Miracle-dz/UniFAR-FTUs huggingface dataset:
To beer or not to beer: Does tapping beer cans prevent beer loss? A randomised controlled trial

Objective: Preventing or minimising beer loss when opening a can of beer is socially and economically desirable. One theoretically grounded approach is tapping the can prior to opening, although this has never been rigorously evaluated. We aimed to evaluate the effect of tapping a can of beer on beer loss. Methods: Single centre parallel-group randomised controlled trial. 1031 cans of cans of beer of 330mL were randomised into one of four groups before the experiment: unshaken/untapped (n=256), unshaken/tapped (n=251), shaken/untapped (n=249), or shaken/tapped (n=244). The intervention was tapping the can of beer three times on its side with a single finger. We compared tapping versus non-tapping for cans that had been shaken for 2 minutes or were unshaken. Three teams weighed, tapped or did not tap, opened cans, absorbed any beer loss using paper towels, then re-weighed cans. The teams recorded the mass of each can before and after opening with an accuracy of +/-0.01 grams. Main outcome measure: The main outcome measure was beer loss (in grams). This was calculated as the difference in the mass of the beer after the can was opened compared to before the can was opened. Results: For shaken cans, there was no statistically significant difference in the mass of beer lost when tapping compared to not tapping (mean difference of -0.159g beer lost with tapping, 95% CI -0.36 to 0.04). For unshaken cans, there was also no statistically significant difference between tapping and not tapping. Conclusion: These findings suggest that tapping shaken beer cans does not prevent beer loss when the container is opened. Thus, the practice of tapping a beer prior to opening is unsupported. The only apparent remedy to avoid liquid loss is to wait for bubbles to settle before opening the can.
A Journey in Applying Blockchain for Cyberphysical Systems

Cyberphysical Systems (CPS) are transforming the way we interact with the physical world around us. However, centralised approaches for CPS systems are not capable of addressing the unique challenges of CPS due to the complexity, constraints, and dynamic nature of the interactions. To realize the true potential of CPS, a decentralized approach that takes into account these unique features is required. Recently, blockchain-based solutions have been proposed to address CPS challenges. Yet, applying blockchain for diverse CPS domains is not straightforward and has its own challenges. In this paper, we share our experiences in applying blockchain technology for CPS to provide insights and highlight the challenges and future opportunities.
On the emergence of gravitational dynamics from tensor networks

Tensor networks are used to describe the ground state wavefunction of the quantum many-body system. Recently, it has been shown that a tensor network can generate the anti-de Sitter (AdS) geometry by using the entanglement renormalization approach, which provides a new way to realize bulk reconstruction in the AdS/conformal field theory correspondence. However, whether the dynamical connections can be found between the tensor network and gravity is an important unsolved problem. In this paper, we give a novel proposal to integrate ideas from tensor networks, entanglement entropy, canonical quantization of quantum gravity and the holographic principle and argue that the gravitational dynamics can be generated from a tensor network if the wave function of the latter satisfies the Wheeler–DeWitt equation.
Examination of the Correlation between Working Time Reduction and Employment

In recent years, it has been debated whether a reduction in working hours would be a viable solution to tackle the unemployment caused by technological change. The improvement of existing production technology is gradually being seen to reduce labor demand. Although this debate has been at the forefront for many decades, the high and persistent unemployment encountered in the European Union has renewed interest in implementing this policy in order to increase employment. According to advocates of reducing working hours, this policy will increase the number of workers needed during the production process, increasing employment. However, the contradiction expressed by advocates of working time reduction is that the increase in labor costs will lead to a reduction in business activity and ultimately to a reduction in demand for human resources. In this article, we will attempt to answer the question of whether reducing working hours is a way of countering the potential decline in employment due to technological change. In order to answer this question, the aforementioned conflicting views will be examined. As we will see during our statistical examination of the existing empirical studies, the reduction of working time does not lead to increased employment and cannot be seen as a solution to the long-lasting unemployment.
Finding entries of maximum absolute value in low-rank tensors

We present an iterative method for the search of extreme entries in low-rank tensors which is based on a power iteration combined with a binary search. In this work we use the HT-format for low-rank tensors but other low-rank formats can be used verbatim. We present two different approaches to accelerate the basic power iteration: an orthogonal projection of Rayleigh-Ritz type, as well as an acceleration of the power iteration itself which can be achieved due to the diagonal structure of the underlying eigenvalue problem. Finally the maximizing index is determined by a binary search based on the proposed iterative method for the approximation of the maximum norm. The iterative method for the maximum norm estimation inherits the linear complexity of the involved tensor arithmetic in the HT-format w.r. t. the tensor order, which is also verified by numerical tests.
Transcatheter mitral valve repair: a high mountain to climb

State-of-the-art treatment for mitral regurgitation (MR) has been and remains surgical mitral valve (MV) repair. Several transcatheter-based innovations however have been introduced to facilitate MV repair. This article aims to relate the recent developments in transcatheter MV repair to the current standard of MV surgery for treatment of MR from a surgeon's point of view.
Radio Burst and Circular Polarization Studies of the Solar Corona at Low Frequencies

Low-frequency (80-240 MHz) radio observations of the solar corona are presented using the Murchison Widefield Array (MWA), and several discoveries are reported. The corona is reviewed, followed by chapters on Type III bursts and circularly-polarized quiescent emission. The second chapter details new Type III burst dynamics. One source component at higher frequencies splits into two at lower frequencies, where the two components rapidly diverge. This is attributed to electron beams traversing a divergent magnetic field configuration, which is supported by extreme ultraviolet jet observations outlining a coronal null point. The third chapter uses Type III burst heights as density probes. Harmonic plasma emission implies ~4x enhancements over background models. This can be explained by electron beams traveling along dense fibers or by propagation effects that elevate apparent source heights. The quiescent corona is compared to model predictions to conclude that propagation effects can largely but not entirely explain the apparent density enhancements. The fourth chapter surveys over 100 spectropolarimetric observing runs. Around 700 compact sources are detected with polarization fractions from less than 0.5% to nearly 100%. They are interpreted as plasma emission noise storm sources down to levels not previously observable. A "bullseye" structure is reported for coronal holes, where an outer ring surrounds an oppositely-polarized central component that does not match the sign expected of thermal bremsstrahlung. The large-scale polarization structure is shown to be well-correlated with that of a global magnetic field model. The last chapter summarizes results and outlines future work. A preliminary comparison of polarization images to model predictions is shared, along with coronal mass ejection observations revealing a radio arc that is morphologically similar to the white-light structure.
Maximum entropy methods for texture synthesis: theory and practice

Recent years have seen the rise of convolutional neural network techniques in exemplar-based image synthesis. These methods often rely on the minimization of some variational formulation on the image space for which the minimizers are assumed to be the solutions of the synthesis problem. In this paper we investigate, both theoretically and experimentally, another framework to deal with this problem using an alternate sampling/minimization scheme. First, we use results from information geometry to assess that our method yields a probability measure which has maximum entropy under some constraints in expectation. Then, we turn to the analysis of our method and we show, using recent results from the Markov chain literature, that its error can be explicitly bounded with constants which depend polynomially in the dimension even in the non-convex setting. This includes the case where the constraints are defined via a differentiable neural network. Finally, we present an extensive experimental study of the model, including a comparison with state-of-the-art methods and an extension to style transfer.
Optimal Farsighted Agents Tend to Seek Power

Some researchers have speculated that capable reinforcement learning (RL) agents pursuing misspecified objectives are often incentivized to seek resources and power in pursuit of those objectives. An agent seeking power is incentivized to behave in undesirable ways, including rationally preventing deactivation and correction. Others have voiced skepticism: humans seem idiosyncratic in their urges to power, which need not be present in the agents we design. We formalize a notion of power within the context of finite Markov decision processes (MDPs). With respect to a neutral class of reward function distributions, our results suggest that farsighted optimal policies tend to seek power over the environment.
Better Understanding Hierarchical Visual Relationship for Image Caption

The Convolutional Neural Network (CNN) has been the dominant image feature extractor in computer vision for years. However, it fails to get the relationship between images/objects and their hierarchical interactions which can be helpful for representing and describing an image. In this paper, we propose a new design for image caption under a general encoder-decoder framework. It takes into account the hierarchical interactions between different abstraction levels of visual information in the images and their bounding-boxes. Specifically, we present CNN plus Graph Convolutional Network (GCN) architecture that novelly integrates both semantic and spatial visual relationships into image encoder. The representations of regions in an image and the connections between images are refined by leveraging graph structure through GCN. With the learned multi-level features, our model capitalizes on the Transformer-based decoder for description generation. We conduct experiments on the COCO image captioning dataset. Evaluations show that our proposed model outperforms the previous state-of-the-art models in the task of image caption, leading to a better performance in terms of all evaluation metrics.
Spectral-GANs for High-Resolution 3D Point-cloud Generation

Point-clouds are a popular choice for robotics and computer vision tasks due to their accurate shape description and direct acquisition from range-scanners. This demands the ability to synthesize and reconstruct high-quality point-clouds. Current deep generative models for 3D data generally work on simplified representations (e.g., voxelized objects) and cannot deal with the inherent redundancy and irregularity in point-clouds. A few recent efforts on 3D point-cloud generation offer limited resolution and their complexity grows with the increase in output resolution. In this paper, we develop a principled approach to synthesize 3D point-clouds using a spectral-domain Generative Adversarial Network (GAN). Our spectral representation is highly structured and allows us to disentangle various frequency bands such that the learning task is simplified for a GAN model. As compared to spatial-domain generative approaches, our formulation allows us to generate high-resolution point-clouds with minimal computational overhead. Furthermore, we propose a fully differentiable block to transform from the spectral to the spatial domain and back, thereby allowing us to integrate knowledge from well-established spatial models. We demonstrate that Spectral-GAN performs well for point-cloud generation task. Additionally, it can learn a highly discriminative representation in an unsupervised fashion and can be used to accurately reconstruct 3D objects. Our codes are available at https://github.com/samgregoost/Spectral-GAN/.
CLOSED IDEALS AND LIE IDEALS OF MINIMAL TENSOR PRODUCT OF CERTAIN C*-ALGEBRAS

Abstract For a locally compact Hausdorff space X and a C*-algebra A with only finitely many closed ideals, we discuss a characterization of closed ideals of C0(X,A) in terms of closed ideals of A and a class of closed subspaces of X. We further use this result to prove that a closed ideal of C0(X)⊗min A is a finite sum of product ideals. We also establish that for a unital C*-algebra A, C0(X,A) has the centre-quotient property if and only if A has the centre-quotient property. As an application, we characterize the closed Lie ideals of C0(X,A) and identify all the closed Lie ideals of HC0(X)⊗minB(H), H being a separable Hilbert space.
A Variant Stellar-to-nebular Dust Attenuation Ratio on Subgalactic and Galactic Scales

The state-of-the-art geometry models of stars/dust suggest that dust attenuation toward nebular regions ($A_{V,gas}$) is always larger than that of stellar regions ($A_{V,star}$). Utilizing the newly released integral field spectroscopic data from the MaNGA survey, we investigate whether and how the $A_{V,star}/A_{V,gas}$ ratio varies from subgalactic to galactic scales. On a subgalactic scale, we report a stronger correlation between $A_{V,star}$ and $A_{V,gas}$ for more active HII regions. The local $A_{V,star}/A_{V,gas}$ is found to have moderate nonlinear correlations with three tracers of diffuse ionized gas (DIG), as well as indicators of gas-phase metallicity and ionization. The DIG regions tend to have larger $A_{V,star}/A_{V,gas}$ compared to classic HII regions excited by young OB stars. Metal-poor regions with a higher ionized level suffer much less nebular attenuation and thus have larger $A_{V,star}/A_{V,gas}$ ratios. A low-$A_{V,gas}$ and high-$A_{V,star}/A_{V,gas}$ sequence, which can be resolved into DIG-dominated and metal-poor regions, on the three BPT diagrams is found. Based on these observations, we suggest that besides the geometry of stars/dust, local physical conditions such as metallicity and ionized level also play an important role in determining the $A_{V,star}/A_{V,gas}$. On a galactic scale, the global $A_{V,star}/A_{V,gas}$ ratio has strong correlations with stellar mass ($M_*$), moderate correlations with SFR and metallicity, and weak correlations with inclination and specific SFR. Galaxies with larger $M_*$ and higher SFR that are more metal-rich tend to have smaller $A_{V,star}/A_{V,gas}$ ratios. Such correlations form a decreasing trend of $A_{V,star}/A_{V,gas}$ along the star-forming main sequence and mass-metallicity relation. The dust growth process accompanied by galaxy growth might be one plausible explanation for our observations.
A Step Towards Exposing Bias in Trained Deep Convolutional Neural Network Models

We present Smooth Grad-CAM++, a technique which combines two recent techniques: SMOOTHGRAD and Grad-CAM++. Smooth Grad-CAM++ has the capability of either visualizing a layer, subset of feature maps, or subset of neurons within a feature map at each instance. We experimented with few images, and we discovered that Smooth Grad-CAM++ produced more visually sharp maps with larger number of salient pixels highlighted in the given input images when compared with other methods. Smooth Grad-CAM++ will give insight into what our deep CNN models (including models trained on medical scan or imagery) learn. Hence informing decisions on creating a representative training set.
A probability theoretic approach to drifting data in continuous time domains

The notion of drift refers to the phenomenon that the distribution, which is underlying the observed data, changes over time. Albeit many attempts were made to deal with drift, formal notions of drift are application-dependent and formulated in various degrees of abstraction and mathematical coherence. In this contribution, we provide a probability theoretical framework, that allows a formalization of drift in continuous time, which subsumes popular notions of drift. In particular, it sheds some light on common practice such as change-point detection or machine learning methodologies in the presence of drift. It gives rise to a new characterization of drift in terms of stochastic dependency between data and time. This particularly intuitive formalization enables us to design a new, efficient drift detection method. Further, it induces a technology, to decompose observed data into a drifting and a non-drifting part.
COSTRA 1.0: A Dataset of Complex Sentence Transformations

We present COSTRA 1.0, a dataset of complex sentence transformations. The dataset is intended for the study of sentence-level embeddings beyond simple word alternations or standard paraphrasing. This first version of the dataset is limited to sentences in Czech but the construction method is universal and we plan to use it also for other languages. The dataset consist of 4,262 unique sentences with average length of 10 words, illustrating 15 types of modifications such as simplification, generalization, or formal and informal language variation. The hope is that with this dataset, we should be able to test semantic properties of sentence embeddings and perhaps even to find some topologically interesting “skeleton” in the sentence embedding space. A preliminary analysis using LASER, multi-purpose multi-lingual sentence embeddings suggests that the LASER space does not exhibit the desired properties.
X-Ray Spectra from Plasmas with High-energy Electrons: κ-distributions and e−–e− Bremsstrahlung

Shocks, turbulence, and winds all influence the electron velocity distribution in hot plasmas, exciting lower-energy electrons and generating a high-energy (typically power-law) tail. This effect, typically described as a κ distribution can affect both the line and continuum X-ray spectra emitted by the plasma. Hahn & Savin proposed a “Maxwellian decomposition” to generate the rate coefficients of κ distributions. Using their method and the AtomDB atomic database, we have developed a general model to calculate the emission from a plasma with a κ distribution. We compare our κ results for the charge state distribution and spectra of oxygen with those from KAPPA package with the ion data available within the CHIANTI atomic database. Sufficiently energetic electrons, created either in a κ distribution or merely a very hot Maxwellian plasma, can also emit via electron–electron (e–e) bremsstrahlung, a process not previously included in AtomDB. We have added this process to AtomDB and apply it to calculate the temperature gradients, as well as the total spectra from the post-shock regions of an accreting magnetic cataclysmic variable. We find the contribution of e–e bremsstrahlung to the total spectra exceeds 10% at KT ∼ 100 keV, with the total emissivity in the post-shock accretion stream differing by more than 10% at energies above 60 keV.
A Simulation Model for Pedestrian Crowd Evacuation Based on Various AI Techniques

This paper attempts to design an intelligent simulation model for pedestrian crowd evacuation. For this purpose, the cellular automata(CA) was fully integrated with fuzzy logic, the kth nearest neighbors (KNN), and some statistical equations. In this model, each pedestrian was assigned a specific speed, according to his/her physical, biological and emotional features. The emergency behavior and evacuation efficiency of each pedestrian were evaluated by coupling his or her speed with various elements, such as environment, pedestrian distribution and familiarity with the exits. These elements all have great impacts on the evacuation process. Several experiments were carried out to verify the performance of the model in different emergency scenarios. The results show that the proposed model can predict the evacuation time and emergency behavior in various types of building interiors and pedestrian distributions. The research provides a good reference to the design of building evacuation systems.
A unified view on Bayesian varying coefficient models

Varying coefficient models are useful in applications where the effect of the covariate might depend on some other covariate such as time or location. Various applications of these models often give rise to case-specific prior distributions for the parameter(s) describing how much the coefficients vary. In this work, we introduce a unified view of varying coefficients models, arguing for a way of specifying these prior distributions that are coherent across various applications, avoid overfitting and have a coherent interpretation. We do this by considering varying coefficients models as a flexible extension of the natural simpler model and capitalising on the recently proposed framework of penalized complexity (PC) priors. We illustrate our approach in two spatial examples where varying coefficient models are relevant.
Strategies for global HIV prevention

Despite significant advances in the development of therapeutic strategies for HIV over the past 25 years, the AIDS pandemic continues to grow worldwide. Access to life-saving medicines is not universal and there is a critical need for better prevention strategies. This review will highlight current research in the area of HIV prevention strategies, including preventive HIV vaccines, effective microbicides, treatment of sexually transmitted infections and the impact on HIV transmission, in addition to the role of male circumcision and pre-exposure prophylaxis with antiretroviral medications in HIV prevention.