Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning for Instance Retrieval: A Survey

In recent years a vast amount of visual content has been generated and shared from many fields, such as social media platforms, medical imaging, and robotics. This abundance of content creation and sharing has introduced new challenges, particularly that of searching databases for similar content — Content Based Image Retrieval (CBIR) — a long-established research area in which improved efficiency and accuracy are needed for real-time retrieval. Artificial intelligence has made progress in CBIR and has significantly facilitated the process of instance search. In this survey we review recent instance retrieval works that are developed based on deep learning algorithms and techniques, with the survey organized by deep feature extraction, feature embedding and aggregation methods, and network fine-tuning strategies. Our survey considers a wide variety of recent methods, whereby we identify milestone work, reveal connections among various methods and present the commonly used benchmarks, evaluation results, common challenges, and propose promising future directions.
Video-Specific Autoencoders for Exploring, Editing and Transmitting Videos

We study video-specific autoencoders that allow a human user to explore, edit, and efficiently transmit videos. Prior work has independently looked at these problems (and sub-problems) and proposed different formulations. In this work, we train a simple autoencoder (from scratch) on multiple frames of a specific video. We observe: (1) latent codes learned by a video-specific autoencoder capture spatial and temporal properties of that video; and (2) autoencoders can project out-of-sample inputs onto the video-specific manifold. These two properties allow us to explore, edit, and efficiently transmit a video using one learned representation. For e.g., linear operations on latent codes allow users to visualize the contents of a video. Associating latent codes of a video and manifold projection enables users to make desired edits. Interpolating latent codes and manifold projection allows the transmission of sparse low-res frames over a network.
Spectral estimation of Hawkes processes from count data

This paper presents a parametric estimation method for ill-observed linear stationary Hawkes processes. When the exact locations of points are not observed, but only counts over time intervals of fixed size, methods based on the likelihood are not feasible. We show that spectral estimation based on Whittle’s method is adapted to this case and provides consistent and asymptotically normal estimators, provided a mild moment condition on the reproduction function. Simulated datasets and a case-study illustrate the performances of the estimation, notably of the reproduction function even when time intervals are relatively large.
Trade-offs between membership privacy&adversarially robust learning

Historically, machine learning methods have not been designed with security in mind. In turn, this has given rise to adversarial examples, carefully perturbed input samples aimed to mislead detection at test time, which have been applied to attack spam and malware classification, and more recently to attack image classification. Consequently, an abundance of research has been devoted to designing machine learning methods that are robust to adversarial examples. Unfortunately, there are desiderata besides robustness that a secure and safe machine learning model must satisfy, such as fairness and privacy. Recent work by Song et al. (2019) has shown, empirically, that there exists a trade-off between robust and private machine learning models. Models designed to be robust to adversarial examples often overfit on training data to a larger extent than standard (non-robust) models. If a dataset contains private information, then any statistical test that separates training and test data by observing a model's outputs can represent a privacy breach, and if a model overfits on training data, these statistical tests become easier. In this work, we identify settings where standard models will overfit to a larger extent in comparison to robust models, and as empirically observed in previous works, settings where the opposite behavior occurs. Thus, it is not necessarily the case that privacy must be sacrificed to achieve robustness. The degree of overfitting naturally depends on the amount of data available for training. We go on to characterize how the training set size factors into the privacy risks exposed by training a robust model on a simple Gaussian data task, and show empirically that our findings hold on image classification benchmark datasets, such as CIFAR-10 and CIFAR-100.
Feedback Refined Local-Global Network for Super-Resolution of Hyperspectral Imagery

Powered by advanced deep-learning technology, multi-spectral image super-resolution methods based on convolutional neural networks have recently achieved great progress. However, the single hyperspectral image super-resolution remains a challenging problem due to the high-dimensional and complex spectral characteristics of hyperspectral data, which make it difficult for general 2D convolutional neural networks to simultaneously capture spatial and spectral prior information. To deal with this issue, we propose a novel Feedback Refined Local-Global Network (FRLGN) for the super-resolution of hyperspectral image. To be specific, we develop a new Feedback Structure and a Local-Global Spectral block to alleviate the difficulty in spatial and spectral feature extraction. The Feedback Structure can transfer the high-level information to guide the generation process of low-level features, which is achieved by a recurrent structure with finite unfoldings. Furthermore, in order to effectively use the high-level information passed back, a Local-Global Spectral block is constructed to handle the feedback connections. The Local-Global Spectral block utilizes the feedback high-level information to correct the low-level feature from local spectral bands and generates powerful high-level representations among global spectral bands. By incorporating the Feedback Structure and Local-Global Spectral block, the FRLGN can fully exploit spatial-spectral correlations among spectral bands and gradually reconstruct high-resolution hyperspectral images. Experimental results indicate that FRLGN presents advantages on three public hyperspectral datasets.
SOME REFLECTIONS ON THE VALIDATION AND ANALYSIS OF HLS DATA

A Hydrostatic Levelling System (HLS) is a powerful tool that can be used effectively in the precise monitoring of vertical motion in sensitive applications. At the European Synchrotron Radiation Facility (ESRF) a high quality hydrostatic levelling system (HLS) was conceived and installed on the principal accelerator –the Storage Ring- at the inception of the facility. This system is composed of 288 devices (HLS pots and captors) installed on 96 magnet supports evenly distributed around the ring. It has a precision in the order of the micron (over the period of several hours). A second similar large scale system composed of 96 devices is installed on the Storage Ring (SR) tunnel roof. This paper will present some reflections and discussion concerning the validation and analysis of a Hydrostatic Levelling System (HLS) using long term data collected from these systems. The coherence of an HLS can be qualified in two ways; internal and external coherence. The first, internal coherence refers how the system reacts with respect to known events such as a variation in the fluid level and monitoring long term behaviour on a marble. The second, external coherence refers how well the HLS results agree with an independent control that has a wellestablished incertitude. Examples of external coherence are monitoring long term stability on a marble, and level and tilt surveys. A detailed discussion of internal and external coherence in HLS systems was made in The European Synchrotron Radiation Facility Hydrostatic Levelling System –Twelve Years Experience with a Large Scale Hydrostatic Levelling System [13]. In that paper several examples were given of both internal and external coherence. This paper will develop and extend upon the work presented there. 2. THE ESRF HLS The ESRF HLS based on a water (or liquid) equi-potential surface common to all measuring points. The ESRF instruments are composed of three parts. The captor vessel which holds the liquid, a capacitive sensor measuring the capacitance (proportional to distance) between its electrode and the water surface and a temperature sensor used to correct the dilation of water as a function of temperature between the different vessels in the system 2.1. The First Generation System – The Storage Ring HLS Note that in this discussion First Generation and SR HLS will be used interchangeably. This original system was conceived and installed to minimize the number of levelling surveys by providing a reliable real time height difference measure over a long (several month) time period, to control the vertical movements made by the Steinsvik Maskinindustrie jacks during a machine
SPATIAL DISTRIBUTION AND TEMPORAL TRENDS IN DAILY AND MONTHLY RAINFALL CONCENTRATION INDICES IN KEBIR-RHUMEL WATERSHED

In this study, the spatial and temporal patterns of rainfall concentration on daily and monthly scale were investigated in the Kebir-Rhumel watershed located in the northeast of Algeria. Based daily rainfall dataset between 1968 and 2013 of 20 weather stations, two indices were used in the study: the Precipitation Concentration Index (PCI) and the Concentration Index (CI), for measuring seasonality and daily heterogeneity of rainfall. In addition, the trends of annual rainfall, PCI and CI were tested by the Mann–Kendall method. The results show a disparity in rainfall amounts between norths sufficiently watered with a significant decrease in southbound. The values of the concentration (for daily and monthly scales) seem generally inversely proportional to the rainfall amounts. The concentration in the watershed is seasonal with a tendency towards the irregularity. The positive trends in annual precipitation, PCI and CI were found in most stations, although the trends were only very little statistically significant.
Effect of plasticizers on the properties of sugar palm nanocellulose/cinnamon essential oil reinforced starch bionanocomposite films

Abstract This work examines the effects of plasticizer type and concentration on mechanical, physical, and antibacterial characteristics of sugar palm nanocellulose/sugar palm starch (SPS)/cinnamon essential oil bionanocomposite films. In this research, the preparation of SPS films were conducted using glycerol (G), sorbitol (S), and their blend (GS) as plasticizers at ratios of 1.5, 3.0, and 4.5 wt%. The bionanocomposite films were developed by the solution casting method. Plasticizer Plasticizers were added to the SPS film-forming solutions to help overcome the fragile and brittle nature of the unplasticized SPS films. Increasing plasticizer contents resulted in an increase in film thickness and moisture contents. On the contrary, the increase in plasticizer concentrations resulted in the decrease of the densities of the plasticized films. The increase in the plasticizer content from 1.5 to 4.5% revealed less influence towards the moisture content of S-plasticised films. For glycerol and glycerol-sorbitol plasticized (G and GS) films, higher moisture content was observed compared to S-plasticised films. Various plasticizer types did not significantly modify the antibacterial activity of bionanocomposite films. The findings of this study showed significant improvement in the properties of bionanocomposite films with different types and concentrations of plasticizers and their potential for food packaging applications was enhanced. Graphical abstract Processing of bionanocomposite film with various types and concentrations of plasticizers.
Common neural functions during children’s naturalistic and controlled laboratory mathematics learning

A major goal of human neuroscience is to understand how the brain functions in the real world, and to measure neural processes under conditions that are ecologically valid. A critical step toward this goal is understanding how brain activity during naturalistic tasks that mimic the real world, relates to brain activity in more traditional laboratory tasks. In the present study, we used intersubject correlations to locate reliable stimulus-driven cerebral processes among children and adults in a naturalistic video lesson and a laboratory forced-choice task that shared the same arithmetic concept. We show that relative to a control condition with grammatical content, naturalistic and laboratory arithmetic tasks evoked overlapping activation within brain regions previously associated with math semantics. The regions of specific functional overlap between the naturalistic mathematics lesson and laboratory mathematics task included bilateral intraparietal cortex, which confirms that this region processes mathematical content independently of differences in task mode. These findings suggest that regions of the intraparietal cortex process mathematical content when children are learning about mathematics in the real world.
Regulation of chromatin accessibility by hypoxia and HIF

Reduced oxygen availability (hypoxia) can act as a signalling cue in physiological processes such as development, but also in pathological conditions such as cancer or ischaemic disease. As such, understanding how cells and organisms respond to hypoxia is of great importance. The family of transcription factors called Hypoxia Inducible Factors (HIFs) coordinate a transcriptional programme required for survival and adaptation to hypoxia. The effects of hypoxia and HIF on the chromatin accessibility landscape are still unclear. Here, using genome wide mapping of chromatin accessibility via ATAC-seq, we find hypoxia induces loci specific changes in chromatin accessibility enriched at hypoxia transcriptionally responsive genes. These changes are predominantly HIF dependent, reversible upon reoxygenation and partially mimicked by chemical HIF stabilisation independent of molecular dioxygenase inhibition. This work demonstrates that indeed, HIF stabilisation is necessary and sufficient to alter chromatin accessibility in hypoxia, with implications for our understanding of gene expression regulation by hypoxia and HIF.
Loss-of-function of OTUD7A in the schizophrenia-associated 15q13.3 deletion impairs synapse development and function in human neurons

Identifying causative gene(s) within disease-associated large genomic regions of copy number variants (CNVs) is challenging. Here, by targeted sequencing of genes within schizophrenia (SZ)-associated CNVs in 1,779 SZ cases and 1,418 controls, we identified three rare putative loss-of-function (LoF) mutations in OTU deubiquitinase 7A (OTUD7A) within the 15q13.3 deletion in cases, but none in controls. To tie OTUD7A LoF with any SZ-relevant cellular phenotypes, we modeled the OTUD7A LoF mutation, rs757148409, in human induced pluripotent stem cell (hiPSC)-derived induced excitatory neurons (iNs) by CRISPR/Cas9 engineering. The mutant iNs showed a ∼50% decrease in OTUD7A expression without undergoing nonsense-mediated mRNA decay. The mutant iNs also exhibited marked reduction of dendritic complexity, density of synaptic proteins GluA1 and PSD-95, and neuronal network activity. Congruent with the neuronal phenotypes in mutant iNs, our transcriptomic analysis showed that the set of OTUD7A LoF-downregulated genes was enriched for those relating to synapse development and function, and was associated with SZ and other neuropsychiatric disorders. These results suggest that OTUD7A LoF impairs synapse development and neuronal function in human neurons, providing mechanistic insight into the possible role of OTUD7A in driving neuropsychiatric phenotypes associated with the 15q13.3 deletion.
Understanding Advanced Level Academic Writing on Syntactic Complexity

Recent publications have highlighted the combination of large-grained and fine-grained indices to tap into syntactic complexity. Yet few studies have examined the writing produced by more advanced academic writers from a cross-linguistic perspective. This paper reports a corpus-based comparison of the native speaker (NS) and the Chinese advanced academic writers’ written abstracts. The study analyzed 120 texts within applied linguistics to discuss the extent to which the Chinese and the native writers’ writing differed in 16 measures of syntactic complexity. The article also compared across levels of English language proficiency and language background. The results revealed that the NS professional group generated more subordination than the Chinese professional writer group. Within-group comparisons yielded statistically significant differences in fine-grained syntactic complexity for the Chinese group. These results show considerable differences between the Chinese academic writers and native speakers residing in subordination. The results have implications for incorporating subordination to writing instruction for L2 writers at a more advanced level.
Machine Learning Approach for Depression Detection in Japanese

In this paper, we describe the results of our research aimed at detecting depression in Japanese sentences. Machine learning approaches to detect depression from language have been demonstrated in the United States and other countries. Howeve, in Japanese, there are only two studies that has tackled the detection of depression from natural language. In order to be able to detect depression based on linguistic features, even in documents that do not explicitly mention the topic of depression, we build a machine learning model that detects depression in Japanese by eliminating topics that suggest depression or depression. We also examine the accuracy when the parts of speech are limited and when the ratio of the number of labels is aligned, to verify the sig-niﬁcance. In the performances of our models with 5-fold cross-validation, we were able to obtain a high evaluation.
A morph-based and a word-based treebank for Beja

The paper presents the first UD treebank for Beja, a Cushitic language spoken in Sudan. It has been built from the conversion and enhancement of an Interlinear Glossed Text (IGT). The paper’s objectives are three-fold: we explain our choice to use a morph-based annotation and its consequences, we describe the processing chain from an IGT to a morph-based dependency treebank and a word-based treebank, and we present several interesting constructions in Beja
Universal Dependencies for Old Turkish

We introduce the ﬁrst treebank for Old Turkic script Old Turkish texts, consisting of 23 sentences from Orkhon corpus and transliterated texts such as poems, annotated according to the Universal Dependencies (UD) guidelines with universal part-of-speech tags and syntactic dependencies. Then, we propose a text processing pipeline for the script that makes the texts easier to encode, input and tokenize. Finally, we present our approach to tokenization and annotation from a cross-lingual perspective by inspecting linguistic constructions compared to other languages
Improving Adversarial Text Generation with n-Gram Matching

In the past few years, generative adversarial networks (GANs) have become increasingly important in natural language generation. However, their performance seems to still have a signiﬁcant margin for improvement. For this reason, in this paper we propose a new adversarial training method that tackles some of the limitations of GAN training in unconditioned generation tasks. In addition to the commonly used reward signal from the discriminator, our approach leverages another reward signal which is based on the occurrence of n -gram matches between the generated sentences and the training corpus. Thanks to the inherent correlation of this reward signal with the commonly used evaluation metrics such as BLEU, our approach implicitly bridges the gap between the objectives used during training and inference. To circumvent the non-differentiability issues associated with a discrete objective, our approach leverages the reinforcement learning policy gradient theorem. Our experimental results show that the model trained with mixed rewards from both n -gram matching and the discriminator has been able to outperform other GAN-based models in terms of BLEU score and quality-diversity trade-off at a parity of computational budget.
Asymptotics of ℓ2 Regularized Network Embeddings

A common approach to solving prediction tasks on large networks, such as node classification or link prediction, begins by learning a Euclidean embedding of the nodes of the network, from which traditional machine learning methods can then be applied. This includes methods such as DeepWalk and node2vec, which learn embeddings by optimizing stochastic losses formed over subsamples of the graph at each iteration of stochastic gradient descent. In this paper, we study the effects of adding an ℓ 2 penalty of the embedding vectors to the training loss of these types of methods. We prove that, under some exchangeability assumptions on the graph, this asymptotically leads to learning a graphon with a nuclear-norm-type penalty, and give guarantees for the asymptotic distribution of the learned embedding vectors. In particular, the exact form of the penalty depends on the choice of subsampling method used as part of stochastic gradient descent. We also illustrate empirically that concatenating node covariates to ℓ 2 regularized node2vec embeddings leads to comparable, when not superior, performance to methods which incorporate node covariates and the network structure in a non-linear manner.
From Near-synonyms to Divergent Viewpoint Foci: A Corpus-based MARVS Driven Account of Two Verbs of Attention

This study attempted to construct the semantic representation of the two near-synonyms of attention, zhu4yi4 and zhuan1xin1, based on MARVS theory. Since cross-strait semantic comparison has been an important topic in lexical semantic research, we also provided a fine-grained comparison of their eventive meanings as well as their usage variations between Mainland and Taiwan. The linguistic patterns were extracted from the one billion-word GigaWord Corpus (2.0) under the interface of the Chinese Word Sketch Engine. We found that zhu4yi4 and zhuan1xin1 differ crucially in the viewpoint focus of the verbal aspect, with zhuan1xin1 focusing on the process while zhu4yi4 focusing on the resultant endpoint. The contrasts can be represented with both event structure and role internal attributes, as supported by different distributional patterns from the corpus. Besides, by shifting viewpoint perspective, we accounted for the salient pattern of ‘zhu4yi4 + gained-end’ in the corpus, which is, comparing with the gain of potential loss, people find it more difficult to deal with the loss of current gain.