Miracle-dz/UniFAR-FTUs huggingface dataset:
Short-term and Long-term Complications of Cervical Disc Arthroplasty

Study Design: A narrative review. Objective: This review discusses the short and long-term complications associated with cervical disc arthroplasty (CDA). Summary of Background Data: CDA is a safe and effective motion-sparing alternative to fusion for the treatment of cervical disc pathology in patients with cervical radiculopathy or myelopathy. Although CDA offers advantages over fusion within a narrower set of indications, it introduces new technical challenges and potential complications. Methods: A systematic search of several large databases, including Cochrane Central, PubMed, ClinicalTrials.gov, and the World Health Organization International Clinical Trials Registry, was conducted from January 2005 to August 2023 to identify published studies and clinical trials evaluating cervical disc replacement complications and outcomes. Results: Short-term complications are primarily related to surgical approach and include dysphagia reported as high as ~70%, laryngeal nerve injury ~0%–1.25%, Horner syndrome ~0.06%, hematoma ~0.01%, gross device extrusion ~0.3%, whereas long-term complications include adjacent segment disease reported at ~3.8%, osteolysis ~44%–64%, heterotopic ossification ~7.3%–69.2%, implant failure ~3.3%–3.7%, and implant wear, which varies depending on design. Conclusions: Approaches for mitigating complications broadly include meticulous dissection, intraoperative techniques, and diligent postoperative follow-up. This review emphasizes the need for a comprehensive understanding and management of complications to enhance the safety, reproducibility, and success of CDA. As CDA continues to evolve, there remains a critical need for ongoing research to delve deeper into evaluating risk for complications and long-term patient outcomes.
Differences in Outcomes by Place of Origin among Hispanic Patients with Kidney Failure

Significance Statement Hispanic patients are known to have a higher risk of kidney failure and lower rates of home dialysis use and kidney transplantation than non-Hispanic White patients. However, it is unknown whether these outcomes differ within the Hispanic community, which is heterogeneous in its members’ places of origins. Using United States Renal Data System data, the authors found similar adjusted rates of home dialysis use for patients originating from places outside the United States and US-born Hispanic patients, whereas the adjusted risk of mortality and likelihood of transplantation differed depending on place (country or territory) of origin. Understanding the heterogeneity in kidney disease outcomes and treatment within the Hispanic community is crucial in designing interventions and implementation strategies to ensure that Hispanic individuals with kidney failure have equitable access to care. Background Compared with non-Hispanic White groups, Hispanic individuals have a higher risk of kidney failure yet lower rates of living donor transplantation and home dialysis. However, how home dialysis, mortality, and transplantation vary within the Hispanic community depending on patients' place of origin is unclear. Methods We identified adult Hispanic patients from the United States Renal Data System who initiated dialysis in 2009–2017. Primary exposure was country or territory of origin (the United States, Mexico, US–Puerto Rico, and other countries). We used logistic regression to estimate differences in odds of initiating home dialysis and competing risk models to estimate subdistribution hazard ratios (SHR) of mortality and kidney transplantation. Results Of 137,039 patients, 44.4% were US-born, 30.9% were from Mexico, 12.9% were from US–Puerto Rico, and 11.8% were from other countries. Home dialysis rates were higher among US-born patients, but not significantly different after adjusting for demographic, medical, socioeconomic, and facility-level factors. Adjusted mortality risk was higher for individuals from US–Puerto Rico (SHR, 1.04; 95% confidence interval [CI], 1.01 to 1.08) and lower for Mexico (SHR, 0.80; 95% CI, 0.78 to 0.81) and other countries (SHR, 0.83; 95% CI, 0.81 to 0.86) compared with US-born patients. The adjusted rate of transplantation for Mexican or US–Puerto Rican patients was similar to that of US-born patients but higher for those from other countries (SHR, 1.22; 95% CI, 1.15 to 1.30). Conclusions Hispanic people from different places of origin have similar adjusted rates of home dialysis but different adjusted rates of mortality and kidney transplantation. Further research is needed to understand the mechanisms underlying these observed differences in outcomes.
Quasi-isometry between two almost contact metric manifolds

In this paper we introduce the notion of quasi-isometry between two almost contact metric manifolds of same dimension. We also impose this idea to study quasi-isometry between $N(k)-$ contact metric manifolds and Sasakian manifolds. Moving further, we investigate some curvature properties of two quasi-isometrically embedded almost contact metric manifolds, $N(k)-$contact metric manifolds and Sasakian manifolds. Next, an illustrative example of a quasi-isometry between two Sasakian structures is constructed. Finally, we establish a relationship between the scalar curvature and the quasi-isometric constants for two quasi-isometric Riemannian manifolds.
Simultaneity of consciousness with physical reality: the key that unlocks the mind-matter problem

The problem of explaining the relationship between subjective experience and physical reality remains difficult and unresolved. In most explanations, consciousness is epiphenomenal, without causal power. The most notable exception is Integrated Information Theory (IIT), which provides a causal explanation for consciousness. However, IIT relies on an identity between subjectivity and a particular type of physical structure, namely with an information structure that has intrinsic causal power greater than the sum of its parts. Any theory that relies on a psycho-phyiscal identity must eventually appeal to panpsychism, which undermines that theory’s claim to be fundamental. IIT has recently pivoted towards a strong version of causal emergence, but macroscopic structures cannot be stronger causally than their microphysical parts without some new physical law or governing principle. The approach taken here is designed to uncover such a principle. The decisive argument is entirely deductive from initial premises that are phenomenologically certain. If correct, the arguments prove that conscious experience is sufficient to create additional degrees of causal freedom independently of the content of experience, and in a manner that is unpredictable and unobservable by any temporally sequential means. This provides a fundamental principle about consciousness, and a conceptual bridge between it and the physics describing what is experienced. The principle makes testable predictions about brain function, with notable differences from IIT, some of which are also empirically testable.
From local constraints to global binocular motion perception

Humans and many other predators have two eyes that are set a short distance apart so that an extensive region of the world is seen simultaneously by both eyes from slightly different points of view. Although the images of the world are essentially two-dimensional, we vividly see the world as three-dimensional. This is true for static as well as dynamic images. We discuss local constraints for the perception of three-dimensional binocular motion in a geometric-probabilistic framework. It is shown that Bayesian models of binocular 3D motion can explain perceptual bias under uncertainty and predict perceived velocity under ambiguity. The models exploit biologically plausible constraints of local motion and disparity processing in a binocular viewing geometry. Results from psychophysical experiments and an fMRI study support the idea that local constraints of motion and disparity processing are combined late in the visual processing hierarchy to establish perceived 3D motion direction. The methods and results reported here are likely to stimulate computational, psychophysical, and neuroscientific research because they address the fundamental issue of how 3D motion is represented in the human visual system.
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study

Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling. High-dimensional speech features such as spectrograms are often used as the input for the subsequent model. However, they can still be redundant. Recent investigations proposed the use of discrete speech units derived from self-supervised learning representations, which significantly compresses the size of speech data. Applying various methods, such as de-duplication and subword modeling, can further compress the speech sequence length. Hence, training time is significantly reduced while retaining notable performance. In this study, we undertake a comprehensive and systematic exploration into the application of discrete units within end-to-end speech processing models. Experiments on 12 automatic speech recognition, 3 speech translation, and 1 spoken language understanding corpora demonstrate that discrete units achieve reasonably good results in almost all the settings. We intend to release our configurations and trained models to foster future research efforts.
InfraParis: A multi-modal and multi-task autonomous driving dataset

Current deep neural networks (DNNs) for autonomous driving computer vision are typically trained on specific datasets that only involve a single type of data and urban scenes. Consequently, these models struggle to handle new objects, noise, nighttime conditions, and diverse scenarios, which is essential for safety-critical applications. Despite ongoing efforts to enhance the resilience of computer vision DNNs, progress has been sluggish, partly due to the absence of benchmarks featuring multiple modalities. We introduce a novel and versatile dataset named InfraParis that supports multiple tasks across three modalities: RGB, depth, and infrared. We assess various state-of-the-art baseline techniques, encompassing models for the tasks of semantic segmentation, object detection, and depth estimation.
3D Reconstruction with Generalizable Neural Fields using Scene Priors

High-fidelity 3D scene reconstruction has been substantially advanced by recent progress in neural fields. However, most existing methods train a separate network from scratch for each individual scene. This is not scalable, inefficient, and unable to yield good results given limited views. While learning-based multi-view stereo methods alleviate this issue to some extent, their multi-view setting makes it less flexible to scale up and to broad applications. Instead, we introduce training generalizable Neural Fields incorporating scene Priors (NFPs). The NFP network maps any single-view RGB-D image into signed distance and radiance values. A complete scene can be reconstructed by merging individual frames in the volumetric space WITHOUT a fusion module, which provides better flexibility. The scene priors can be trained on large-scale datasets, allowing for fast adaptation to the reconstruction of a new scene with fewer views. NFP not only demonstrates SOTA scene reconstruction performance and efficiency, but it also supports single-image novel-view synthesis, which is underexplored in neural fields. More qualitative results are available at: https://oasisyang.github. io/neural-prior
SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets

We propose SeMAnD, a Self-supervised Anomaly Detection technique to detect geometric anomalies in Multimodal geospatial datasets. SeMAnD consists of (i) a simple data augmentation strategy, called RandPolyAugment, capable of generating diverse augmentations of vector geometries, and (ii) a self-supervised training objective with three components that incentivize learning representations of multimodal data that are discriminative to local changes in one modality which are not corroborated by the other modalities. Our empirical study on test sets of different types of real-world geometric geospatial anomalies across 3 diverse geographical regions demonstrates that SeMAnD is able to detect real-world defects and outperforms domain-agnostic anomaly detection strategies by 4.8--19.7% as measured using anomaly classification AUC. We also show that model performance increases (i) up to 20.4% as the number of input modalities increase and (ii) up to 22.9% as the diversity and strength of training data augmentations increase.
Cross-Modal Multi-Tasking for Speech-to-Text Translation via Hard Parameter Sharing

Recent works in end-to-end speech-to-text translation (ST) have proposed multi-tasking methods with soft parameter sharing which leverage machine translation (MT) data via secondary encoders that map text inputs to an eventual cross-modal representation. In this work, we instead propose a ST/MT multi-tasking framework with hard parameter sharing in which all model parameters are shared cross-modally. Our method reduces the speech-text modality gap via a pre-processing stage which converts speech and text inputs into two discrete token sequences of similar length -- this allows models to indiscriminately process both modalities simply using a joint vocabulary. With experiments on MuST-C, we demonstrate that our multi-tasking framework improves attentional encoder-decoder, Connectionist Temporal Classification (CTC), transducer, and joint CTC/attention models by an average of +0.5 BLEU without any external MT data. Further, we show that this framework incorporates external MT data, yielding +0.8 BLEU, and also improves transfer learning from pre-trained textual models, yielding +1.8 BLEU.
MR Object Identification and Interaction

The increasing number of objects in ubiquitous computing environments creates a need for effective object detection and identification mechanisms that permit users to intuitively initiate interactions with these objects. While multiple approaches to such object detection -- including through visual object detection, fiducial markers, relative localization, or absolute spatial referencing -- are available, each of these suffers from drawbacks that limit their applicability. In this paper, we propose ODIF, an architecture that permits the fusion of object situation information from such heterogeneous sources and that remains vertically and horizontally modular to allow extending and upgrading systems that are constructed accordingly. We furthermore present BLEARVIS, a prototype system that builds on the proposed architecture and integrates computer-vision (CV) based object detection with radio-frequency (RF) angle of arrival (AoA) estimation to identify BLE-tagged objects. In our system, the front camera of a Mixed Reality (MR) head-mounted display (HMD) provides a live image stream to a vision-based object detection module, while an antenna array that is mounted on the HMD collects AoA information from ambient devices. In this way, BLEARVIS is able to differentiate between visually identical objects in the same environment and can provide an MR overlay of information (data and controls) that relates to them. We include experimental evaluations of both, the CV-based object detection and the RF-based AoA estimation, and discuss the applicability of the combined RF and CV pipelines in different ubiquitous computing scenarios. This research can form a starting point to spawn the integration of diverse object detection, identification, and interaction approaches that function across the electromagnetic spectrum, and beyond.
Kinematics of the India-Eurasia collision zone from GPS measurements

We use geodetic techniques to study the India-Eurasia collision zone. Six years of GPS data constrain maximum surface contraction rates across the Nepal Himalaya to 18 ± 2 mm/yr at 12°N ±13° (1σ). These surface rates across the 150-km-wide deforming zone are well fitted with a dislocation model of a buried north dipping detachment fault striking 105°, which aseismically slips at a rate of 20 ± 1 mm/yr, our preferred estimate for the India-to-southern-Tibet convergence rate. This is in good agreement with various geologic predictions of 18 ± 7 mm/yr for the Himalaya. A better fit can be achieved with a two-fault model, where the western and eastern faults strike 112° and 101°, respectively, in approximate parallelism with the Himalayan arc and a seismicity lineament. We find eastward directed extension of 11 ± 3 mm/yr between northwestern Nepal Lhasa, also in good agreement with geologic and seismic studies across the southern Tibetan plateau. Continuous GPS sites are used to further constrain the style and rates of deformation throughout the collision zone. Sites in India, Uzbekistan, and Russia agree within error with plate model prediction.
Analyses of Amboy Crater, Mojave Desert, California, as an Analog for Small Martian Volcanoes

MARTIAN VOLCANOES. Jeffrey M. Byrnes, David C. Finnegan, Steven W. Anderson, and Michael S. Ramsey, U.S. Geological Survey, Astrogeology Team, 2255 North Gemini Drive, Flagstaff, AZ 86001-1637 (jmbyrnes@usgs.gov), Cold Regions Research and Engineering Lab (CRREL), 72 Lyme Road, Hanover, NH 03755-1290, Planetary Science Institute, 1700 East Fort Lowell Road, Suite 106, Tucson, AZ 85719-2395, Department of Geology and Planetary Science, University of Pittsburgh, Pittsburgh, PA 15260.
Lidar determination of mixing layer height with high resolution

Ecological monitoring and analysis of the planetary boundary layer (PBL) dynamics require determination of the mixing layer height (MLH) on a continuous basis. In a number of cases it is necessary to determine the MLH with sufficiently high resolution - both altitude and temporal. The backscatter lidar provides a convenient tool for such determination, using the aerosol as tracer and determining its vertical profile and its time-evolution, with the capability for continuous measurements. Although methods already exist, based on the altitude derivative of the backscatter lidar signal (altitude Gradient method) and its time-variance (Variance method), the application of these methods with high resolution is limited by the background noise presence. We report here a further development of backscatter lidar gradient and variance methods for MLH determination, allowing higher resolutions. In it, the MLH determination from the gradient and the variance of the lidar signal is supported by a convenient filter technique. Time scale of increased temporal resolution allows the investigation of the fine atmospheric dynamic structures like convective motion. A number of examples in MLH retrieval are presented. The examples are based on backscatter lidar measurements performed in the PBL above Neuchatel, Switzerland (47.00°N, 6.95°S, 485m asl). The examples show the applicability and the usefulness of the reported technique in measurements of the daily cycle of the MLH dynamics.
Contrastive Continual Multi-view Clustering with Filtered Structural Fusion

Multi-view clustering thrives in applications where views are collected in advance by extracting consistent and complementary information among views. However, it overlooks scenarios where data views are collected sequentially, i.e., real-time data. Due to privacy issues or memory burden, previous views are not available with time in these situations. Some methods are proposed to handle it but are trapped in a stability-plasticity dilemma. In specific, these methods undergo a catastrophic forgetting of prior knowledge when a new view is attained. Such a catastrophic forgetting problem (CFP) would cause the consistent and complementary information hard to get and affect the clustering performance. To tackle this, we propose a novel method termed Contrastive Continual Multi-view Clustering with Filtered Structural Fusion (CCMVC-FSF). Precisely, considering that data correlations play a vital role in clustering and prior knowledge ought to guide the clustering process of a new view, we develop a data buffer with fixed size to store filtered structural information and utilize it to guide the generation of a robust partition matrix via contrastive learning. Furthermore, we theoretically connect CCMVC-FSF with semi-supervised learning and knowledge distillation. Extensive experiments exhibit the excellence of the proposed method.
Effects of Guiding Ground Rules and Individual Differences on the Accuracy of Children’s Free Recall and Suggestibility

ABSTRACT This research examines how the presence or absence of ground rules and children’s temperamental tendencies affect children’s free recall accuracy and suggestibility. Participating children showed richer free recall with open-ended questions and displayed greater resistance to suggestive questions when provided ground rules during the interview. In addition, children’s recall accuracy varied based on their prosocial orientation: the presence or absence of ground rules influenced memory accuracy more in children with a low prosocial orientation than in those with a high prosocial orientation. These results demonstrate the importance of ground rules for obtaining reliable statements from children during investigative interviews. Findings further suggest that children can provide more detailed information when temperamental characteristics (e.g. prosocial tendencies) are considered.
Retrospective Outcome Analysis of Allogenic Bone Graft

Abstract Mohit Dhingra Bone bank has become an essential requirement for centers that perform tumor and reconstructive surgeries. It provides allogenic bone procured from cadavers as well as live donors in the form of surgical residues. Thus, we conducted a retrospective observational study on recipients who underwent various reconstructive procedures using fresh frozen allograft obtsined from the live donors through surgical residues in a newly established bone bank. The outcomes of cases operated where allogenic bone grafts were used between January 2018 and November 2020 were analyzed in terms of infection and time taken for the grafts to incorporate, allowing weight-bearing in the lower limbs. A total of 223 grafts were obtained as surgical residues from replacement surgeries and traumatic amputations performed on non-salvageable limbs. Out of these, 70 grafts were transplanted into eligible recipients, who were followed up for at least one year. Among the 70 recipients, 15 were lost to follow-up. The outcome data of the remaining 55 recipients was tabulated, including infections (early, delayed and late) and the achievement of weight-bearing milestones when transplanted in lower limbs. Out of the 55 cases, allografts were used alone in 20 cases, while in 35 cases, they were augmented by implants or cement. Two cases (3.6%) experienced acute infections, and another two cases (3.6%) had chronic infection. The mean time for weight-bearing was found to be 7 months when used alone and 3.5 months when augmented with cement or an implant. Additionally, five patients did not show complete integration of the graft. The results of using allogenic bone graft are quite encouraging, suggesting their potential as biological adjuvants in reconstructive surgeries.