Miracle-dz/UniFAR-FTUs huggingface dataset:
Norwegian Community Pharmacists’ Experiences with COVID-19 Vaccination—A Qualitative Interview Study

Background: Immunising the population became important during the COVID-19 pandemic. Community pharmacies in Norway collaborated with municipalities to offer a vaccination services to increase the vaccination rate. Only some pharmacies were allowed to offer this service in the pandemic’s early phase. This study learns about pharmacists’ experiences during this first period of COVID-19 vaccination services in community pharmacies, which is relevant for informing policy and organisational decision makers about the feasibility and acceptability of pharmacy vaccination. Methods: Individual interviews were conducted with 13 pharmacists in community pharmacies offering a COVID-19 vaccination service. Informants were recruited from the eleven pharmacies that first offered COVID-19 vaccinations. The key themes in the interview were COVID-19 vaccination, what the pharmacists think about the vaccination service, and how it is performed. The data were analysed using systematic text condensation. Results: Three main themes and eight subthemes were identified. The main themes were creative solutions, organising and making resources available, and professionally satisfying and an important mission. The interviewed pharmacists experienced the COVID-19 vaccination service as hectic but something important that they would prioritise. They experienced their efforts to be substantial in the pandemic’s early phase. Conclusions: Pharmacists in community pharmacies were a resource for increasing the vaccination rate during the COVID-19 pandemic. The pharmacies’ easy accessibility and the pharmacists’ ability to adjust their daily workflow for a new service should be considered when an expanded healthcare service is needed.
Greater socioenvironmental risk factors and higher chronic pain stage are associated with thinner bilateral temporal lobes

Introduction: Previous research indicates ethnic/race group differences in pain and neurodegenerative diseases. Accounting for socioenvironmental factors reduces ethnic/race group differences in clinical and experimental pain. In the current study sample, we previously reported that in individuals with knee pain, ethnic/race group differences were observed in bilateral temporal lobe thickness, areas of the brain associated with risk for Alzheimer's disease, and related dementias. The purpose of the study was to determine if socioenvironmental factors reduce or account for previously observed ethnic/race group differences and explore if a combined effect of socioenvironmental risk and chronic pain severity on temporal lobe cortices is evident.
Preliminary report on a novel technique for endoscopic transaxillary thyroidectomy: a case–control study

Background: Endoscopic transaxillary approaches to thyroidectomy have been well described and gasless transaxillary endoscopic thyroidectomy (GTET) is the most popular method. However, this require a single long axillary incision which is longer than most remote access thyroidectomy procedures. The authors improved the GTET and provided a novel way to access the thyroid. The purpose of this study was to test the feasibility of our novel transaxillary thyroidectomy procedure and to attempt to reduce the size of the scar and reduce the flap creation area. Methods: One hundred sixteen patients who underwent our novel transaxillary thyroidectomy procedure were compared with the patients who underwent open and GTET procedures. The patients’ demographics, outcomes, and complications were analyzed. Results: Although the operation time (121.48±23.91 mins) was longer in the novel endoscopic group compare to the open group, it was shorter than GTET group. Intraoperative blood loss was similar between the groups. However, the novel procedure group had more drainage volume within 48 postoperative hours compare to other two groups. Despite the VAS pain score did not reveal a difference between the open and novel endoscopic procedure, it was lower in the novel procedure than GTET. The hospital stay days did not show a difference between the two groups. The number of resected central lymph nodes was similar between the groups. Differences did not reveal between the groups regarding to the complications rate. Conclusion: Our results showed that our novel transaxillary thyroidectomy procedure is feasible and safe. This procedure can be an alternative endoscopic transaxillary method for thyroidectomy.
A Comparative Analysis of Perioperative Complications in Octogenarians and Patients under 60 Years of Age after Primary Cemented Total Knee Arthroplasty

Introduction: Knee arthroplasty is exposed to demographic changes as patients age. An analysis of risk factors for surgical treatment decisions in patients over 80 years old is crucial. This study compared perioperative complications between groups of patients undergoing primary knee arthroplasty, under 60 years old and over 80 years old. Materials and Methods: For this retrospective study, data from 400 patients with primary cemented bi- and unicondylar total knee endoprosthesis during inpatient stay from 2017 to 2018 were analyzed. Patients aged 61–79 years (257) were excluded. An analysis of the remaining 143 patients was performed. The incidence of surgery-related and systemic complications (eg, urinary tract infections, electrolyte imbalances, and cases of pneumonia), the blood supply and C-reactive protein (CRP) as well as hemoglobin progression were compared across both age groups. Furthermore, a correlation between prevalent diseases and systemic complications were investigated. Statistical analysis was performed using IBM SPSS (Armonk, US). Results: Data analyses showed a significant difference in the occurrence of systemic complications and blood transfusion between the age groups (P = 0.001, phi = 0.44; phi= 0.55, P = 0.001). Surgical complications did not differ significantly between the age groups. Age-typical pre-existing conditions, especially arterial hypertension (P = 0.003), showed a significant association with the occurrence of systemic complications. In addition, high postoperative CRP values in elderly patients revealed an association to systemic complications (P = 0.008). Discussion and Conclusion: The study shows that primary cemented knee arthroplasty is a safe procedure without an increased incidence of surgical complications, even in elderly patients. The increased incidence of internal complications in octogenarians, in turn, should receive more attention in the perioperative course. The interdisciplinary preoperative optimization of pre-existing conditions and drug therapy, as well as close interdisciplinary assessments of elderly patients, should be ensured.
Equipping Pretrained Unconditional Music Transformers with Instrument and Genre Controls

The “pretraining-and-finetuning” paradigm has become a norm for training domain-specific models in natural language processing and computer vision. In this work, we aim to examine this paradigm for symbolic music generation through leveraging the largest ever symbolic music dataset sourced from the MuseScore forum. We first pretrain a large unconditional transformer model using 1.5 million songs. We then propose a simple technique to equip this pretrained unconditional music transformer model with instrument and genre controls by finetuning the model with additional control tokens. Our proposed representation offers improved high-level controllability and expressiveness against two existing representations. The experimental results show that the proposed model can successfully generate music with user-specified instruments and genre. In a subjective listening test, the proposed model outperforms the pretrained baseline model in terms of coherence, harmony, arrangement and overall quality.
Probing reheating with graviton bremsstrahlung

We investigate the stochastic gravitational wave (GW) spectrum resulting from graviton bremsstrahlung during inflationary reheating. We focus on an inflaton ϕ oscillating around a generic monomial potential V(ϕ) ∝ ϕn , considering two different reheating scenarios: i) inflaton decay and ii) inflaton annihilation. We show that in the case of a quadratic potential, the scattering of the inflatons can give rise to larger GW amplitude than the decay channel. On the other hand, the GW spectrum exhibits distinct features and redshifts in each scenario, which makes it possible to distinguish them in the event of a discovery. Specifically, in the case of annihilation, the GW frequency can be shifted to values higher than those of decay, whereas the GW amplitude generated by annihilation turns out to be smaller than that in the decay case for n ≥ 4, due to the different scaling of radiation during reheating. We also show that the differences in the GW spectrum become more prominent with increasing n. Finally, we highlight the potential of future high-frequency GW detectors to distinguish between the different reheating scenarios.
Exact cube-root fluctuations in an area-constrained random walk model

This article is devoted to the study of the behaviour of a (1+1)-dimensional model of random walk conditioned to enclose an area of order $N^2$. Such a conditioning enforces a globally concave trajectory. We study the local deviations of the walk from its convex hull. To this end, we introduce two quantities -- the mean facet length $\mathsf{MeanFL}$ and the mean local roughness $\mathsf{MeanLR}$ -- measuring the typical longitudinal and transversal fluctuations around the boundary of the convex hull of the random walk. Our main result is that $\mathsf{MeanFL}$ is of order $N^{2/3}$ and $\mathsf{MeanLR}$ is of order $N^{1/3}$. Moreover, following the strategy of Hammond (Ann. Prob., 2012), we identify the polylogarithmic corrections in the scaling of the maximal facet length and of the maximal local roughness, showing that the former one scales as $N^{2/3}(\log N)^{1/3}$, while the latter scales as $N^{1/3}(\log N)^{2/3}$. The object of study is intended to be a toy model for the interface of a two-dimensional statistical mechanics model (such as the Ising model) in the phase separation regime -- we discuss this issue at the end of this work.
GLAD: Global-Local View Alignment and Background Debiasing for Unsupervised Video Domain Adaptation with Large Domain Gap

In this work, we tackle the challenging problem of unsupervised video domain adaptation (UVDA) for action recognition. We specifically focus on scenarios with a substantial domain gap, in contrast to existing works primarily deal with small domain gaps between labeled source domains and unlabeled target domains. To establish a more realistic setting, we introduce a novel UVDA scenario, denoted as Kinetics->BABEL, with a more considerable domain gap in terms of both temporal dynamics and background shifts. To tackle the temporal shift, i.e., action duration difference between the source and target domains, we propose a global-local view alignment approach. To mitigate the background shift, we propose to learn temporal order sensitive representations by temporal order learning and background invariant representations by background augmentation. We empirically validate that the proposed method shows significant improvement over the existing methods on the Kinetics->BABEL dataset with a large domain gap. The code is available at https://github.com/KHUVLL/GLAD.
How Far Have We Gone in Vulnerability Detection Using Large Language Models

As software becomes increasingly complex and prone to vulnerabilities, automated vulnerability detection is critically important, yet challenging. Given the significant successes of large language models (LLMs) in various tasks, there is growing anticipation of their efficacy in vulnerability detection. However, a quantitative understanding of their potential in vulnerability detection is still missing. To bridge this gap, we introduce a comprehensive vulnerability benchmark VulBench. This benchmark aggregates high-quality data from a wide range of CTF (Capture-the-Flag) challenges and real-world applications, with annotations for each vulnerable function detailing the vulnerability type and its root cause. Through our experiments encompassing 16 LLMs and 6 state-of-the-art (SOTA) deep learning-based models and static analyzers, we find that several LLMs outperform traditional deep learning approaches in vulnerability detection, revealing an untapped potential in LLMs. This work contributes to the understanding and utilization of LLMs for enhanced software security.
Deciphering Non-Gaussianity of Diffusion Based on the Evolution of Diffusivity

Non-Gaussianity indicates complex dynamics related to extreme events or significant outliers. However, the correlation between non-Gaussianity and the dynamics of heterogeneous environments in anomalous diffusion remains uncertain. Inspired by a recent study by Alexandre et al. [Phys. Rev. Lett. 130, 077101 (2023)], we demonstrate that non-Gaussianity can be deciphered through the spatiotemporal evolution of heterogeneity-dependent diffusivity. Using diffusion experiments in a linear temperature field and Brownian dynamics simulations, we found that short- and long-time non-Gaussianity can be predicted based on diffusivity distribution. Non-Gaussianity variation is determined by an effective P\'eclet number (a ratio of the varying rate of diffusivity to the diffusivity of diffusivity), which clarifies whether the tail distribution expands or contracts. The tail is more Gaussian than exponential over long times, with exceptions significantly dependent on the diffusivity distribution. Our findings shed light on heterogeneity mapping in complex environments using non-Gaussian statistics.
ESTIMATION OF STATURE FROM HAND LENGTH AND LENGTH OF PHALANGES

CONTEXT: Stature or body height is one of the most important and useful anthropometric parameter that determine the physical identity of an individual. The study was done to estimate stature from the hand and length of phalanges. Study design: descriptive cross sectional study. Place of Study: department of Anatomy, Dr.S. N. Medical College, Jodhpur MATERIAL: One hundred males and one hundred females with the age of 18to 25yrs of, Dr.S. N. Medical College, Jodhpur. METHOD: Measurement of stature using standiometer and hand length and phalangeal length of right hand with a slide caliper respectively. RESULT: The present study showed significant correlation between the stature and hand lengths and phalangeal length.provide multiplication factors for stature with other parameters.
Instance-aware 3D Semantic Segmentation powered by Shape Generators and Classifiers

Existing 3D semantic segmentation methods rely on point-wise or voxel-wise feature descriptors to output segmentation predictions. However, these descriptors are often supervised at point or voxel level, leading to segmentation models that can behave poorly at instance-level. In this paper, we proposed a novel instance-aware approach for 3D semantic segmentation. Our method combines several geometry processing tasks supervised at instance-level to promote the consistency of the learned feature representation. Specifically, our methods use shape generators and shape classifiers to perform shape reconstruction and classification tasks for each shape instance. This enforces the feature representation to faithfully encode both structural and local shape information, with an awareness of shape instances. In the experiments, our method significantly outperform existing approaches in 3D semantic segmentation on several public benchmarks, such as Waymo Open Dataset, SemanticKITTI and ScanNetV2.
Carbohydrate NMR chemical shift predictions using E(3) equivariant graph neural networks

Carbohydrates, vital components of biological systems, are well-known for their structural diversity. Nuclear Magnetic Resonance (NMR) spectroscopy plays a crucial role in understanding their intricate molecular arrangements and is essential in assessing and verifying the molecular structure of organic molecules. An important part of this process is to predict the NMR chemical shift from the molecular structure. This work introduces a novel approach that leverages E(3) equivariant graph neural networks to predict carbohydrate NMR spectra. Notably, our model achieves a substantial reduction in mean absolute error, up to threefold, compared to traditional models that rely solely on two-dimensional molecular structure. Even with limited data, the model excels, highlighting its robustness and generalization capabilities. The implications are far-reaching and go beyond an advanced understanding of carbohydrate structures and spectral interpretation. For example, it could accelerate research in pharmaceutical applications, biochemistry, and structural biology, offering a faster and more reliable analysis of molecular structures. Furthermore, our approach is a key step towards a new data-driven era in spectroscopy, potentially influencing spectroscopic techniques beyond NMR.
IEKM: A Model Incorporating External Keyword Matrices

A customer service platform system with a core text semantic similarity (STS) task faces two urgent challenges: Firstly, one platform system needs to adapt to different domains of customers, i.e., different domains adaptation (DDA). Secondly, it is difficult for the model of the platform system to distinguish sentence pairs that are literally close but semantically different, i.e., hard negative samples. In this paper, we propose an incorporation external keywords matrices model (IEKM) to address these challenges. The model uses external tools or dictionaries to construct external matrices and fuses them to the self-attention layers of the Transformer structure through gating units, thus enabling flexible corrections to the model results. We evaluate the method on multiple datasets and the results show that our method has improved performance on all datasets. To demonstrate that our method can effectively solve all the above challenges, we conduct a flexible correction experiment, which results in an increase in the F1 value from 56.61 to 73.53. Our code will be publicly available.
Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models

We present a method to create interpretable concept sliders that enable precise control over attributes in image generations from diffusion models. Our approach identifies a low-rank parameter direction corresponding to one concept while minimizing interference with other attributes. A slider is created using a small set of prompts or sample images; thus slider directions can be created for either textual or visual concepts. Concept Sliders are plug-and-play: they can be composed efficiently and continuously modulated, enabling precise control over image generation. In quantitative experiments comparing to previous editing techniques, our sliders exhibit stronger targeted edits with lower interference. We showcase sliders for weather, age, styles, and expressions, as well as slider compositions. We show how sliders can transfer latents from StyleGAN for intuitive editing of visual concepts for which textual description is difficult. We also find that our method can help address persistent quality issues in Stable Diffusion XL including repair of object deformations and fixing distorted hands. Our code, data, and trained sliders are available at https://sliders.baulab. info/
A Hilbert--Mumford criterion for nilsolitons

We give an algebraic criterion for a nilpotent real Lie algebra and prove that it provides a necessary and sufficient condition for the associated nilpotent Lie group to admit left-invariant Ricci solitons, called nilsolitons. As an application of this result, we generalise Nikolayevsky's criterion for the existence of nilsolitons to nilpotent Lie algebras without nice bases. We further prove a modified version of the Taketomi--Tamaru conjecture for nilpotent Lie groups which gives an obstruction to the existence of nilsolitons.
A tensor network view of multilayer multiconfiguration time-dependent Hartree methods

The multilayer multiconfiguration time-dependent Hartree (ML-MCTDH) method and the density matrix renormalization group (DMRG) are powerful workhorses applied mostly in different scientific fields. Although both methods are based on tensor network states, very different mathematical languages are used for describing them. This severely limits knowledge transfer and sometimes leads to re-inventions of ideas well known in the other field. Here, we review ML-MCTDH and DMRG theory using both MCTDH expressions and tensor network diagrams. We derive the ML-MCTDH equations of motions using diagrams and compare them with time-dependent and time-independent DMRG algorithms. We further review two selected recent advancements. The first advancement is related to optimizing unoccupied single-particle functions in MCTDH, which corresponds to subspace enrichment in the DMRG. The second advancement is related to finding optimal tree structures and on highlighting similarities and differences of tensor networks used in MCTDH and DMRG theories. We hope that this contribution will foster more fruitful cross-fertilization of ideas between ML-MCTDH and DMRG.
On anomalous diffusion in the Kraichnan model and correlated-in-time variants

We provide a concise PDE-based proof of anomalous diffusion in the Kraichan model -- a stochastic, white-in-time model of passive scalar turbulence. That is, we show an exponential rate of $L^2$ decay in expectation of a passive scalar advected by a certain white-in-time, correlated-in-space, divergence-free Gaussian field, uniform in the initial data and the diffusivity of the passive scalar. Additionally, we provide examples of correlated-in-time versions of the Kraichnan model which fail to exhibit anomalous diffusion despite their (formal) white-in-time limits exhibiting anomalous diffusion. As part of this analysis, we prove that anomalous diffusion of a scalar advected by some flow implies non-uniqueness of the ODE trajectories of that flow.
Optimal Portfolio with Ratio Type Periodic Evaluation under Short-Selling Prohibition

This paper studies some unconventional utility maximization problems when the ratio type relative portfolio performance is periodically evaluated over an infinite horizon. Meanwhile, the agent is prohibited from short-selling stocks. Our goal is to understand the impact of the periodic reward structure on the long-run constrained portfolio strategy. For power and logarithmic utilities, we can reformulate the original problem into an auxiliary one-period optimization problem. To cope with the auxiliary problem with no short-selling, the dual control problem is introduced and studied, which gives the characterization of the candidate optimal portfolio within one period. With the help of the results from the auxiliary problem, the value function and the optimal constrained portfolio for the original problem with periodic evaluation can be derived and verified, allowing us to discuss some financial implications under the new performance paradigm.