the agent faces an adversarial environment. Our experiments show it significantly outperforms the prior algorithms for the problem, which do not have such guarantees. It can be used in any application for which regret minimization is useful: approximating Nash equilibrium or quantal response equilibrium, approximating coarse correlated equilibrium in multi-player games, learning a best response, learning safe opponent exploitation, and online play against an unknown opponent/environment.
Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning

Demand Response (DR) has a widely recognized potential for improving grid stability and reliability while reducing customers' energy bills. However, the conventional DR techniques come with several shortcomings, such as inability to handle operational uncertainties and incurring customer disutility, impeding their wide spread adoption in real-world applications. This paper proposes a new multiagent Reinforcement Learning (RL) based decision-making environment for implementing a Real-Time Pricing (RTP) DR technique in a prosumer dominated microgrid. The proposed technique addresses several shortcomings common to traditional DR methods and provides significant economic benefits to the grid operator and prosumers. To show its better efficacy, the proposed DR method is compared to a baseline traditional operation scenario in a small-scale microgrid system. Finally, investigations on the use of prosumers' energy storage capacity in this microgrid highlight the advantages of the proposed method in establishing a balanced market setup.
Contingency tables and the generalized Littlewood–Richardson coefficients

The Littlewood-Richardson coefficients $c^{\lambda}_{\mu\nu}$ give the multiplicity of an irreducible polynomial ${\rm GL}_n$-representation $F^{\lambda}_n$ in the tensor product of polynomial representations $F^{\mu}_n\otimes F^{\nu}_n$. In this paper, we generalize these coefficients to an $r$-fold tensor product of rational representations, and give a new method for computing them using an analogue of statistical contingency tables. We demonstrate special cases in which our method reduces to counting statistical contingency tables with prescribed margins. Finally, we extend our result from the general linear group to both the orthogonal and symplectic groups.
Taste Dysfunction and Long COVID-19.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent for the coronavirus disease 2019 (COVID-19), has imposed unprecedented morbidity and mortality worldwide. As of June 2021, globally over 163 million individuals are infected and nearly 3.4 million individuals have died. Emerging concerns include complaints of persistent symptoms for extended periods in recovered individuals. Cellular damage due to disease and/or treatment, prolonged viral shedding, chronic immune inflammatory response, and pro-coagulant state induced by SARS-CoV-2 infection are suggested mechanisms contributing to the symptom sequelae (Estiri et al., 2021; Tran et al., 2021).
Is Climate Change a Threat to International Peace and Security

Is climate change a threat to international peace and security? If so, what actions, if any, should the United Nations Security Council (“Council”) take to combat the climate crisis? Scientists and security professionals alike predict massive climate destabilization this century to include climate migration, competing resource wars, and violent conflict. And four island nations will be uninhabitable by mid-century, threatening their territorial integrity and very sovereignty. In this Article I argue that climate change can no longer be conceptualized as a future environmental and sustainability issue that the Council can ignore. Rather, climate change is a complex security threat that will increasingly stress and test international institutions and existing governance models. Yet is a “Climate-Security Council” truly realistic in light of the Council’s institutional design and current political paralysis? What could spark Council climate action? And how should the Council respond to the climate crisis? I address these questions, and others, proceeding in four parts. I first describe climate change’s security impacts, analyzing climate change’s cataclysmic impacts on international peace and security. Second, I analyze the Council’s gradual willingness to tackle non-traditional security threats, thus offering a possible roadmap for future climate action. In doing so, I analyze climate change’s existential threat to several island nations, a vanguard climate-security challenge that strikes at the core of the Council’s authority and legal responsibilities. Third, I address the challenges and opportunities represented by any Council climate action. I conclude by arguing that the Council should explicitly declare climate change a threat to international peace and security. Doing so unlocks the door to a menu of legally binding Council actions necessary to meet the existential challenges of our “climate security century.”
Consistent depth of moving objects in video

We present a method to estimate depth of a dynamic scene, containing arbitrary moving objects, from an ordinary video captured with a moving camera. We seek a geometrically and temporally consistent solution to this under-constrained problem: the depth predictions of corresponding points across frames should induce plausible, smooth motion in 3D. We formulate this objective in a new test-time training framework where a depth-prediction CNN is trained in tandem with an auxiliary scene-flow prediction MLP over the entire input video. By recursively unrolling the scene-flow prediction MLP over varying time steps, we compute both short-range scene flow to impose local smooth motion priors directly in 3D, and long-range scene flow to impose multi-view consistency constraints with wide baselines. We demonstrate accurate and temporally coherent results on a variety of challenging videos containing diverse moving objects (pets, people, cars), as well as camera motion. Our depth maps give rise to a number of depth-and-motion aware video editing effects such as object and lighting insertion.
Towards Unifying Feature Attribution and Counterfactual Explanations: Different Means to the Same End

Feature attributions and counterfactual explanations are popular approaches to explain a ML model. The former assigns an importance score to each input feature, while the latter provides input examples with minimal changes to alter the model's predictions. To unify these approaches, we provide an interpretation based on the actual causality framework and present two key results in terms of their use. First, we present a method to generate feature attribution explanations from a set of counterfactual examples. These feature attributions convey how important a feature is to changing the classification outcome of a model, especially on whether a subset of features is necessary and/or sufficient for that change, which attribution-based methods are unable to provide. Second, we show how counterfactual examples can be used to evaluate the goodness of an attribution-based explanation in terms of its necessity and sufficiency. As a result, we highlight the complimentary of these two approaches. Our evaluation on three benchmark datasets --- Adult-Income, LendingClub, and German-Credit --- confirms the complimentary. Feature attribution methods like LIME and SHAP and counterfactual explanation methods like Wachter et al. and DiCE often do not agree on feature importance rankings. In addition, by restricting the features that can be modified for generating counterfactual examples, we find that the top-k features from LIME or SHAP are often neither necessary nor sufficient explanations of a model's prediction. Finally, we present a case study of different explanation methods on a real-world hospital triage problem.
Testing degree corrections in stochastic block models

Nous etudions les seuils optimaux de detection de la correlation en degre dans des modeles par blocs stochastiques dans le cadre du probleme de validite de l’ajustement, et explorons les effets de la non connaissance de l’affectation des communautes (qui est un parametre de nuisance de grande dimension) et la densite du graphe en testant des corrections de degre. Quand les corrections de degre sont assez denses, un test simple base sur le nombre total d’aretes est asymptotiquement optimal. Pour les corrections de degre parcimonieuses, les resultats font apparaitre plusieurs differences dans le comportement, dependant de la densite du modele par blocs stochastiques sous-jacent. Pour les graphes qui ne sont pas extremement parcimonieux, les tests optimaux sont bases sur des tests de haute criticite ou sur des tests du type du degre maximum portant sur une combinaison lineaire de chacun des degres (estimes) des communautes. Dans le cas special des communautes equilibrees un simple test de haute criticite base sur le degre (Mukherjee, Mukherjee, Sen (2021)) est optimal si le graphe n’est pas completement dense, mais une approche plus compliquee basee sur des combinaisons lineaires est necessaire si le graphe n’est pas completement dense. La “necessite” de cette approche en deux etapes est demontree dans le cas des communautes equilibrees par l’echec du test classique du degre maximum a atteindre les constantes optimales. Enfin, pour les graphes extremement parcimonieux, les vitesses optimales changent, et une version du test du degre maximum avec une region de rejet differente est montree etre optimale.
Deficiency zero for random reaction networks under a stochastic block model framework

Deficiency zero is an important network structure and has been the focus of many celebrated results within reaction network theory. In our previous paper Prevalence of deficiency zero reaction networks in an Erdős-Renyi framework, we provided a framework to quantify the prevalence of deficiency zero among randomly generated reaction networks. Specifically, given a randomly generated binary reaction network with n species, with an edge between two arbitrary vertices occurring independently with probability $$p_n$$ , we established the threshold function $$r(n)=\frac{1}{n^3}$$ such that the probability of the random network being deficiency zero converges to 1 if $$\frac{p_n}{r(n)}\rightarrow 0$$ and converges to 0 if $$\frac{p_n}{r(n)}\rightarrow \infty $$ , as $$n \rightarrow \infty $$ . With the base Erdős-Renyi framework as a starting point, the current paper provides a significantly more flexible framework by weighting the edge probabilities via control parameters $$\alpha _{i,j}$$ , with $$i,j\in \{0,1,2\}$$ enumerating the types of possible vertices (zeroth, first, or second order). The control parameters can be chosen to generate random reaction networks with a specific underlying structure, such as “closed” networks with very few inflow and outflow reactions, or “open” networks with abundant inflow and outflow. Under this new framework, for each choice of control parameters $$\{\alpha _{i,j}\}$$ , we establish a threshold function $$r(n,\{\alpha _{i,j}\})$$ such that the probability of the random network being deficiency zero converges to 1 if $$\frac{p_n}{r(n,\{\alpha _{i,j}\})}\rightarrow 0$$ and converges to 0 if $$\frac{p_n}{r(n,\{\alpha _{i,j}\})}\rightarrow \infty $$ .
Randomized sequential importance sampling for estimating the number of perfect matchings in bipartite graphs

Abstract We introduce and study randomized sequential importance sampling algorithms for estimating the number of perfect matchings in bipartite graphs. In analyzing their performance, we establish various non-standard central limit theorems. We expect our methods to be useful for other applied problems.
Cluster many-body expansion: A many-body expansion of the electron correlation energy about a cluster mean field reference.

The many-body expansion (MBE) is an efficient tool that has a long history of use for calculating interaction energies, binding energies, lattice energies, and so on. In the past, applications of MBE to correlation energy have been unfeasible for large systems, but recent improvements to computing resources have sparked renewed interest in capturing the correlation energy using the generalized nth order Bethe–Goldstone equation. In this work, we extend this approach, originally proposed for a Slater determinant, to a tensor product state (TPS) based wavefunction. By partitioning the active space into smaller orbital clusters, our approach starts from a cluster mean field reference TPS configuration and includes the correlation contribution of the excited TPSs using the MBE. This method, named cluster MBE (cMBE), improves the convergence of MBE at lower orders compared to directly doing a block-based MBE from a RHF reference. We present numerical results for strongly correlated systems, such as the one- and two-dimensional Hubbard models and the chromium dimer. The performance of the cMBE method is also tested by partitioning the extended π space of several large π-conjugated systems, including a graphene nano-sheet with a very large active space of 114 electrons in 114 orbitals, which would require 1066 determinants for the exact FCI solution.
Deconvolution of Expression for Nascent RNA sequencing data (DENR) highlights pre-RNA isoform diversity in human cells.

Motivation Quantification of isoform abundance has been extensively studied at the mature-RNA level using RNA-seq but not at the level of precursor RNAs using nascent RNA sequencing. Results We address this problem with a new computational method called Deconvolution of Expression for Nascent RNA sequencing data (DENR), which models nascent RNA sequencing read counts as a mixture of user-provided isoforms. The baseline algorithm is enhanced by machine-learning predictions of active transcription start sites and an adjustment for the typical "shape profile" of read counts along a transcription unit. We show that DENR outperforms simple read-count-based methods for estimating gene and isoform abundances, and that transcription of multiple pre-RNA isoforms per gene is widespread, with frequent differences between cell types. In addition, we provide evidence that a majority of human isoform diversity derives from primary transcription rather than from post-transcriptional processes. Availability DENR and nascentRNASim are freely available at https://github.com/CshlSiepelLab/DENR (version v1.0.0) and https://github.com/CshlSiepelLab/nascentRNASim (version v0.3.0). Supplementary information Supplementary data are available at Bioinformatics online.
The Effects of Temperature Acclimation on Swimming Performance in the Pelagic Mahi-Mahi (Coryphaena hippurus)

Mahi-mahi (Coryphaena hippurus) are a highly migratory pelagic fish, but little is known about what environmental factors drive their broad distribution. This study examined how temperature influences aerobic scope and swimming performance in mahi. Mahi were acclimated to four temperatures spanning their natural range (20, 24, 28, and 32°C; 5-27 days) and critical swimming speed (Ucrit), metabolic rates, aerobic scope, and optimal swim speed were measured. Aerobic scope and Ucrit were highest in 28°C-acclimated fish. 20°C-acclimated mahi experienced significantly decreased aerobic scope and Ucrit relative to 28°C-acclimated fish (57 and 28% declines, respectively). 32°C-acclimated mahi experienced increased mortality and a significant 23% decline in Ucrit, and a trend for a 26% decline in factorial aerobic scope relative to 28°C-acclimated fish. Absolute aerobic scope showed a similar pattern to factorial aerobic scope. Our results are generally in agreement with previously observed distribution patterns for wild fish. Although thermal performance can vary across life stages, the highest tested swim performance and aerobic scope found in the present study (28°C), aligns with recently observed habitat utilization patterns for wild mahi and could be relevant for climate change predictions.
High-speed 2D and 3D mid-IR imaging with an InGaAs camera

Recent work on mid-infrared (MIR) detection through the process of non-degenerate two-photon absorption (NTA) in semiconducting materials has shown that wide-field MIR imaging can be achieved with standard Si cameras. While this approach enables MIR imaging at high pixel densities, the low nonlinear absorption coefficient of Si prevents fast NTA-based imaging at lower illumination doses. Here, we overcome this limitation by using InGaAs as the photosensor. Taking advantage of the much higher nonlinear absorption coefficient of this direct bandgap semiconductor, we demonstrate high-speed MIR imaging up to 500 fps with under 1 ms exposure per frame, enabling 2D or 3D mapping without pre- or post-processing of the image.
Stochastic functional Kolmogorov equations, I: Persistence

Abstract This work (Part (I)) together with its companion (Part (II)) develops a new framework for stochastic functional Kolmogorov equations, which are nonlinear stochastic differential equations depending on the current as well as the past states. Because of the complexity of the results, it seems to be instructive to divide our contributions to two parts. In contrast to the existing literature, our effort is to advance the knowledge by allowing delay and past dependence, yielding essential utility to a wide range of applications. A long-standing question of fundamental importance pertaining to biology and ecology is: What are the minimal necessary and sufficient conditions for long-term persistence and extinction (or for long-term coexistence of interacting species) of a population? Regardless of the particular applications encountered, persistence and extinction are properties shared by Kolmogorov systems. While there are many excellent treaties of stochastic-differential-equation-based Kolmogorov equations, the work on stochastic Kolmogorov equations with past dependence is still scarce. Our aim here is to answer the aforementioned basic question. This work, Part (I), is devoted to characterization of persistence, whereas its companion, Part (II) is devoted to extinction. The main techniques used in this paper include the newly developed functional Ito formula and asymptotic coupling and Harris-like theory for infinite dimensional systems specialized to functional equations. General theorems for stochastic functional Kolmogorov equations are developed first. Then a number of applications are examined covering, improving, and substantially extending the existing literature. Furthermore, our results reduce to that in the existing literature of Kolmogorov systems when there is no past dependence.
Phase transition in random tensors with multiple independent spikes

Consider a spiked random tensor obtained as a mixture of two components: noise in the form of a symmetric Gaussian p-tensor for p≥3 and signal in the form of a symmetric low-rank random tensor. The latter is defined as a linear combination of k independent symmetric rank-one random tensors, referred to as spikes, with weights referred to as signal-to-noise ratios (SNRs). The entries of the vectors that determine the spikes are i.i.d. sampled from general probability distributions supported on bounded subsets of R. This work focuses on the problem of detecting the presence of these spikes, and establishes the phase transition of this detection problem for any fixed k≥1. In particular, it shows that for a set of relatively low SNRs it is impossible to distinguish between the spiked and nonspiked Gaussian tensors. Furthermore, in the interior of the complement of this set, where at least one of the k SNRs is relatively high, these two tensors are distinguishable by the likelihood ratio test. In addition, when the total number of low-rank components, k, of the p-tensor of size N grows in the order o(N(p−2)/4) as N tends to infinity, the problem exhibits an analogous phase transition. This theory for spike detection is also shown to imply that recovery of the spikes by the minimum mean square error exhibits the same phase transition. The main methods used in this work arise from the study of mean field spin glass models, where the phase transition thresholds are identified as the critical inverse temperatures distinguishing the high and low-temperature regimes of the free energies. In particular, our result formulates the first full characterization of the high temperature regime for vector-valued spin glass models with independent coordinates.
NexRAN: Closed-loop RAN slicing in POWDER -A top-to-bottom open-source open-RAN use case

Much like earlier "network softwarization" efforts, the Open RAN concept is poised to have a transformative impact on the manner in which radio access networks (RANs) are realized and operated. The inherent complexity of the RAN ecosystem and the fact that it is rapidly evolving makes Open RAN a rich area of research into use cases, system realization, security, and more. This same complexity, however, hampers research efforts. Specifically, there is a lack of end-to-end open source software and fully-developed use cases associated with the Open RAN ecosystem. Further, to truly advance the state of the art will require use cases to be explored in realistic wireless environments. This paper describes our efforts to address these shortcomings by realizing NexRAN, a top-to-bottom, open-source Open RAN use case in the POWDER mobile and wireless research platform. Specifically, NexRAN allows closed-loop control of a RAN slicing realization in an O-RAN ecosystem. RAN slicing is implemented in the srsRAN open source mobility stack and is exposed through a custom service model to the NexRAN xApp, which executes on a RAN intelligent controller (RIC) from the O-RAN Alliance. The NexRAN xApp realizes policy driven closed-loop control of RAN slices by reading the current state of RAN elements (using the O-RAN key performance measurements (KPM) service model) and controlling slice behavior via the custom slicing service model. We demonstrate and evaluate NexRAN in the POWDER platform and have open sourced all aspects of our realization to enable research into this domain.
Automatic Polygon Layout for Primal-Dual Visualization of Hypergraphs.

N-ary relationships, which relate N entities where N is not necessarily two, can be visually represented as polygons whose vertices are the entities of the relationships. Manually generating a high-quality layout using this representation is labor-intensive. In this paper, we provide an automatic polygon layout generation algorithm for the visualization of N-ary relationships. At the core of our algorithm is a set of objective functions motivated by a number of design principles that we have identified. These objective functions are then used in an optimization framework that we develop to achieve high-quality layouts. Recognizing the duality between entities and relationships in the data, we provide a second visualization in which the roles of entities and relationships in the original data are reversed. This can lead to additional insight about the data. Furthermore, we enhance our framework for a joint optimization on the primal layout (original data) and the dual layout (where the roles of entities and relationships are reversed). This allows users to inspect their data using two complementary views. We apply our visualization approach to a number of datasets that include co-authorship data and social contact pattern data.
Group-Free 3D Object Detection via Transformers

Recently, directly detecting 3D objects from 3D point clouds has received increasing attention. To extract object representation from an irregular point cloud, existing methods usually take a point grouping step to assign the points to an object candidate so that a PointNet-like network could be used to derive object features from the grouped points. However, the inaccurate point assignments caused by the hand-crafted grouping scheme decrease the performance of 3D object detection. In this paper, we present a simple yet effective method for directly detecting 3D objects from the 3D point cloud. Instead of grouping local points to each object candidate, our method computes the feature of an object from all the points in the point cloud with the help of an attention mechanism in the Transformers \cite{vaswani2017attention}, where the contribution of each point is automatically learned in the network training. With an improved attention stacking scheme, our method fuses object features in different stages and generates more accurate object detection results. With few bells and whistles, the proposed method achieves state-of-the-art 3D object detection performance on two widely used benchmarks, ScanNet V2 and SUN RGB-D. The code and models are publicly available at \url{this https URL}
Laser Triple-Quantum Photoionization of Cesium

Abstract : A report is given of observations of the ionization of atomic cesium by the simulataneous absorption of three ruby laser quanta. The results appear to be in qualitative agreement with theory, and indicate that circularly polarized light produces ionization about twice as efficiently as linearly polarized light, suggesting a new kind of polarized electron source. (Author)
Evaluating Music Recommendations with Binary Feedback for Multiple Stakeholders.

High quality user feedback data is essential to training and evaluating a successful music recommendation system, particularly one that has to balance the needs of multiple stakeholders. Most existing music datasets suffer from noisy feedback and self-selection biases inherent in the data collected by music platforms. Using the Piki Music dataset of 500k ratings collected over a two-year time period, we evaluate the performance of classic recommendation algorithms on three important stakeholders: consumers, well-known artists and lesser-known artists. We show that a matrix factorization algorithm trained on both likes and dislikes performs significantly better compared to one trained only on likes for all three stakeholders.
Extensions of the Maximum Bichromatic Separating Rectangle Problem.

In this paper, we study two extensions of the maximum bichromatic separating rectangle (MBSR) problem introduced in \cite{Armaselu-CCCG, Armaselu-arXiv}. One of the extensions, introduced in \cite{Armaselu-FWCG}, is called \textit{MBSR with outliers} or MBSR-O, and is a more general version of the MBSR problem in which the optimal rectangle is allowed to contain up to $k$ outliers, where $k$ is given as part of the input. For MBSR-O, we improve the previous known running time bounds of $O(k^7 m \log m + n)$ to $O(k^3 m + m \log m + n)$. The other extension is called \textit{MBSR among circles} or MBSR-C and asks for the largest axis-aligned rectangle separating red points from blue unit circles. For MBSR-C, we provide an algorithm that runs in $O(m^2 + n)$ time.
Bridge Networks: Relating Inputs through Vector-Symbolic Manipulations

Despite rapid progress, current deep learning methods face a number of critical challenges. These include high energy consumption, catastrophic forgetting, dependance on global losses, and an inability to reason symbolically. By combining concepts from information theory and vector-symbolic architectures, we propose and implement a novel information processing architecture, the ‘Bridge network.’ We show this architecture provides unique advantages which can address the problem of global losses and catastrophic forgetting. Furthermore, we argue that it provides a further basis for increasing energy efficiency of execution and the ability to reason symbolically.
Regression Networks for Calculating Englacial Layer Thickness

Ice thickness estimation is an important aspect of ice sheet modelling. In this work, we use convolutional neural networks (CNN) with multiple output nodes to regress and learn the thickness of internal ice layers in Snow Radar images captured over northwest Greenland. We experiment with some state-of-the-art CNNs to obtain a mean absolute error of 1.251 pixels of thickness estimation over the test set. Such regression-based networks can further be improved by embedding domain knowledge and radar information in the neural network in order to reduce the requirement of manual annotations.
(Un)clear and (In)conspicuous: The Right to Opt-out of Sale under CCPA

The California Consumer Privacy Act (CCPA)---which began enforcement on July 1, 2020---grants California users the right to opt-out of sale of their personal information. In this work, we perform a series of manual observational studies (conducted in July 2020, January 2021, and July 2021) to understand how websites implement this right. We find that the vast majority of sites that implement opt-out mechanisms do so with a Do Not Sell link rather than with a privacy banner, and that many of opt-out controls exhibit features such as nudging and inconvenience factors (e.g., fillable forms). We then perform a pair of user studies with 4357 unique users (recruited from Google Ads and Amazon Mechanical Turk) in which we observe how users interact with different opt-out mechanisms and evaluate how the observed implementation choices---exclusive use of links, nudging, and inconvenience factors---affect the rate at which users exercise their right to opt-out of sale. We find that these design elements significantly deter interactions with opt-out mechanisms---including reducing the opt-out rate for users who are uncomfortable with the sale of their information---and that they reduce users' awareness of their right to opt-out.
Evaluation of Adjuvant Radiation and Endocrine Therapy in Elderly Patients with Early-Stage Breast Cancer.

PURPOSE/OBJECTIVE(S) Several prospective trials demonstrated the feasibility of the omission of adjuvant radiation therapy (RT) among elderly breast cancer patients without worsening overall mortality. However, 50-70% of patients could not finish the full course of adjuvant ET, and select elderly patients may be at risk for undertreatment and tumor recurrences. This study compares the survival outcome of various adjuvant therapies in elderly patients with breast cancer. MATERIALS/METHODS The National Cancer Database (NCDB) was queried for female patients above 65 years old, diagnosed between 2010 and 2015 with hormone receptor (HR)-positive, human epidermal growth factor receptor 2 (HER2)-negative, pT1-2N0 (tumor size up to 3 cm) breast cancer who underwent BCS followed by either adjuvant RT, ET, or both. Primary endpoint was overall survival (OS), evaluated by Kaplan-Meier method, log-rank test, and Cox multivariable analysis. Propensity score matching was used to address selection bias. RESULTS A total of 16494 patients with a median age of 69 (interquartile range [IQR] 67-73) were analyzed, including 814, 1425, and 14255 patients treated with RT, ET, or both, respectively. On Cox multivariable analysis, ET alone was not associated with improved survival outcome (hazards ratio [HR] 1.00, 95% confidence interval [CI] 0.73-1.38, P = 1.00) compared to RT alone. However, RT combined with ET was associated with improved OS (HR 0.59, 95% CI 0.45-0.76, P < 0.001) compared to RT alone. When propensity score matching was performed, 424 and 683 pairs were matched for RT vs ET alone and RT vs RT and ET. All patient and tumor characteristics were well balanced. There was no difference in OS between RT and ET alone (HR 0.99, 95% CI 0.60-1.65, P = 0.97), while RT combined with ET was associated with improved OS (HR 0.54, 95% CI 0.34-0.86, P = 0.009). CONCLUSION RT alone may be considered as an alternative option in elderly patients who decline or may not tolerate ET, while RT combined with ET may be preferred for select elderly patients.
Paths and pathways that generate cell-type heterogeneity and developmental progression in hematopoiesis

Mechanistic studies of Drosophila lymph gland hematopoiesis are limited by the availability of cell-type specific markers. Using a combination of bulk RNA-Seq of FACS-sorted cells, single cell RNA-Seq, and genetic dissection, we identify new blood cell subpopulations along a developmental trajectory with multiple paths to mature cell types. This provides functional insights into key developmental processes and signaling pathways. We highlight metabolism as a driver of development, show that graded Pointed expression allows distinct roles in successive developmental steps, and that mature crystal cells specifically express an alternate isoform of Hypoxia-inducible factor (Hif/Sima). Mechanistically, the Musashi-regulated protein Numb facilitates Sima-dependent non-canonical, and inhibits canonical, Notch signaling. Broadly, we find that prior to making a fate choice, a progenitor selects between alternative, biologically relevant, transitory states allowing smooth transitions reflective of combinatorial expressions rather than stepwise binary decisions. Increasingly, this view is gaining support in mammalian hematopoiesis.
Occupational characteristics moderate personality–performance relations in major occupational groups

Abstract Personality predicts performance, but the moderating influence of occupational characteristics on its performance relations remains underexamined. Accordingly, we conduct second-order meta-analyses of the Big Five traits and occupational performance (i.e., supervisory ratings of overall job performance or objective performance outcomes). We identify 15 meta-analyses reporting 47 effects for 9 major occupational groups (clerical, customer service, healthcare, law enforcement, management, military, professional, sales, and skilled/semiskilled), which represent N = 89,639 workers across k = 539 studies. We also integrate data from the Occupational Information Network (O*NET) concerning two occupational characteristics: 1) expert ratings of Big Five trait relevance to its occupational requirements; and 2) its level of occupational complexity. We report three major findings. First, relations differ considerably across major occupational groups. Conscientiousness predicts across all groups, but other traits have higher validities when they are more relevant to occupational requirements: agreeableness for healthcare; emotional stability for skilled/semiskilled, law enforcement, and military; extraversion for sales and management; and openness for professional. Second, expert ratings of trait relevance mostly converge with empirical relations. For 77% of occupational groups, the top-two most highly rated traits match the top-two most highly predictive traits. Third, occupational complexity moderates personality–performance relations. When groups are ranked by complexity, multiple correlations generally follow an inverse-U shaped pattern, which suggests that moderate complexity levels may be a “goldilocks range” for personality prediction. Altogether, results demonstrate that occupational characteristics are important, if often overlooked, contextual variables. We close by discussing implications of findings for research, practice, and policy.
TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up

The recent explosive interest on transformers has suggested their potential to become powerful ``universal" models for computer vision tasks, such as classification, detection, and segmentation. While those attempts mainly study the discriminative models, we explore transformers on some more notoriously difficult vision tasks, e.g., generative adversarial networks (GANs). Our goal is to conduct the first pilot study in building a GAN completely free of convolutions, using only pure transformer-based architectures. Our vanilla GAN architecture, dubbed TransGAN, consists of a memory-friendly transformer-based generator that progressively increases feature resolution, and correspondingly a multi-scale discriminator to capture simultaneously semantic contexts and low-level textures. On top of them, we introduce the new module of grid self-attention for alleviating the memory bottleneck further, in order to scale up TransGAN to high-resolution generation. We also develop a unique training recipe including a series of techniques that can mitigate the training instability issues of TransGAN, such as data augmentation, modified normalization, and relative position encoding. Our best architecture achieves highly competitive performance compared to current state-of-the-art GANs using convolutional backbones. Specifically, TransGAN sets new state-of-the-art inception score of 10.43 and FID of 18.28 on STL-10, outperforming StyleGAN-V2. When it comes to higher-resolution (e.g. 256 x 256) generation tasks, such as on CelebA-HQ and LSUN-Church, TransGAN continues to produce diverse visual examples with high fidelity and impressive texture details. In addition, we dive deep into the transformer-based generation models to understand how their behaviors differ from convolutional ones, by visualizing training dynamics. The code is available at this https URL.
Prochlorococcus extracellular vesicles: molecular composition and adsorption to diverse microbes.

Extracellular vesicles are small (~50-200 nm diameter) membrane-bound structures released by cells from all domains of life. While vesicles are abundant in the oceans, their functions, both for cells themselves and the emergent ecosystem, remain a mystery. To better characterize these particles - a prerequisite for determining function - we analysed the lipid, protein, and metabolite content of vesicles produced by the marine cyanobacterium Prochlorococcus. We show that Prochlorococcus exports a diverse array of cellular compounds into the surrounding seawater enclosed within discrete vesicles. Vesicles produced by two different strains contain some materials in common, but also display numerous strain-specific differences, reflecting functional complexity within vesicle populations. The vesicles contain active enzymes, indicating that they can mediate extracellular biogeochemical reactions in the ocean. We further demonstrate that vesicles from Prochlorococcus and other bacteria associate with diverse microbes including the most abundant marine bacterium, Pelagibacter. Together, our data point toward hypotheses concerning the functional roles of vesicles in marine ecosystems including, but not limited to, possibly mediating energy and nutrient transfers, catalysing extracellular biochemical reactions, and mitigating toxicity of reactive oxygen species.