Miracle-dz/UniFAR-FTUs huggingface dataset:
CD-Net: Histopathology Representation Learning using Pyramidal Context-Detail Network

Extracting rich phenotype information, such as cell density and arrangement, from whole slide histology images (WSIs), requires analysis of large field of view, i.e more contexual information. This can be achieved through analyzing the digital slides at lower resolution. A potential drawback is missing out on details present at a higher resolution. To jointly leverage complementary information from multiple resolutions, we present a novel transformer based Pyramidal Context-Detail Network (CD-Net). CD-Net exploits the WSI pyramidal structure through co-training of proposed Context and Detail Modules, which operate on inputs from multiple resolutions. The residual connections between the modules enable the joint training paradigm while learning self-supervised representation for WSIs. The efficacy of CD-Net is demonstrated in classifying Lung Adenocarcinoma from Squamous cell carcinoma.
Mismatched Rate-Distortion Theory: Ensembles, Bounds, and General Alphabets

In this paper, we consider the mismatched rate-distortion problem, in which the encoding is done using a codebook, and the encoder chooses the minimum-distortion codeword according to a mismatched distortion function that differs from the true one. For the case of discrete memoryless sources, we establish achievable rate-distortion bounds using multi-user coding techniques, namely, superposition coding and expurgated parallel coding. We study examples where these attain the matched rate-distortion trade-off but a standard ensemble with independent codewords fails to do so. On the other hand, in contrast with the channel coding counterpart, we show that there are cases where structured random codebooks can perform worse than their unstructured counterparts. In addition, in view of the difficulties in adapting the existing and above-mentioned results to general alphabets, we consider a simpler i.i. d. random coding ensemble, and establish its achievable rate-distortion bounds for general alphabets.
Semi-Supervised Image-to-Image Translation using Latent Space Mapping

Recent image-to-image translation works have been transferred from supervised to unsupervised settings due to the expensive cost of capturing or labeling large amounts of paired data. However, current unsupervised methods using the cycle-consistency constraint may not find the desired mapping, especially for difficult translation tasks. On the other hand, a small number of paired data are usually accessible. We therefore introduce a general framework for semi-supervised image translation. Unlike previous works, our main idea is to learn the translation over the latent feature space instead of the image space. Thanks to the low dimensional feature space, it is easier to find the desired mapping function, resulting in improved quality of translation results as well as the stability of the translation model. Empirically we show that using feature translation generates better results, even using a few bits of paired data. Experimental comparisons with state-of-the-art approaches demonstrate the effectiveness of the proposed framework on a variety of challenging image-to-image translation tasks
Probability of Forming Gaps in the GD-1 Stream by Close Encounters of Globular Clusters

One of the most intriguing properties of the GD-1 stellar stream is the existence of three gaps. If these gaps were formed by close encounters with dark matter subhalos, the GD-1 stream opens an exciting window through which we can see the size, mass, and velocity distributions of the dark matter subhalos in the Milky Way. However, in order to use the GD-1 stream as a probe of the dark matter substructure, we need to disprove that these gaps are not due to the perturbations from baryonic components of the Milky Way. Here we ran a large number of test-particle simulations to investigate the probability that each of the known globular clusters (GCs) can form a GD-1-like gap, by using the kinematical data of the GD-1 stream and GCs from Gaia early data release 3 and by fully taking the observational uncertainty into account. We found that the probability that all of the three gaps were formed by GCs is as low as 1.7 × 10−5, and the expected number of gaps formed by GCs is only 0.057 in our fiducial model. Our result highly disfavors a scenario in which GCs form the gaps. Given that other baryonic perturbers (e.g., giant molecular clouds) are even less likely to form a gap in the retrograde-moving GD-1 stream, we conclude that at least one of the gaps in the GD-1 stream was formed by dark matter subhalos if the gaps were formed by flyby perturbations.
Comparing in context: Improving cosine similarity measures with a metric tensor

Cosine similarity is a widely used measure of the relatedness of pre-trained word embeddings, trained on a language modeling goal. Datasets such as WordSim-353 and SimLex-999 rate how similar words are according to human annotators, and as such are often used to evaluate the performance of language models. Thus, any improvement on the word similarity task requires an improved word representation. In this paper, we propose instead the use of an extended cosine similarity measure to improve performance on that task, with gains in interpretability. We explore the hypothesis that this approach is particularly useful if the word-similarity pairs share the same context, for which distinct contextualized similarity measures can be learned. We first use the dataset of Richie et al. (2020) to learn contextualized metrics and compare the results with the baseline values obtained using the standard cosine similarity measure, which consistently shows improvement. We also train a contextualized similarity measure for both SimLex-999 and WordSim-353, comparing the results with the corresponding baselines, and using these datasets as independent test sets for the all-context similarity measure learned on the contextualized dataset, obtaining positive results for a number of tests.
Effects of critical correlations on quantum percolation in two dimensions

We analyze the out-of-equilibrium dynamics of a quantum particle coupled to local magnetic degrees of freedom that undergo a classical phase transition. Speciﬁcally, we consider a two-dimensional tight-binding model that interacts with a background of classical spins in thermal equilibrium, which are subject to Ising interactions and act as emergent, correlated disorder for the quantum particle. Particular attention is devoted to temperatures close to the ferromagnet-to-paramagnet transition. To capture the salient features of the classical transition, namely the eﬀects of long-range correlations, we focus on the strong coupling limit, in which the model can be mapped onto a quantum percolation problem on spin clusters generated by the Ising model. By inspecting several dynamical probes such as energy level statistics, inverse participation ratios, and wave-packet dynamics, we provide evidence that the classical phase transition might induce a delocalization-localization transition in the quantum system at certain energies. We also identify further important features due to the presence of Ising correlations, such as the suppression of compact localized eigenstates.
On Metric Learning for Audio-Text Cross-Modal Retrieval

Audio-text retrieval aims at retrieving a target audio clip or caption from a pool of candidates given a query in another modality. Solving such cross-modal retrieval task is challenging because it not only requires learning robust feature representations for both modalities, but also requires capturing the fine-grained alignment between these two modalities. Existing cross-modal retrieval models are mostly optimized by metric learning objectives as both of them attempt to map data to an embedding space, where similar data are close together and dissimilar data are far apart. Unlike other cross-modal retrieval tasks such as image-text and video-text retrievals, audio-text retrieval is still an unexplored task. In this work, we aim to study the impact of different metric learning objectives on the audio-text retrieval task. We present an extensive evaluation of popular metric learning objectives on the AudioCaps and Clotho datasets. We demonstrate that NT-Xent loss adapted from self-supervised learning shows stable performance across different datasets and training settings, and outperforms the popular triplet-based losses. Our code is available at https://github.com/XinhaoMei/audio-text_retrieval.
Contrasting the landscape of contrastive and non-contrastive learning

A lot of recent advances in unsupervised feature learning are based on designing features which are invariant under semantic data augmentations. A common way to do this is contrastive learning, which uses positive and negative samples. Some recent works however have shown promising results for non-contrastive learning, which does not require negative samples. However, the non-contrastive losses have obvious"collapsed"minima, in which the encoders output a constant feature embedding, independent of the input. A folk conjecture is that so long as these collapsed solutions are avoided, the produced feature representations should be good. In our paper, we cast doubt on this story: we show through theoretical results and controlled experiments that even on simple data models, non-contrastive losses have a preponderance of non-collapsed bad minima. Moreover, we show that the training process does not avoid these minima.
Higher order moments for SPDE with monotone nonlinearities

This paper introduces a new p -dependent coercivity condition through which L p moments for solutions can be obtained for a large class of SPDEs in the variational framework. If p = 2, our condition reduces to the classically coercivity condition, which only yields second moments for the solution. The abstract result is shown to be optimal. Moreover, the results are applied to obtain L p -moments of solutions for several classical SPDEs such as stochastic heat equations with Dirichlet and Neumann boundary conditions, Burgers’ equation and the Navier- Stokes equations in two spatial dimensions. Furthermore, we can recover recent results for systems of SPDEs and higher order SPDEs using our unifying coercivity condition.
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach

Deep Learning (DL) based methods for object detection achieve remarkable performance at the cost of computationally expensive training and extensive data labeling. Robots embodiment can be exploited to mitigate this burden by acquiring automatically annotated training data via a natural interaction with a human showing the object of interest, hand-held. However, learning solely from this data may introduce biases (the so-called domain shift), and prevents adaptation to novel tasks. While Weakly-supervised Learning offers a well-established set of techniques to cope with these problems in general-purpose Computer Vision, its adoption in challenging robotic domains is still at a preliminary stage. In this work, we target the scenario of a robot trained in a teacher-learner setting to detect handheld objects. The aim is to improve detection performance in different settings by letting the robot explore the environment with a limited human labeling budget. We compare several techniques for WSL in detection pipelines to reduce model re-training costs without compromising accuracy, proposing solutions which target the considered robotic scenario. We show that the robot can improve adaptation to novel domains, either by interacting with a human teacher (Active Learning) or with an autonomous supervision (Semi-supervised Learning). We integrate our strategies into an on-line detection method, achieving efficient model update capabilities with few labels. We experimentally benchmark our method on challenging robotic object detection tasks under domain shift1.
Joint Distribution of Distance and Angles in Finite Wireless Networks

Directional beamforming will play a paramount role in 5G and beyond networks to combat the higher path losses incurred at millimeter wave bands. Appropriate modeling and analysis of the angles and distances between transmitters and receivers in these networks are thus essential to understand performance and limiting factors. Most existing literature considers either infinite and uniform networks, where nodes are drawn according to a Poisson point process, or finite networks with the reference receiver placed at the origin of a disk. Under either of these assumptions, the distance and azimuth angle between transmitter and receiver are independent, and the angle follows a uniform distribution between 0 and $2\pi$. Here, we consider a more realistic case of finite networks where the reference node is placed at any arbitrary location. We obtain the joint distribution between the distance and azimuth angle and demonstrate that these random variables do exhibit certain correlation, which depends on the shape of the region and the location of the reference node. To conduct the analysis, we present a general mathematical framework that is specialized to exemplify the case of a rectangular region. We also derive the statistics for the 3D case where, considering antenna heights, the joint distribution of distance, azimuth, and zenith angles is obtained. Finally, we describe some immediate applications of the present work, including the design of analog codebooks, wireless routing algorithms, and the analysis of directional beamforming, which is illustrated by analyzing the coverage probability of an indoor scenario considering misaligned beams.
Effect of Feed Rate, Concave Clearance and Peripheral Speed on the Performance Evaluation of Pre Thresher for Onion Umbels (Allium cepa var. aggregatum L.)

Freshly harvested, sun dried onion umbels consists of florets attached to the hard umbel heads. Conventional threshing method involves tractor treading, rubbing umbels using rubber slippers on hard surface and beating with sticks. This method resulted in seed damage and affected the germination and vigor index. High quality seeds can be produced through effective threshing and separation process. Threshing of onion umbels was carried out in a lab model thresher resulted in chocking of umbel heads in the threshing chamber and reduced the threshing efficiency of the thresher. Pre threshing is an important unit operation carried out before threshing of onion umbels. The main aim of the study was to develop a pre threshing unit to separate the florets from hard umbel heads. Hence, a pre thresher with peg tooth type threshing cylinder was designed and developed for separating the florets from onion umbels further florets are conveyed to the thresher for separation of seeds from the florets. The performance of the unit was evaluated in terms of florets threshing efficiency, floret separation efficiency, percentage floret loss and seed damage at different feed rate, concave clearance and peripheral speed. The peg tooth type pre thresher’s performance was good at 100 kg·h -1 feed rate 6 mm concave clearance and 7 m·s -1 peripheral speed.
Earth and life evolve together—a comment on Yamahira et al.

The provocative study by Yamahira et al. [1] hypothesizes that one species,Oryzias setnai (figure 1a,b), endemic to coastal areas ofwest-flowing streams of theWestern Ghats, is the sister species of all other ricefishes and that it diverged in the late Mesozoic. They conclude India is the centre of origin of ricefishes, the ancestral lineage of which subsequently diversified and dispersed to occupy its current broad range throughout Asia and Southeast Asia. This scenario is presented as the only possible conclusion from the molecular phylogenetic analysis. We challenge their scenario and conclusions based on a reanalysis of their data.
Catastrophe risk can accelerate unlikely evolutionary transitions

Intelligent life has emerged late in Earth’s habitable lifetime, and required a preceding series of key evolutionary transitions. A simple model (the Carter model) explains the late arrival of intelligent life by positing these evolutionary transitions were exceptionally unlikely ‘critical steps’. An alternative model (the neocatastrophism hypothesis) proposes that intelligent life was delayed by frequent catastrophes that served to set back evolutionary innovation. Here, we generalize the Carter model and explore this hypothesis by including catastrophes that can ‘undo’ an evolutionary transition. Introducing catastrophes or evolutionary dead ends can create situations in which critical steps occur rapidly or in clusters, suggesting that past estimates of the number of critical steps could be underestimated. If catastrophes affect complex life more than simple life, the critical steps will also exhibit a pattern of acceleration towards the present, suggesting that the increase in biological complexity over the past 500 Myr could reflect previously overlooked evolutionary transitions. Furthermore, our results have implications for understanding the different explanations (critical steps versus neo-catastrophes) for the evolution of intelligent life and the so-called Fermi paradox—the observation that intelligent life appears rare in the observable Universe.
A predictive multiscale model of in-stent restenosis in femoral arteries: linking haemodynamics and gene expression with an agent-based model of cellular dynamics

In-stent restenosis (ISR) is a maladaptive inflammatory-driven response of femoral arteries to percutaneous transluminal angioplasty and stent deployment, leading to lumen re-narrowing as consequence of excessive cellular proliferative and synthetic activities. A thorough understanding of the underlying mechanobiological factors contributing to ISR is still lacking. Computational multiscale models integrating both continuous- and agent-based approaches have been identified as promising tools to capture key aspects of the complex network of events encompassing molecular, cellular and tissue response to the intervention. In this regard, this work presents a multiscale framework integrating the effects of local haemodynamics and monocyte gene expression data on cellular dynamics to simulate ISR mechanobiological processes in a patient-specific model of stented superficial femoral artery. The framework is based on the coupling of computational fluid dynamics simulations (haemodynamics module) with an agent-based model (ABM) of cellular activities (tissue remodelling module). Sensitivity analysis and surrogate modelling combined with genetic algorithm optimization were adopted to explore the model behaviour and calibrate the ABM parameters. The proposed framework successfully described the patient lumen area reduction from baseline to one-month follow-up, demonstrating the potential capabilities of this approach in predicting the short-term arterial response to the endovascular procedure.
Theoretical analysis on the effect of seed Raman noise on the stimulated Raman scattering suppression of fiber amplifier

In this paper, the quantitative relationship between the seed Raman suppression and output of Yb-doped fiber amplifier is studied theoretically. The analytical formula showing the relationship between the Raman suppression (in dB) in the seed light and output of the fiber amplifier is derived. It is revealed that Raman suppression of the fiber amplifier increases linearly with that of the seed light. It is found that such a relationship can be well kept regardless of the pumping scheme. By the comparison of analytic results and numerical calculation results, it is revealed that there is indeed a linear relationship between two Raman suppressions with the slope agreeing well with the theoretical prediction. However, such a relationship only occurs when the Raman noise power of the seed light is greater than the microwatts level. Once it is less than the microwatts level, the output Raman suppression of the fiber amplifier will not raise with a further increase of seed Raman suppression. It is also found that the law is independent of the output signal power of the seed light as well as the pumping scheme of the fiber amplifier, which is also coincident with the theoretical result.
Eliciting P 12 Mexican Teachers’ Images Of Engineering: What Do Engineers Do?

This study collected data using a modified Draw an Engineer (DAE) test followed up by unstructured informal interviews. The 134 Mexican teachers participating in the study were given the DAE test at the beginning of an unrelated workshop. The purpose of the DAE test was to determine individual conceptions of engineers and engineering. Analysis of the teachers’ drawings and answers to question prompts indicated the emergence of three main categories: 1) Engineers in action, 2) Occurrence of gender, and 3) Engineering tools. Drawings recorded as Repairing-Building represent 22% and portrayed mainly engineers working on a construction site. Further, 83% of the drawings were recorded as Designing-Supervising-Experimenting, depicting individuals who are mainly supervising others work. Even though the female participants (107) in this program were almost four times the number of male participants, the majority (72%) of the drawings depicted a male engineer at work. The DAE test appears to be an appropriate tool to elicit perception of the engineering profession among Mexican teachers. Analysis of constructed responses indicates that most teachers held common misconceptions about engineers while very few were knowledgeable about what engineers do.
Expanding the Capabilities of Constraint-based Metabolic Models for Biotechnology Purposes

Over the past decade, the constraint-based approach to metabolic modeling has become an important tool for understanding and controlling biology. Unfortunately, the application of this novel approach to systems biology in biotechnology has been limited by three significant technical issues: existing metabolic modeling methods cannot completely model the overflow metabolism, cannot model the metabolism of microbial communities, and cannot design strains optimized for productivity and titer. Three computational methods – the Flux Balance Analysis with Membrane Economics (FBA) method, the Dynamic Multi-species Metabolic Modeling (DyMMM) framework, and the Dynamic Strain Scanning Optimization (DySScO) strategy – have been developed to resolve these issues respectively. First, the FBA method, which adopts the membrane occupancy limitation hypothesis, was used to explain and predict the phenomenon of overflow metabolism, an important metabolic phenomenon in industrial fermentation, in Escherichia coli. Then, the DyMMM framework was used to investigate the community metabolism during uranium bioremediation, and demonstrated that the simultaneous addition of acetate and Fe(III)
Empowering or addicting? An analysis of the effects of team members–supervisor relationships on job satisfaction in healthcare

Patient-centredness relies on integrated care, which requires teamwork to be effectively implemented. Positive relationships between team members and supervisors are crucial to enhance health professionals’ satisfaction with their job and, consequently, to increase their commitment to organizational excellence. However, the positive effects of team members–supervisor exchange may dilute when the team is endowed with autonomy at work. The article proposes a moderation analysis aimed at collecting evidence of the implications of team members–supervisor relationships on job satisfaction (JS) with team autonomy (TA) as a moderating variable. Results of the study suggest that positive exchanges between team members and supervisors enhance health professionals’ satisfaction with their job. However, TA acts as a negative and statistically significant moderator. Good exchanges between team members and their supervisor seem to lose their positive effects on JS when health professionals are provided with a greater self-determination to accomplish their organizational activities. Tailored management interventions should be designed to sustain teamwork when increased autonomy at work is assigned to health professionals.