cssi/SciSciGPT-SciSciNet huggingface dataset:
Strategic Liability in the Corporate Group

The typical large corporation divides itself into numerous subsidiaries but then overrides the liability barriers between them by having the subsidiaries and the parent company cross-guarantee each other’s major debts. Previous scholarly theories of the corporate group cannot explain why. The leading theory posits that the subsidiaries make it easier for creditors to evaluate risk because they enable each creditor to lend against a discrete asset pool within the broader enterprise. But any such efficiency would be undercut by the guarantees, which transmit credit risk across subsidiary boundaries. This Article argues that the combination of subsidiaries and intragroup guarantees reflects a type of shareholder opportunism termed correlation-seeking. Because the insolvency risks of the entities in the typical corporate group are highly correlated, the intragroup guarantees provide the group’s shareholders with a one-way bet. The guarantees lower the interest rates on the guaranteed debts, thus enriching the shareholders as long as the group stays solvent. And if the group falls insolvent, the triggering of liability on the guarantees makes no difference to the shareholders, whose equity stakes are wiped out anyway. The guarantees instead dilute the recoveries of the group’s nonguaranteed creditors. This separation of burden and benefit induces firms to form too many subsidiaries and to overuse guarantees, thereby undermining transparency, complicating bankruptcy proceedings, and introducing other distortions. Current fraudulent transfer doctrine perversely upholds those guarantees that are most likely to be overused. Doctrinal reform based on risk correlations would deter guarantee overuse and would reduce bankruptcy courts’ dependence on the controversial remedy of substantive consolidation.
When the highest bidder loses the auction: theory and evidence from public procurement

When bids do not represent binding commitments, the use of a …rst price sealed bid auction favors those bidders who are less penalized from reneging on their bids. These bidders are the most likely to win but also the most likely to default on their bid. In this paper I study theoretically two methods often used in public procurement to deal with this problem: (1) augmenting the …rst price auction with an ex-post veri…cation of the responsiveness of the bids and (2) using an average bid auction in which the winner is the bidder whose bid is closest to the simple average of all the bids. The average bid auction is new to economics but has been proposed in civil engineering literature. I show that when penalties for defaulting are asymmetric across bidders and when their valuations are characterized by a predominant common component, the average bid auction is preferred over the standard …rst price by an auctioneer when the costs due to the winner’s bankruptcy are high enough. Depending on the cost of the ex-post veri…cation, the average bid auction can be dominated by the …rst price with monitoring. I use a new dataset of Italian public procurement auctions, run alternately using a form of the average bid auction or the augmented …rst price, to structurally estimate the bids’veri…cation cost, the …rms’mark up and the ine¢ ciency generated by the average bid auctions.
Law Firm Diversity Scholarships: Good Intentions, Incomplete Solutions

This article suggests ways that firms can fully benefit from diversity scholarships. It aims to discuss issues faced by both diverse and non-diverse associates within the law firm. It proposes solutions to these issues and attempts to highlight ways firms can get the most out of diversity scholarships, while at the same time reducing costs and overhead. Most important, this article aims to discuss diversity scholarships from the eyes of the group it is created to benefit – the diverse candidate.
Do three-part tariffs improve efficiency in residential broadband networks?

We analyze subscriber usage data from an Internet Service Provider that sells service using three-part tariff and unlimited plans. Subscribers facing three-part tariffs have lower average usage than subscribers on unlimited plans, and differences among heavy users explain nearly all the overall difference. Hence, the three-part tariff saves network costs and narrows the gap, between light and heavy users, in price per Gigabyte used. However, subscribers facing three-part tariffs cut usage similarly during peak and off-peak hours. Since off-peak usage adds no network costs, these off-peak usage reductions lower welfare. Differentially pricing peak usage could further enhance efficiency.
Composition and acquisition of the microbiome in solitary, ground-nesting alkali bees

Increasing evidence suggests the microbiome plays an important role in bee ecology and health. However, the relationship between bees and their bacterial symbionts has only been explored in a handful of species. We characterized the microbiome across the life cycle of solitary, ground-nesting alkali bees (Nomia melanderi). We find that feeding status is a major determinant of microbiome composition. The microbiome of feeding larvae was similar to that of pollen provisions, but the microbiome of post-feeding larvae (pre-pupae) was similar to that of the brood cell walls and newly-emerged females. Feeding larvae and pollen provisions had the lowest beta diversity, suggesting the composition of larval diet is highly uniform. Comparisons between lab-reared, newly-emerged, and nesting adult females suggest that the hindgut bacterial community is largely shaped by the external environment. However, we also identified taxa that are likely acquired in the nest or which increase or decrease in relative abundance with age. Although Lactobacillus micheneri was highly prevalent in pollen provisions, it was only detected in one lab-reared female, suggesting it is primarily acquired from environmental sources. These results provide the foundation for future research on metagenomic function and development of probiotics for these native pollinators.
Hyperplane arrangements and mixed Hodge numbers of the Milnor fiber

For each complex central essential hyperplane arrangement $\mathcal{A}$, let $F_{\mathcal{A}}$ denote its Milnor fiber. We use Tevelev's theory of tropical compactifications to study invariants related to the mixed Hodge structure on the cohomology of $F_{\mathcal{A}}$. We prove that the map taking each arrangement $\mathcal{A}$ to the Hodge-Deligne polynomial of $F_{\mathcal{A}}$ is locally constant on the realization space of any loop-free matroid. When $\mathcal{A}$ consists of distinct hyperplanes, we also give a combinatorial description for the homotopy type of the boundary complex of any simple normal crossing compactification of $F_{\mathcal{A}}$. As a direct consequence, we obtain a combinatorial formula for the top weight cohomology of $F_{\mathcal{A}}$, recovering a result of Dimca and Lehrer.
The Refractory Period Matters: Unifying Mechanisms of Macroscopic Brain Waves

The relationship between complex brain oscillations and the dynamics of individual neurons is poorly understood. Here we utilize maximum caliber, a dynamical inference principle, to build a minimal yet general model of the collective (mean field) dynamics of large populations of neurons. In agreement with previous experimental observations, we describe a simple, testable mechanism, involving only a single type of neuron, by which many of these complex oscillatory patterns may emerge. Our model predicts that the refractory period of neurons, which has often been neglected, is essential for these behaviors.
Learning-Based Stopping Power Mapping on Dual-Energy CT for Proton Radiation Therapy

Purpose Dual-energy computed tomography (DECT) has been used to derive relative stopping power (RSP) maps by obtaining the energy dependence of photon interactions. The DECT-derived RSP maps could potentially be compromised by image noise levels and the severity of artifacts when using physics-based mapping techniques. This work presents a noise-robust learning-based method to predict RSP maps from DECT for proton radiation therapy. Materials and methods The proposed method uses a residual attention cycle-consistent generative adversarial network to bring DECT-to-RSP mapping close to a 1-to-1 mapping by introducing an inverse RSP-to-DECT mapping. To evaluate the proposed method, we retrospectively investigated 20 head-and-neck cancer patients with DECT scans in proton radiation therapy simulation. Ground truth RSP values were assigned by calculation based on chemical compositions and acted as learning targets in the training process for DECT datasets; they were evaluated against results from the proposed method using a leave-one-out cross-validation strategy. Results The predicted RSP maps showed an average normalized mean square error of 2.83% across the whole body volume and an average mean error less than 3% in all volumes of interest. With additional simulated noise added in DECT datasets, the proposed method still maintained a comparable performance, while the physics-based stoichiometric method suffered degraded inaccuracy from increased noise level. The average differences from ground truth in dose volume histogram metrics for clinical target volumes were less than 0.2 Gy for D95% and Dmax with no statistical significance. Maximum difference in dose volume histogram metrics of organs at risk was around 1 Gy on average. Conclusion These results strongly indicate the high accuracy of RSP maps predicted by our machine-learning-based method and show its potential feasibility for proton treatment planning and dose calculation.
On the Kodaira dimension of base spaces of families of manifolds

Abstract We prove that the variation in a smooth projective family of varieties admitting a good minimal model forms a lower bound for the Kodaira dimension of the base, if the dimension of the base is at most five and its Kodaira dimension is non-negative. This gives an affirmative answer to the conjecture of Kebekus and Kovacs for base spaces of dimension at most five.
A novel allele of SIR2 reveals a heritable intermediate state of gene silencing

Genetic information acquires additional meaning through epigenetic regulation, the process by which genetically identical cells can exhibit heritable differences in gene expression and phenotype. Inheritance of epigenetic information is a critical step in maintaining cellular identity and organismal health. In Saccharomyces cerevisiae, one form of epigenetic regulation is the transcriptional silencing of two mating-type loci, HML and HMR, by the SIR-protein complex. To focus on the epigenetic dimension of this gene regulation, we conducted a forward mutagenesis screen to identify mutants exhibiting an epigenetic or metastable silencing defect. We utilized fluorescent reporters at HML and HMR, and screened yeast colonies for epigenetic silencing defects. We uncovered numerous independent sir1 alleles, a gene known to be required for stable epigenetic inheritance. More interestingly, we recovered a missense mutation within SIR2, which encodes a highly conserved histone deacetylase. In contrast to sir1Δ, which exhibits states that are either fully silenced or fully expressed, this sir2 allele exhibited heritable states that were either fully silenced or expressed at an intermediate level. The heritable nature of this unique silencing defect was influenced by, but not completely dependent on, changes in rDNA copy number. Therefore, this study revealed a heritable state of intermediate silencing and linked this state to a central silencing factor, Sir2.
Factors correlating to enhanced surface diffusion in metallic glasses

The enhancement of surface diffusion (DS) over the bulk (DV) in metallic glasses (MGs) is well documented and likely to strongly influence the properties of glasses grown by vapor deposition. Here, we use classical molecular dynamics (MD) simulations to identify different factors influencing the enhancement of surface diffusion in MGs. MGs have a simple atomic structure and belong to the category of moderately fragile glasses that undergo pronounced slowdown of bulk dynamics with cooling close to the glass transition temperature (Tg). We observe that DS exhibits a much more moderate slowdown compared to DV when approaching Tg, and DS/DV at Tg varies by two orders of magnitude among the MGs investigated. We demonstrate that both the surface energy and the fraction of missing bonds for surface atoms show good correlation to DS/DV, implying that the loss of nearest neighbors at the surface directly translates into higher mobility, unlike the behavior of network-bonded and hydrogen-bonded organic glasses. Fragility, a measure of the slowdown of bulk dynamics close to Tg, also correlates to DS/DV, with more fragile systems having larger surface enhancement of mobility. The deviations observed in the fragility–DS/DV relationship are shown to be correlated to the extent of segregation or depletion of the mobile element at the surface. Finally, we explore the relationship between the diffusion pre-exponential factor (D0) and the activation energy (Q) and compare it to a ln(D0)–Q correlation previously established for bulk glasses, demonstrating similar correlations from MD as in the experiments and that the surface and bulk have very similar ln(D0)–Q correlations.
Gutzwiller hybrid quantum-classical computing approach for correlated materials

The authors develop a hybrid quantum-classical simulation framework for correlated quantum materials based on Gutzwiller variational embedding theory.
MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling

Boosting methods are among the best generalpurpose and off-the-shelf machine learning approaches, gaining widespread popularity. In this paper, we seek to develop a boosting method that yields comparable accuracy to popular AdaBoost and gradient boosting methods, yet is faster computationally and whose solution is more interpretable. We achieve this by developing MP-Boost, an algorithm loosely based on AdaBoost that learns by adaptively selecting small subsets of instances and features, or what we term minipatches (MP), at each iteration. By sequentially learning on tiny subsets of the data, our approach is computationally faster than classic boosting algorithms. MP-Boost upweights important features and challenging instances, hence adaptively selects the most relevant minipatches for learning. The learned probability distributions aid in interpretation of our method. We empirically demonstrate the interpretability and comparative accuracy of our algorithm on a variety of binary classification tasks.
Analysis of high-order velocity moments in a strained channel flow

Abstract In the current study, model expressions for fifth-order velocity moments obtained from the truncated Gram-Charlier series expansions model for a turbulent flow field probability density function are validated using data from direct numerical simulation (DNS) of a planar turbulent flow in a strained channel. Simplicity of the model expressions, the lack of unknown coefficients, and their applicability to non-Gaussian turbulent flows make this approach attractive to use for closing turbulent models based on the Reynolds-averaged Navier-Stokes equations. The study confirms validity of the model expressions. It also shows that the imposed flow deformation improves an agreement between the model and DNS profiles for the fifth-order moments in the flow buffer zone including when the flow reverses its direction. The study reveals sensitivity of particularly odd velocity moments to the grid resolution. A new length scale is proposed as a criterion for the grid generation near the wall and in the other flow areas dominated by high mean velocity gradients when higher-order statistics have to be collected from DNS.
Detection and Localization of Load Redistribution Attacks on Large-Scale Systems

A nearest neighbor-based detection scheme against load redistribution attacks is presented. The detector is designed to scale from small to very large systems while guaranteeing consistent detection performance. Extensive testing is performed on a realistic, large scale system to evaluate the performance of the proposed detector against a wide range of attacks, from simple random noise attacks to sophisticated load redistribution attacks. The detection capability is analyzed against different attack parameters to evaluate its sensitivity. Finally, a statistical test that leverages the proposed detection algorithm is introduced to identify which loads are likely to have been maliciously modified, thus, localizing the attack subgraph. This test is based on ascribing to each load a risk measure (probability of being attacked) and then computing the best posterior likelihood that minimizes log-loss.
Hidden Time-Reversal Symmetry, Quantum Detailed Balance and Exact Solutions of Driven-Dissipative Quantum Systems

A formulation of quantum detailed balance, based on hidden time-reversal symmetry, is presented, enabling an efficient way for finding non-trivial steady states of a driven dissipative quantum system.
Ricci Curvature, Reeb Flows and Contact 3-Manifolds

Given a contact 3-manifold, we consider the problem of when a given function can be realized as the Ricci curvature of a Reeb vector field for the contact structure. We will use topological tools to show that every admissible function can be realized as such Ricci curvature for a singular metric which is an honest compatible metric away from a measure zero set. However, we will see that resolving such singularities depends on contact topological data and is yet to be fully understood.
Atypical Mediofrontal Theta Oscillations Underlying Cognitive Control in Kindergarteners With Autism Spectrum Disorder.

Abstract Background Children with autism spectrum disorder (ASD) often exhibit deficits in cognitive control. Neuroimaging approaches have implicated disruptions to mediofrontal cortex structure and function. However, previous work is limited in testing whether young children with ASD exhibit disruptions to task-related theta oscillations thought to arise from the mediofrontal cortex. Methods Children with ASD (n = 43) and age- and sex-matched typically developing peers (n = 24) at kindergarten entry performed a child-friendly Go/NoGo task while 64-channel electroencephalography was recorded. Time-frequency approaches were employed to assess the magnitude of mediofrontal theta oscillations immediately after error (vs. correct) responses (early theta) as well as later emerging theta oscillations (late theta). We tested whether error-related mediofrontal theta oscillations differed as a function of diagnosis (ASD/typical) and timing (early/late theta). In addition, links to social and academic outcomes were tested. Results Overall, children showed increased theta power after error versus correct responses. Compared with typically developing children, children with ASD exhibited a selective reduction in error-related mediofrontal theta power during the late time window. There were no significant group differences for early theta power. Moreover, reduced error-related theta power during the late, but not early, time window significantly predicted poorer academic and social skills. Conclusions Kindergarteners with ASD demonstrated a selective reduction in error-related mediofrontal theta power during a relatively late time window, which is consistent with impairments in specific cognitive processes that recruit top-down control. Targeting these particular cognitive control processes via intervention prior to school entry may promote more successful functional outcomes for children with ASD.
Nucleon momentum fraction, helicity and transversity from 2+1-flavor lattice QCD

A detailed analysis of the systematic uncertainties in the calculation of the isovector momentum fraction, 〈x〉u − d, helicity moment, 〈x〉Δu − Δd, and the transversity moment, 〈x〉δu − δd, of the nucleon is presented using high-statistics data on seven ensembles of gauge configurations generated by the JLab/W&M/LANL/MIT collaborations using 2 + 1-flavors of dynamical Wilson-clover quarks. The much higher statistics have facilitated better control over all systematics compared to previous lattice calculations. The least understood systematic — excited-state contamination — is quantified by studying the variation of the results as a function of different estimates of the mass gap of the first excited state, obtained from two- and three-point correlation functions, and as a function of the pion mass Mπ. The final results are obtained using a simultaneous fit in the lattice spacing a, pion mass Mπ and the finite volume parameter MπL keeping leading order corrections. The data show no significant dependence on the lattice spacing and some evidence for finite-volume corrections. Our final results, in the $$ \overline{\mathrm{MS}} $$ scheme at 2 GeV, are 〈x〉u − d = 0.155(17)(20), 〈x〉Δu − Δd = 0.183(14)(20) and 〈x〉δu − δd = 0.220(18)(20), where the first error is the overall analysis uncertainty assuming excited-state contributions have been removed, and the second is an additional systematic uncertainty due to possible residual excited-state contributions. These results are consistent with phenomenological global fit values.
Kv1.1 subunits localize to cardiorespiratory brain networks in mice where their absence induces astrogliosis and microgliosis.

Abstract Cardiorespiratory collapse following a seizure is a suspected cause of sudden unexpected death in epilepsy (SUDEP), the leading cause of epilepsy-related mortality. In the commonly used Kcna1 gene knockout (Kcna1−/−) mouse model of SUDEP, cardiorespiratory profiling reveals an array of aberrant breathing patterns that could contribute to risk of seizure-related mortality. However, the brain structures mediating these respiratory abnormalities remain unknown. We hypothesize that Kv1.1 deficiency in respiratory control centers of the brain contribute to respiratory dysfunction in Kcna1−/− mice leading to increased SUDEP risk. Thus, in this study, we first used immunohistochemistry to map expression of Kv1.1 protein in cardiorespiratory brain regions of wild-type Kcna1+/+ (WT) mice. Next, GFAP and Iba1 immunostaining was used to test for the presence of astrogliosis and microgliosis, respectively, in the cardiorespiratory centers of Kcna1−/− mice, which could be indicative of seizure-related brain injury that could impair breathing. In WT mice, we detected Kv1.1 protein in all cardiorespiratory centers examined, including the basolateral amygdala, dorsal respiratory group, dorsal motor nucleus of vagus, nucleus ambiguus, ventral respiratory column, and pontine respiratory group, as well as chemosensory centers including the retrotrapezoid and median raphae nuclei. Extensive gliosis was observed in the same areas in Kcna1−/− mice suggesting that seizure-associated brain injury could contribute to respiratory abnormalities.
Investigating 3,4-bis(3-nitrofurazan-4-yl)furoxan detonation with a rapidly tuned density functional tight binding model.

We describe a machine learning approach to rapidly tune density functional tight binding models for the description of detonation chemistry in organic molecular materials. Resulting models enable simulations on the several 10s of ps scales characteristic to these processes, with “quantum-accuracy.” We use this approach to investigate early shock chemistry in 3,4-bis(3-nitrofurazan-4-yl)furoxan, a hydrogen-free energetic material known to form onion-like nanocarbon particulates following detonation. We find that the ensuing chemistry is significantly characterized by the formation of large CxNyOz species, which are likely precursors to the experimentally observed carbon condensates. Beyond utility as a means of investigating detonation chemistry, the present approach can be used to generate quantum-based reference data for the development of full machine-learned interatomic potentials capable of simulation on even greater time and length scales, i.e., for applications where characteristic time scales exceed the reach of methods including Kohn–Sham density functional theory, which are commonly used for reference data generation.
#StayHome #WithMe: How Do YouTubers Help with COVID-19 Loneliness?

Loneliness threatens public mental wellbeing during COVID-19. In response, YouTube creators participated in the #StayHome #WithMe movement (SHWM) and made myriad videos for people experiencing loneliness or boredom at home. User-shared videos generate parasocial attachment and virtual connectedness. However, there is limited knowledge of how creators contributed videos during disasters to provide social provisions as disaster-relief. Grounded on Weiss’s loneliness theory, this work analyzed 1488 SHWM videos to examine video sharing as a pathway to social provisions. Findings suggested that skill and knowledge sharing, entertaining arts, homelife activities, live chatting, and gameplay were the most popular video styles. YouTubers utilized parasocial relationships to form a space for staying away from the disaster. SHWM YouTubers provided friend-like, mentor-like, and family-like provisions through videos in different styles. Family-like provisions led to the highest overall viewer engagement. Based on the findings, design implications for supporting viewers’ mental wellbeing in disasters are discussed.
ICASSP 2021 Acoustic Echo Cancellation Challenge: Datasets, Testing Framework, and Results

The ICASSP 2021 Acoustic Echo Cancellation Challenge is intended to stimulate research in the area of acoustic echo cancellation (AEC), which is an important part of speech enhancement and still a top issue in audio communication and conferencing systems. Many recent AEC studies report good performance on synthetic datasets where the train and test samples come from the same underlying distribution. However, the AEC performance often degrades significantly on real recordings. Also, most of the conventional objective metrics such as echo return loss enhancement (ERLE) and perceptual evaluation of speech quality (PESQ) do not correlate well with subjective speech quality tests in the presence of background noise and reverberation found in realistic environments. In this challenge, we open source two large datasets to train AEC models under both single talk and double talk scenarios. These datasets consist of recordings from more than 2,500 real audio devices and human speakers in real environments, as well as a synthetic dataset. We open source two large test sets, and we open source an online subjective test framework for researchers to quickly test their results. The winners of this challenge will be selected based on the average Mean Opinion Score (MOS) achieved across all different single talk and double talk scenarios.
Functorial properties of pro-p-Iwahori cohomology

Suppose $F$ is a finite extension of $\mathbb{Q}_p$, $G$ is the group of $F$-points of a connected reductive $F$-group, and $I_1$ is a pro-$p$-Iwahori subgroup of $G$. We construct two spectral sequences relating derived functors on mod-$p$ representations of $G$ to the analogous functors on Hecke modules coming from pro-$p$-Iwahori cohomology. More specifically: (1) using results of Ollivier--Vigneras, we provide a link between the right adjoint of parabolic induction on pro-$p$-Iwahori cohomology and Emerton's functors of derived ordinary parts; and (2) we establish a "Poincare duality spectral sequence" relating duality on pro-$p$-Iwahori cohomology to Kohlhaase's functors of higher smooth duals. As applications, we calculate various examples of the Hecke modules $\textrm{H}^i(I_1,\pi)$.
Shareholder activism and firms' voluntary disclosure of climate change risks

Research Summary This article examines whether—in the absence of mandated disclosure requirements—shareholder activism can elicit greater disclosure of firms' exposure to climate change risks. We find that environmental shareholder activism increases the voluntary disclosure of climate change risks, especially if initiated by institutional investors, and even more so if initiated by long‐term institutional investors. We also find that companies that voluntarily disclose climate change risks following environmental shareholder activism achieve a higher valuation postdisclosure, suggesting that investors value transparency with respect to firms' exposure to climate change risks. Managerial Summary Climate change poses increasing risks to companies. Yet, despite the growing importance of climate change risks, little is known about companies' exposure to climate change risks, their disclosure of these risks, and what strategic actions they take to manage and mitigate these risks. In this study, we examine whether—in the absence of mandatory disclosure—shareholders can elicit greater corporate transparency with respect to climate change risks. We find that shareholder activism is effective, especially if initiated by long‐term institutional investors. We also find that the stock market reacts positively to companies' climate risk disclosure following environmental shareholder activism, suggesting that investors value transparency with respect to firms' exposure to climate change risks.
Metrology for Measuring Custom Periodicities on Diffraction Gratings

We present a new, inexpensive, bench-top method for measuring groove period over large areas with high mapping resolution and high measurement accuracy, dubbed the grating mapper for accurate perio...
Distinct microbial communities alter litter decomposition rates in a fertilized coastal plain wetland

Abstract Human activities have led to increased deposition of nitrogen (N) and phosphorus (P) into soils. Nutrient enrichment of soils is known to increase plant biomass and rates of microbial litter decomposition. However, interacting effects of hydrologic position and associated changes to soil moisture can constrain microbial activity and lead to unexpected nutrient feedbacks on microbial community structure-function relationships. Examining how feedbacks of nutrient enrichment on decomposition rates is essential for predicting microbial contributions to carbon (C) cycling as atmospheric deposition of nutrients persists. This study explores how long-term nutrient addition and contrasting litter chemical quality influence soil bacterial community structure and function. We hypothesize that long-term nutrient enrichment of low fertility soils alters bacterial community structure and leads to higher rates of litter decomposition with decreasing C:N ratio of litter; but low nutrient and dry conditions limit constrain microbial decomposition of high C:N ratio litter. We leverage a long-term fertilization experiment to test how nutrient enrichment and hydrologic manipulation (due to ditches) affects decomposition and soil bacterial community structure in a nutrient poor coastal plain wetland. We conducted a litter bag experiment and characterized litter-associated and bulk soil microbiomes using 16S rRNA bacterial sequencing and quantified litter mass losses and soil physicochemical properties. Results revealed that distinct bacterial communities were involved in decomposing higher C:N ratio litter more quickly in fertilized compared to unfertilized especially under drier soil conditions, while decomposition rates of green tea litter (lower C:N ratio) were similar between fertilized and unfertilized plots. Bacterial community structure in part explained litter decomposition rates, and long-term fertilization and drier hydrologic status affected bacterial diversity and increased decomposition rates. However, community composition associated with high C:N litter was similar in wetter plots with available nitrate detected, regardless of fertilization treatment. This study provides insight into long-term fertilization effects on soil bacterial diversity and composition, decomposition, and the increased potential for soil C loss as nutrient enrichment and hydrology interact to affect historically low nutrient ecosystems.
Provably Efficient Reinforcement Learning for Discounted MDPs with Feature Mapping

Modern tasks in reinforcement learning have large state and action spaces. To deal with them efficiently, one often uses predefined feature mapping to represent states and actions in a low-dimensional space. In this paper, we study reinforcement learning for discounted Markov Decision Processes (MDPs), where the transition kernel can be parameterized as a linear function of certain feature mapping. We propose a novel algorithm that makes use of the feature mapping and obtains a $\tilde O(d\sqrt{T}/(1-\gamma)^2)$ regret, where $d$ is the dimension of the feature space, $T$ is the time horizon and $\gamma$ is the discount factor of the MDP. To the best of our knowledge, this is the first polynomial regret bound without accessing the generative model or making strong assumptions such as ergodicity of the MDP. By constructing a special class of MDPs, we also show that for any algorithms, the regret is lower bounded by $\Omega(d\sqrt{T}/(1-\gamma)^{1.5})$. Our upper and lower bound results together suggest that the proposed reinforcement learning algorithm is near-optimal up to a $(1-\gamma)^{-0.5}$ factor.
Multiplicative Noise and Heavy Tails in Stochastic Optimization

Although stochastic optimization is central to modern machine learning, the precise mechanisms underlying its success, and in particular, the precise role of the stochasticity, still remain unclear. Modelling stochastic optimization algorithms as discrete random recurrence relations, we show that multiplicative noise, as it commonly arises due to variance in local rates of convergence, results in heavy-tailed stationary behaviour in the parameters. A detailed analysis is conducted for SGD applied to a simple linear regression problem, followed by theoretical results for a much larger class of models (including non-linear and non-convex) and optimizers (including momentum, Adam, and stochastic Newton), demonstrating that our qualitative results hold much more generally. In each case, we describe dependence on key factors, including step size, batch size, and data variability, all of which exhibit similar qualitative behavior to recent empirical results on state-of-the-art neural network models from computer vision and natural language processing. Furthermore, we empirically demonstrate how multiplicative noise and heavy-tailed structure improve capacity for basin hopping and exploration of non-convex loss surfaces, over commonly-considered stochastic dynamics with only additive noise and light-tailed structure.
Sensifi: A Wireless Sensing System for Ultra-High-Rate Applications

Wireless Sensor Networks (WSNs) are being used in various applications such as structural health monitoring and industrial control. Since energy efficiency is one of the major design factors, the existing WSNs primarily rely on low-power, low-rate wireless technologies such as 802.15.4 and Bluetooth. In this paper, by proposing , we strive to tackle the challenges of developing ultra-high-rate WSNs based on the 802.11 (WiFi) standard. As an illustrative structural health monitoring application, we consider spacecraft vibration test and identify system design requirements and challenges. Our main contributions are as follows. First, we propose packet encoding methods to reduce the overhead of assigning accurate timestamps to samples. Second, we propose energy efficiency methods to enhance the system’s lifetime. Third, to enhance sampling rate and mitigate sampling rate instability, we reduce the overhead of processing outgoing packets through the network stack. Fourth, we study and reduce the delay of processing time synchronization packets through the network stack. Fifth, we propose a low-power node design particularly targeting vibration monitoring. Sixth, we use our node design to empirically evaluate energy efficiency, sampling rate, and data rate. We leave large-scale evaluations as future work.
How to Test the Randomness From the Wireless Channel for Security

We revisit the traditional framework of wireless secret key generation, where two parties leverage the wireless channel randomness to establish a secret key. The essence in the framework is to quantify channel randomness into bit sequences for key generation. Conducting randomness tests on such bit sequences has been a common practice to provide the confidence to validate whether they are random. Interestingly, despite different settings in the tests, existing studies interpret the results the same: passing tests means that the bit sequences are indeed random. In this paper, we investigate how to properly test the wireless channel randomness to ensure enough security strength and key generation efficiency. In particular, we define an adversary model that leverages the imperfect randomness of the wireless channel to search the generated key, and create a guideline to set up randomness testing and privacy amplification to eliminate security loss and achieve efficient key generation rate. We use theoretical analysis and comprehensive experiments to reveal that common practice misuses randomness testing and privacy amplification: (i) no security insurance of key strength, (ii) low efficiency of key generation rate. After revision by our guideline, security loss can be eliminated and key generation rate can be increased significantly.
Sparse sketches with small inversion bias

For a tall $n\times d$ matrix $A$ and a random $m\times n$ sketching matrix $S$, the sketched estimate of the inverse covariance matrix $(A^\top A)^{-1}$ is typically biased: $E[(\tilde A^\top\tilde A)^{-1}]\ne(A^\top A)^{-1}$, where $\tilde A=SA$. This phenomenon, which we call inversion bias, arises, e.g., in statistics and distributed optimization, when averaging multiple independently constructed estimates of quantities that depend on the inverse covariance. We develop a framework for analyzing inversion bias, based on our proposed concept of an $(\epsilon,\delta)$-unbiased estimator for random matrices. We show that when the sketching matrix $S$ is dense and has i.i.d. sub-gaussian entries, then after simple rescaling, the estimator $(\frac m{m-d}\tilde A^\top\tilde A)^{-1}$ is $(\epsilon,\delta)$-unbiased for $(A^\top A)^{-1}$ with a sketch of size $m=O(d+\sqrt d/\epsilon)$. This implies that for $m=O(d)$, the inversion bias of this estimator is $O(1/\sqrt d)$, which is much smaller than the $\Theta(1)$ approximation error obtained as a consequence of the subspace embedding guarantee for sub-gaussian sketches. We then propose a new sketching technique, called LEverage Score Sparsified (LESS) embeddings, which uses ideas from both data-oblivious sparse embeddings as well as data-aware leverage-based row sampling methods, to get $\epsilon$ inversion bias for sketch size $m=O(d\log d+\sqrt d/\epsilon)$ in time $O(\text{nnz}(A)\log n+md^2)$, where nnz is the number of non-zeros. The key techniques enabling our analysis include an extension of a classical inequality of Bai and Silverstein for random quadratic forms, which we call the Restricted Bai-Silverstein inequality; and anti-concentration of the Binomial distribution via the Paley-Zygmund inequality, which we use to prove a lower bound showing that leverage score sampling sketches generally do not achieve small inversion bias.
Ultrafast optical melting of trimer superstructure in layered 1T′-TaTe2

Quasi-two-dimensional transition-metal dichalcogenides are a key platform for exploring emergent nanoscale phenomena arising from complex interactions. Access to the underlying degrees-of-freedom on their natural time scales motivates the use of advanced ultrafast probes sensitive to self-organised atomic-scale patterns. Here, we report the ultrafast investigation of TaTe2, which exhibits unique charge and lattice trimer order characterised by a transition upon cooling from stripe-like chains into a (3 × 3) superstructure of trimer clusters. Utilising MeV-scale ultrafast electron diffraction, we capture the photo-induced TaTe2 structural dynamics – exposing a rapid ≈ 1.4 ps melting of its low-temperature ordered state followed by recovery via thermalisation into a hot cluster superstructure. Density-functional calculations indicate that the initial quench is triggered by intra-trimer Ta charge transfer which destabilises the clusters, unlike melting of charge density waves in other TaX2 compounds. Our work paves the way for further exploration and ultimately rapid optical and electronic manipulation of trimer superstructures.
A parametric study of fine-scale turbulence mixing noise

The present paper is a study of aerodynamic noise spectra from model functions that describe the source. The study is motivated by the need to improve the spectral shape of the MGBK jet noise prediction methodology at high frequency. The predicted spectral shape usually appears less broadband than measurements and faster decaying at high frequency. Theoretical representation of the source is based on Lilley's equation. Numerical simulations of high-speed subsonic jets as well as some recent turbulence measurements reveal a number of interesting statistical properties of turbulence correlation functions that may have a bearing on radiated noise. These studies indicate that an exponential spatial function may be a more appropriate representation of a two-point correlation compared to its Gaussian counterpart. The effect of source non-compactness on spectral shape is discussed. It is shown that source non-compactness could well be the differentiating factor between the Gaussian and exponential model functions. In particular, the fall-off of the noise spectra at high frequency is studied and it is shown that a non-compact source with an exponential model function results in a broader spectrum and better agreement with data. An alternate source model that represents the source as a covariance of the convective derivative of fine-scale turbulence kinetic energy is also examined.
ATHLETE: Trading Complexity for Mass in Roving Vehicles

This paper describes a scaling analysis of ATHLETE for exploration of the moon, Mars and Near-Earth Asteroids (NEAs) in comparison to a more conventional vehicle configuration. Recently, the focus of human exploration beyond LEO has been on NEAs. A low gravity testbed has been constructed in the ATHLETE lab, with six computer-controlled winches able to lift ATHLETE and payloads so as to simulate the motion of the system in the vicinity of a NEA or to simulate ATHLETE on extreme terrain in lunar or Mars gravity. Test results from this system are described.
Investigation of high-pressure turbulent jets using direct numerical simulation

Direct numerical simulations of free round jets at a Reynolds number ( is reduced, large mean strain rates in the transition region of laminar-inflow jets significantly enhance velocity fluctuations (non-dimensionalized by local mean velocity) and scalar mixing, whereas the effects are minimal in jets from turbulent inflow.
Language-based Decisions.

In Savage's classic decision-theoretic framework, actions are formally defined as functions from states to outcomes. But where do the state space and outcome space come from? Expanding on recent work by Blume, Easley, and Halpern (BEH), we consider a language-based framework in which actions are identified with (conditional) descriptions in a simple underlying language, while states and outcomes (along with probabilities and utilities) are constructed as part of a representation theorem. Our work expands the role of language from that of BEH by using it not only for the conditions that determine which actions are taken, but also the effects. More precisely, we take the set of actions to be built from those of the form "do(phi)", for formulas phi in the underlying language. This presents a problem: how do we interpret the result of do(phi) when phi is underspecified (i.e., compatible with multiple states)? We answer this using tools familiar from the semantics of counterfactuals: roughly speaking, do(phi) maps each state to the "closest" phi-state. This notion of "closest" is also something we construct as part of the representation theorem; in effect, then, we prove that (under appropriate assumptions) the agent is acting as if each underspecified action is first made definite and then evaluated (i.e., by maximizing expected utility). Of course, actions in the real world are often not presented in a fully precise manner, yet agents reason about and form preferences among them all the same. Our work brings the abstract tools of decision theory into closer contact with such real-world scenarios.
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function

The optimal performance of robotic systems is usually achieved near the limit of state and input bounds. Model predictive control (MPC) is a prevalent strategy to handle these operational constraints, however, safety still remains an open challenge for MPC as it needs to guarantee that the system stays within an invariant set. In order to obtain safe optimal performance in the context of set invariance, we present a safety-critical model predictive control strategy utilizing discrete-time control barrier functions (CBFs), which guarantees system safety and accomplishes optimal performance via model predictive control. We analyze the feasibility and the stability properties of our control design. We verify the properties of our method on a 2D double integrator model for obstacle avoidance. We also validate the algorithm numerically using a competitive car racing example, where the ego car is able to overtake other racing cars.
Altruism Design in Networked Public Goods Games

Many collective decision-making settings feature a strategic tension between agents acting out of individual self-interest and promoting a common good. These include wearing face masks during a pandemic, voting, and vaccination. Networked public goods games capture this tension, with networks encoding strategic interdependence among agents. Conventional models of public goods games posit solely individual self-interest as a motivation, even though altruistic motivations have long been known to play a significant role in agents' decisions. We introduce a novel extension of public goods games to account for altruistic motivations by adding a term in the utility function that incorporates the perceived benefits an agent obtains from the welfare of others, mediated by an altruism graph. Most importantly, we view altruism not as immutable, but rather as a lever for promoting the common good. Our central algorithmic question then revolves around the computational complexity of modifying the altruism network to achieve desired public goods game investment profiles. We first show that the problem can be solved using linear programming when a principal can fractionally modify the altruism network. While the problem becomes in general intractable if the principal's actions are all-or-nothing, we exhibit several tractable special cases.