Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-motion with Hearing Impairment and (Directional) Hearing Aids

When listening to a sound source in everyday situations, typical movement behavior is highly individual and may not result in the listener directly facing the sound source. Behavioral differences can affect the performance of directional algorithms in hearing aids, as was shown in previous work by using head movement trajectories of normal-hearing (NH) listeners in acoustic simulations for noise-suppression performance predictions. However, the movement behavior of hearing-impaired (HI) listeners with or without hearing aids may differ, and hearing-aid users might adapt their self-motion to improve the performance of directional algorithms. This work investigates the influence of hearing impairment on self-motion, and the interaction of hearing aids with self-motion. In order to do this, the self-motion of three HI participant groups—aided with an adaptive differential microphone (ADM), aided without ADM, and unaided—was measured and compared to previously measured self-motion data from younger and older NH participants. Self-motion was measured in virtual audiovisual environments (VEs) in the laboratory, and the signal-to-noise ratios (SNRs) and SNR improvement of the ADM resulting from the head movements of the participants were estimated using acoustic simulations. HI participants did almost all of the movement with their head and less with their eyes compared to NH participants, which led to a 0.3 dB increase in estimated SNR and to differences in estimated SNR improvement of the ADM. However, the self-motion of the HI participants aided with ADM was similar to that of other HI participants, indicating that the ADM did not cause listeners to adapt their self-motion.
Geroscience‐guided repurposing of FDA‐approved drugs to target aging: A proposed process and prioritization

Common chronic diseases represent the greatest driver of rising healthcare costs, as well as declining function, independence, and quality of life. Geroscience‐guided approaches seek to delay the onset and progression of multiple chronic conditions by targeting fundamental biological pathways of aging. This approach is more likely to improve overall health and function in old age than treating individual diseases, by addressing aging the largest and mostly ignored risk factor for the leading causes of morbidity in older adults. Nevertheless, challenges in repurposing existing and moving newly discovered interventions from the bench to clinical care have impeded the progress of this potentially transformational paradigm shift. In this article, we propose the creation of a standardized process for evaluating FDA‐approved medications for their geroscience potential. Criteria for systematically evaluating the existing literature that spans from animal models to human studies will permit the prioritization of efforts and financial investments for translating geroscience and allow immediate progress on the design of the next Targeting Aging with MEtformin (TAME)‐like study involving such candidate gerotherapeutics.
Creating New Design-Build-Test Experiences as Outputs of Undergraduate Design-Build-Test Projects

This paper describes a methodology of using individual engineering undergraduate student projects as a means of effectively and efficiently developing new Design-Build-Test (DBT) learning experiences and challenges. A key aspect of the rationale for this approach is that it benefits all parties. The student undertaking the individual project gets an authentic experience of producing a functional artefact, which has been the result of a design process that addresses conception, design, implementation and operation. The supervising faculty member benefits from live prototyping of new curriculum content and resources with a student who is at a similar level of knowledge and experience as the intended end users of the DBT outputs. The multiple students who ultimately undertake the DBT experiences / challenges benefit from the enhanced nature of a learning experience which has been “road tested” and optimised. To demonstrate the methodology the paper will describe a case study example of an individual project completed in 2015. This resulted in a DBT design challenge with a theme of designing a catapult for throwing table tennis balls, the device being made from components laser cut from medium density fibreboard (MDF). Further three different modes of operation will be described which use the same resource materials but operate over different timescales and with different learning outcomes, from an icebreaker exercise focused on developing team dynamics through to full DBT where students get an opportunity to experience the full impact of their design decisions by competing against other students with a catapult they have designed and built themselves.
Outcomes in Obese vs Non-Obese Injured Patients at a Level 1 Trauma Center and Bariatric Surgery Center of Excellence

Background We hypothesized that the outcomes of trauma patients with a body mass index (BMI) equal to or greater than 30 compared to patients with BMI less than 30 would not differ at a level 1 trauma center that is also a Metabolic and Bariatric Surgery Center of Excellence in the Metabolic and Bariatric Surgery Accreditation Quality Improvement Program (MBSAQIP). Study Design Patients equal to and greater than 18 years old treated between 1/1/2018 and 12/31/2020 were included. Demographics, BMI, comorbidities, and outcomes (hospital-LOS, ICU-LOS, blood products used, and mortality) were compared between 2 groups: obese (BMI ≥30) vs non-obese (BMI <30). Results Of the 4192 patients identified, 3821 met the inclusion criteria; 3019 patients had a BMI <30, and 802 had a BMI ≥30. There was a statistically significant difference between the 2 groups with respect to gender (females: 57% vs 47%, P < .0001) and age (median: 80 [IQR: 63-88] vs 69 [IQR: 55-81], P < .0001). When adjusted for age, sex, DM, dementia, ISS, and ICU admission, there was no statistically significant difference in hospital-LOS (4.30 [95% CI: 4.10, 4.52] vs 4.48 [95% CI: 4.18, 4.79]) or mortality. No statistical differences were seen between the 2 groups in blood product use. Conclusions Obesity did not correlate with poorer outcomes at an ACS-verified level 1 Trauma Center and Bariatric Surgery Center of Excellence. Further studies are needed to determine whether outcomes vary at hospitals without both designations.
Current methods to analyze lysosome morphology, positioning, motility and function

Since the discovery of lysosomes more than 70 years ago, much has been learned about the functions of these organelles. Lysosomes were regarded as exclusively degradative organelles, but more recent research has shown that they play essential roles in several other cellular functions, such as nutrient sensing, intracellular signalling and metabolism. Methodological advances played a key part in generating our current knowledge about the biology of this multifaceted organelle. In this review, we cover current methods used to analyze lysosome morphology, positioning, motility and function. We highlight the principles behind these methods, the methodological strategies and their advantages and limitations. To extract accurate information and avoid misinterpretations, we discuss the best strategies to identify lysosomes and assess their characteristics and functions. With this review, we aim to stimulate an increase in the quantity and quality of research on lysosomes and further ground‐breaking discoveries on an organelle that continues to surprise and excite cell biologists.
Coarse to Fine Two-Stage Approach to Robust Tensor Completion of Visual Data

Tensor completion is the problem of estimating the missing values of high-order data from partially observed entries. Data corruption due to prevailing outliers poses major challenges to traditional tensor completion algorithms, which catalyzed the development of robust algorithms that alleviate the effect of outliers. However, existing robust methods largely presume that the corruption is sparse, which may not hold in practice. In this article, we develop a two-stage robust tensor completion approach to deal with tensor completion of visual data with a large amount of gross corruption. A novel coarse-to-fine framework is proposed which uses a global coarse completion result to guide a local patch refinement process. To efficiently mitigate the effect of a large number of outliers on tensor recovery, we develop a new M-estimator-based robust tensor ring recovery method which can adaptively identify the outliers and alleviate their negative effect in the optimization. The experimental results demonstrate the superior performance of the proposed approach over state-of-the-art robust algorithms for tensor completion.
Computing hydrodynamic eigenmodes of channel flow with slip—A highly accurate algorithm

The transient start-up flow solution with slip is a useful tool to verify Computational Fluid Dynamics (CFD) simulations. However, a highly accurate, open-source black box solution does not seem to be available. Our method provides a fast, automated, and rigorously verified open-source implementation that can compute the hydrodynamic eigenmodes of a two-dimensional channel flow beyond the standard floating-point precision. This allows for a very accurate computation of the corresponding Fourier series solution. We prove that all roots are found in all special cases for the general flow problem with different slip lengths on the channel walls. The numerical results confirm analytically derived asymptotic power laws for the leading hydrodynamic eigenmode and the characteristic timescale in the limiting cases of small and large slip. The code repository including test cases is publicly available here https://git.rwth-aachen.de/fricke/start-up-flow
Learning to Aggregate Multi-Scale Context for Instance Segmentation in Remote Sensing Images.

The task of instance segmentation in remote sensing images, aiming at performing per-pixel labeling of objects at the instance level, is of great importance for various civil applications. Despite previous successes, most existing instance segmentation methods designed for natural images encounter sharp performance degradations when they are directly applied to top-view remote sensing images. Through careful analysis, we observe that the challenges mainly come from the lack of discriminative object features due to severe scale variations, low contrasts, and clustered distributions. In order to address these problems, a novel context aggregation network (CATNet) is proposed to improve the feature extraction process. The proposed model exploits three lightweight plug-and-play modules, namely, dense feature pyramid network (DenseFPN), spatial context pyramid (SCP), and hierarchical region of interest extractor (HRoIE), to aggregate global visual context at feature, spatial, and instance domains, respectively. DenseFPN is a multi-scale feature propagation module that establishes more flexible information flows by adopting interlevel residual connections, cross-level dense connections, and feature reweighting strategy. Leveraging the attention mechanism, SCP further augments the features by aggregating global spatial context into local regions. For each instance, HRoIE adaptively generates RoI features for different downstream tasks. Extensive evaluations of the proposed scheme on iSAID, DIOR, NWPU VHR-10, and HRSID datasets demonstrate that the proposed approach outperforms state-of-the-arts under similar computational costs. Source code and pretrained models are available at https://github.com/yeliudev/CATNet.
STaR: Bootstrapping Reasoning With Reasoning

Generating step-by-step"chain-of-thought"rationales improves language model performance on complex reasoning tasks like mathematics or commonsense question-answering. However, inducing language model rationale generation currently requires either constructing massive rationale datasets or sacrificing accuracy by using only few-shot inference. We propose a technique to iteratively leverage a small number of rationale examples and a large dataset without rationales, to bootstrap the ability to perform successively more complex reasoning. This technique, the"Self-Taught Reasoner"(STaR), relies on a simple loop: generate rationales to answer many questions, prompted with a few rationale examples; if the generated answers are wrong, try again to generate a rationale given the correct answer; fine-tune on all the rationales that ultimately yielded correct answers; repeat. We show that STaR significantly improves performance on multiple datasets compared to a model fine-tuned to directly predict final answers, and performs comparably to fine-tuning a 30$\times$ larger state-of-the-art language model on CommensenseQA. Thus, STaR lets a model improve itself by learning from its own generated reasoning.
Feature Selective Transformer for Semantic Image Segmentation

Recently, it has attracted more and more attentions to fuse multi-scale features for semantic image segmentation. Various works were proposed to employ progressive local or global fusion, but the feature fusions are not rich enough for modeling multi-scale context features. In this work, we focus on fusing multi-scale features from Transformer-based backbones for semantic segmentation, and propose a Feature Selective Transformer (FeSeFormer), which aggregates features from all scales (or levels) for each query feature. Specifically, we first propose a Scale-level Feature Selection (SFS) module, which can choose an informative subset from the whole multi-scale feature set for each scale, where those features that are important for the current scale (or level) are selected and the redundant are discarded. Furthermore, we propose a Full-scale Feature Fusion (FFF) module, which can adaptively fuse features of all scales for queries. Based on the proposed SFS and FFF modules, we develop a Feature Selective Transformer (FeSeFormer), and evaluate our FeSeFormer on four challenging semantic segmentation benchmarks, including PASCAL Context, ADE20K, COCO-Stuff 10K, and Cityscapes, outperforming the state-of-the-art.
Improvement on the blow-up for a weakly coupled wave equations with scale-invariant damping and mass and time derivative nonlinearity

An improvement of [18] on the blow-up region and the lifespan estimate of a weakly coupled system of wave equations with damping and mass in the scale-invariant case and with time-derivative nonlinearity is obtained in this article. Indeed, thanks to a better understanding of the dynamics of the solutions, we give here a better characterization of the blow-up region. Furthermore, the techniques used in this article may be extended to other systems and interestingly they simplify the proof of the blow-up result in [3] which is concerned with the single wave equation in the same context as in the present work.
NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows

Due to NUNet’s ability to super-resolve only regions of interest, it predicts the same target 1024 × 1024 spatial resolution 7 − 28 . 5 × faster than state-of-the-art DL methods and reduces the memory usage by 4 . 4 − 7 . 65 × , showcasing improved scalability.
Three-Dimensional stochastic Navier-Stokes equations with Markov switching

A finite-state Markov chain is introduced in the noise terms of the three-dimensional stochastic Navier-Stokes equations in order to allow for transitions between two types of multiplicative noises. We call such systems as stochastic Navier-Stokes equations with Markov switching. To solve such a system, a family of regularized stochastic systems is introduced. For each such regularized system, the existence of a unique strong solution (in the sense of stochastic analysis) is established by the method of martingale problems and pathwise uniqueness. The regularization is removed in the limit by obtaining a weakly convergent sequence from the family of regularized solutions, and identifying the limit as a solution of the three-dimensional stochastic Navier-Stokes equation with Markov switching.
On the Intrinsic and Extrinsic Fairness Evaluation Metrics for Contextualized Language Representations

Multiple metrics have been introduced to measure fairness in various natural language processing tasks. These metrics can be roughly categorized into two categories: 1) extrinsic metrics for evaluating fairness in downstream applications and 2) intrinsic metrics for estimating fairness in upstream contextualized language representation models. In this paper, we conduct an extensive correlation study between intrinsic and extrinsic metrics across bias notions using 19 contextualized language models. We find that intrinsic and extrinsic metrics do not necessarily correlate in their original setting, even when correcting for metric misalignments, noise in evaluation datasets, and confounding factors such as experiment configuration for extrinsic metrics.
Knowledge Distillation with the Reused Teacher Classifier

Knowledge distillation aims to compress a powerful yet cumbersome teacher model into a lightweight student model without much sacrifice of performance. For this purpose, various approaches have been proposed over the past few years, generally with elaborately designed knowledge rep-resentations, which in turn increase the difficulty of model development and interpretation. In contrast, we empirically show that a simple knowledge distillation technique is enough to significantly narrow down the teacher-student performance gap. We directly reuse the discriminative classifier from the pre-trained teacher model for student inference and train a student encoder through feature alignment with a single ℓ2 loss. In this way, the student model is able to achieve exactly the same performance as the teacher model provided that their extracted features are perfectly aligned. An additional projector is developed to help the student encoder match with the teacher classifier, which renders our technique applicable to various teacher and student architectures. Extensive experiments demonstrate that our technique achieves state-of-the-art results at the modest cost of compression ratio due to the added projector.
3D Uplink Channel Modeling of UAV-Based mmWave Fronthaul Links for Future Small Cell Networks

In this study, we consider an unmanned aerial vehicle (UAV)-assisted heterogeneous network that is offered as a cost effective and easy to deploy solution to solve the problem related to transferring traffic of distributed small cells to the core network. For any given distribution of small cell base stations (SBSs), we first characterize an accurate millimeter wave (mmWave) channel model for SBS to networked flying platform (NFP) by taking into consideration real parameters such as UAV's vibrations, distribution of SBSs, position of UAVs in the sky, real three-dimensional (3D) antenna pattern model provided by 3GPP along with interference caused by antenna side lobes and frequency reuse. Then, for the characterized channel, we derive an analytical closed-form expression for the end-to-end signal-to-noise plus interference ratio (SINR). Based on that, we derive the closed-form expressions for the outage probability and channel capacity of the considered UAV-based mmWave uplinks. The accuracy of the derived analytical expressions is verified by Monte Carlo simulations. Finally, we investigate the effects of different channel parameters such as antenna pattern gain, strength of UAV's vibrations, UAVs' positions in the sky, distribution of SBSs, and frequency reuse on the performance of the considered UAV-based uplink channel in terms of average capacity and outage probability.
Mugs: A Multi-Granular Self-Supervised Learning Framework

In self-supervised learning, multi-granular features are heavily desired though rarely investigated, as different downstream tasks (e.g., general and fine-grained classification) often require different or multi-granular features, e.g.~fine- or coarse-grained one or their mixture. In this work, for the first time, we propose an effective MUlti-Granular Self-supervised learning (Mugs) framework to explicitly learn multi-granular visual features. Mugs has three complementary granular supervisions: 1) an instance discrimination supervision (IDS), 2) a novel local-group discrimination supervision (LGDS), and 3) a group discrimination supervision (GDS). IDS distinguishes different instances to learn instance-level fine-grained features. LGDS aggregates features of an image and its neighbors into a local-group feature, and pulls local-group features from different crops of the same image together and push them away for others. It provides complementary instance supervision to IDS via an extra alignment on local neighbors, and scatters different local-groups separately to increase discriminability. Accordingly, it helps learn high-level fine-grained features at a local-group level. Finally, to prevent similar local-groups from being scattered randomly or far away, GDS brings similar samples close and thus pulls similar local-groups together, capturing coarse-grained features at a (semantic) group level. Consequently, Mugs can capture three granular features that often enjoy higher generality on diverse downstream tasks over single-granular features, e.g.~instance-level fine-grained features in contrastive learning. By only pretraining on ImageNet-1K, Mugs sets new SoTA linear probing accuracy 82.1$\%$ on ImageNet-1K and improves previous SoTA by $1.1\%$. It also surpasses SoTAs on other tasks, e.g. transfer learning, detection and segmentation.
From Intervention to Domain Transportation: A Novel Perspective to Optimize Recommendation

The interventional nature of recommendation has attracted increasing attention in recent years. It particularly motivates researchers to formulate learning and evaluating recommendation as causal inference and data missing-not-at-random problems. However, few take seriously the consequence of violating the critical assumption of overlapping, which we prove can significantly threaten the validity and interpretation of the outcome. We find a critical piece missing in the current understanding of information retrieval (IR) systems: as interventions, recommendation not only affects the already observed data, but it also interferes with the target domain (distribution) of interest. We then rephrase optimizing recommendation as finding an intervention that best transports the patterns it learns from the observed domain to its intervention domain. Towards this end, we use domain transportation to characterize the learning-intervention mechanism of recommendation. We design a principled transportation-constraint risk minimization objective and convert it to a two-player minimax game. We prove the consistency, generalization, and excessive risk bounds for the proposed objective, and elaborate how they compare to the current results. Finally, we carry out extensive real-data and semi-synthetic experiments to demonstrate the advantage of our approach, and launch online testing with a real-world IR system.