Miracle-dz/UniFAR-FTUs huggingface dataset:
Strong Electronic Winds Blowing under Liquid Flows on Carbon Surfaces

The interface between a liquid and a solid is the location of plethora of intrincate mechanisms at the nanoscale, at the root of their specific emerging properties in natural processes or technological applications. However, while the structural properties and chemistry of interfaces have been intensively explored, the effect of the solid-state electronic transport at the fluid interface has been broadly overlooked up to now. It has been reported that water flowing against carbon-based nanomaterials, such as carbon nanotubes or graphene sheets, does induce electronic currents, but the mechanism at stake remains controversial. Here, we unveil the molecular mechanisms underlying the hydro-electronic couplings by investigating the electronic conversion under flow at the nanoscale. We use a tuning fork-Atomic Force Microscope (AFM) to deposit and displace a micrometric droplet of both ionic and non-ionic liquids on a multilayer graphene sample, while recording the electrical current across the carbon flake. We report measurements of an oscillation-induced current which is several orders of magnitude larger than previously reported for water on carbon , and further boosted by the presence of surface wrinkles on the carbon layer. Our results point to a peculiar momentum transfer mechanism between fluid molecules and charge carriers in the carbon walls mediated by phonon excitations in the solid. Our findings pave the way for active control of fluid transfer at the nanoscale by harnessing the complex interplay between collective excitations in the solid and the molecules in the fluid.
GRB 211211A: a Prolonged Central Engine under a Strong Magnetic Field Environment

Recently, a kilonova-associated gamma-ray burst (GRB 211211A), whose light curve consists of a precursor (∼0.2 s), a hard spiky emission (∼10 s), and a soft long extended emission (∼40 s), has attracted great attention. Kilonova association could prove its merger origin, while the detection of the precursor can be used to infer at least one highly magnetized neutron star (NS) being involved in the merger. In this case, a strong magnetic flux Φ is expected to surround the central engine of GRB 211211A. Here we suggest that when Φ is large enough, the accretion flow could be halted far from the innermost stable radius, which will significantly prolong the lifetime of the accretion process, and so the GRB duration. For example, we show that as long as the central black hole (BH) is surrounded by a strong magnetic flux Φ ∼ 1029cm2 G, an accretion flow with Ṁini≃0.1M⊙s−1 could be halted at 40 times the gravitational radius and be slowly transferred into the black hole on the order of ∼10 s, which naturally explains the duration of hard spiky emission. After most of the disk mass has been accreted onto the BH, the inflow rate will be reduced, so a long and soft extended emission is expected when a new balance between the magnetic field and the accretion current is reconstructed at a larger radius. Our results further support that the special behavior of GRB 211211A is mainly due to the strong magnetic field of its progenitor stars.
MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation

The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent sparse inter-slice information and dense intra-slice information in a balanced way, leading to severe underfitting to inter-slice features (for vanilla 2D CNNs) and overfitting to noise from long-range slices (for vanilla 3D CNNs). In this work, a novel mesh network (MNet) is proposed to balance the spatial representation inter axes via learning. 1) Our MNet latently fuses plenty of representation processes by embedding multi-dimensional convolutions deeply into basic modules, making the selections of representation processes flexible, thus balancing representation for sparse inter-slice information and dense intra-slice information adaptively. 2) Our MNet latently fuses multi-dimensional features inside each basic module, simultaneously taking the advantages of 2D (high segmentation accuracy of the easily recognized regions in 2D view) and 3D (high smoothness of 3D organ contour) representations, thus obtaining more accurate modeling for target regions. Comprehensive experiments are performed on four public datasets (CT\&MR), the results consistently demonstrate the proposed MNet outperforms the other methods. The code and datasets are available at: https://github.com/zfdong-code/MNet
Bridging the prosody GAP: Genetic Algorithm with People to efficiently sample emotional prosody

The human voice effectively communicates a range of emotions with nuanced variations in acoustics. Existing emotional speech corpora are limited in that they are either (a) highly curated to induce specific emotions with predefined categories that may not capture the full extent of emotional experiences, or (b) entangled in their semantic and prosodic cues, limiting the ability to study these cues separately. To overcome this challenge, we propose a new approach called 'Genetic Algorithm with People' (GAP), which integrates human decision and production into a genetic algorithm. In our design, we allow creators and raters to jointly optimize the emotional prosody over generations. We demonstrate that GAP can efficiently sample from the emotional speech space and capture a broad range of emotions, and show comparable results to state-of-the-art emotional speech corpora. GAP is language-independent and supports large crowd-sourcing, thus can support future large-scale cross-cultural research.
On Causality in Domain Adaptation and Semi-Supervised Learning: an Information-Theoretic Analysis

The establishment of the link between causality and unsupervised domain adaptation (UDA)/semi-supervised learning (SSL) has led to methodological advances in these learning problems in recent years. However, a formal theory that explains the role of causality in the generalization performance of UDA/SSL is still lacking. In this paper, we consider the UDA/SSL setting where we access m labeled source data and n unlabeled target data as training instances under a parametric probabilistic model. We study the learning performance (e.g., excess risk) of prediction in the target domain. Speciﬁcally, we distinguish two scenarios: the learning problem is called causal learning if the feature is the cause and the label is the eﬀect, and is called anti-causal learning otherwise. We show that in causal learning, the excess risk depends on the size of the source sample at a rate of O ( 1 m ) only if the labelling distribution between the source and target domains remains unchanged. In anti-causal learning, we show that the unlabeled data dominate the performance at a rate of typically O ( 1 n ). Our analysis is based on the notion of potential outcome random variables and information theory. These results bring out the relationship between the data sample size and the hardness of the learning problem with diﬀerent causal mechanisms. rate. We empirically depict the rate of the learning performance under diﬀerent causal mechanisms and domain shift conditions, from which the usefulness of the source and target data is manifested.
Clean two-dimensional Floquet time crystal

We consider the two-dimensional quantum Ising model, in absence of disorder, subject to periodic imperfect global spin ﬂips. We show by a combination of exact diagonalization and tensor-network methods that the system can sustain a spontaneously broken discrete time-translation symmetry. Employing careful scaling analysis, we show the feasibility of a two-dimensional discrete time-crystal (DTC) phase. Despite an unbounded energy pumped into the system, in the high-frequency limit, a well-deﬁned eﬀective Hamiltonian controls a ﬁnite-temperature intermediate regime, wherein local time averages are described by thermal averages. As a consequence, the long-lived stability of the DTC relies on the existence of a long-range ordered phase at ﬁnite temperature. Interestingly, even for large deviations from the perfect spin ﬂip, we observe a non-perturbative change in the decay rate of the order parameter, which is related to the long-lived stability of the magnetic domains in 2D.
Unsupervised Slot Schema Induction for Task-oriented Dialog

Carefully-designed schemas describing how to collect and annotate dialog corpora are a prerequisite towards building task-oriented dialog systems. In practical applications, manually designing schemas can be error-prone, laborious, iterative, and slow, especially when the schema is complicated. To alleviate this expensive and time consuming process, we propose an unsupervised approach for slot schema induction from unlabeled dialog corpora. Leveraging in-domain language models and unsupervised parsing structures, our data-driven approach extracts candidate slots without constraints, followed by coarse-to-fine clustering to induce slot types. We compare our method against several strong supervised baselines, and show significant performance improvement in slot schema induction on MultiWoz and SGD datasets. We also demonstrate the effectiveness of induced schemas on downstream applications including dialog state tracking and response generation.
List of Papers

I Asman P, Britt J, Mills R P & Heijl A (1988): Evaluation of adaptive spatial enhancement in suprathreshold visual field screening. Ophthalmology 95: 1656-1662. I1 Asman P & Heijl A (1989): A clinical study of perimetric probability maps. Arch OphthalmollO7: 199-203. I11 Heijl A, Lindgren G, Olsson J & Asman P (1989): Visual field interpretation with empirical probability maps. Arch OphthalmollO7: 204-208. IV Heijl A & Asman P (1989): Clustering of depressed points in the normal visual field. In: Heijl A (ed). Perimetry Update 1988/89, pp 185-189. Amsterdam, Kugler & Ghedini. V Heijl A, Lindgren G , Lindgren A, Olsson J, Asman P, Myers S & Patella M (1991): Extended empirical statistical package for evaluation of single and multiple fields in glaucoma. In: Mills R P & Heijl A (eds). Perimetry Update 1990/91, pp 303-315. Amsterdam, Kugler & Ghedini. VI Asman P & Heijl A (1992): Glaucoma Hemifield Test: automated visual field evaluation. Arch Ophthalmol 110: 812-819. VII Asman P & Heijl A (1992): Evaluation of methods for automated hemifield analysis in perimetry. Arch Ophthalmol 110:
Shadow-Aware Dynamic Convolution for Shadow Removal

With a wide range of shadows in many collected images, shadow removal has aroused increasing attention since uncontaminated images are of vital importance for many downstream multimedia tasks. Current methods consider the same convolution operations for both shadow and non-shadow regions while ignoring the large gap between the color mappings for the shadow region and the non-shadow region, leading to poor quality of reconstructed images and a heavy computation burden. To solve this problem, this paper introduces a novel plug-and-play Shadow-Aware Dynamic Convolution (SADC) module to decouple the interdependence between the shadow region and the non-shadow region. Inspired by the fact that the color mapping of the non-shadow region is easier to learn, our SADC processes the non-shadow region with a lightweight convolution module in a computationally cheap manner and recovers the shadow region with a more complicated convolution module to ensure the quality of image reconstruction. Given that the non-shadow region often contains more background color information, we further develop a novel intra-convolution distillation loss to strengthen the information flow from the non-shadow region to the shadow region. Extensive experiments on the ISTD and SRD datasets show our method achieves better performance in shadow removal over many state-of-the-arts. Our code is available at https://github.com/xuyimin0926/SADC.
Tensor-based Collaborative Filtering With Smooth Ratings Scale

Conventional collaborative filtering techniques don't take into consideration the effect of discrepancy in users' rating perception. Some users may rarely give 5 stars to items while others almost always assign 5 stars to the chosen item. Even if they had experience with the same items this systematic discrepancy in their evaluation style will lead to the systematic errors in the ability of recommender system to effectively extract right patterns from data. To mitigate this problem we introduce the ratings' similarity matrix which represents the dependency between different values of ratings on the population level. Hence, if on average the correlations between ratings exist, it is possible to improve the quality of proposed recommendations by off-setting the effect of either shifted down or shifted up users' rates.
Temperature-induced dephasing in high-order harmonic generation from solids

High harmonic generation (HHG) in solid and gaseous targets has been proven to be a powerful avenue for the generation of attosecond pulses, whereas the inﬂuence of electron-phonon scattering on HHG is a critical outstanding problem. Here we ﬁrst introduce a temperature dependent lattice vibration model by characterizing the spacing ﬂuctuation. Our results reveal that (i) structural disorder induced by lattice vibration does not lead to generation of even-order harmonics; (ii) dephasing of HHG occurs as the lattice temperature is growing; (iii) an open-trajectory picture predicts the maximal photon energy in the temperature-dependent HHG spectra. Moreover, a formula assessing dephasing time with lattice temperature is proposed to identify the timescale of electron-phonon scattering. This work paves a way to study non-Born-Oppenheimer eﬀect in solids driven by strong ﬁeld.
STDC-MA Network for Semantic Segmentation

Semantic segmentation is applied extensively in autonomous driving and intelligent transportation with methods that highly demand spatial and semantic information. Here, an STDC-MA network is proposed to meet these demands. First, the STDC-Seg structure is employed in STDC-MA to ensure a lightweight and efficient structure. Subsequently, the feature alignment module (FAM) is applied to understand the offset between high-level and low-level features, solving the problem of pixel offset related to upsampling on the high-level feature map. Our approach implements the effective fusion between high-level features and low-level features. A hierarchical multiscale attention mechanism is adopted to reveal the relationship among attention regions from two different input sizes of one image. Through this relationship, regions receiving much attention are integrated into the segmentation results, thereby reducing the unfocused regions of the input image and improving the effective utilization of multiscale features. STDC- MA maintains the segmentation speed as an STDC-Seg network while improving the segmentation accuracy of small objects. STDC-MA was verified on the verification set of Cityscapes. The segmentation result of STDC-MA attained 76.81% mIOU with the input of 0.5x scale, 3.61% higher than STDC-Seg.
Crypto Pump and Dump Detection via Deep Learning Techniques

Despite the fact that cryptocurrencies themselves have experienced an astonishing rate of adoption over the last decade, cryptocurrency fraud detection is a heavily under-researched problem area. Of all fraudulent activity regarding cryptocurrencies, pump and dump schemes are some of the most common. Though some studies have been done on these kinds of scams in the stock market, the lack of labelled stock data and the volatility unique to the cryptocurrency space constrains the applicability of studies on the stock market toward this problem domain. Furthermore, the only work done in this space thus far has been either statistical in nature, or has been concerned with classical machine learning models such as random forest trees. We propose the novel application of two existing neural network architectures to this problem domain and show that deep learning solutions can significantly outperform all other existing pump and dump detection methods for cryptocurrencies.
Shadow thermodynamics of AdS black hole in regular spacetime

The dependence of the black hole (BH) shadow and thermodynamics may be structured in the regular spacetime. Taking the regular Bardeen-AdS BH as an example, the relationship between the shadow radius and the event horizon radius is derived. It is found that these two radii display a positive correlation, implying that the BH temperature can be rewritten as a function of shadow radius in the regular spacetime. By analyzing the phase transition curves under the shadow context, we found that the shadow radius can replace the event horizon radius to present the BH phase transition process, and the phase transition grade can also be revealed by the shadow radius, indicating that the shadow radius may serve as a probe for the phase structure in the regular spacetime. Utilizing the temperature-shadow radius function, the thermal profile of the Bardeen-AdS BH is established. We obtained that the temperature shows an N-type change trend in $P<P_{\rm c}$ situation. These results suggest that the phase transition process of the regular AdS BH can be completely presented in the thermal profile, and the relationship between the BH shadow and thermodynamics can also be established in the regular spacetime.
A social network perspective of building information modelling in Greek construction projects

ABSTRACT Building information modelling (BIM) is a technology promoted by governments as a solution to the problems of inefficient communication and limited collaboration in the construction industry. However, there remains a limited understanding of the changes that BIM introduces to the structure of inter-organizational communication and its impact on project participants’ roles and relationships. In this study, these issues are addressed through a comparative study of two construction projects in Greece: one that utilized BIM, and one that did not. Social network analysis (SNA) was employed as an analytical method to examine the inter-organizational communication networks in relation to two types of information exchange – design development and cost management during the design stages of the two projects. Comparative SNA studies were conducted focusing on network density, tie strength, path length, and actor centrality. The research findings revealed the capacity for BIM to improve inter-organizational communication with the BIM-enabled project, exhibiting a higher density of communication, stronger ties, and shorter path lengths between project participants, indicating timely access to higher quality of information. The findings also identified the high centrality of the ‘BIM manager’ and ‘BIM coordinator’, demonstrating the effectiveness of these two new roles in managing the flow of communication in construction teams.
Impact of L1 Batch Normalization on Analog Noise Resistant Property of Deep Learning Models

Analog hardware has become a popular choice for machine learning on resource-constrained devices recently due to its fast execution and energy efficiency. However, the inherent presence of noise in analog hardware and the negative impact of the noise on deployed deep neural network (DNN) models limit their usage. The degradation in performance due to the noise calls for the novel design of DNN models that have excellent noise-resistant property, leveraging the properties of the fundamental building block of DNN models. In this work, the use of L1 or TopK BatchNorm type, a fundamental DNN model building block, in designing DNN models with excellent noise-resistant property is proposed. Specifically, a systematic study has been carried out by training DNN models with L1/TopK BatchNorm type, and the performance is compared with DNN models with L2 BatchNorm types. The resulting model noise-resistant property is tested by injecting additive noise to the model weights and evaluating the new model inference accuracy due to the noise. The results show that L1 and TopK BatchNorm type has excellent noise-resistant property, and there is no sacrifice in performance due to the change in the BatchNorm type from L2 to L1/TopK BatchNorm type.
Voltage control of the quantum scattering time in InAs/GaSb/InAs trilayer quantum wells

We study the evolution of the quantum scattering time by gate voltage training in the topological insulator (TI) based on InAs/GaSb/InAs trilayer quantum wells. Depending on the minimal gate voltage applied during a gate voltage sweep cycle, the quantum scattering time can be improved by 50% from 0.08 ps to 0.12 ps albeit the transport scattering time is rather constant around 1.0 ps. The ratio of the quantum scattering time versus transport scattering time scales linearly with the charge carrier density and varies from 10 to 30, indicating Coulombic scattering as the dominant scattering mechanism. Our findings may enable to improve bulk and edge properties of TIs based on InAs/GaSb quantum well heterostructures solely by means of an electric field rather than temperature which opens the paths towards their application for macroscopic devices.
Concordance between Observations and Simulations in the Evolution of the Mass Relation between Supermassive Black Holes and Their Host Galaxies

We carry out a comparative analysis of the relation between the mass of supermassive black holes (BHs) and the stellar mass of their host galaxies at 0.2 < z < 1.7 using well-matched observations and multiple state-of-the-art simulations (e.g., MassiveBlackII, Horizon-AGN, Illustris, TNG, and a semianalytic model). The observed sample consists of 646 uniformly selected Sloan Digital Sky Survey quasars (0.2 < z < 0.8) and 32 broad-line active galactic nuclei (AGNs; 1.2 < z < 1.7) with imaging from Hyper Suprime-Cam (HSC) for the former and Hubble Space Telescope (HST) for the latter. We first add realistic observational uncertainties to the simulation data and then construct a simulated sample in the same manner as the observations. Over the full redshift range, our analysis demonstrates that all simulations predict a level of intrinsic scatter of the scaling relations comparable to the observations that appear to agree with the dispersion of the local relation. Regarding the mean relation, Horizon-AGN and TNG are in closest agreement with the observations at low and high redshift (z ∼ 0.2 and 1.5, respectively), while the other simulations show subtle differences within the uncertainties. For insight into the physics involved, the scatter of the scaling relation, seen in the SAM, is reduced by a factor of two and closer to the observations after adopting a new feedback model that considers the geometry of the AGN outflow. The consistency in the dispersion with redshift in our analysis supports the importance of both quasar- and radio-mode feedback prescriptions in the simulations. Finally, we highlight the importance of increasing the sensitivity (e.g., using the James Webb Space Telescope), thereby pushing to lower masses and minimizing biases due to selection effects.