Miracle-dz/UniFAR-FTUs huggingface dataset:
Substructures in the Keplerian disc around the O-type (proto-)star G17.64+0.16 (Corrigendum)

We present the highest angular resolution (∼20 × 15 mas–44 × 33 au) Atacama Large Millimeter/sub-millimeter Array (ALMA) observations that are currently possible of the proto-O-star G17.64+0.16 in Band 6. The Cycle 5 observations with baselines out to 16 km probe scales < 50 au and reveal the rotating disc around G17.64+0.16, a massive forming O-type star. The disc has a ring-like enhancement in the dust emission that is especially visible as arc structures to the north and south. The Keplerian kinematics are most prominently seen in the vibrationally excited water line, H2O 55, 0−64, 3 ν2 = 1 (Eu = 3461.9 K). The mass of the central source found by modelling the Keplerian rotation is consistent with 45 ± 10 M⊙. The H30α (231.9 GHz) radio-recombination line and the SiO (5-4) molecular line were detected at up to the ∼10σ level. The estimated disc mass is 0.6 − 2.6 M⊙ under the optically thin assumption. Analysis of the Toomre Q parameter in the optically thin regime indicates that the disc stability is highly dependent on temperature. The disc currently appears stable for temperatures > 150 K; this does not preclude that the substructures formed earlier through disc fragmentation.
Invertibility of generalized g-frame multipliers in Hilbert spaces

ABSTRACT In this paper, we investigate the invertibility of generalized g-Bessel multipliers. Sufficient and necessary conditions for invertibility are determined depending on the optimal g-frame bounds. Moreover, we show that, for semi-normalized symbols, the inverse of any invertible generalized g-frame multiplier can be represented as a generalized g-frame multiplier with the reciprocal symbol and dual g-frames of the given ones. Furthermore, we investigate some equivalent conditions for the special case, when both dual g-frames can be chosen to be the canonical duals. Finally, we give several approaches for constructing invertible generalized g-frame multipliers from the given ones. It is worth mentioning that some of our results are quite different from those studied in the previous literatures on this topic.
Inhibition of Xnos1 Translation by Structural Elementsin the Open Reading Frame

of a thesis at the University of Miami. Thesis supervised by Professor Mary Lou King. No. of pages in text. (41) The spatio-temporal regulation of translation is critical to the proper development of all organisms. The presence of Translational Control Elements (TCEs) in Un-Translated Regions (UTRs) is one feature common to all regulated eukaryotic mRNAs examined to date. These TCEs serve as binding sites for sequence specific proteins or small regulatory RNAs that recruit other accessory proteins that inhibit translation. Xnos1, a localized RNA in the germ plasm of Xenopus, is negatively regulated by an unknown mechanism. We used in vivo and in vitro translation assays, competition assays, and ribosome binding assays in order to determine the location of the Xnos1 TCE and its mechanism of repression. The Xnos1 TCE is located in the open reading frame, a departure from the canonical translational regulation mechanisms. This TCE is predicted to form conserved secondary structures in the first 75 nucleotides of the open reading frame (ORF). In vitro translation assays demonstrated that the repression can be partially relieved by either denaturing the transcripts or by introducing point mutations that weaken the secondary structure. This TCE cannot be relieved by competition and therefore is likely not to require the presence of a repressor protein. The structural regulation of translation by a TCE in the open reading frame is a novel mechanism of repression for a eukaryotic mRNA.
On Lazy Training in Differentiable Programming

In a series of recent theoretical works, it was shown that strongly over-parameterized neural networks trained with gradient-based methods could converge exponentially fast to zero training loss, with their parameters hardly varying. In this work, we show that this "lazy training" phenomenon is not specific to over-parameterized neural networks, and is due to a choice of scaling, often implicit, that makes the model behave as its linearization around the initialization, thus yielding a model equivalent to learning with positive-definite kernels. Through a theoretical analysis, we exhibit various situations where this phenomenon arises in non-convex optimization and we provide bounds on the distance between the lazy and linearized optimization paths. Our numerical experiments bring a critical note, as we observe that the performance of commonly used non-linear deep convolutional neural networks in computer vision degrades when trained in the lazy regime. This makes it unlikely that "lazy training" is behind the many successes of neural networks in difficult high dimensional tasks.
Nonreciprocal Yagi–Uda Filtering Antennas

This letter proposes a novel and compact nonreciprocal filtering antenna based on temporal modulation. The device is composed of a third-order filtering section integrated into a Yagi–Uda planar printed antenna. Strong nonreciprocity in transmission and reception is achieved at the same operation frequency by time-modulating the resonators of the filtering section. These resonators are implemented as quarter-wavelength microstrip lines terminated with varactors located on the ground plane. Such plane is also employed as a reflector for the Yagi–Uda antenna and to host the coplanar waveguides that feed low-frequency signals to the varactors, thus leading to a very compact design. A prototype is manufactured and successfully tested at 2.4 GHz, showing isolations greater than 20 dB between transmission and reception modes in both E- and H-planes for all directions in space and a gain drop of only 3.5 dB compared to a reference antenna. The proposed devices can easily be integrated with other electronic circuits and may find exciting applications in communication, radar, and sensing systems.
Amortized Bethe Free Energy Minimization for Learning MRFs

We propose to learn deep undirected graphical models (i.e., MRFs), with a non-ELBO objective for which we can calculate exact gradients. In particular, we optimize a saddle-point objective deriving from the Bethe free energy approximation to the partition function. Unlike much recent work in approximate inference, the derived objective requires no sampling, and can be efficiently computed even for very expressive MRFs. We furthermore amortize this optimization with trained inference networks. Experimentally, we find that the proposed approach compares favorably with loopy belief propagation, but is faster, and it allows for attaining better held out log likelihood than other recent approximate inference schemes.
The Black Sea Zooplankton: Composition, Spatial/Temporal Distribution and History of Investigations

Investigations into the composition, spatial and temporal distribution of the Black Sea zooplankton were reviewed in context with its historical development. The review comprises the main zooplankton studies published by the riparian countries of the Black Sea beginning with the earliest faunistic publications in the 1860s and 1870s up to the recent multinational ecosystem investigations carried out within the framework of the NATO-TU-Black Sea Project. The reviewed material covers a wide subject range of zooplankton investigations from descriptive studies to distribution and the production mechanisms in the ecosystem. The main findings of recently performed large scale investigations into the horizontal and vertical distiribution of zooplankton were also summarized and short and long term fluctuations in the composition and biomass were also analysed. Additionally, the influences of natural and anthropogenic factors on these changes were discussed and problem areas to be investigated in future were underlined.
Effect of performance appraisal on employee job satisfaction in Kenya revenue authority

Effective performance system can lead an organization to take strides towards success but an ineffective performance appraisal system can seal the fate of the organization by creating chaos and confusion from the top management to bottom in the organization. Organizations have continued with the performance appraisal system without establishing whether it has an effect on the employee job satisfaction. The Kenya Revenue Authority conducts performance appraisal for its employees using the objective approach, which focuses on individual goals and objectives but does not assess the skills and abilities required of the employee to perform their duties. However, this is not an appropriate use of performance appraisal system, because the main outcome of this system is the determination of the appropriateness of the pay increase. The objective of this study was to establish the effect of performance appraisal system on employee job satisfaction at the Kenya Revenue Authority. The research was conducted in the form of a case study which sought to find the effect of performance appraisal on employee job satisfaction in Kenya Revenue Authority. The study focused on KRA staff based in Nairobi. Both primary and secondary data was used. The data was collected from one hundred and twenty two employees who were selected through stratified sampling approach. The data was analyzed using both descriptive and inferential specifically the mean score, standard deviation, Pearson correlation, and chi square tests were used. The findings were presented using charts and tables. The findings of the study show that performance appraisal helps KRA and individual employees meet set targets, however there is a lack of job satisfaction. The staff are not satisfied because the management does not use the performance appraisal to determine salary increments, rewards/penalties, promotions, fringe benefits, training needs and career growth opportunities. In conclusion the findings have indicated a positive and significant relationship between performance appraisals and job satisfaction.
INQUIRY OF DISEASE , ILLNESS AND SICKNESS

The fundamental idea for this article is a review of the theoretical discussion among experts in biomedicine and the social sciences: Arthur Kleinman, Leon Eisenberg, Horacio Fabrega, Byron Good, and Andrew Twaddle, among others; on the concepts of disease, illness, and sickness. The main objective is to explore the conceptual distinction between the triad from the field of anthropology, particularly on how culturally defined concepts of ill health are created. It is generally argued that the complexity of different opinions on human ailment causes difficulty in providing a clear distinction between the triad for the use among medical practitioners, social scientists, and laymen community at large. In particular the analysis reveals that most signs or symptoms of distress are reflective of sufferers’ personal and cultural experiences. They are the cultural processes of illness and sickness, rather than purely understood by medical profession as disease. Additionally, the article demonstrates that with the effort of the experts of biomedicine and social sciences, clear definitions of the different concepts of disease, illness, and sickness indicate different aspects of ill health. Finally, the author focuses the discussion on a proposal to explore how the full triad can be of use amongst laymen patients, particularly in times of physician-patient interactions over ailment diagnosis.
Adaptive mitigation of the Air-Time pressure in LoRa multi-gateway architectures

LoRa is a promising technology in the current Internet of Things market, which operates in un-licensed bands achieving long-range communications and with ultra power devices. In this work we capitalize on the idea introduced in [1], i.e. balance the Air-Time of the different modulation spreading factors (SF), and adapt it to operate in a typical metropolitan scenario comprising multiple gateways (GWs) interconnected to a same network server. Our proposed approach, named ADaptive Mitigation of the AIr-time pressure in lORa (AD MAIORA), relies on a suitable measure of the per-spreading-factor load at each GW - quantified by means of a so-called pressure table -, and on a relevant heuristic algorithm which attempts to balance such a per-SF-pressure. Especially in cases of very loaded scenarios, where a high number of nodes insist on the same GWs, the use of AD MAIORA shows significant performance gains, up to a factor of 5 improvements with respect to the legacy LoRaWAN's Adaptive Data Rate.
Stacked Capsule Autoencoders

Objects are composed of a set of geometrically organized parts. We introduce an unsupervised capsule autoencoder (SCAE), which explicitly uses geometric relationships between parts to reason about objects. Since these relationships do not depend on the viewpoint, our model is robust to viewpoint changes. SCAE consists of two stages. In the first stage, the model predicts presences and poses of part templates directly from the image and tries to reconstruct the image by appropriately arranging the templates. In the second stage, SCAE predicts parameters of a few object capsules, which are then used to reconstruct part poses. Inference in this model is amortized and performed by off-the-shelf neural encoders, unlike in previous capsule networks. We find that object capsule presences are highly informative of the object class, which leads to state-of-the-art results for unsupervised classification on SVHN (55%) and MNIST (98.7%). The code is available at this https URL
Efficiency of four-wave mixing in injection-locked InAs/GaAs quantum-dot lasers

Frequency conversion using highly non-degenerate four-wave mixing is investigated in optically injection-locked InAs/GaAs quantum-dot Fabry-Perot lasers with different ridge waveguide dimensions. Conversion efficiencies up to -16 dB with a large optical signal-to-noise ratios of 36 dB are unveiled. The conversion bandwidth is extended to 4 THz with a quasi-symmetrical response between up- and down-converted signals.
Optimal orthogonal approximations to symmetric tensors cannot always be chosen symmetric

We study the problem of finding orthogonal low-rank approximations of symmetric tensors. In the case of matrices, the approximation is a truncated singular value decomposition which is then symmetric. Moreover, for rank-one approximations of tensors of any dimension, a classical result proven by Banach in 1938 shows that the optimal approximation can always be chosen to be symmetric. In contrast to these results, this article shows that the corresponding statement is no longer true for orthogonal approximations of higher rank. Specifically, for any of the four common notions of tensor orthogonality used in the literature, we show that optimal orthogonal approximations of rank greater than one cannot always be chosen to be symmetric.
Absorption by black hole remnants in metric-affine gravity

Using numerical methods, we investigate the absorption properties of a family of nonsingular solutions {which arise in different metric-affine theories, such as quadratic and Born-Infeld gravity. } These solutions continuously interpolate between Schwarzschild black holes and naked solitons with wormhole topology. The resulting spectrum is characterized by a series of quasibound states excitations, associated with the existence of a stable photonsphere.
Accelerating Neural ODEs with Spectral Elements

This paper proposes the use of spectral element methods \citep{canuto_spectral_1988} for fast and accurate training of Neural Ordinary Differential Equations (ODE-Nets; \citealp{Chen2018NeuralOD}) for system identification. This is achieved by expressing their dynamics as a truncated series of Legendre polynomials. The series coefficients, as well as the network weights, are computed by minimizing the weighted sum of the loss function and the violation of the ODE-Net dynamics. The problem is solved by coordinate descent that alternately minimizes, with respect to the coefficients and the weights, two unconstrained sub-problems using standard backpropagation and gradient methods. The resulting optimization scheme is fully time-parallel and results in a low memory footprint. Experimental comparison to standard methods, such as backpropagation through explicit solvers and the adjoint technique \citep{Chen2018NeuralOD}, on training surrogate models of small and medium-scale dynamical systems shows that it is at least one order of magnitude faster at reaching a comparable value of the loss function. The corresponding testing MSE is one order of magnitude smaller as well, suggesting generalization capabilities increase.
Optimization of a Terahertz Radiator

At present, the utilization of the terahertz band for communications remains a hypothetical scenario. This thesis presents a design for a communications setup for terahertz frequencies of kilometer range distances using a reflector antenna design. The primary focus is the terahertz radiating element of the system. Building upon published experimental work a simulation based approach is developed that duplicates experimental work. Next, this simulation setup is used with a new proposed design methodology is used to develop the primary radiators. A structure intended to be used with a photoconductive switch is selected and various design parameters are studied. The summation of the study concludes with a proposed experimental design that will be built and studied.
Optimal Combination of Frequency Control and Peak Shaving With Battery Storage Systems

Combining revenue streams by providing multiple services with battery storage systems increases profitability and enhances the investment case. In this work, we present a novel optimisation and control framework that enables a storage system to optimally combine the provision of primary frequency control services with peak shaving of a consumption profile. We adopt a dynamic programming framework to connect the daily bidding in frequency control markets with the longer term peak shaving objective: reducing the maximum consumption peak over an entire billing period. The framework also allows to aggregate frequency control capacity of multiple batteries installed at different sites, creating synergies when the consumption profile peaks occur on different times. Using a case study of two batteries at two industrial sites, we show that the presented approach increases net profit of the batteries significantly compared to using the batteries for only peak shaving or frequency control.
UK Housing Stock Models Using SAP: The Case for Heating Regime Change

Cutting energy use in housing will play a key role in the UK’s efforts to reduce climate change emissions in line with international commitments. Much UK Government policy is based on modelling present and future emissions using assumptions from SAP, the Standard Assessment Procedure. This paper compares SAP-based modelling against measured gas consumption in 405 dwellings that were monitored in the Energy Follow-Up Survey, an extension of the English Housing Survey. The combined EFUS/EHS provides comprehensive information about space heating energy use for a sample of dwellings: detailed physical data, user behaviour, and measured energy use. Very poor model versus measurement agreement is observed at the individual dwelling level – the average difference is 45%. Much better agreement is observed when applying typical EFUS regimes of 20°C mean demand temperature, 10 hours of heating a day for weekdays and weekends, and a heating season of six months, and comparing average results. Comparisons for the 405 dwellings and an EFUS subset of 1,191 dwellings are both in agreement to within 2%, whilst average 2010 and 2011 sub-national estimates are in agreement to 3% of DUKES figures. The authors recommend changing SAP heating regimes to a mean demand temperature of 20°C, 10 hours of heating a day for weekdays and weekends, and a heating season of six months.
Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory

In this paper, we provide a unified analysis of temporal difference learning algorithms with linear function approximators by exploiting their connections to Markov jump linear systems (MJLS). We tailor the MJLS theory developed in the control community to characterize the exact behaviors of the first and second order moments of a large family of temporal difference learning algorithms. For both the IID and Markov noise cases, we show that the evolution of some augmented versions of the mean and covariance matrix of the TD estimation error exactly follows the trajectory of a deterministic linear time-invariant (LTI) dynamical system. Applying the well-known LTI system theory, we obtain closed-form expressions for the mean and covariance matrix of the TD estimation error at any time step. We provide a tight matrix spectral radius condition to guarantee the convergence of the covariance matrix of the TD estimation error, and perform a perturbation analysis to characterize the dependence of the TD behaviors on learning rate. For the IID case, we provide an exact formula characterizing how the mean and covariance matrix of the TD estimation error converge to the steady state values. For the Markov case, we use our formulas to explain how the behaviors of TD learning algorithms are affected by learning rate and the underlying Markov chain. For both cases, upper and lower bounds for the mean square TD error are provided. The mean square TD error is shown to converge linearly to an exact limit.
Resilient Consensus Through Event-Based Communication

We consider resilient versions of discrete-time multiagent consensus in the presence of faulty or even malicious agents in the network. In particular, we develop event-triggered update rules that can mitigate the influence of the malicious agents and, at the same time, reduce the communication. Each regular agent updates its state based on a given rule using its neighbors’ information. Only when the triggering condition is satisfied, the regular agents send their current states to their neighbors. Otherwise, the neighbors will continue to use the state received the last time. Assuming that a bound on the number of malicious nodes is known, we propose two update rules with event-triggered communication. They follow the so-called mean subsequence reduced-type algorithms and ignore values received from potentially malicious neighbors. We characterize the necessary connectivity in the network for the algorithms to perform correctly, which are stated in terms of the notion of graph robustness. A numerical example is provided to demonstrate the effectiveness of the proposed approach.
On the Strength of Uniqueness Quantification in Primitive Positive Formulas

Uniqueness quantification ($\exists ! $) is a quantifier in first-order logic where one requires that exactly one element exists satisfying a given property. In this paper we investigate the strength of uniqueness quantification when it is used in place of existential quantification in conjunctive formulas over a given set of relations $\Gamma$, so-called primitive positive definitions (pp-definitions). We fully classify the Boolean sets of relations where uniqueness quantification has the same strength as existential quantification in pp-definitions and give several results valid for arbitrary finite domains. We also consider applications of $\exists ! $-quantified pp-definitions in computer science, which can be used to study the computational complexity of problems where the number of solutions is important. Using our classification we give a new and simplified proof of the trichotomy theorem for the unique satisfiability problem, and prove a general result for the unique constraint satisfaction problem.