Miracle-dz/UniFAR-FTUs huggingface dataset:
Flexible FOND Planning with Explicit Fairness Assumptions

We consider the problem of reaching a propositional goal condition in fully-observable nondeterministic (FOND) planning under a general class of fairness assumptions that are given explicitly. The fairness assumptions are of the form A/B and say that state trajectories that contain infinite occurrences of an action a from A in a state s and finite occurrence of actions from B, must also contain infinite occurrences of action a in s followed by each one of its possible outcomes. The infinite trajectories that violate this condition are deemed as unfair, and the solutions are policies for which all the fair trajectories reach a goal state. We show that strong and strong-cyclic FOND planning, as well as QNP planning, a planning model introduced recently for generalized planning, are all special cases of FOND planning with fairness assumptions of this form which can also be combined. FOND+ planning, as this form of planning is called, combines the syntax of FOND planning with some of the versatility of LTL for expressing fairness constraints. A sound and complete FOND+ planner is implemented by reducing FOND+ planning to answer set programs, and its performance is evaluated in comparison with FOND and QNP planners, and LTL synthesis tools. Two other FOND+ planners are introduced as well which are more scalable but are not complete.
Numerical simulation of pulmonary airway reopening by the EOS-based multiphase lattice Boltzmann method

The aerosol formation is associated with the rupture of the liquid plug during the pulmonary airway reopening. The fluid dynamics of this process is difficult to predict because the rupture involved complex liquid-gas transition. Equation of state (EOS) plays a key role in the thermodynamic process of liquid-gas transition. Here, we propose an EOS-based multiphase lattice Boltzmann model, in which the nonideal force is directly evaluated by EOSs. This multiphase model is used to model the pulmonary airway reopening and study aerosol formation during exhalation. The numerical model is first validated with the simulations of Fujioka et al.(2008). and the result is in reasonable agreement with their study. Furthermore, two rupture cases with and without aerosol formation are contrasted and analyzed. It is found that the injury on the epithelium in the case with aerosol formation is essentially the same that of without aerosol formation even while the pressure drop in airway increases by about 67%. Then extensive simulations are performed to investigate the effects of pressure drop, thickness of liquid plug and film on aerosol size and the mechanical stresses. The results show that aerosol size and the mechanical stresses increase as the pressure drop enlarges and thickness of liquid plug become thicken, while aerosol size and the mechanical stresses decrease as thickness of liquid film is thicken. The present multiphase model can be extended to study the generation and transmission of bioaerosols which can carry the bioparticles of influenza or coronavirus.
A new interpretation of (Tikhonov) regularization

Tikhonov regularization with square-norm penalty for linear forward operators has been studied extensively in the literature. However, the results on convergence theory are based on technical proofs and sometimes difficult to interpret. It is also often not clear how those results translate into the discrete, numerical setting. In this paper we present a new strategy to study the properties of a regularization method on the example of Tikhonov regularization. The technique is based on the well-known observation that Tikhonov regularization approximates the unknown exact solution in the range of the adjoint of the forward operator. This is closely related to the concept of approximate source conditions, which we generalize to describe not only the approximation of the unknown solution, but also noise-free and noisy data; all from the same source space. Combining these three approximation results we derive the well-known convergence results in a concise way and improve the understanding by tightening the relation between concepts such as convergence rates, parameter choice, and saturation. The new technique is not limited to Tikhonov regularization, it can be applied also to iterative regularization, which we demonstrate by relating Tikhonov regularization and Landweber iteration. All results are accompanied by numerical examples.
Rare events of a peculiar thermonuclear supernova that precedes a core-collapse supernova

We study stellar binary evolution that leads to the formation of a white dwarf (WD) that explodes in a thermonuclear supernova at the termination of a common envelope evolution (CEE) shortly before the core of its companion explodes as a core-collapse supernova (CCSN). The CCSN explosion of the core, which is the remnant of a red supergiant (RSG) star, might take place few months to several years after the explosion of the WD as a thermonuclear supernova, i.e., a type Ia peculiar supernova (peculiar SN Ia). Using the evolutionary code mesa-binary we simulate evolution of binary systems with stars of initial masses of 6 − 7.5M . The more massive star, the primary, transfers mass to the secondary star and leaves a CO WD remnant. The secondary becomes massive enough to end in a CCSN. As the secondary evolves to the RSG phase it engulfs the WD and the system experience a CEE that ends with a WD-core binary system at an orbital separation of af ' 1 − 5R . Our simulations show that the core explodes as a CCSN at tCEE−CCSN ' 3000 − 10 yr after the CEE. We assume that if the WD accretes helium-rich gas from the core it might explode as a SN Ia in the frame of the double detonation scenario for SNe Ia and peculiar SNe Ia. We predict the very rare occurrence of a peculiar SN Ia followed within months to years by a CCSN.
DHASP: Differentiable Hearing Aid Speech Processing

Hearing aids are expected to improve speech intelligibility for listeners with hearing impairment. An appropriate amplification fitting tuned for the listener’s hearing disability is critical for good performance. The developments of most prescriptive fittings are based on data collected in subjective listening experiments, which are usually expensive and time-consuming. In this paper, we explore an alternative approach to finding the optimal fitting by introducing a hearing aid speech processing framework, in which the fitting is optimised in an automated way using an intelligibility objective function based on the HASPI physiological auditory model. The framework is fully differentiable, thus can employ the back-propagation algorithm for efficient, data-driven optimisation. Our initial objective experiments show promising results for noise-free speech amplification, where the automatically optimised processors outperform one of the well recognised hearing aid prescriptions.
Improving Adversarial Robustness via Channel-wise Activation Suppressing

The study of adversarial examples and their activation has attracted significant attention for secure and robust learning with deep neural networks (DNNs). Different from existing works, in this paper, we highlight two new characteristics of adversarial examples from the channel-wise activation perspective: 1) the activation magnitudes of adversarial examples are higher than that of natural examples; and 2) the channels are activated more uniformly by adversarial examples than natural examples. We find that the state-of-the-art defense adversarial training has addressed the first issue of high activation magnitudes via training on adversarial examples, while the second issue of uniform activation remains. This motivates us to suppress redundant activation from being activated by adversarial perturbations via a Channel-wise Activation Suppressing (CAS) strategy. We show that CAS can train a model that inherently suppresses adversarial activation, and can be easily applied to existing defense methods to further improve their robustness. Our work provides a simple but generic training strategy for robustifying the intermediate layer activation of DNNs.
Route to chaos in two-dimensional discrete parametric maps with bistable potentials

The texture of phase space and bifurcation diagrams of two-dimensional discrete maps describing a lattice of interacting oscillators, confined in on-site potentials with deformable double-well shapes, are examined. The two double-well potentials considered belong to a family proposed by Dikandé and Kofané (A. M. Dikandé and T. C. Kofané, Solid State Commun. vol. 89, p. 559, 1994), whose shapes can be tuned distinctively: one has a variable barrier height and the other has variable minima positions. However the two parametrized double-well potentials reduce to the φ substrate, familiar in the studies of structural phase transitions in centro-symmetric crystals or bistable processes in biophysics. It is shown that although the parametric maps are area preserving their routes to chaos display different characteristic features: the first map exhibits a cascade of period-doubling bifurcations with respect to the potential amplitude, but period-halving bifurcations with respect to the shape deformability parameter. On the other hand the first bifurcation of the second map always coincides with the first pitchfork bifurcation of the φ map. However, an increase of the deformability parameter shrinks the region between successive perioddoubling bifurcations. The two opposite bifurcation cascades characterizing the first map, and the shrinkage of regions between successive bifurcation cascades which is characteristic of the second map, suggest a non-universal character of the Feigenbaumnumber sequences associate with the two discrete parametric double-well maps.
Quasi-Equivalence Discovery for Zero-Shot Emergent Communication

Effective communication is an important skill for enabling information exchange in multi-agent settings and emergent communication is now a vibrant field of research, with common settings involving discrete cheap-talk channels. Since, by definition, these settings involve arbitrary encoding of information, typically they do not allow for the learned protocols to generalize beyond training partners. In contrast, in this work, we present a novel problem setting and the Quasi-Equivalence Discovery (QED) algorithm that allows for zero-shot coordination (ZSC), i.e., discovering protocols that can generalize to independently trained agents. Real world problem settings often contain costly communication channels, e.g., robots have to physically move their limbs, and a non-uniform distribution over intents. We show that these two factors lead to unique optimal ZSC policies in referential games, where agents use the energy cost of the messages to communicate intent. Other-Play was recently introduced for learning optimal ZSC policies, but requires prior access to the symmetries of the problem. Instead, QED can iteratively discovers the symmetries in this setting and converges to the optimal ZSC policy.
Solving Compositional Reinforcement Learning Problems via Task Reduction

We propose a novel learning paradigm, Self-Imitation via Reduction (SIR), for solving compositional reinforcement learning problems. SIR is based on two core ideas: task reduction and self-imitation. Task reduction tackles a hard-to-solve task by actively reducing it to an easier task whose solution is known by the RL agent. Once the original hard task is successfully solved by task reduction, the agent naturally obtains a self-generated solution trajectory to imitate. By continuously collecting and imitating such demonstrations, the agent is able to progressively expand the solved subspace in the entire task space. Experiment results show that SIR can significantly accelerate and improve learning on a variety of challenging sparse-reward continuous-control problems with compositional structures.
SaNet: Scale-aware neural Network for Parsing Multiple Spatial Resolution Aerial Images

: Assigning the geospatial objects of aerial images with categorical information at the pixel-level is a basic task in urban scene understanding. However, the huge differencc in remote sensing sensors makes the acqured aerial images in multiple spatial resolution (MSR), which brings two issues: the increased scale variation of geospatial objects and informative feature loss as spatial resolution drops. To address the two issues, we propose a novel scale-aware neural network (SaNet) for parsing MSR aerial images. For coping with the imbalanced segmentation quality between larger and smaller objects caused by the scale variation, the SaNet deploys a densely connected feature network (DCFPN) module to capture quality multi-scale context with large receptive fields. To alleviate the informative feature loss, a SFR module is incorporated into the network to learn scale-invariant features with spatial relation enhancement. Extensive experimental results on the ISPRS Vaihingen 2D Dataset and ISPRS Potsdam 2D Dataset demonstrate the outstanding cross-resolution segmentation ability of the proposed SaNet compared to other state-of-the-art networks.
How do biofilms feel their environment?

The ability of bacteria to colonize and grow on different surfaces is an essential process for biofilm development and depends on complex biomechanical interactions between the biofilm and the underlying substrate. Changes in the physical properties of the underlying substrate are known to alter biofilm expansion, but the mechanisms by which biofilms sense and respond to physical features of their environment are still poorly understood. Here, we report the use of synthetic polyacrylamide hydrogels with tunable stiffness and controllable pore size to assess physical effects of the substrate on biofilm development. Using time lapse microscopy to track the growth of expanding Serratia marcescens colonies, we find that biofilm colony growth can increase with increasing substrate stiffness on purely elastic substrates, unlike what is found on traditional agar substrates. Using traction force microscopy, we find that biofilms exert transient stresses correlated over length scales much larger than a single bacterium. Our results are consistent with a model of biofilm development in which the interplay between osmotic pressure arising from the biofilm and the poroelastic response of the underlying substrate controls biofilm growth and morphology.
Automated Fact-Checking for Assisting Human Fact-Checkers

The reporting and the analysis of current events around the globe has expanded from professional, editor-lead journalism all the way to citizen journalism. Nowadays, politicians and other key players enjoy direct access to their audiences through social media, bypassing the filters of official cables or traditional media. However, the multiple advantages of free speech and direct communication are dimmed by the misuse of media to spread inaccurate or misleading claims. These phenomena have led to the modern incarnation of the fact-checker --- a professional whose main aim is to examine claims using available evidence and to assess their veracity. Here, we survey the available intelligent technologies that can support the human expert in the different steps of her fact-checking endeavor. These include identifying claims worth fact-checking, detecting relevant previously fact-checked claims, retrieving relevant evidence to fact-check a claim, and actually verifying a claim. In each case, we pay attention to the challenges and the potential impact on real-world fact-checking.
Chronopharmacokinetics of Methotrexate in Mice: Modulation by Administration of 5-Fluorouracil in Combination Chemotherapy

Summary The basic pharmacokinetic parameters of MTX administered as monotherapy as well as in the combined therapy with simultaneous and prior administation of FU were investigated. The parameters were determined in the two-compartment open system on the basis of measurement of MTX levels in mice after i.p. administration of a single dose 20 mg/ kg of the drug. The FU dose was 20 mg/kg, too. The effect of the circadian rhythm on the kinetics of MTX have been taken into consideration. In one series of experiments the drugs were administered in the morning and in the other during midday hours . The statistical analysis of the obtained results showed the essential effect of the administration time on the pharmacokinetic parameters of MTX in mice. The statistical significal differences could be found: C max, AUC, CL, T0,5 . The differences in T0,5 , parameter resulting from coadministration of FU with the same circadian rhythm of MTX administration have been observed.
Magneto-Plasmons in Grounded Graphene-Based Structures with Anisotropic Cover and Substrate

This paper aims to study the magneto-plasmons in an anisotropic graphene nano-waveguide with bigyrotropic cover and substrate. The substrate is backed by a perfect electromagnetic conductor (PEMC) layer, a general and ideal boundary, which can be transformed easily into the perfect electric conductor (PEC) or the perfect magnetic conductor (PMC) boundaries. The upper and bottom layers of the graphene sheet are made of different magnetic materials, each one has the permittivity and permeability tensors of ɛ ̿and ? ̿? , respectively. The external magnetic field is applied perpendicularly to the structure surface, which can be provided by a permanent magnet placed underneath the ground plane. Hence, the graphene sheet has anisotropic conductivity tensor (σ ̿). A novel analytical model has been proposed for the general nano-waveguide to find its propagation properties. As special cases of the proposed general structure, two important new waveguides have been introduced and studied to show, first the richness of the proposed general nano-waveguide regarding the related specific plasmonic wave phenomena and effects, and second the validity and the high accuracy of the proposed model. The analytical and the simulation results are in an excellent agreement. It is shown that the modal properties of the proposed structure can be tuned effectively via the external magnetic field and the chemical potential of the graphene. Harnessing the non-reciprocity effect of anisotropic materials and the graphene sheet, the presented analytical model can be exploited to design tunable innovative devices in THz frequencies. Key-words: Anisotropic graphene sheet, Permeability tensor, Analytical model, PEMC, Bi-gyrotropic media, Permittivity tensor, Effective index, Propagation loss
Massless polarized particle and Faraday rotation of light in the Schwarzschild spacetime

We present the manifestly covariant Lagrangian of a massless polarized particle, that implies all dynamic and algebraic equations as the conditions of extreme of this variational problem. The model allows for minimal interaction with a gravitational field, leading to the equations, coinciding with Maxwell equations in the geometrical optics approximation. The model allows also a wide class of non minimal interactions, which suggests an alternative way to study the electromagnetic radiation beyond the leading order of geometrical optics. As a specific example, we construct a curvaturedependent interaction in Schwarzschild spacetime, predicting the Faraday rotation of polarization plane, linearly dependent on the wave frequency. As a result, the Schwarzschild spacetime generates a kind of angular rainbow of light: waves of different frequencies, initially linearly-polarized in one direction, acquire different orientations of their polarization planes when propagate along the same ray.
Using Twitter Social Media for Depression Detection in the Canadian Population

Depression is a serious public health problem, and an economic burden for the society. Therefore, identifying individuals with depression and providing the support for the people in need is a crucial step for creating a healthier environment. In this research, we explore the task of detecting signs of depression from tweets where people often express their feelings, thoughts, interests, and opinions. We utilize personal narratives collected users with self-reported depression to build a model suitable for predicting depression in a sample of Twitter users that is representative for the Canadian population. The training dataset contains 1,402 users who self-reported depression diagnoses during 2016. Using classical machine learning techniques, we achieved 0.961 F1-score using 10-fold cross validation. We used the CLPsych 2015 dataset as a test dataset, and we reached 0.898 F1-score using deep learning models. Then, we applied the same model on a population sample and obtained a result in line with the depression statistics reported by Statistics Canada for 2015.
THE REGIONAL CONSEQUENCES OF NEW DIGITAL INFRASTRUCTURE: CAN WELSH SMES GAIN AN EDGE FROM ACCESS AND ADOPTION OF SUPERFAST BROADBAND?

Across the United Kingdom public (and private) resources have been targeted on improving broadband infrastructure. While this has served to provide new opportunities for households and firms, there has been some debate around the ability of firms to take full advantage of the opportunities that arise through this evolving infrastructure. In this respect, there has been particular debate on how far small- and medium-sized enterprises (SMEs) have taken up the challenge of effectively engaging with the resource. Drawing on the case of Wales, this paper explores the digital maturity of a sample of Welsh SMEs. The paper provides evidence of how far SMEs are successfully engaging with the new opportunities available through broadband, and develops a typology of firms according to their engagement. The paper then explores how these differences produce policy implications.
Effect of Pre-Harvest Calcium Sprays on Calcium Concentrations in the Skin and Flesh of Apples

ABSTRACT During 2004 and 2006, experiments were conducted that measured the absorption of calcium (Ca) by the fruit and assessed the effects of Ca sprays on the skin and flesh of apples. Frequent (1 spray/month for 2 or 4 months) Ca treatments increased the concentration of Ca in the skin, but not in the flesh of fruit, and several sprays were needed to promote a prolonged increase in the concentration of Ca in the skin. Calcium sprays did not influence the concentrations of magnesium (Mg) and potassium (K). Foliar analyses confirmed the absorption of topical Ca by the apple tree following the Ca sprays as the concentration of Ca in leaves increased.
Elastically Graded Titanium Alloy Produced by Mechanical Surface Deformation

The objective of this study was to develop a thermo-mechanical strategy to create a radial elasticity gradient in a β metastable Ti-Nb-Zr alloy, and to characterize it in terms of microstructural and mechanical properties. A first investigation was conducted on thin samples of Ti-20Nb-6Zr (at.%) submitted to various thermo-mechanical treatments. Microstructure-properties relationships and elastic variability of this alloy were determined performing uniaxial tensile tests, X-ray diffraction and scanning and transmission electron microscopies. Based on these preliminary results, mechanical deformation was identified as a potential way to lower the elastic modulus of the alloy. In order to create elastically graded pieces, shot-peening was therefore carried out on thicker samples to engender surface deformation. In this second part of the work, local mechanical properties were evaluated by instrumented micro-indentation. Experimental observations demonstrated that shot-peening enabled to locally induce martensitic transformation on surface, and a decrease in indentation elastic modulus from 85 to 65 GPa over 400 μm was highlighted. Surface deformation proved to be an efficient way of creating an elasticity gradient in β metastable titanium alloys. This combination of material and process could be suitable to produce dental implants with mechanically enhanced biocompatibility.