Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly Stable Perylenediimide‐Based Self‐Assembled Monolayers Studied with Spectroelectrochemistry

Perylenediimide (PDI) based self-assembled monolayers (SAMs) have been studied by quartz crystal microbalance, X-ray photoelectron spectroscopy, cyclic voltammetry and spectroelectrochemistry (SEC). The high stability of PDI based SAMs has allowed probing very low signals by absorption and emission SEC and extracting voltabsorptograms. The wavelengths of absorption maxima of PDI, anion radical and dianion species. In contrast, the magnitudes of the molar extinction coefficient of the reduced forms were not preserved in SAM. The quenching of PDI fluorescence was confirmed on gold substrate.
Visual Relationship Forecasting in Videos

Real-world scenarios often require the anticipation of object interactions in unknown future, which would assist the decision-making process of both humans and agents. To meet this challenge, we present a new task named Visual Relationship Forecasting (VRF) in videos to explore the prediction of visual relationships in a reasoning manner. Specifically, given a subject-object pair with H existing frames, VRF aims to predict their future interactions for the next T frames without visual evidence. To evaluate the VRF task, we introduce two video datasets named VRF-AG and VRF-VidOR, with a series of spatio-temporally localized visual relation annotations in a video. These two datasets densely annotate 13 and 35 visual relationships in 1923 and 13447 video clips, respectively. In addition, we present a novel Graph Convolutional Transformer (GCT) framework, which captures both object-level and frame-level dependencies by spatio-temporal Graph Convolution Network and Transformer. Experimental results on both VRF-AG and VRF-VidOR datasets demonstrate that GCT outperforms the state-of-the-art sequence modelling methods on visual relationship forecasting.
Audio-visual Attentive Fusion for Continuous Emotion Recognition

—We propose an audio-visual spatial-temporal deep neural network with: (1) a visual block containing a pretrained 2D-CNN followed by a temporal convolutional network (TCN); (2) an aural block containing several parallel TCNs; and (3) a leader-follower attentive fusion block combining the audio-visual information. The TCN with large history coverage enables our model to exploit spatial-temporal information within a much larger window length (i.e., 300) than that from the baseline and state-of-the-art methods (i.e., 36 or 48). The fusion block emphasizes the visual modality while exploits the noisy aural modality using the inter-modality attention mechanism. To make full use of the data and alleviate over-ﬁtting, the cross-validation is carried out on the training and validation set. The concordance correlation coefﬁcient (CCC) centering is used to merge the results from each fold. On the development set, the achieved CCC is 0 . 410 for valence and 0 . 661 for arousal, which signiﬁcantly outperforms the baseline method with the corresponding CCC of 0 . 210 and 0 . 230 for valence and arousal, respectively. The code is available at https://github.com/sucv/ABAW2.
Vox Populi, Vox DIY: Benchmark Dataset for Crowdsourced Audio Transcription

Domain-speciﬁc data is the crux of the successful transfer of machine learning systems from benchmarks to real life. Crowdsourcing has become one of the standard tools for cheap and time-efﬁcient data collection for simple problems such as image classiﬁcation: thanks in large part to advances in research on aggregation methods. However, the applicability of crowdsourcing to more complex tasks (e.g., speech recognition) remains limited due to the lack of principled aggregation methods for these modalities. The main obstacle towards designing advanced aggregation methods is the absence of training data, and in this work, we focus on bridging this gap in speech recognition . For this, we collect and release C ROWD S PEECH — the ﬁrst publicly available large-scale dataset of crowdsourced audio transcriptions. Evaluation of existing aggregation methods on our data shows room for improvement, suggesting that our work may entail the design of better algorithms. At a higher level, we also contribute to the more general challenge of collecting high-quality datasets using crowdsourcing : we develop a principled pipeline for constructing datasets of crowdsourced audio transcriptions in any novel domain. We show its applicability on an under-resourced language by constructing V OX DIY — a counterpart of C ROWD S PEECH for the Russian language. We also release the code that allows a full replication of our data collection pipeline and share various insights on best practices of data collection via crowdsourcing. 1
Long–Short Ensemble Network for Bipolar Manic-Euthymic State Recognition Based on Wrist-Worn Sensors

Manic episodes of bipolar disorder can lead to uncritical behavior and delusional psychosis, often with destructive consequences for those affected and their surroundings. Early detection and intervention of a manic episode are crucial to prevent escalation, hospital admission, and premature death. However, people with bipolar disorder may not recognize that they are experiencing a manic episode and symptoms, such as euphoria and increased productivity can also deter affected individuals from seeking help. This work proposes to perform user-independent, automatic mood-state detection based on actigraphy and electrodermal activity acquired from a wrist-worn device during mania and after recovery (euthymia). This article proposes a new deep learning-based ensemble method leveraging long (20 h) and short (5 min) time intervals to discriminate between the mood states. When tested on 47 bipolar patients, the proposed classification scheme achieves an average accuracy of 91.59% in euthymic/manic mood-state recognition.
4C: A Computation, Communication, and Control Co-Design Framework for CAVs

Connected and autonomous vehicles (CAVs) are promising due to their potential safety and efficiency benefits, and have attracted massive investment and interest from government agencies, industry, and academia. With more computing and communication resources available, both vehicles and edge servers are equipped with a set of cam-era-based vision sensors, also known as Visual IoT (VIoT) techniques, for sensing and perception. Tremendous efforts have been made to achieve programmable communication, computation, and control. However, they are conducted mainly in silo mode, limiting the responsiveness and efficiency of handling challenging scenarios in the real world. To improve the end-to-end performance, we envision that future CAVs require co-design of communication, computation, and control. This article presents our vision of the end-to-end design principle for CAVs, called 4C, which extends the VIoT system by providing a unified communication, computation, and control co-design framework. With programmable communications, fine-grained heterogeneous computation, and efficient vehicle controls in 4C, CAVs can handle critical scenarios and achieve energy-efficient autonomous driving. Finally, we present several challenges to achieving the vision of the 4C framework.
Mitigating Uncertainty of Classifier for Unsupervised Domain Adaptation

Understanding unsupervised domain adaptation has been an important task that has been well explored. However, the wide variety of methods have not analyzed the role of a classifier's performance in detail. In this paper, we thoroughly examine the role of a classifier in terms of matching source and target distributions. We specifically investigate the classifier ability by matching a) the distribution of features, b) probabilistic uncertainty for samples and c) certainty activation mappings. Our analysis suggests that using these three distributions does result in a consistently improved performance on all the datasets. Our work thus extends present knowledge on the role of the various distributions obtained from the classifier towards solving unsupervised domain adaptation.
Unsupervised Single Image Super-resolution Under Complex Noise

While the researches on single image super-resolution (SISR), especially equipped with deep neural networks (DNNs), have achieved tremendous successes recently, they still suffer from two major limitations. Firstly, the real image degradation is usually unknown and highly variant from one to another, making it extremely hard to train a single model to handle the general SISR task. Secondly, most of current methods mainly focus on the downsampling process of the degradation, but ignore or underestimate the inevitable noise contamination. For example, the commonly-used independent and identically distributed (i.i.d.) Gaussian noise distribution always largely deviates from the real image noise (e.g., camera sensor noise), which limits their performance in real scenarios. To address these issues, this paper proposes a model-based unsupervised SISR method to deal with the general SISR task with unknown degradations. Instead of the traditional i.i. d. Gaussian noise assumption, a novel patch-based non-i.i. d. noise modeling method is proposed to ﬁt the complex real noise. Besides, a deep generator parameterized by a DNN is used to map the latent variable to the high-resolution image, and the conventional hyper-Laplacian prior is also elaborately embedded into such generator to further constrain the image gradients. Finally, a Monte Carlo EM algorithm is designed to solve our model, which provides a general inference framework to update the image generator both w.r. t. the latent variable and the network parameters. Comprehensive experiments demonstrate that the proposed method can evidently surpass the current state of the art (SotA
F-LOAM : Fast LiDAR Odometry and Mapping

Simultaneous Localization and Mapping (SLAM) has wide robotic applications such as autonomous driving and unmanned aerial vehicles. Both computational efficiency and localization accuracy are of great importance towards a good SLAM system. Existing works on LiDAR based SLAM often formulate the problem as two modules: scan-to-scan match and scan-to-map refinement. Both modules are solved by iterative calculation which are computationally expensive. In this paper, we propose a general solution that aims to provide a computationally efficient and accurate framework for LiDAR based SLAM. Specifically, we adopt a non-iterative two-stage distortion compensation method to reduce the computational cost. For each scan input, the edge and planar features are extracted and matched to a local edge map and a local plane map separately, where the local smoothness is also considered for iterative pose optimization. Thorough experiments are performed to evaluate its performance in challenging scenarios, including localization for a warehouse Automated Guided Vehicle (AGV) and a public dataset on autonomous driving. The proposed method achieves a competitive localization accuracy with a processing rate of more than 10 Hz in the public dataset evaluation, which provides a good trade-off between performance and computational cost for practical applications. It is one of the most accurate and fastest open-sourced SLAM systems1 in KITTI dataset ranking.
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments

Stock market prediction has been an important topic for investors, researchers, and analysts. Because it is affected by too many factors, stock market prediction is a difficult task to handle. In this study, we propose a novel method that is based on deep reinforcement learning methodologies for the direction prediction of stocks using sentiments of community and knowledge graph. For this purpose, we firstly construct a social knowledge graph of users by analyzing relations between connections. After that, time series analysis of related stock and sentiment analysis is blended with deep reinforcement methodology. Turkish version of Bidirectional Encoder Representations from Transformers (BerTurk) is employed to analyze the sentiments of the users while deep Q-learning methodology is used for the deep reinforcement learning side of the proposed model to construct the deep Q network. In order to demonstrate the effectiveness of the proposed model, Garanti Bank (GARAN), Akbank (AKBNK), T\"urkiye \. I\c{s} Bankas{\i} (ISCTR) stocks in Istanbul Stock Exchange are used as a case study. Experiment results show that the proposed novel model achieves remarkable results for stock market prediction task.
User Role Discovery and Optimization Method based on K-means + Reinforcement learning in Mobile Applications

With the widespread use of mobile phones, users can share their location and activity anytime, anywhere, as a form of check-in data. These data reflect user features. Long-term stable, and a set of user-shared features can be abstracted as user roles. The role is closely related to the user’s social background, occupation, and living habits. This study provides four main contributions. Firstly, user feature models from different views for each user are constructed from the analysis of check-in data. Secondly, K-Means algorithm is used to discover user roles from user features. Thirdly, a reinforcement learning algorithm is proposed to strengthen the clustering effect of user roles and improve the stability of the clustering result. Finally, experiments are used to verify the validity of the method, the results of which show the effectiveness of the method.
Constraining particle acceleration in Sgr A* with simultaneous GRAVITY, Spitzer, NuSTAR, and Chandra observations

We report the time-resolved spectral analysis of a bright near-infrared and moderate X-ray flare of Sgr A*. We obtained light curves in the $M$-, $K$-, and $H$-bands in the mid- and near-infrared and in the $2-8~\mathrm{keV}$ and $2-70~\mathrm{keV}$ bands in the X-ray. The observed spectral slope in the near-infrared band is $\nu L_\nu\propto \nu^{0.5\pm0.2}$; the spectral slope observed in the X-ray band is $\nu L_\nu \propto \nu^{-0.7\pm0.5}$. We tested synchrotron and synchrotron self-Compton (SSC) scenarios. The observed near-infrared brightness and X-ray faintness, together with the observed spectral slopes, pose challenges for all models explored. We rule out a scenario in which the near-infrared emission is synchrotron emission and the X-ray emission is SSC. A one-zone model in which both the near-infrared and X-ray luminosity are produced by SSC and a model in which the luminosity stems from a cooled synchrotron spectrum can explain the flare. In order to describe the mean SED, both models require specific values of the maximum Lorentz factor $\gamma_{max}$, which however differ by roughly two orders of magnitude: the SSC model suggests that electrons are accelerated to $\gamma_{max}\sim 500$, while cooled synchrotron model requires acceleration up to $\gamma_{max}\sim5\times 10^{4}$. The SSC scenario requires electron densities of $10^{10}~\mathrm{cm^{-3}}$ much larger than typical ambient densities in the accretion flow, and thus require in an extraordinary accretion event. In contrast, assuming a source size of $1R_s$, the cooled synchrotron scenario can be realized with densities and magnetic fields comparable with the ambient accretion flow. For both models, the temporal evolution is regulated through the maximum acceleration factor $\gamma_{max}$, implying that sustained particle acceleration is required to explain at least a part of the temporal evolution of the flare.
AÇAI: Ascent Similarity Caching with Approximate Indexes

Similarity search is a key operation in multimedia retrieval systems and recommender systems, and it will play an important role also for future machine learning and augmented reality applications. When these systems need to serve large objects with tight delay constraints, edge servers close to the end-user can operate as similarity caches to speed up the retrieval. In this paper we present AÇAI, a new similarity caching policy which improves on the state of the art by using (i) an (approximate) index for the whole catalog to decide which objects to serve locally and which to retrieve from the remote server, and (ii) a mirror ascent algorithm to update the set of local objects with strong guarantees even when the request process does not exhibit any statistical regularity.
DUKweb: Diachronic word representations from the UK Web Archive corpus

Lexical semantic change (detecting shifts in the meaning and usage of words) is an important task for social and cultural studies as well as for Natural Language Processing applications. Diachronic word embeddings (time-sensitive vector representations of words that preserve their meaning) have become the standard resource for this task. However, given the significant computational resources needed for their generation, very few resources exist that make diachronic word embeddings available to the scientific community. In this paper we present DUKweb, a set of large-scale resources designed for the diachronic analysis of contemporary English. DUKweb was created from the JISC UK Web Domain Dataset (1996-2013), a very large archive which collects resources from the Internet Archive that were hosted on domains ending in `. uk'. DUKweb consists of a series word co-occurrence matrices and two types of word embeddings for each year in the JISC UK Web Domain dataset. We show the reuse potential of DUKweb and its quality standards via a case study on word meaning change detection.
Collaborative Visual Navigation

As a fundamental problem for Artificial Intelligence, multi-agent system (MAS) is making rapid progress, mainly driven by multi-agent reinforcement learning (MARL) techniques. However, previous MARL methods largely focused on grid-world like or game environments; MAS in visually rich environments has remained less explored. To narrow this gap and emphasize the crucial role of perception in MAS, we propose a large-scale 3D dataset, CollaVN, for multi-agent visual navigation (MAVN). In CollaVN, multiple agents are entailed to cooperatively navigate across photo-realistic environments to reach target locations. Diverse MAVN variants are explored to make our problem more general. Moreover, a memory-augmented communication framework is proposed. Each agent is equipped with a private, external memory to persistently store communication information. This allows agents to make better use of their past communication information, enabling more efficient collaboration and robust long-term planning. In our experiments, several baselines and evaluation metrics are designed. We also empirically verify the efficacy of our proposed MARL approach across different MAVN task settings.
Radio emission from dust-obscured galaxies

The coevolution of galaxies and their central supermassive black holes is a subject of intense research. A class of objects, the dust-obscured galaxies (DOGs) are particularly interesting in this respect as they are thought to represent a short evolutionary phase when violent star formation activity in the host galaxy may coexist with matter accretion on to the black hole powering the active nucleus. Here, we investigate different types of DOGs classified by their mid-infrared spectral energy distributions to reveal whether they can be distinguished by their arcsec-scale radio properties. Radio emission is unaffected by dust obscuration and may originate from both star formation and an active nucleus. We analyse a large sample of 661 DOGs complied from the literature and find that only a small fraction of them (∼2 per cent) are detected with flux densities exceeding ∼1 mJy in the Faint Images of the Radio Sky at Twenty-Centimeters (FIRST) survey. These radio-detected objects are almost exclusively ‘power-law’ DOGs. Stacking analysis of the FIRST image cutouts centred on the positions of individually radio-undetected sources suggests that weak radio emission is present in ‘power-law’ DOGs. On the other hand, radio emission from ‘bump’ DOGs is only marginally detected in the median-stacked FIRST image.
Measurements of the Electroweak Diboson Production Cross Sections in Proton-Proton Collisions at sqrt[s]=5.02  TeV Using Leptonic Decays.

The first measurements of diboson production cross sections in proton-proton interactions at a center-of-mass energy of 5.02 TeV are reported. They are based on data collected with the CMS detector at the LHC, corresponding to an integrated luminosity of 302 pb^{-1}. Events with two, three, or four charged light leptons (electrons or muons) in the final state are analyzed. The WW, WZ, and ZZ total cross sections are measured as σ_{WW}=37. 0_{-5.2}^{+5.5}(stat)_{-2.6}^{+2.7}(syst) pb, σ_{WZ}=6. 4_{-2.1}^{+2.5}(stat)_{-0.3}^{+0.5}(syst) pb, and σ_{ZZ}=5. 3_{-2.1}^{+2.5}(stat)_{-0.4}^{+0.5}(syst) pb. All measurements are in good agreement with theoretical calculations at combined next-to-next-to-leading order quantum chromodynamics and next-to-leading order electroweak accuracy.
Black Hole Shadows : How to Fix the Extended Gravity Theory

The first images of black hole shadows open new possibilities to develop modern extended gravity theories. We discuss the shadow calculations in non-rotating case both when g11 = −g 00 and g11 6= −g 00 . We demonstrate the application to few different models: Horndesky theory with Gauss-Bonnet invariant, loop quantum gravity and conformal gravity. The difference of these theories from shadow models with the theory of general relativity is shown. In addition we show that when the rotation is taken into account the requirements to the observational accuracy decrease.
Depth-based Outlier Detection for Grouped Smart Meters: a Functional Data Analysis Toolbox

—Smart metering infrastructures collect data almost continuously in the form of ﬁne-grained long time series. These massive time series often have common daily patterns that are repeated between similar days or seasons and shared between grouped meters. Within this context, we propose a method to highlight individuals with abnormal daily dependency patterns, which we term evolution outliers . To this end, we approach the problem from the standpoint of Functional Data Analysis (FDA), by treating each daily record as a function or curve. We then focus on the morphological aspects of the observed curves, such as daily magnitude, daily shape, derivatives, and inter-day evolution. The proposed method for evolution outliers relies on the concept of functional depth , which has been a cornerstone in the literature of FDA to build shape and magnitude outlier detection methods. In conjunction with our evolution outlier proposal, these methods provide an outlier detection toolbox for smart meter data that covers a wide palette of functional outliers classes. We illustrate the outlier identiﬁcation ability of this toolbox using actual smart-metering data corresponding to photovoltaic energy generation and circuit voltage records.
The Star Formation Reference Survey – V. The effect of extinction, stellar mass, metallicity, and nuclear activity on star-formation rates based on H α emission

We present new H α photometry for the Star Formation Reference Survey (SFRS), a representative sample of star-forming galaxies in the local Universe. Combining these data with the panchromatic coverage of the SFRS, we provide calibrations of H α-based star-formation rates (SFRs) with and without correction for the contribution of [N ii] emission. We consider the effect of extinction corrections based on the Balmer decrement, infrared excess, and spectral energy distribution (SED) fits. We compare the SFR estimates derived from SED fits, polycyclic aromatic hydrocarbons, hybrid indicators such as 24 µm + H α, 8 µm + H α, FIR + FUV, and H α emission for a sample of purely star-forming galaxies. We provide a new calibration for 1.4 GHz-based SFRs by comparing to the H α emission, and we measure a dependence of the radio-to-H α emission ratio based on galaxy stellar mass. Active galactic nuclei introduce biases in the calibrations of different SFR indicators but have only a minimal effect on the inferred SFR densities from galaxy surveys. Finally, we quantify the correlation between galaxy metallicity and extinction.