Miracle-dz/UniFAR-FTUs huggingface dataset:
Implications of single field inflation in general cosmological scenarios on the nature of dark energy given the swampland conjectures

Swampland Conjectures have attracted quite some interest in the Cosmological Community. They have been shown to have wide ranging implications, like constraints on inflationary models, primordial black holes, etc. to name a few. A particularly revealing insight on dark energy also shows that one can have the dark energy equation-of-state for a quintessence scenario to be significantly different than [Formula: see text] after one takes into account the refined dS conjecture. Another interesting issue with the swampland conjectures is that they have been shown to be incompatible with single field inflationary models in GR-based cosmology. In our previous work, we have, however, shown that single field inflationary models are quite compatible with swampland conjectures in their usual string theoretic form in a large class of modified cosmological scenarios. Building on that work, we now show that in modified cosmological scenarios where the early universe expansion was driven by single field inflation, one can have the dark energy equation of state to be significantly different from [Formula: see text] even if we just take into account the original dS conjecture, let alone the refined form of that. We thereby show that one does not need to apply a step function approach towards inflation in order to have an observable distinction between constant and non-constant dark energy models in the context of the swampland conjectures.
One-step replica symmetry breaking of random regular NAE-SAT

In a broad class of sparse random constraint satisfaction problems (CSP), deep heuristics from statistical physics predict that there is a condensation phase transition before the satisfiability threshold, governed by one-step replica symmetry breaking (1RSB). In fact, in random regular k-NAE-SAT, which is one of such random CSPS, it was verified [1] that its free energy is well-defined and the explicit value follows the 1RSB prediction. However, for any model of sparse random CSP, it has been unknown whether the solution space indeed condensates on O(1) clusters according to the 1RSB prediction. In this paper, we give an affirmative answer to this question for the random regular k-NAE-SAT model. Namely, we prove that with probability close to one, most of the solutions lie inside a bounded number of solution clusters whose sizes are comparable to the scale of the free energy. Furthermore, we establish that the overlap between two independently drawn solutions concentrates precisely at two values. This is the defining property of the one-step replica symmetry breaking class which we establish for the first time in a sparse random CSP, Our proof is based on a detailed moment analysis of a spin system, which has an infinite spin space that encodes the structure of solution clusters. We develop new techniques to study the partition function as well as enhance previous approaches which were only applicable to spin systems with finitely many spins. We believe that our method is applicable to a broad range of random CSPS in the 1RSB universality class.
i3DMM: Deep Implicit 3D Morphable Model of Human Heads

We present the first deep implicit 3D morphable model (i3DMM) of full heads. Unlike earlier morphable face models it not only captures identity-specific geometry, texture, and expressions of the frontal face, but also models the entire head, including hair. We collect a new dataset consisting of 64 people with different expressions and hairstyles to train i3DMM. Our approach has the following favorable properties: (i) It is the first full head morphable model that includes hair. (ii) In contrast to mesh-based models it can be trained on merely rigidly aligned scans, without requiring difficult non-rigid registration. (iii) We design a novel architecture to decouple the shape model into an implicit reference shape and a deformation of this reference shape. With that, dense correspondences between shapes can be learned implicitly. (iv) This architecture allows us to semantically disentangle the geometry and color components, as color is learned in the reference space. Geometry is further disentangled as identity, expressions, and hairstyle, while color is disentangled as identity and hairstyle components. We show the merits of i3DMM using ablation studies, comparisons to state-of-the-art models, and applications such as semantic head editing and texture transfer. We will make our model publicly available1.
Compound sawtooth study in ohmically heated TFTR plasmas

Compound sawtooth activity has been observed in ohmically heated, high current, high density TFTR plasmas. Commonly called ''double sawteeth,'' such sequences consist of a repetitive series of subordinate relaxations followed by a main relaxation with a different inversion radius. The period of such compound sawteeth can be as long as 100 msec. In other cases, however, no compound sawteeth or bursts of them can be observed in discharges with essentially the same parameters.
Many-body effects and quantum fluctuations for discrete time crystals in Bose–Einstein condensates

We present a fully comprehensive multi-mode quantum treatment based on the truncated Wigner approximation (TWA) to study many-body effects and effects of quantum fluctuations on the formation of a discrete time crystal (DTC) in a Bose–Einstein condensate (BEC) bouncing resonantly on a periodically driven atom mirror. Zero-range contact interactions between the bosonic atoms are assumed. Our theoretical approach avoids the restrictions both of mean-field theory, where all bosons are assumed to remain in a single mode, and of time-dependent Bogoliubov theory, which assumes boson depletion from the condensate mode is small. We show that the mean-field and time-dependent Bogoliubov approaches can be derived as approximations to the TWA treatment. Differing initial conditions, such as a finite temperature BEC, can also be treated. For realistic initial conditions corresponding to a harmonic trap condensate mode function, our TWA calculations performed for period-doubling agree broadly with recent mean-field calculations for times out to at least 2000 mirror oscillations, except at interaction strengths very close to the threshold value for DTC formation where the position probability density differs significantly from that determined from mean-field theory. For typical attractive interaction strengths above the threshold value for DTC formation and for the chosen trap and driving parameters, the TWA calculations indicate a quantum depletion due to quantum many-body fluctuations of less than about two atoms out of a total of 600 atoms at times corresponding to 2000 mirror oscillations, in agreement with time-dependent Bogoliubov theory calculations. On the other hand, for interaction strengths very close to the threshold value for DTC formation, the TWA calculations predict a large quantum depletion—as high as about 260 atoms out of 600. We also show that the mean energy per particle of the DTC does not increase significantly for times out to at least 2000 mirror oscillations and typically oscillates around an average value close to its initial value; so TWA theory predicts the absence of thermalisation. Finally, we find that the dynamical behaviour of our system is largely independent of whether the boson–boson interaction is attractive or repulsive, and that it is possible to create a stable DTC based on repulsive interactions.
Discrete gravity dynamics from effective spin foams

The first computation of a spin foam dynamics that provides a test of the quantum equations of motions of gravity is presented. Specifically, a triangulation that includes an inner edge is treated. The computation leverages the recently introduced effective spin foam models, which are particularly numerically efficient. Previous work has raised the concern of a flatness problem in spin foam dynamics, identifying the potential for the dynamics to lead to flat geometries in the small ℏ semiclassical limit. The numerical results presented here expose a rich semiclassical regime, but one that must be understood as an interplay between the various parameters of the spin foam model. In particular, the scale of the triangulation, fixed by the areas of its boundary triangles, the discreteness of the area spectrum, input from loop quantum gravity, and the curvature scales around the bulk triangles, all enter the characterization of the semiclassical regime identified here. In addition to these results on the dynamics, we show that the subtle nature of the semiclassical regime is a generic feature of the path integral quantization of systems with second class constraints.
Is It Implicit Detection or Perception During Change Blindness?

Purpose Implicit detection differs from implicit perception. The former includes implicit registration, localisation, identification and comparison of an object. Implicit comparison is not necessary for implicit perception, and should not involve the identification or localisation of objects. While many studies have reported evidence of implicit detection in change blindness, they may, in fact, have only observed implicit perception. In this study, we aimed to find out whether there is implicit detection or perception during the change blindness period. Methods In Experiments 1 and 2, we used a simple change detection paradigm, coupled with a speeded attribute discrimination task. Reaction times (RTs) and accuracy of the participants were measured for the speeded attribute discrimination task. We compared differences in RT and accuracy of the invalid and congruent cue trials to find evidence for implicit detection. Invalid trials referred to stimuli where the appearance of the cue does not change, whereas congruent trials involved cued objects with the same attributes as that of the change object. In Experiment 3, a one-shot change detection experiment was conducted, where subjects were required to report whether the objects were the same or different as quickly as possible. We compared the differences in RTs between trials in which the stimulus exhibited a change but participants reported “same” (change blindness trails) and trials in which the stimulus exhibited no change and participants reported “same” (baseline trials), to find evidence for implicit perception. Results In Experiments 1 and 2, the difference in accuracy and RTs under invalid and congruent conditions was not significant. We did not observe a validity effect as evidence for implicit localisation or a congruency effect as evidence for identification. In Experiment 3, the RTs were longer in the change blindness relative no-change trials, which indicated that there was implicit perception. Conclusion The results of this study showed that there was no evidence supporting implicit detection in colour or orientation as a single or a combination of features. However, we report evidence for implicit perception during the change blindness period. Change may be implicitly perceived, but not located or identified before there is conscious detection.
Performance Analysis of Cell-Free Massive MIMO Systems in LoS/ NLoS Channels

In cellular communication systems, it is conventional to assume the absence of a line of sight (LoS) path between the users and theirassociated access points (APs). This assumption however becomes questionable in the context of recent developments in the direction of cell-free (CF) massive MIMO systems. In the CF massive MIMO, the AP density is assumed to be comparable with the user density, which increases probability of existence of an LoS path between the users and their associated APs. In this paper, we analyze the performance of an uplink CF massive MIMO system, with a probabilistic LoS channel model. Here, we first derive the effective statistics of this channel model, and argue that their behaviour is fundamentally different from that of the conventional rich scattering channels. Utilizing these statistics, we next compare the rates achievable by CF massive MIMO systems, under both stream-wise and joint decoding at the central processing unit. Following this, we also discuss the centralized MMSE based data detection to obtain a complexity/ performance trade-off. Finally, using detailed Monte-Carlo simulations, we validate our analytical results, and evaluate the performance of the three data detection schemes.
Online Search with Maximum Clearance

We study the setting in which a mobile searcher must locate a hidden target in a bounded or unbounded search domain, with no information about the hider's position. In particular, we consider online search, in which the performance of the search strategy is evaluated by its worst case competitive ratio. We introduce a multi-criteria search problem in which the searcher has a budget on its allotted search time, and the objective is to design strategies that are competitively efficient, respect the budget, and maximize the total searched ground. We give analytically optimal strategies for the line and the star domains, and efficient heuristics for general networks.
Twenty years of experimental and numerical studies on microwave-assisted breakage of rocks and minerals-a review.

Microwaves have been used for a variety of applications in the past two decades. However, there has been a significant and growing interest in the applications of microwaves in hard rock breakage and mineral processing industries. The purpose of this review paper is to focus on these applications and to present a careful review of the state-of-the-art experimental and numerical modeling techniques introduced in the literature from 2000 to 2020. The challenges involved in this research area are surveyed, and the efforts that should be made regarding the potential practical implementation of microwaves in industry are discussed.
Dynamic Curriculum Learning for Low-Resource Neural Machine Translation

Large amounts of data has made neural machine translation (NMT) a big success in recent years. But it is still a challenge if we train these models on small-scale corpora. In this case, the way of using data appears to be more important. Here, we investigate the effective use of training data for low-resource NMT. In particular, we propose a dynamic curriculum learning (DCL) method to reorder training samples in training. Unlike previous work, we do not use a static scoring function for reordering. Instead, the order of training samples is dynamically determined in two ways - loss decline and model competence. This eases training by highlighting easy samples that the current model has enough competence to learn. We test our DCL method in a Transformer-based system. Experimental results show that DCL outperforms several strong baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De.
Learning Affinity-Aware Upsampling for Deep Image Matting*

We show that learning affinity in upsampling provides an effective and efficient approach to exploit pairwise interactions in deep networks. Second-order features are commonly used in dense prediction to build adjacent relations with a learnable module after upsampling such as non-local blocks. Since upsampling is essential, learning affinity in upsampling can avoid additional propagation layers, offering the potential for building compact models. By looking at existing upsampling operators from a unified mathematical perspective, we generalize them into a second-order form and introduce Affinity-Aware Upsampling (A2U) where upsampling kernels are generated using a light-weight lowrank bilinear model and are conditioned on second-order features. Our upsampling operator can also be extended to downsampling. We discuss alternative implementations of A2U and verify their effectiveness on two detail-sensitive tasks: image reconstruction on a toy dataset; and a largescale image matting task where affinity-based ideas constitute mainstream matting approaches. In particular, results on the Composition-1k matting dataset show that A2U achieves a 14% relative improvement in the SAD metric against a strong baseline with negligible increase of parameters (< 0.5%). Compared with the state-of-the-art matting network, we achieve 8% higher performance with only 40% model complexity.
On Generalization of Adaptive Methods for Over-parameterized Linear Regression

Over-parameterization and adaptive methods have played a crucial role in the success of deep learning in the last decade. The widespread use of over-parameterization has forced us to rethink generalization by bringing forth new phenomena, such as implicit regularization of optimization algorithms and double descent with training progression. A series of recent works have started to shed light on these areas in the quest to understand -- why do neural networks generalize well? The setting of over-parameterized linear regression has provided key insights into understanding this mysterious behavior of neural networks. In this paper, we aim to characterize the performance of adaptive methods in the over-parameterized linear regression setting. First, we focus on two sub-classes of adaptive methods depending on their generalization performance. For the first class of adaptive methods, the parameter vector remains in the span of the data and converges to the minimum norm solution like gradient descent (GD). On the other hand, for the second class of adaptive methods, the gradient rotation caused by the pre-conditioner matrix results in an in-span component of the parameter vector that converges to the minimum norm solution and the out-of-span component that saturates. Our experiments on over-parameterized linear regression and deep neural networks support this theory.
Clozapine Use in Patients with Early-Stage Schizophrenia in a Chinese Psychiatric Hospital

Objective Previous studies suggest that clozapine is commonly underutilized and that its initiation is delayed in patients with first-episode schizophrenia. Knowledge regarding clozapine use among Chinese patients with early-stage schizophrenia is limited. The aim of the present study was to investigate the point prevalence of and patterns and factors associated with clozapine use in patients with early-stage schizophrenia discharged from a psychiatric hospital in China. Methods A retrospective study was conducted to analyze the prescriptions of 867 consecutive patients with early-stage schizophrenia who were admitted to the Affiliated Brain Hospital of Guangzhou Medical University between Jan 1, 2011 and Dec 31, 2016. Results At discharge from the hospital, 114 (13.1%) patients were prescribed clozapine. Among the patients taking clozapine, 93 patients (81.6%) were prescribed clozapine polypharmacy, and only 21 patients (18.4%) were prescribed clozapine monotherapy. None of the patients were prescribed an overdose of clozapine. The mean daily dosage of clozapine was 160.97 mg, 149.05 mg and 213.69 mg among all patients taking clozapine, patients taking clozapine polypharmacy and patients taking clozapine monotherapy, respectively. The antipsychotic most frequently combined with clozapine was risperidone. Logistic regression suggested that the length of hospital stay, high school education, lower benzodiazepine use and antipsychotic polypharmacy were independently and significantly associated with clozapine use (P<0.05). Conclusion Although clozapine has been commonly used in China in recent years, the present study found that clozapine was not commonly used in patients with early-stage schizophrenia. An underutilization and delayed initiation of clozapine may exist in a portion of patients with early-stage schizophrenia. Given the unfavorable outcomes of underutilized and delayed clozapine use, future studies may be needed to assess and increase clozapine use in this population.
Relating force balances and flow length scales in geodynamo simulations

In fluid dynamics, the scaling behaviour of flow length scales is commonly used to infer the governing force balance of a system. The key to a successful approach is to measure length scales that are simultaneously representative of the energy contained in the solution (energetically relevant) and also indicative of the established force balance (dynamically relevant). In the case of numerical simulations of rotating convection and magnetohydrodynamic dynamos in spherical shells, it has remained difficult to measure length scales that are both energetically and dynamically relevant, a situation that has led to conflicting interpretations, and sometimes misrepresentations of the underlying force balance. By analysing an extensive set of magnetic and non-magnetic models, we focus on two length scales that achieve both energetic and dynamical relevance. The first one is the peak of the poloidal kinetic energy spectrum, which we successfully compare to crossover points on spectral representations of the force balance. In most dynamo models, this result confirms that the dominant length scale of the system is controlled by a previously proposed quasi-geostrophic (QG-) MAC (Magneto-Archimedean-Coriolis) balance. In non-magnetic convection models, the analysis generally favours a QG-CIA (Coriolis-Inertia-Archimedean) balance. Viscosity, which is typically a minor contributor to the force balance, does not control the dominant length scale at high convective supercriticalities in the non-magnetic case, and in the dynamo case, once the generated magnetic energy largely exceeds the kinetic energy. In dynamo models, we introduce a second energetically relevant length scale associated with the loss of axial invariance in the flow. We again relate this length scale to another crossover point in scale-dependent force balance diagrams, which marks the transition between large-scale geostrophy (the equilibrium of Coriolis and pressure forces) and small-scale magnetostrophy, where the Lorentz force overtakes the Coriolis force. Scaling analysis of these two energetically and dynamically relevant length scales suggests that the Earth’s dynamo is controlled by a QG-MAC balance at a dominant scale of about $200 \, \mathrm{km}$, while magnetostrophic effects are deferred to scales smaller than $50 \, \mathrm{km}$.
Markov and almost Markov properties in one, two or more directions.

In this review-type paper written at the occasion of the Oberwolfach workshop {\em One-sided vs. Two-sided stochastic processes} (february 22-29, 2020), we discuss and compare Markov properties and generalisations thereof in more directions, as well as weaker forms of conditional dependence, again either in one or more directions. In particular, we discuss in both contexts various extensions of Markov chains and Markov fields and their properties, such as $g$-measures, Variable Length Markov Chains, Variable Neighbourhood Markov Fields, Variable Neighbourhood (Parsimonious) Random Fields, and Generalized Gibbs Measures.