Miracle-dz/UniFAR-FTUs huggingface dataset:
CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation

Many recent approaches in contrastive learning have worked to close the gap between pretraining on iconic images like ImageNet and pretraining on complex scenes like COCO. This gap exists largely because commonly used random crop augmentations obtain semantically inconsistent content in crowded scene images of diverse objects. Previous works use preprocessing pipelines to localize salient objects for improved cropping, but an end-to-end solution is still elusive. In this work, we propose a framework which accomplishes this goal via joint learning of representations and segmentation. We leverage segmentation masks to train a model with a mask-dependent contrastive loss, and use the partially trained model to bootstrap better masks. By iterating between these two components, we ground the contrastive updates in segmentation information, and simultaneously improve segmentation throughout pretraining. Experiments show our representations transfer robustly to downstream tasks in classification, detection and segmentation.
Bag of Instances Aggregation Boosts Self-supervised Distillation

Recent advances in self-supervised learning have experienced remarkable progress, especially for contrastive learning based methods, which regard each image as well as its augmentations as an individual class and try to distinguish them from all other images. However, due to the large quantity of exemplars, this kind of pretext task intrinsically suffers from slow convergence and is hard for optimization. This is especially true for small-scale models, in which we find the performance drops dramatically comparing with its supervised counterpart. In this paper, we propose a simple but effective distillation strategy for unsupervised learning. The highlight is that the relationship among similar samples counts and can be seamlessly transferred to the student to boost the performance. Our method, termed as BINGO, which is short for Bag of InstaNces aGgregatiOn, targets at transferring the relationship learned by the teacher to the student. Here bag of instances indicates a set of similar samples constructed by the teacher and are grouped within a bag, and the goal of distillation is to aggregate compact representations over the student with respect to instances in a bag. Notably, BINGO achieves new state-of-the-art performance on small-scale models, i.e., 65.5% and 68.9% top-1 accuracies with linear evaluation on ImageNet, using ResNet-18 and ResNet-34 as the backbones respectively, surpassing baselines (52.5% and 57.4% top-1 accuracies) by a significant margin. The code is available at https://github.com/haohang96/bingo.
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization

We introduce PRIMERA, a pre-trained model for multi-document representation with a focus on summarization that reduces the need for dataset-specific architectures and large amounts of fine-tuning labeled data. PRIMERA uses our newly proposed pre-training objective designed to teach the model to connect and aggregate information across documents. It also uses efficient encoder-decoder transformers to simplify the processing of concatenated input documents. With extensive experiments on 6 multi-document summarization datasets from 3 different domains on zero-shot, few-shot and full-supervised settings, PRIMERA outperforms current state-of-the-art dataset-specific and pre-trained models on most of these settings with large margins.
Edinburgh Research Explorer Understanding Privacy Switching Behaviour on Twitter

Changing a Twitter account’s privacy setting between public and protected changes the visibility of past tweets. By inspecting the privacy setting of more than 100K Twitter users over 3 months, we noticed that over 40% of those users changed their privacy setting at least once with around 16% changing it over 5 times. This observation motivated us to explore the reasons why people switch their privacy settings. We studied these switching phenomena quantitatively by comparing the tweeting behaviour of users when public vs protected, and qualitatively using two follow-up surveys (n=100, n=324) to understand potential reasoning behind the observed behaviours. Our quantitative analysis shows that users who switch privacy settings mention others and share hashtags more when their setting is public. Our surveys highlighted that users turn protected to share personal content and regulate boundaries while they turn public to interact with others in ways the protected setting prevents.
Few-Shot Learning on Graphs: A Survey

Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. However, prevailing supervised graph representation learning models for specific tasks often suffer from label sparsity issue as data labeling is always time and resource consuming. In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge. There have been many studies working on FSLG recently. In this paper, we comprehensively survey these work in the form of a series of methods and applications. Specifically, we first introduce FSLG challenges and bases, then categorize and summarize existing work of FSLG in terms of three major graph mining tasks at different granularity levels, i.e., node, edge, and graph. Finally, we share our thoughts on some future research directions of FSLG. The authors of this survey have contributed significantly to the AI literature on FSLG over the last few years.
Modeling Dual Read/Write Paths for Simultaneous Machine Translation

Simultaneous machine translation (SiMT) outputs translation while reading source sentence and hence requires a policy to decide whether to wait for the next source word (READ) or generate a target word (WRITE), the actions of which form a read/write path. Although the read/write path is essential to SiMT performance, no direct supervision is given to the path in the existing methods. In this paper, we propose a method of dual-path SiMT which introduces duality constraints to direct the read/write path. According to duality constraints, the read/write path in source-to-target and target-to-source SiMT models can be mapped to each other. As a result, the two SiMT models can be optimized jointly by forcing their read/write paths to satisfy the mapping. Experiments on En-Vi and De-En tasks show that our method can outperform strong baselines under all latency.
Optimal schemes for combinatorial query problems with integer feedback

A query game is a pair of a set $Q$ of queries and a set $\mathcal{F}$ of functions, or codewords $f:Q\rightarrow \mathbb{Z}. $ We think of this as a two-player game. One player, Codemaker, picks a hidden codeword $f\in \mathcal{F}$. The other player, Codebreaker, then tries to determine $f$ by asking a sequence of queries $q\in Q$, after each of which Codemaker must respond with the value $f(q)$. The goal of Codebreaker is to uniquely determine $f$ using as few queries as possible. Two classical examples of such games are coin-weighing with a spring scale, and Mastermind, which are of interest both as recreational games and for their connection to information theory. In this paper, we will present a general framework for finding short solutions to query games. As applications, we give new self-contained proofs of the query complexity of variations of the coin-weighing problems, and prove new results that the deterministic query complexity of Mastermind with $n$ positions and $k$ colors is $\Theta(n \log k/ \log n + k)$ if only black-peg information is provided, and $\Theta(n \log k / \log n + k/n)$ if both black- and white-peg information is provided. In the deterministic setting, these are the first up to constant factor optimal solutions to Mastermind known for any $k\geq n^{1-o(1)}$.
PanoFormer: Panorama Transformer for Indoor 360° Depth Estimation

Existing panoramic depth estimation methods based on convolutional neural networks (CNNs) focus on removing panoramic distortions, failing to perceive panoramic structures efficiently due to the fixed receptive field in CNNs. This paper proposes the panorama transformer (named PanoFormer) to estimate the depth in panorama images, with tangent patches from spherical domain, learnable token flows, and panorama specific metrics. In particular, we divide patches on the spherical tangent domain into tokens to reduce the negative effect of panoramic distortions. Since the geometric structures are essential for depth estimation, a self-attention module is redesigned with an additional learnable token flow. In addition, considering the characteristic of the spherical domain, we present two panorama-specific metrics to comprehensively evaluate the panoramic depth estimation models' performance. Extensive experiments demonstrate that our approach significantly outperforms the state-of-the-art (SOTA) methods. Furthermore, the proposed method can be effectively extended to solve semantic panorama segmentation, a similar pixel2pixel task. Code will be available.
Counterdiabatic driving for periodically driven open quantum systems

We discuss dynamics of periodically driven open quantum systems. The time evolution of the quantum state is described by the quantum master equation, and the form of the dissipator is chosen so that the instantaneous stationary state is given by the Gibbs distribution. We find that the correlation between the population part and the coherence part of the density operator is induced by an adiabatic gauge potential. Although the introduction of the counterdiabatic term eliminates the correlation, additional correlations prevent convergence to the Gibbs distribution. We study the performance of the control by the counterdiabatic term. The system has three different scales and the performance strongly depends on the relations between their magnitudes. This article is part of the theme issue ‘Shortcuts to adiabaticity: theoretical, experimental and interdisciplinary perspectives’.
SoK: Differential Privacy on Graph-Structured Data

In this work, we study the applications of differential privacy (DP) in the context of graph-structured data. We discuss the formulations of DP applicable to the publication of graphs and their associated statistics as well as machine learning on graph-based data, including graph neural networks (GNNs). The formulation of DP in the context of graph-structured data is difficult, as individual data points are interconnected (often non-linearly or sparsely). This connectivity complicates the computation of individual privacy loss in differentially private learning. The problem is exacerbated by an absence of a single, well-established formulation of DP in graph settings. This issue extends to the domain of GNNs, rendering private machine learning on graph-structured data a challenging task. A lack of prior systematisation work motivated us to study graph-based learning from a privacy perspective. In this work, we systematise different formulations of DP on graphs, discuss challenges and promising applications, including the GNN domain. We compare and separate works into graph analysis tasks and graph learning tasks with GNNs. Finally, we conclude our work with a discussion of open questions and potential directions for further research in this area.
Towards Understanding Asynchronous Advantage Actor-Critic: Convergence and Linear Speedup

Asynchronous and parallel implementation of standard reinforcement learning (RL) algorithms is a key enabler of the tremendous success of modern RL. Among many asynchronous RL algorithms, arguably the most popular and effective one is the asynchronous advantage actor-critic (A3C) algorithm. Although A3C is becoming the workhorse of RL, its theoretical properties are still not well-understood, including its non-asymptotic analysis and the performance gain of parallelism (a.k.a. linear speedup). This paper revisits the A3C algorithm and establishes its non-asymptotic convergence guarantees. Under both i.i. d. and Markovian sampling, we establish the local convergence guarantee for A3C in the general policy approximation case and the global convergence guarantee in softmax policy parameterization. Under i.i. d. sampling, A3C obtains sample complexity of <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(\epsilon ^{-2.5}/N)$</tex-math></inline-formula> per worker to achieve <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> accuracy, where <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula> is the number of workers. Compared to the best-known sample complexity of <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(\epsilon ^{-2.5})$</tex-math></inline-formula> for two-timescale AC, A3C achieves <italic>linear speedup</italic>, which justifies the advantage of parallelism and asynchrony in AC algorithms theoretically for the first time. Numerical tests on synthetic environment, OpenAI Gym environments and Atari games have been provided to verify our theoretical analysis.
Jordanian University Students' Lived Experience of Misusing Amphetamine (Captagon)

Abstract Background Substance misuse is a growing problem among Jordanian university students. Purpose The aim of this study was to explore the lived experiences of university students who misuse Captagon (amphetamines). Methods The interpretative phenomenological analysis methodology was used. In-depth face-to-face interviews were conducted with 10 Jordanian university students, aged 17–22 years, who were using Captagon (amphetamines) for the last 6 months. Results Three major themes detailed participants' experiences with Captagon: (a) causes for use, (b) effects of taking amphetamines, and (c) seeking help behaviors and support. Participants who experienced academic and personal stress sought help from friends, who provided them with Captagon pills as a way to overcome their life challenges. Initially, taking Captagon provided participants with a sense of control, but it did not solve their problems. Later or as the days passed by, they experienced increased level of stress, felt disorganized in a way that they missed classes, and were being socially isolated. Participants finally sought community help for their problem, but this was difficult because of stigmatizing attitudes in their community toward substance misuse. Conclusions Increasing university students' knowledge about the negative consequences of substance misuse and raising awareness of strategies to address the problem will help young people to make more informed choices, because today's young generation are tomorrow's citizens.
Synchronized Swept-Sine: Theory, Application, and Implementation

Exponential, or sometimes called logarithmic, swept-sine signal is very often used to analyze nonlinear audio systems. In this paper, the theory of exponential swept-sine measurements of nonlinear systems is reexamined. The synchronization procedure necessary for a proper analysis of higher harmonics is detailed leading to an improvement of the formula for the exponential swept-sine signal generation. Moreover, an analytical expression of spectra of the swept-sine signal is derived and used in the deconvolution of the impulse response. A Matlab code for generation of the synchronized swept-sine, deconvolution, and separation of the impulse responses is given with discussion of some application issues and an illustrative example of harmonic analysis of current distortion of a woofer is provided. pp.
Media Smart Be Adwise 2: an evaluation

Be Adwise 2 was the second set of teaching materials produced as part of the UK Media Smart initiative. The materials were launched in late 2004, and were sent on request free of charge to over 7000 UK primary schools. Developed with leading UK media literacy experts, the materials were designed for use with pupils at Key Stage 2 (aged 7-11). They aimed to support a range of curriculum areas, focusing particularly on Literacy/English and Citizenship. The Centre for the Study of Children, Youth and Media was engaged by Media Smart in mid-2006 to undertake an independent, objective evaluation of its Be Adwise 2 materials. The evaluation sought to address the following questions: • How and where were teachers using the materials? • How well did they connect with the existing curriculum? • Were the materials easy to use with different groups of children? • How did the materials relate to teachers’ own views on advertising and media literacy? • Did the materials help children to understand advertising better?
EXPLICIT CHOICES AND EMERGENT WORK PRACTICES IN GLOBAL TEAMWORK

The technical advances in computer hardware, software, and infrastructure have increasingly optimized conditions for the application of Internet-based tools to support collaborative teamwork in the past decade. This paper presents the valuable findings that resulted from of a number of studies in industry and education environments that highlight the process, critical explicit choices, and emergent work practices in global teamwork. Key questions are (1) What people experience when interacting with the technologies? (2) Why people practice in the way they do? (3) How the practice fits into the environment and changes the work patterns. Observations indicate that (1) explicit choices of core team values, team’s mode of operation, and collaboration technologies affect teamwork patterns and lead to emergent behaviors and work practices, (2) high performance teams that use the collaboration technologies effectively exhibit collaboration readiness at an early stage and manage to define a “third way” to meet the demands of the cross-disciplinary, multi cultural and geographically distributed AEC teamwork. The observations represent the blueprint for innovations and improvements in both the design and development of collaboration technologies and work processes.
RHD exon 5, 7 and 10 targeted non-invasive prenatal screening of fetal Rhesus-D (RhD) in selected RhD negative pregnant women in Ethiopia

Background A majority of non-invasive prenatal screening studies determining fetal RhD status have been tested on Caucasian and Asian populations, but limited or no studies have been conducted on the Ethiopian population. In the current study, we carried non-invasive prenatal screening of fetal RHD genotype in selected RhD negative Ethiopian pregnant women. Methods Cell-free DNA was extracted from the plasma samples of 117 RhD pregnant women between 9 and 38 weeks of gestation. Fetal RHD genotypes were detected by targeting exons 5, 7 and 10 of the RHD gene by using real-time PCR assay. RHD genotypic results were confirmed by neonatal cord blood serology. Results Fetal RHD genotyping was conclusive in all 117 subjects. RHD genotype was correctly predicted in 115 of 117 cases, thus the test yielded 98.3% accuracy (95%CI: 97.3–99.1%). Among 115 cases, 105 were genotyped as RHD positive and 12 were genotyped as RHD negative. The sensitivity and specificity of the test were 99.1% (95% CI: 94.8–99.9%) and 91.7% (95%CI: 61.5–99.7%) respectively. The negative and positive predictive values were 99.9% (95%CI: 99.2–99.9%) and 54.0% (95% CI: 15.2–88.4%) respectively. SRY genotyping results were in complete concordance with fetal sex. Conclusion Multi exon targeted non-invasive prenatal screening test for fetal RhD determination exhibited high accuracy and sensitivity. A confirmatory study with a bigger size of study subjects is warranted before enabling clinical implementation.
Long-read sequencing reveals the evolutionary drivers of intra-host diversity across natural RNA mycovirus infections

Abstract Intra-host dynamics are a core component of virus evolution but most intra-host data come from a narrow range of hosts or experimental infections. Gaining broader information on the intra-host diversity and dynamics of naturally occurring virus infections is essential to our understanding of evolution across the virosphere. Here we used PacBio long-read HiFi sequencing to characterize the intra-host populations of natural infections of the RNA mycovirus Cryphonectria hypovirus 1 (CHV1). CHV1 is a biocontrol agent for the chestnut blight fungus (Cryphonectria parasitica), which co-invaded Europe alongside the fungus. We characterized the mutational and haplotypic intra-host virus diversity of thirty-eight natural CHV1 infections spread across four locations in Croatia and Switzerland. Intra-host CHV1 diversity values were shaped by purifying selection and accumulation of mutations over time as well as epistatic interactions within the host genome at defense loci. Geographical landscape features impacted CHV1 inter-host relationships through restricting dispersal and causing founder effects. Interestingly, a small number of intra-host viral haplotypes showed high sequence similarity across large geographical distances unlikely to be linked by dispersal.
Two attentive strategies reducing subjective distortions in serial duration perception

Humans tend to perceptually distort (dilate/shrink) the duration of brief stimuli presented in a sequence when discriminating the duration of a second stimulus (Comparison) from the duration of a first stimulus (Standard). This type of distortion, termed “Time order error” (TOE), is an important window into the determinants of subjective perception. We hypothesized that stimulus durations would be optimally processed, suppressing subjective distortions in serial perception, if the events to be compared fell within the boundaries of rhythmic attentive sampling (4–8 Hz, theta band). We used a two-interval forced choice (2IFC) experimental design, and in three separate experiments tested different Standard durations: 120-ms, corresponding to an 8.33 Hz rhythmic attentive window; 160 ms, corresponding to a 6.25 Hz window; and 200 ms, for a 5 Hz window. We found that TOE, as measured by the Constant Error metric, is sizeable for a 120-ms Standard, is reduced for a 160-ms Standard, and statistically disappears for 200-ms Standard events, confirming our hypothesis. For 120- and 160-ms Standard events, to reduce TOEs it was necessary to increase the interval between the Standard and the Comparison event from sub-second (400, 800 ms) to supra-second (1600, 2000 ms) lags, suggesting that the orienting of attention in time waiting for the Comparison event to onset may work as a back-up strategy to optimize its encoding. Our results highlight the flexible use of two different attentive strategies to optimize subjective time perception.
Comparative metabolomic analysis of polyphenic horn development in the dung beetle Onthophagus taurus

Organisms alter their phenotypes in response to changing environmental conditions. The developmental basis of this phenomenon, phenotypic plasticity, is a topic of broad interest in many fields of biology. While insects provide a suitable model for studying the genetic basis of phenotypic plasticity, the physiological aspects of plasticity are not fully understood. Here, we report the physiological basis of polyphenism, an extreme form of phenotypic plasticity by utilizing a dung beetle species, Onthophagus taurus. We highlighted the metabolome between sexes as well as two distinct male morphs—large and small horns. Unlike results from previous transcriptomic studies, the comparative metabolomic study revealed that differences in metabolite level were more prominent between animals with different body sizes than different sexes. Our results also indicate that specific metabolites and biochemical pathways may be active during horn size determination.