Miracle-dz/UniFAR-FTUs huggingface dataset:
Electric fields and neutral winds from monostatic incoherent scatter measurements by means of stochastic inversion

[1] A new method utilizing stochastic inversion in determining the electric field and neutral wind from monostatic beam swing incoherent scatter measurements is described. The method consists of two stages. In the first stage, beam-aligned ion velocities from a chosen F region height interval and a set of subsequent beam directions are taken as measurements. The unknowns are the two electric field components and the field-aligned ion velocity profile. The solution gives the most probable values of the unknowns with error estimates. In the second stage, the measurements consist of beam-aligned ion velocities from the E region, and the electric fields given by the first inversion problem are also used as measurements. The number of applied beam directions may be greater than in the first inversion problem. This is a feasible approach since the neutral wind usually changes more slowly than the electric field. The solution of the second inversion problem gives the most probable values of the three neutral wind components. Results of the method are shown for 11 September 2005, when the European Incoherent Scatter (EISCAT) UHF radar was running in the CP2 experiment mode, which is a four-position 6 min monostatic cycle. In addition, from each beam direction a tristatic measurement at one F region range gate was made using two additional receivers. That allowed comparison between the monostatic and tristatic electric field results, which were in excellent agreement. The calculated neutral wind components were in good accordance with previous measurements during disturbed conditions from the same site.
MANU MILITARI: THE INSTITUTIONAL CONTINGENCIES OF MANU MILITARI: THE INSTITUTIONAL CONTINGENCIES OF STAKEHOLDER RELATIONSHIPS ON ENTREPRENEURIAL STAKEHOLDER RELATIONSHIPS ON ENTREPRENEURIAL PERFORMANCE PERFORMANCE

This study examines how ventures can leverage relationships with heterogeneous government stakeholders to enhance survival in different institutional environments. We consider how the distinct resources provided from venture ties to military and political actors represent complementary strategic assets that differentially influence performance in varying political and economic environments as well as under conditions of violence and political conflict. Empirically, we examine the effect of these respective stakeholder relationships on new venture survival across 10 countries over a 65-year period. By distinguishing between the resources obtained through relationships with different types of government stakeholders and showing how the value of these resources varies in different contexts, this study contributes to nonmarket strategy and stakeholder management research and highlights the need for studies to take a pluralistic view of government stakeholders. The paper also presents managerial insights to firms seeking to address the prevalent challenges associated with political, economic, and physical security issues in developing and underdeveloped economies. Data Shortcomings. We have annual data on military and political ties for 80% of the firms. For the remaining 20%, we cannot accurately measure from the data when a military tie ends. To address this issue, we ran an analysis with only the 80% of firms for which we have annual tie data. The results did not change. Additionally, of the firms for which we do have annual data, in only three instances did the military officer leave the business or sell his holdings before an event (e.g., failure, expropriation, acquisition, or exit) occurred, suggesting that the ties are particularly durable. We also ran survival analyses with military ties ending after 10, 5, and 3 years, and we lagged the military-tie variable by 2 years. In all these analyses, we found the results to be significant for the failure. We also compared small and large airlines to tease out any differences in advisory-board changes. Research suggests that smaller firms are less likely to experience advisory-board turnover than large firms and thus would be likely to maintain military ties longer (Harrison al. 1988). We conducted failure analyses comparing the two groups and found the organizational effects of military ties to be significant for both.
Contextual Effects in Modeling for Small Domains

During last two decades, different Small Area Estimation (SAE) methods have been proposed to overcome the challenge of finding reliable small area estimates. This happens a lot that the required data for various research purposes are available at different levels. Based on availability of data, individual-level or aggregatedlevel models are implied in SAE. However, the estimated values for model parameters obtained from individual-level analysis can be different from the one obtained based on analysis of aggregate data. Generally, this is referred to as the ecological fallacy. This happens due to some substantial contextual or area-level effects in the covariates. To have a good interpretation of available data, possible contextual effects must be carefully included, measured, and accounted for in statistical models for calculating reliable estimates. Ignoring these effects leads to misleading results. The main advantage of contextual models is to help statisticians in studying aggregated-level data without concerning about the issue of ecological fallacy. In this paper, synthetic estimators and Empirical Best Linear Unbiased Predictors (EBLUPs) are studied based on different levels of linear mixed models. Using a numerical simulation study, the key role of contextual area-level effects is examined for model selection in SAE.
Entropy Augmented Reinforcement Learning

Deep reinforcement learning was instigated with the presence of trust region methods, being scalable and efficient. However, the pessimism of such algorithms, among which it forces to constrain in a trust region by all means, has been proven to suppress the exploration and harm the performance. Exploratory algorithm such as SAC, while utilizes the entropy to encourage exploration, implicitly optimizing another objective yet. We first observed this inconsistency, and therefore put forward an analogous augmentation technique, which combines well with the on-policy algorithms, when a value critic is involved. Surprisingly, the proposed method consistently satisfies the soft policy improvement theorem, while being more extensible. As the analysis advises, it is crucial to control the temperature coefficient to balance the exploration and exploitation. Empirical tests on MuJoCo benchmark tasks show that the agent is heartened towards higher reward regions, and enjoys a finer performance. Furthermore, we verify the exploration bonus of our method on a set of custom environments.
Gauged quintessence

Despite its dominance in the present universe's energy budget, dark energy is the least understood component in the universe. Although there is a popular model for the dynamical dark energy, the quintessence scalar, the investigation is limited because of its highly elusive character. We present a model where the quintessence is gauged by an Abelian gauge symmetry. The quintessence is promoted to be a complex scalar whose real part is the dark energy field while the imaginary part is the longitudinal component of a new gauge boson. It brings interesting characters to dark energy physics. We study the general features of the model, including how the quintessence behavior is affected and how the solicited dark energy properties constrain its gauge interaction. We also note that while the uncoupled quintessence models are suffered greatly from the Hubble tension, it can be alleviated if the quintessence is under the gauge symmetry.
A Risk-Sensitive Approach to Policy Optimization

Standard deep reinforcement learning (DRL) aims to maximize expected reward, considering collected experiences equally in formulating a policy. This differs from human decision-making, where gains and losses are valued differently and outlying outcomes are given increased consideration. It also fails to capitalize on opportunities to improve safety and/or performance through the incorporation of distributional context. Several approaches to distributional DRL have been investigated, with one popular strategy being to evaluate the projected distribution of returns for possible actions. We propose a more direct approach whereby risk-sensitive objectives, specified in terms of the cumulative distribution function (CDF) of the distribution of full-episode rewards, are optimized. This approach allows for outcomes to be weighed based on relative quality, can be used for both continuous and discrete action spaces, and may naturally be applied in both constrained and unconstrained settings. We show how to compute an asymptotically consistent estimate of the policy gradient for a broad class of risk-sensitive objectives via sampling, subsequently incorporating variance reduction and regularization measures to facilitate effective on-policy learning. We then demonstrate that the use of moderately "pessimistic" risk profiles, which emphasize scenarios where the agent performs poorly, leads to enhanced exploration and a continual focus on addressing deficiencies. We test the approach using different risk profiles in six OpenAI Safety Gym environments, comparing to state of the art on-policy methods. Without cost constraints, we find that pessimistic risk profiles can be used to reduce cost while improving total reward accumulation. With cost constraints, they are seen to provide higher positive rewards than risk-neutral approaches at the prescribed allowable cost.
Federated Learning with Noisy Labels

Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets, where the labeling effort is entrusted to the clients. While most existing FL approaches assume high-quality labels are readily available on users’ devices; in reality, label noise can naturally occur in FL and follows a non-i.i. d. distribution among clients. Due to the “ non-iid-ness ” challenges, existing state-of-the-art centralized approaches exhibit unsatisfactory performance, while previous FL studies rely on data exchange or repeated server-side aid to improve model’s performance. Here, we propose FedLN , a framework to deal with label noise across different FL training stages; namely, FL initialization, on-device model training, and server model aggregation. Specifically, FedLN computes per-client noise-level estimation in a single federated round and improves the models’ performance by correcting (or limiting the effect of) noisy samples. Extensive experiments on various publicly available vision and audio datasets demonstrate a 24% improvement on average compared to other existing methods for a label noise level of 70%. We further validate the efficiency of FedLN in human-annotated real-world noisy datasets and report a 9% increase on average in models’ recognition rate, highlighting that FedLN can be useful for improving FL services provided to everyday users.
Implicit Session Contexts for Next-Item Recommendations

\noindent Session-based recommender systems capture the short-term interest of a user within a session. Session contexts (i.e., a user's high-level interests or intents within a session) are not explicitly given in most datasets, and implicitly inferring session context as an aggregation of item-level attributes is crude. In this paper, we propose \method, which implicitly contextualizes sessions. \method first generates implicit contexts for sessions by creating a session-item graph, learning graph embeddings, and clustering to assign sessions to contexts. \method then trains a session context predictor and uses the predicted contexts' embeddings to enhance the next-item prediction accuracy. Experiments on four datasets show that \method has superior next-item prediction accuracy than state-of-the-art models. A case study of \method on the Reddit dataset confirms that assigned session contexts are unique and meaningful.
GJ 1252b: A Hot Terrestrial Super-Earth with No Atmosphere

In recent years, the discovery of increasing numbers of rocky, terrestrial exoplanets orbiting nearby stars has drawn increased attention to the possibility of studying these planets’ atmospheric and surface properties. This is especially true for planets orbiting M dwarfs, whose properties can best be studied with existing observatories. In particular, the minerological composition of these planets and the extent to which they can retain their atmospheres in the face of intense stellar irradiation both remain unresolved. Here, we report the detection of the secondary eclipse of the terrestrial exoplanet GJ 1252b, obtained via 10 eclipse observations using the Spitzer Space Telescope’s IRAC2 4.5 μm channel. We measure an eclipse depth of 149−32+25 ppm, corresponding to a dayside brightness temperature of 1410−125+91 K. This measurement is consistent with the prediction for a bare rock surface. Comparing the eclipse measurement to a large suite of simulated planetary spectra indicates that GJ 1252b has a surface pressure of ≲10 bar, i.e., substantially thinner than the atmosphere of Venus. Assuming energy-limited escape, even a 100 bar atmosphere would be lost in <1 Myr, far shorter than our gyrochronological age estimate of 3.9 ± 0.4 Gyr. The expected mass loss could be overcome by mantle outgassing, but only if the mantle’s carbon content were >7% by mass—over two orders of magnitude greater than that found in Earth. We therefore conclude that GJ 1252b has no significant atmosphere. Model spectra with granitoid or feldspathic surface composition, but with no atmosphere, are disfavored at >2σ. The eclipse occurs just +1.4 −1.0+2.8 minutes after orbital phase 0.5, indicating ecosω = +0.0025 −0.0018+0.0049 , consistent with a circular orbit. Tidal heating is therefore likely to be negligible with regard to GJ 1252b’s global energy budget. Finally, we also analyze additional, unpublished TESS transit photometry of GJ 1252b, which improves the precision of the transit ephemeris by a factor of 10, provides a more precise planetary radius of 1.180 ± 0.078 R ⊕, and rules out any transit-timing variations with amplitudes ≳1 minute.
Counterexamples expose gaps in the proof of time complexity for cover trees introduced in 2006

This paper is motivated by the k-nearest neighbors search: given an arbitrary metric space, and its finite subsets (a reference set R and a query set Q), design a fast algorithm to find all k-nearest neighbors in R for every point q ∈ Q. In 2006, Beygelzimer, Kakade, and Langford introduced cover trees to justify a near-linear time complexity for the neighbor search in the sizes of Q,R.Section 5.3 of Curtin’s PhD (2015) pointed out that the proof of this result was wrong. The key step in the original proof attempted to show that the number of iterations can be estimated by multiplying the length of the longest root-to-leaf path in a cover tree by a constant factor. However, this estimate can miss many potential nodes in several branches of a cover tree, that should be considered during the neighbor search. The same argument was unfortunately repeated in several subsequent papers using cover trees from 2006.This paper explicitly constructs challenging datasets that provide counterexamples to the past proofs of time complexity for the cover tree construction, the k-nearest neighbor search presented at ICML 2006, and the dual-tree search algorithm published in NIPS 2009.The corrected near-linear time complexities with extra parameters are proved in another forthcoming paper by using a new compressed cover tree simplifying the original tree structure.
Microbiome and metabolome insights into the role of the gastrointestinal-brain axis in neurodegenerative diseases: unveiling potential therapeutic targets

Due to the aging of the world population and westernization of lifestyles, the prevalence of neurodegenerative diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) is rapidly rising and is expected to put a strong socioeconomic burden on health systems worldwide. Due to the limited success of clinical trials of therapies against neurodegenerative diseases, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome as well as metabolome studies along the gastrointestinal-brain axis have already revealed important insights into disease pathomechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the association between microbiota, metabolites, and neurodegeneration in light of the gastrointestinal-brain axis. In this context, we also illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude our individual host tissues. Metabolic exchanges between bacteria and colon occur via a luminal compartment, metabolic exchanges between host tissues are mediated by the blood stream. modeling
Video Interpolation by Event-driven Anisotropic Adjustment of Optical Flow

Video frame interpolation is a challenging task due to the ever-changing real-world scene. Previous methods often calculate the bi-directional optical flows and then predict the intermediate optical flows under the linear motion assumptions, leading to isotropic intermediate flow generation. Follow-up research obtained anisotropic adjustment through estimated higher-order motion information with extra frames. Based on the motion assumptions, their methods are hard to model the complicated motion in real scenes. In this paper, we propose an end-to-end training method A^2OF for video frame interpolation with event-driven Anisotropic Adjustment of Optical Flows. Specifically, we use events to generate optical flow distribution masks for the intermediate optical flow, which can model the complicated motion between two frames. Our proposed method outperforms the previous methods in video frame interpolation, taking supervised event-based video interpolation to a higher stage.
Verifiable Differential Privacy For When The Curious Become Dishonest

Many applications seek to produce differentially private statistics on sensitive data. Traditional approaches in the centralised model rely on a trusted aggregator to gather the raw data, aggregate statistics and introduce appropriate noise. Recent work has tried to relax the trust assumptions and reduce the need for trusted entities. However, such systems can trade off trust for increased noise and still require complete trust in some participants. Moreover, they do not prevent a malicious entity from introducing adversarial noise to skew the result or unmask some inputs. In this paper, we introduce the notion of “veriﬁable differential privacy with covert security”. The purpose is to ensure both privacy of the client’s data and assurance that the output is not subject to any form of adversarial manipulation. The result is that everyone is assured that the noise used for differential privacy has been generated correctly, but no one can determine what the noise was. In the event of a malicious entity attempting to pervert the protocol, their actions will be detected with a constant probability negligibly close to one. We show that such veriﬁable privacy is practical and can be implemented at scale.
Reservoir-induced stabilisation of a periodically driven classical spin chain: local vs. global relaxation

Floquet theory is an indispensable tool for analysing periodically-driven quantum many-body systems. Although it does not universally extend to classical systems, some of its methodologies can be adopted in the presence of well-separated timescales. Here we use these tools to investigate the stroboscopic behaviours of a classical spin chain that is driven by a periodic magnetic ﬁeld and coupled to a thermal reservoir. We detail and expand our previous work: we investigate the signiﬁcance of higher-order corrections to the classical Floquet-Magnus expansion in both the high-and low-frequency regimes; explicitly probe the evolution the dynamics of the reservoir; and further explore how the driven system synchronises with the applied ﬁeld at low frequencies. In line with our earlier results, we ﬁnd that the high-frequency regime is characterised by a local Floquet-Gibbs ensemble with the reservoir acting as a nearly-reversible heatsink. At low frequencies, the driven system rapidly enters a synchronised state, which can only be fully described in a global picture accounting for the concurrent relaxation of the reservoir in a ﬁctitious magnetic ﬁeld arising from the drive. We highlight how the evolving nature of the reservoir may still be incorporated in a local picture by introducing an eﬀective temperature. Finally, we argue that dissipative equations of motion for periodically-driven many-body systems, at least at intermediate frequencies, must generically be non-Markovian.
A General Purpose Exact Solution Method for Mixed Integer Concave Minimization Problems

In this article, we discuss an exact algorithm for solving mixed integer concave minimization problems. A piecewise inner-approximation of the concave function is achieved using an auxiliary linear program that leads to a bilevel program, which provides a lower bound to the original problem. The bilevel program is reduced to a single level formulation with the help of Karush-Kuhn-Tucker (KKT) conditions. Incorporating the KKT conditions lead to complementary slackness conditions that are linearized using BigM, for which we identify a tight value for general problems. Multiple bilevel programs, when solved over iterations, guarantee convergence to the exact optimum of the original problem. Though the algorithm is general and can be applied to any optimization problem with concave function(s), in this paper, we solve two common classes of operations and supply chain problems; namely, the concave knapsack problem, and the concave production-transportation problem. The computational experiments indicate that our proposed approach outperforms the customized methods that have been used in the literature to solve the two classes of problems by an order of magnitude in most of the test cases.