Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-ionization structures in planetary nebulae – II. Densities, temperatures, abundances and excitation of 6 PNe

Here we present the spatially resolved study of six Galactic planetary nebulae (PNe), namely IC 4593, Hen 2-186, Hen 2-429, NGC 3918, NGC 6543 and NGC 6905, from intermediate-resolution spectra of the 2.5 m Isaac Newton Telescope and the 1.54 m Danish telescope. The physical conditions (electron densities, Ne, and temperatures, Te), chemical compositions and dominant excitation mechanisms for the different regions of these objects are derived, in an attempt to go deeper on the knowledge of the low-ionization structures (LISs) hosted by these PNe. We reinforce the previous conclusions that LISs are characterized by lower (or at most equal) Ne than their associated rims and shells. As for the Te, we point out a possible different trend between the N and O diagnostics. Te[N ii] does not show significant variations throughout the nebular components, whereas Te[O iii] appears to be slightly higher for LISs. The much larger uncertainties associated with the Te[O iii] of LISs do not allow robust conclusions. Moreover, the chemical abundances show no variation from one to another PN components, not even contrasting LISs with rims and shells, as also found in a number of other works. By discussing the ionization photon flux due to shocks and stellar radiation, we explore the possible mechanisms responsible for the excitation of LISs. We argue that the presence of shocks in LISs is not negligible, although there is a strong dependence on the orientation of the host PNe and LISs.
Prompting for a conversation: How to control a dialog model?

Dialog modelling faces a difficult trade-off. Models are trained on a large amount of text, yet their responses need to be limited to a desired scope and style of a dialog agent. Because the datasets used to achieve the former contain language that is not compatible with the latter, pre-trained dialog models are fine-tuned on smaller curated datasets. However, the fine-tuning process robs them of the ability to produce diverse responses, eventually reducing them to dull conversation partners. In this paper we investigate if prompting can help with mitigating the above trade-off. Specifically, we experiment with conditioning the prompt on the query, rather than training a single prompt for all queries. By following the intuition that freezing the pre-trained language model will conserve its expressivity, we find that compared to fine-tuning, prompting can achieve a higher BLEU score and substantially improve the diversity and novelty of the responses.
GOPROBIT: Stata module to estimate generalized ordered probit models

goprobit is a user-written procedure to estimate generalized ordered probit models in Stata. The actual values taken on by the dependent variable are irrelevant except that larger values are assumed to correspond to "higher" outcomes. The generalized ordered probit model relaxes the parallel regression assumption of the standard ordered probit model.
Period Change Rates of Large Magellanic Cloud Cepheids using MESA

Pulsating stars, such as Cepheids and RR Lyrae, offer us a window to measure and study changes due to stellar evolution. In this work, we study the former by calculating a set of evolutionary tracks of stars with an initial mass of 4 to 7 $M_\odot$, varying the initial rotation rate and metallicity, using the stellar evolution code Modules for Experiments in Stellar Astrophysics (MESA). Using Radial Stellar Pulsations (RSP), a recently added functionality of MESA, we obtained theoretical instability strip (IS) edges and linear periods for the radial fundamental mode. Period-age, period-age-temperature, period-luminosity, and period-luminosity-temperature relationships were derived for three rotation rates and metallicities, showing a dependence on crossing number, position in the IS, rotation, and metallicity. We calculated period change rates (PCRs) based on the linear periods from RSP. We compared our models with literature results using the Geneva code, and found large differences, as expected due to the different implementations of rotation between codes. In addition, we compared our theoretical PCRs with those measured in our recent work for Large Magellanic Cloud Cepheids. We found good overall agreement, even though our models do not reach the short-period regime exhibited by the empirical data. Implementations of physical processes not yet included in our models, such as pulsation-driven mass loss, an improved treatment of convection that may lead to a better description of the instability strip edges, as well as consideration of a wider initial mass range, could all help improve the agreement with the observed PCRs.
Robust Forecasting for Robotic Control: A Game-Theoretic Approach

Modern robots require accurate forecasts to make optimal decisions in the real world. For example, self-driving cars need an accurate forecast of other agents' future actions to plan safe trajectories. Current methods rely heavily on historical time series to accurately predict the future. However, relying entirely on the observed history is problematic since it could be corrupted by noise, have outliers, or not completely represent all possible outcomes. To solve this problem, we propose a novel framework for generating robust forecasts for robotic control. In order to model real-world factors affecting future forecasts, we introduce the notion of an adversary, which perturbs observed historical time series to increase a robot's ultimate control cost. Specifically, we model this interaction as a zero-sum two-player game between a robot's forecaster and this hypothetical adversary. We show that our proposed game may be solved to a local Nash equilibrium using gradient-based optimization techniques. Furthermore, we show that a forecaster trained with our method performs 30.14% better on out-of-distribution real-world lane change data than baselines.
Leptoquarks, axions and the unification of B, L, and Peccei-Quinn symmetries

In this paper, axion models supplemented by leptoquarks and diquarks are systematically analyzed. Turning on some couplings to and among these latter states permits to unify the Peccei-Quinn symmetry with baryon (B) and lepton (L) numbers, such that the axion becomes associated to the spontaneous breaking of the three $U(1)$ symmetries. All possible four- and six-fermion patterns of B and L violation are discussed, including those inducing proton decay, with $\Delta B = 1$ and $\Delta L = \pm 1, \pm 3$, neutron-antineutron oscillations with $\Delta B = 2$, and Majorana neutrino masses with $\Delta L = 2$. Scenarios in which one or two axion fields necessarily appear in any B and/or L violating operators are also constructed. Nucleon decays would then necessarily involve an axion in the final state, while neutron-antineutron oscillations would only happen in an axionic background. This could have implications for the neutron lifetime puzzle, and more generally, opens the door to new phenomenological and cosmological applications.
Proximal Point Imitation Learning

This work develops new algorithms with rigorous efficiency guarantees for infinite horizon imitation learning (IL) with linear function approximation without restrictive coherence assumptions. We begin with the minimax formulation of the problem and then outline how to leverage classical tools from optimization, in particular, the proximal-point method (PPM) and dual smoothing, for online and offline IL, respectively. Thanks to PPM, we avoid nested policy evaluation and cost updates for online IL appearing in the prior literature. In particular, we do away with the conventional alternating updates by the optimization of a single convex and smooth objective over both cost and Q-functions. When solved inexactly, we relate the optimization errors to the suboptimality of the recovered policy. As an added bonus, by re-interpreting PPM as dual smoothing with the expert policy as a center point, we also obtain an offline IL algorithm enjoying theoretical guarantees in terms of required expert trajectories. Finally, we achieve convincing empirical performance for both linear and neural network function approximation.
Cross-domain Voice Activity Detection with Self-Supervised Representations

Voice Activity Detection (VAD) aims at detecting speech segments on an audio signal, which is a necessary first step for many today's speech based applications. Current state-of-the-art methods focus on training a neural network exploiting features directly contained in the acoustics, such as Mel Filter Banks (MFBs). Such methods therefore require an extra normalisation step to adapt to a new domain where the acoustics is impacted, which can be simply due to a change of speaker, microphone, or environment. In addition, this normalisation step is usually a rather rudimentary method that has certain limitations, such as being highly susceptible to the amount of data available for the new domain. Here, we exploited the crowd-sourced Common Voice (CV) corpus to show that representations based on Self-Supervised Learning (SSL) can adapt well to different domains, because they are computed with contextualised representations of speech across multiple domains. SSL representations also achieve better results than systems based on hand-crafted representations (MFBs), and off-the-shelf VADs, with significant improvement in cross-domain settings.
Learning Interpretable Latent Dialogue Actions With Less Supervision

We present a novel architecture for explainable modeling of task-oriented dialogues with discrete latent variables to represent dialogue actions. Our model is based on variational recurrent neural networks (VRNN) and requires no explicit annotation of semantic information. Unlike previous works, our approach models the system and user turns separately and performs database query modeling, which makes the model applicable to task-oriented dialogues while producing easily interpretable action latent variables. We show that our model outperforms previous approaches with less supervision in terms of perplexity and BLEU on three datasets, and we propose a way to measure dialogue success without the need for expert annotation. Finally, we propose a novel way to explain semantics of the latent variables with respect to system actions.
Programming With Private State

In a purely-functional language, reasoning about program equivalence is rather straightforward. However, the inclusion of states and references can complicate such reasoning. The problem is that expressions have the ability to alter the state, and therefore depend on the state for their values. Consequently, we must resort to a notion of contextual equivalence, where equivalence depends on two expressions having the same value no matter what the complete program in which they appear. We consider privatizing state to functions by adding a new function declaration to ML. If the use of references in the state is limited, proving equivalence may not need to go as far as contextual equivalence. We present the operational semantics and type system for the language, as well as a type soundness proof. Finally, we present some examples in the language, as well as its encoding in the Elf programming language.
Characteristics of cooperative spontaneous emission with applications to atom microscopy and coherent XUV radiation generation

Characteristics of Cooperative Spontaneous Emission with Applications to Atom Microscopy and Coherent XUV Radiation Generation. (December 2008) Juntao Chang, B.S., Peking University; M.S., Institute of Physics, Chinese Academy of Sciences Co–Chairs of Advisory Committee: Dr. M. Suhail Zubairy Dr. Marlan O. Scully Cooperative effect in the radiation process has been studied in for more than half a century. It is important in the sense of both basic physics and applied science. In this work, we study the dynamics of the cooperative spontaneous emission from an ensemble of N atoms which is uniformly excited by absorbing a single photon. We reveal that there are two different regimes in which the system exhibits totally different behaviors. One of them is the superradiance type of behavior: the system decays much quicker than single atom decay, with a decay rate proportional to N(λ/R), where N is the atom numbers, R is the size of the atom cloud, and λ is the wavelength. We call it Markovian regime because the sytem does not persist memory effect. The other regime is called non-Markovian regime and the system oscillates with effective Rabi oscillation frequency Ω while slowly decaying with a rate proportional to the photon escaping rate. The effective Rabi oscillation is a new type of dynamics which analogs well known Cavity QED behavior. Particularly in the Markovian regime, we study the system dynamics as a manybody eigenfunction and eigenvalue problem. For a dense cloud, we find analytical
MEASURE OF REGULAR LANGUAGES

This paper reviews and extends the recent work on signed real measure of regular languages within a unified framework. The language measure provides total ordering of partially ordered sets of sublanguages of a regular language to allow quantitative evaluation of the controlled behavior of deterministic finite state automata under different supervisors. The paper presents a procedure by which performance of different supervisors can be evaluated based on a common quantitative tool. Two algorithms are provided for computation of the language measure and their equivalence is established along with a physical interpretation from the probabilistic perspective.
C/Zm fractional branes

We construct several geometric representatives for theC/Zm fractional branes on either a partially or the completely resolved orbifold. In the process we use large radius and conifold-type monodromies, and provide a strong consistency check. In particular, for C /Z5 we give three different sets of geometric representatives. We also find the explicit Seiberg-duality which connects our fractional branes to the ones given by the McKay correspondence.
Three-dimensional light capsule enclosing perfect supersized darkness via anti-resolution

We theoretically and experimentally demonstrate the focusing of macroscopic 3D darkness surrounded by all light in free space. The object staying in the darkness is similar to staying in an empty light capsule because light just bypasses it by resorting to destructive interference. Its functionality of controlling the direction of energy flux of light macroscopically is fascinating, similar in some sense to the transformation-based cloaking effect. Binary-optical system exhibiting anti-resolution (AR) is designed and fabricated, by which electromagnetic energy flux avoids and bends smoothly around a nearly perfect darkness region. AR remains an unexplored topic hitherto, in contrast to the super-resolution for realizing high spatial resolution. This novel scheme replies on smearing out the PSF and thus poses less stringent limitations upon the object’s size and position since the created dark (zero-field) area reach 8 orders of magnitude larger than λ 2 in size. It functions very well at arbitrarily polarized beams in three dimensions, which is also frequency-scalable in the whole electromagnetic spectrum.
Review of incursive, hyperincursive and anticipatory systems-foundation of anticipation in electromagnetism

The main purpose of this paper is to show that anticipation is not only a property of biosystems but is also a fundamental property of physical systems. In electromagnetism, the anticipation is related to the Lorentz transform. In this framework the anticipation is a strong anticipation because it is not based on a prediction from a model of the physical system but is embedded in the fundamental system. So, Robert Rosen’s anticipatory systems deal with weak anticipation. Contrary to Robert Rosen’s affirmation, anticipation is thus not a characteristic of living systems. Finality is implicitly embedded in any system and thus the final cause of Aristotle is implicitly embedded in any physical and biological systems, contrary to what Robert Rosen argued. This paper will review some incursive and hyperincursive systems giving rise to strong anticipation. Space-time incursive parabolic systems show non-local properties. Hyperincursive crisp systems are related to catastrophe theory. Finally it will be shown th.
A large class of graphs with a small subclass of Cohen–Macaulay members

Abstract Let R be a finite commutative ring with nonzero identity. The unit graph of R is the graph in which the vertex set is R, and two distinct vertices x and y are adjacent if and only if x + y is a unit in R. In this paper, we determine when these graphs are well-covered, and then, by applying this result, we characterize the unit graphs whose edge rings are Cohen–Macaulay (Gorenstein). This characterization gives us a large class of non-Cohen–Macaulay graphs.
Thermal duality and gravitational collapse in heterotic string theories

The thermal duality of E(8)$ x $E(8) and SO(32) heterotic string theories may underpin a mechanism that would convert the kinetic energy of infalling matter during gravitational collapse to form a region of a hot string phase that would expel gravitational gradients. This phase would be the continuation of a Ginzburg-Landau like superconductor in the Euclidean regime. In this scenario, there would be no event horizon or singularity produced in gravitational collapse. Solutions are presented for excitations of the string vacuum that may form during gravitational collapse and drive the transition to the hot phase. The proposed mechanism is developed here for the case of approximately spherical gravitational collapse in 4 uncompactified spacetime dimensions. Astrophysical objects such as stellar or galactic cores which have undergone extreme gravitational collapse would currently be sites of an on-going conversion process to shells of this high temperature phase. The relationship of this proposal to the `firewall paradox' is noted.
Adoption of Green IT Behaviours: A Perceived Knowledge Effect on Responsible Digital Practices?

The Green IT approach aims to reduce the environmental, social and economic impact of digital technology. A possible lever for change can be to study factors that encourage individuals to adopt pro-Green IT behaviours. However, few studies have explored these factors, especially the influence of perceived knowledge. To fill this gap, this study examines the relationship between perceived knowledge of Green IT, attitude and intention towards adopting specific Green IT behaviours. The study includes 143 participants, who completed an online survey and answered various questions on Green IT (e.g., on perceived knowledge, attitude, intention to adopt behaviours). The results of the study reveal that the more perceived knowledge of Green IT the participants had, the more positive their attitude towards Green IT and the greater their intention to adopt Green IT behaviours. This study paves the way for public policies promoting knowledge on Green IT and digital responsible technologies.
How Is the Stroke? Inferring Shot Influence in Badminton Matches via Long Short-term Dependencies

Identifying significant shots in a rally is important for evaluating players’ performance in badminton matches. While there are several studies that have quantified player performance in other sports, analyzing badminton data has remained untouched. In this article, we introduce a badminton language to fully describe the process of the shot, and we propose a deep-learning model composed of a novel short-term extractor and a long-term encoder for capturing a shot-by-shot sequence in a badminton rally by framing the problem as predicting a rally result. Our model incorporates an attention mechanism to enable the transparency between the action sequence and the rally result, which is essential for badminton experts to gain interpretable predictions. Experimental evaluation based on a real-world dataset demonstrates that our proposed model outperforms the strong baselines. We also conducted case studies to show the ability to enhance players’ decision-making confidence and to provide advanced insights for coaching, which benefits the badminton analysis community and bridges the gap between the field of badminton and computer science.
Time-Aware Anonymization of Knowledge Graphs

Knowledge graphs (KGs) play an essential role in data sharing because they can model both users’ attributes and their relationships. KGs can tailor many data analyses, such as classification where a sensitive attribute is selected and the analyst analyzes the associations between users and the sensitive attribute’s values (aka sensitive values). Data providers anonymize their KGs and share the anonymized versions to protect users’ privacy. Unfortunately, an adversary can exploit these attributes and relationships to infer sensitive information by monitoring either one or many snapshots of a KG. To cope with this issue, in this paper, we introduce (k, l)-Sequential Attribute Degree ((k, l)-sad), an extension of the kw-tad principle[10], to ensure that sensitive values of re-identified users are diverse enough to prevent them from being inferred with a confidence higher than \(\frac{1}{l} \) even though adversaries monitor all published KGs. In addition, we develop the Time-Aware Knowledge Graph Anonymization Algorithm to anonymize KGs such that all published anonymized versions of a KG satisfy the (k, l)-sad principle, by, at the same time, preserving the utility of the anonymized data. We conduct experiments on four real-life datasets to show the effectiveness of our proposal and compare it with kw-tad.
Notice of Retraction A Novel Forecasting Model of Fuzzy Time Series Based on K-means Clustering

Fuzzy time series (FTS) is an effective method in forecasting problems due to its salient capabilities of tracking uncertainty and vagueness in observation data. However, in FTS forecasting, it is required about 5-7 intervals in the universe of discourse, as a result, the method of partition intervals become a major consideration. Recently, some studies have demonstrated that the method using length-variant intervals is able to improve the forecasting accuracy at a large scale. In this paper, a K-means clustering technique is used while selecting the length of each interval, and then a novel model of FTS is introduced accordingly. The empirical results show that this model provides a more general platform for partitioning universe of discourse, and gets higher forecasting accuracy rates than those of the existing methods.
Uranus as a radio source

The complex nature of the Uranus radio emissions, both magnetospheric and atmospheric, is reviewed, with emphasis on the identification of distinct components and the determination of their source locations. Seven radii components were discovered in addition to the RF signature of lightning in the planet's atmosphere. Six of the seven magnetospheric components are freely propagating emissions; one component, the nonthermal continuum, is trapped in the density cavity between the magnetopause and the dense inner magnetosphere. The radio components are divided into two types according to their emission signature: bursty emission and smooth emission. The inferred source location for the dominant nightside emission is above the nightside magnetic pole, largely overlapping the UV auroral region and the magnetic polar cap. The N-burst component appears to be associated with solar-wind enhancements at Uranus, consistent with the idea that the solar wind was triggering magnetospheric substormlike activity during the encounter.