Miracle-dz/UniFAR-FTUs huggingface dataset:
Magnetic proximity effect on excitonic spin states in Mn-doped layered hybrid perovskites.

Materials combining the optoelectronic functionalities of semiconductors with control of the spin degree of freedom are highly sought after for the advancement of quantum technology devices. Here, we report the paramagnetic Ruddlesden-Popper hybrid perovskite Mn:(PEA)2PbI4 (PEA = phenethylammonium) in which the interaction of isolated Mn2+ ions with magnetically brightened excitons leads to circularly polarized photoluminescence. Using a combination of superconducting quantum interference device (SQUID) magnetometry and magneto-optical experiments, we find that the Brillouin-shaped polarization curve of the photoluminescence follows the magnetization of the material. This indicates coupling between localized manganese magnetic moments and exciton spins via a magnetic proximity effect. The saturation polarization of 15% at 4 K and 6 T indicates a highly imbalanced spin population and demonstrates that manganese doping enables efficient control of excitonic spin states in Ruddlesden-Popper perovskites. Our finding constitutes the first example of polarization control in magnetically doped hybrid perovskites and will stimulate research on this highly tuneable material platform that promises tailored interactions between magnetic moments and electronic states.
Biology, cytopathology and molecular identification of an Egyptian isolate of zucchini yellow mosaic potyvirus (ZYMV-EG).

Squash is considered as one of the most important crops in Egypt and worldwide. Zucchini yellow mosaic potyvirus (ZYMV) is considered as one of the most important viruses infects squash. We used the biological, serological and cytopathological studies to identify an Egyptian isolate of ZYMV. Results showed that the Egyptian isolate of ZYMV showed systemic symptoms in the form of severe mosaic and vein banding on Cucurbita pepo cv. Eskandarani under open field conditions. Direct antigen coated (DAC)-enzyme-linked immunosorbent assay (ELISA) using polyclonal antibodies was used as a diagnostic tool for detecting ZYMV in the virus-infected C. pepo samples. It showed positive reactions with ZYMV antiserum. Filamentous virus-like particles measuring 750X13 nm was successfully purified from ZYMV-infected squash plants, based on the use of polyethylene glycol and ultracentrifugation. The electron microscope of ultrathin sections of virusinfected leaf tissues revealed the presence of cylindrical inclusions as pinwheels, laminated aggregates and scrolls in the cytoplasm of cells infected with ZYMV. In addition, disorganization of plastids, nucleus and mitochondria in the virus-cells infected was also observed. In addition, the nucleotide of CI gene of the viral isolate under investigation was determined and its similarities to some overseas isolates were addressed.
Montage: A General System for Buffered Durably Linearizable Data Structures

The recent emergence of fast, dense, nonvolatile main memory suggests that certain long-lived data might remain in its natural pointer-rich format across program runs and hardware reboots. Operations on such data must be instrumented with explicit write-back and fence instructions to ensure consistency in the wake of a crash. Techniques to minimize the cost of this instrumentation are an active topic of research. We present what we believe to be the first general-purpose approach to building buffered durably linearizable persistent data structures, and a system, Montage, to support that approach. Montage is built on top of the Ralloc nonblocking persistent allocator. It employs a slow-ticking epoch clock, and ensures that no operation appears to span an epoch boundary. It also arranges to persist only that data minimally required to reconstruct the structure after a crash. If a crash occurs in epoch $e$, all work performed in epochs $e$ and $e-1$ is lost, but work from prior epochs is preserved. We describe the implementation of Montage, argue its correctness, and report unprecedented throughput for persistent queues, sets/mappings, and general graphs.
Black-Hole Models in Loop Quantum Gravity

Dynamical black-hole scenarios have been developed in loop quantum gravity in various ways, combining results from mini and midisuperspace models. In the past, the underlying geometry of space-time has often been expressed in terms of line elements with metric components that differ from the classical solutions of general relativity, motivated by modified equations of motion and constraints. However, recent results have shown by explicit calculations that most of these constructions violate general covariance and slicing independence. The proposed line elements and black-hole models are therefore ruled out. The only known possibility to escape this sentence is to derive not only modified metric components but also a new space-time structure which is covariant in a generalized sense. Formally, such a derivation is made available by an analysis of the constraints of canonical gravity, which generate deformations of hypersurfaces in space-time, or generalized versions if the constraints are consistently modified. A generic consequence of consistent modifications in effective theories suggested by loop quantum gravity is signature change at high density. Signature change is an important ingredient in long-term models of black holes that aim to determine what might happen after a black hole has evaporated. Because this effect changes the causal structure of space-time, it has crucial implications for black-hole models that have been missed in several older constructions, for instance in models based on bouncing black-hole interiors. Such models are ruled out by signature change even if their underlying space-times are made consistent using generalized covariance. The causal nature of signature change brings in a new internal consistency condition, given by the requirement of deterministic behavior at low curvature. Even a causally disconnected interior transition, opening back up into the former exterior as some kind of astrophysical white hole, is then ruled out. New versions consistent with both generalized covariance and low-curvature determinism are introduced here, showing a remarkable similarity with models developed in other approaches, such as the final-state proposal or the no-transition principle obtained from the gauge-gravity correspondence.
Measurements of the z ∼ 6 Intergalactic Medium Optical Depth and Transmission Spikes Using a New z > 6.3 Quasar Sample

We report new measurements of the intergalactic medium (IGM) Lyα and Lyβ effective optical depth at 5.3 < z < 6.5, using a new sample of quasar sight lines including 32 quasars at 6.308 ≤ z ≤ 7.00. These quasars provide a large statistical sample to measure the IGM evolution during the transition phase of the reionization epoch. We construct a data set of deep optical spectra of these quasars using VLT, Keck, Gemini, LBT, and MMT. We measure the Lyα effective optical depth at 5.36 < z < 6.57 using the Lyα forests of both individual spectra and the stacked spectrum. The large scatter of individual measurements is consistent with previous work, suggesting an inhomogeneous reionization process. Combining our new measurements and previous results, we obtain a best fit for the Lyα effective optical depth evolution at z > 5.3, . We then estimate the observed Lyβ effective optical depth using Lyβ forests and convert them to Lyα optical depth for comparison, which provides additional constraints on the evolution of the IGM optical depth. The Lyβ-based measurements are generally in agreement with the best-fit evolution obtained from Lyα forests. Using this new sample, we identify 389 Lyα and 50 Lyβ transmission spikes at 5.5 < z < 6.3. The upper limits of Lyα optical depth estimated using transmission spikes are well consistent with our best-fit evolution. The evolution in number density of these high-redshift transmission spikes suggests a rapid transition phase at the end of the reionization. Comparison of our optical depth measurements with hydrodynamical simulations indicates an IGM neutral hydrogen fraction at z = 6.
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution

Reinforcement Learning algorithms require a large number of samples to solve complex tasks with sparse and delayed rewards. Complex tasks can often be hierarchically decomposed into sub-tasks. A step in the Q-function can be associated with solving a sub-task, where the expectation of the return increases. RUDDER has been introduced to identify these steps and then redistribute reward to them, thus immediately giving reward if sub-tasks are solved. Since the problem of delayed rewards is mitigated, learning is considerably sped up. However, for complex tasks, current exploration strategies as deployed in RUDDER struggle with discovering episodes with high rewards. Therefore, we assume that episodes with high rewards are given as demonstrations and do not have to be discovered by exploration. Typically the number of demonstrations is small and RUDDER's LSTM model as a deep learning method does not learn well. Hence, we introduce Align-RUDDER, which is RUDDER with two major modifications. First, Align-RUDDER assumes that episodes with high rewards are given as demonstrations, replacing RUDDER's safe exploration and lessons replay buffer. Second, we replace RUDDER's LSTM model by a profile model that is obtained from multiple sequence alignment of demonstrations. Profile models can be constructed from as few as two demonstrations as known from bioinformatics. Align-RUDDER inherits the concept of reward redistribution, which considerably reduces the delay of rewards, thus speeding up learning. Align-RUDDER outperforms competitors on complex artificial tasks with delayed reward and few demonstrations. On the MineCraft ObtainDiamond task, Align-RUDDER is able to mine a diamond, though not frequently. Github: this https URL, YouTube: this https URL
Log-unimodality for free positive
multiplicative Brownian motion

We prove that the marginal law $\sigma_{t}\boxtimes\nu$ of free positive multiplicative Brownian motion is log-unimodal for all $t>0$ if $\nu$ is a multiplicatively symmetric log-unimodal distribution, and that $\sigma_{t}\boxtimes\nu$ is log-unimodal for sufficiently large $t$ if $\nu$ is supported on a suitably chosen finite interval. Counterexamples are given when $\nu$ is not assumed to be symmetric or having a bounded support.
Fast Gravitational Approach for Rigid Point Set Registration With Ordinary Differential Equations

This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA). In FGA, the source and target point sets are interpreted as rigid particle swarms with masses interacting in a globally multiply-linked manner while moving in a simulated gravitational force field. The optimal alignment is obtained by explicit modeling of forces acting on the particles as well as their velocities and displacements with second-order ordinary differential equations of n-body motion. Additional alignment cues can be integrated into FGA through particle masses. We propose a smooth-particle mass function for point mass initialization, which improves robustness to noise and structural discontinuities. To avoid the quadratic complexity of all-to-all point interactions, we adapt a Barnes-Hut tree for accelerated force computation and achieve quasilinear complexity. We show that the new method class has characteristics not found in previous alignment methods such as efficient handling of partial overlaps, inhomogeneous sampling densities, and coping with large point clouds with reduced runtime compared to the state of the art. Experiments show that our method performs on par with or outperforms all compared competing deep-learning-based and general-purpose techniques (which do not take training data) in resolving transformations for LiDAR data and gains state-of-the-art accuracy and speed when coping with different data.
VIVO: Surpassing Human Performance in Novel Object Captioning with Visual Vocabulary Pre-Training

It is highly desirable yet challenging to generate image captions that can describe novel objects which are unseen in caption-labeled training data, a capability that is evaluated in the novel object captioning challenge (nocaps). In this challenge, no additional image-caption training data, other than COCO Captions, is allowed for model training. Thus, conventional Vision-Language Pre-training (VLP) methods cannot be applied. This paper presents VIsual VOcabulary pre-training (VIVO) that performs pre-training in the absence of caption annotations. By breaking the dependency of paired image-caption training data in VLP, VIVO can leverage large amounts of paired image-tag data to learn a visual vocabulary. This is done by pre-training a multi-layer Transformer model that learns to align image-level tags with their corresponding image region features. To address the unordered nature of image tags, VIVO uses a Hungarian matching loss with masked tag prediction to conduct pre-training. We validate the effectiveness of VIVO by fine-tuning the pre-trained model for image captioning. In addition, we perform an analysis of the visual-text alignment inferred by our model. The results show that our model can not only generate fluent image captions that describe novel objects, but also identify the locations of these objects. Our single model has achieved new state-of-the-art results on nocaps and surpassed the human CIDEr score.
TEL: Low-Latency Failover Traffic Engineering in Data Plane

Modern network applications demand low-latency traffic engineering in the presence of network failure, while preserving the quality of service constraints like delay and capacity. Fast Re-Route (FRR) mechanisms are widely used for traffic re-routing purposes in failure scenarios. Control plane FRR typically computes the backup forwarding rules to detour the traffic in the data plane when the failure occurs. This mechanism could be computed in the data plane with the emergence of programmable data planes. In this paper, we propose a system (called <monospace>TEL</monospace>) that contains <italic>two</italic> FRR mechanisms, namely, TEL-C and TEL-D. The first one computes backup forwarding rules in the control plane, satisfying max-min fair allocation. The second mechanism provides FRR in the data plane. Both algorithms require minimal memory on programmable data planes and are well-suited with modern line rate match-action forwarding architectures (e.g., PISA). We implement both mechanisms on P4 programmable software switches (e.g., BMv2 and Tofino) and measure their performance on various topologies. The obtained results from a datacenter topology show that our FRR mechanism can improve the flow completion time up to 4.6<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula>–7.3<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> (i.e., small flows) and 3.1<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula>–12<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> (i.e., large flows) compared to recirculation-based mechanisms, such as F10, respectively.
Tracking collective cell motion by topological data analysis

By modifying and calibrating an active vertex model to experiments, we have simulated numerically a confluent cellular monolayer spreading on an empty space and the collision of two monolayers of different cells in an antagonistic migration assay. Cells are subject to inertial forces and to active forces that try to align their velocities with those of neighboring ones. In agreement with experiments in the literature, the spreading test exhibits formation of fingers in the moving interfaces, there appear swirls in the velocity field, and the polar order parameter and the correlation and swirl lengths increase with time. Numerical simulations show that cells inside the tissue have smaller area than those at the interface, which has been observed in recent experiments. In the antagonistic migration assay, a population of fluidlike Ras cells invades a population of wild type solidlike cells having shape parameters above and below the geometric critical value, respectively. Cell mixing or segregation depends on the junction tensions between different cells. We reproduce the experimentally observed antagonistic migration assays by assuming that a fraction of cells favor mixing, the others segregation, and that these cells are randomly distributed in space. To characterize and compare the structure of interfaces between cell types or of interfaces of spreading cellular monolayers in an automatic manner, we apply topological data analysis to experimental data and to results of our numerical simulations. We use time series of data generated by numerical simulations to automatically group, track and classify the advancing interfaces of cellular aggregates by means of bottleneck or Wasserstein distances of persistent homologies. These techniques of topological data analysis are scalable and could be used in studies involving large amounts of data. Besides applications to wound healing and metastatic cancer, these studies are relevant for tissue engineering, biological effects of materials, tissue and organ regeneration.
Shot noise distinguishes Majorana fermions from vortices injected in the edge mode of a chiral p-wave superconductor

The chiral edge modes of a topological superconductor support two types of excitations: fermionic quasiparticles known as Majorana fermions and \bm\pi𝛑-phase domain walls known as edge vortices. Edge vortices are injected pairwise into counter-propagating edge modes by a flux bias or voltage bias applied to a Josephson junction. An unpaired edge mode carries zero electrical current on average, but there are time-dependent current fluctuations. We calculate the shot noise power produced by a sequence of edge vortices and find that it increases logarithmically with their spacing — even if the spacing is much larger than the core size so the vortices do not overlap. This nonlocality produces an anomalous \bm{V\ln V}𝐕ln𝐕 increase of the shot noise in a voltage-biased geometry, which serves as a distinguishing feature in comparison with the linear-in-\bm V𝐕 Majorana fermion shot noise.
A Simple but Tough-to-Beat Data Augmentation Approach for Natural Language Understanding and Generation

Adversarial training has been shown effective at endowing the learned representations with stronger generalization ability. However, it typically requires expensive computation to determine the direction of the injected perturbations. In this paper, we introduce a set of simple yet effective data augmentation strategies dubbed cutoff, where part of the information within an input sentence is erased to yield its restricted views (during the fine-tuning stage). Notably, this process relies merely on stochastic sampling and thus adds little computational overhead. A Jensen-Shannon Divergence consistency loss is further utilized to incorporate these augmented samples into the training objective in a principled manner. To verify the effectiveness of the proposed strategies, we apply cutoff to both natural language understanding and generation problems. On the GLUE benchmark, it is demonstrated that cutoff, in spite of its simplicity, performs on par or better than several competitive adversarial-based approaches. We further extend cutoff to machine translation and observe significant gains in BLEU scores (based upon the Transformer Base model). Moreover, cutoff consistently outperforms adversarial training and achieves state-of-the-art results on the IWSLT2014 German-English dataset.
Weighted Sum-Rate Maximization for Intelligent Reflecting Surface Assisted Multi-Cell MU-MIMO Communications

In this paper, we investigate an intelligent reflecting surface (IRS) assisted multi-cell downlink communication system, where the IRS is dedicatedly deployed at cells boundary for mitigating the intra-cell and crosscell interference. We assume that each base station (BS) equipped with multiple antennas serves multiple users with multiple antennas in each cell, and an IRS consists of a large number of reflective elements to reflect the incident electromagnetic wave by changing phase shift and amplitude controlled by all BSs cooperatively. In other words, the IRS can adjust the propagation conditions near-instantaneously. For this configuration, the weighted sum-rate (WSR) of all the users is maximized under the individual maximum transmit power constraint at each BS and the phase shift constraint at each reflection element by jointly optimizing the active transmit beamforming at BSs and passive reflection coefficient matrix at IRS. Since the original problem is expressed as a sum-of-logarithmic function of signal-to-inerference-plus-noise (SINR) and the optimizing variables of SINR are coupled in fractional form, it is non-convex and hard to solve. To address the problem, we propose an efficient jointly optimizing algorithm based on Lagrangian Dual Transform and Fractional Transform and Successive Convex Approximation (SCA) technique is used to reduce complexity. Extensive simulations results indicate that the proposed jointly optimizing algorithm offers a substantial improvement performance gain over baseline schemes.
Parameter Critic: a Model Free Variance Reduction Method Through Imperishable Samples

We consider the problem of finding a policy that maximizes an expected reward throughout the trajectory of an agent that interacts with an unknown environment. Frequently denoted Reinforcement Learning, this framework suffers from the need of large amount of samples in each step of the learning process. To this end, we introduce parameter critic, a formulation that allows samples to keep their validity even when the parameters of the policy change. In particular, we propose the use of a function approximator to directly learn the relationship between the parameters and the expected cumulative reward. Through convergence analysis, we demonstrate the parameter critic outperforms gradient-free parameter space exploration techniques as it is robust to noise. Empirically, we show that our method solves the cartpole problem which corroborates our claim as the agent can successfully learn an optimal policy while learning the relationship between the parameters and the cumulative reward.
Stable cubulations, bicombings, and barycenters

We prove that the hierarchical hulls of finite sets of points in mapping class groups and Teichmuller spaces are stably approximated by a CAT(0) cube complexes, strengthening a result of Behrstock-Hagen-Sisto. As applications, we prove that mapping class groups are semihyperbolic and Teichmuller spaces are coarsely equivariantly bicombable, and both admit stable coarse barycenters. Our results apply to the broader class of "colorable" hierarchically hyperbolic spaces and groups.
Negative superluminal velocity and violation of Kramers-Kronig relations in causal optical systems

We investigate nonanalyticities (e.g., zeros and poles) of refractive index $n(\omega)$ and group index $n_g(\omega)$ in different optical setups. We first demonstrate that: while a Lorentzian dielectric has no nonanalyticity in the upper half of the complex frequency plane (CFP), its group index -- which governs the pulse-center propagation -- violates the Kramers-Kronig relations (KKRs). Thus, we classify the nonanalyticities as in the (a) first-order (refractive index or reflection) and (b) second-order (group index or group delay). The latter contains the derivative of the former. Then, we study a possible connection between the negative superluminal velocities and the presence of nonanalyticities in the upper half of the CFP. We show that presence of nonanalyticities in the upper half of the CFP for (a) the first-order response and (b) the second-order response are accompanied by the appearance of negative (a) phase velocity and (b) group velocity, respectively. We also distinguish between two kinds of superluminosity, $v>c$ and $v<0$, where we show that the second one ($v<0$) appears with the violation of KKRs.