Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality of Life of Cancer Patients Receiving Home Infusion Services

Q uality of life (QOL) research originated more than 40 years ago in the social sciences. In the past 10 years there has been escalating interest in health-related QOL. This has lead to a proliferation of studies and measurement tools worldwide. Unfortunately, there are significant differences in the conceptual models and tools used to measure QOL. Today, there are no standard definitions and there is little consistency in measurements. These differences frequently prevent comparative analysis. Additionally, they limit the extrapolations that can be made from the results. Despite these constraints, however, experts generally agree on the following points:
Relevant genetic polymorphisms and kidney expression of TLR 5 and TLR 9 in lupus nephritis Running title : Toll like receptor in lupus nephritis Authors

Nesrine Elloumi PhD, Immunology Department, Habib Bourguiba Hospital, University of Sfax, Tunisia, elloumi_nesrine@hotmail.fr Raouia Fakhfakh PhD, Immunology Department, Habib Bourguiba Hospital, University of Sfax, Tunisia. raouiafakh2@yahoo.fr Olfa Abida PhD, Immunology Department, Habib Bourguiba Hospital, University of Sfax, Tunisia, olfaabida@yahoo.fr Lobna Ayadi MD, Anatomopathology Department, Habib Bourguiba Hospital, University of Sfax, Tunisia, ayadilobna@yahoo.fr Sameh Marzouk MD, Internal Medicine Department, Hedi Chaker Hospital, University of Sfax, Tunisia, sameh74tn@yahoo.fr Hend Hachicha MD, Immunology Department, Habib Bourguiba Hospital, University of Sfax, Tunisia, hendhachcihca@yahoo.fr Mohamed Fourati MD, Urology Department, Habib Bourguiba Hospital, University of Sfax, Tunisia, drfourati@live.fr Zouhir bahloul MD, Internal Medicine Department, Hedi Chaker Hospital, University of Sfax, Tunisia. zouhir.bahloul@rns.tn Mohamed Nabil Mhiri MD, Urology Department, Habib Bourguiba Hospital, University of Sfax, Tunisia, nabil.mhiri@rns.tn Khawla. Kammoun MD, Nephrology Department, Hedi Chaker Hospital, University of Sfax,Tunisia,khawlakammoun2002@yahoo.fr Hatem Masmoudi MD/PhD, Immunology Department, Habib Bourguiba Hospital, University of Sfax, Tunisia. hatem.masmoudi@yahoo.com
Relationship between respiratory muscle strength and physical performance in elderly hospitalized patients.

UNLABELLED Background: Age-related changes in pulmonary function increase respiratory muscle work. In the face of this increased demand, poor muscle mass, frequently associated with age and multi-morbidity, can reduce endurance and strength of respiratory muscles. Furthermore, poor muscle mass may per se contribute to exercise intolerance and lower physical performance. The aim of the study was to evaluate if respiratory muscle strength has a significant impact on physical performance in an elderly population. METHODS We included 68 patients (28 men and 40 women) aged over 65 years (mean 79 years, standard deviation [SD] 6) in stable condition at discharge from our acute care geriatric ward. We assessed the function of respiratory muscle by measuring maximal inspiratory and expiratory pressures (MIP, MEP) and physical function using the 6-Minute Walk Test (6MWT). RESULTS The mean age of our sample was 78.2 years (SD 6.1). There was a statistically significant correlation between MIP or MEP and 6MWT distance (MIP, r=0.43, p<0.001; MEP, r=0.41, p=0.001). The association between respiratory pressures and 6MWT was maintained after adjustment for forced expiratory volume in 1 sec (FEV1), forced vital capacity (FVC), age, sex, fat-free mass index (FFMI), and leg strength. The multiple regression model showed a significant relation between 6-Minute Walk Test distance (6MWD) and both MIP and MEP after correction for sex, age, FEV1, and FVC. Furthermore, MEP can significant predict poorer performance at 6MWD in a multiple logistic regression model. CONCLUSION Reduced respiratory muscle strength is independently associated with worse physical performance in elderly patients.
Medicinal Immunoglobulin G Products (2020) Show High Infectivity Neutralizing Activity Against Seasonal Influenza Virus Strains Selected for Future Vaccines (2020–2022)

Abstract Immunoglobulin (Ig)G medicinal products manufactured in 2020 were tested for infectivity neutralization and hemagglutination inhibition against World Health Organization-selected influenza strains included in worldwide vaccines 2020–2022. The IgG batches (from US plasma) showed potent activity. Intravenous immunoglobulin could potentially add to therapies for serious influenza cases in immunocompromised patients. Further study is warranted.
Non-Hermitian Hamiltonian Beyond Pt -Symmetry for Time-Dependant Su(1,1) and Su(2) Systems  --- Exact Solution and Geometric Phase in Pseudo-Invariant Theory

We investigate in this paper time-dependent non-Hermitian Hamiltonians, which consist respectively of SU (1 , 1) and SU (2) generators. The former Hamiltonian is PT symmetric but the latter one is not. A time-dependent non-unitary operator is proposed to construct the non-Hermitian invariant, which is veriﬁed as pseudo-Hermitian with real eigenvalues. The exact solutions are obtained in terms of the eigenstates of the pseudo-Hermitian invariant operator for both the SU (1 , 1)and SU (2)systems in a uniﬁed manner. Then, we derive the LR phase, which can be separated to the dynamic phase and the geometrical phase. The analytical results are exactly in agreement with those of corresponding Hermitian Hamiltonians in the literature.
Filtering and Imaging of Frequency-Degenerate Spin Waves Using Nanopositioning of a Single-Spin Sensor

Nitrogen-vacancy (NV) magnetometry is a new technique for imaging spin waves in magnetic materials. It detects spin waves by their microwave magnetic stray fields, which decay evanescently on the scale of the spin-wavelength. Here, we use nanoscale control of a single-NV sensor as a wavelength filter to characterize frequency-degenerate spin waves excited by a microstrip in a thin-film magnetic insulator. With the NV probe in contact with the magnet, we observe an incoherent mixture of thermal and microwave-driven spin waves. By retracting the tip, we progressively suppress the small-wavelength modes until a single coherent mode emerges from the mixture. In-contact scans at low drive power surprisingly show occupation of the entire isofrequency contour of the two-dimensional spin-wave dispersion despite our one-dimensional microstrip geometry. Our distance-tunable filter sheds light on the spin-wave band occupation under microwave excitation and opens opportunities for imaging magnon condensates and other coherent spin-wave modes.
A Learning System for Motion Planning of Free-Float Dual-Arm Space Manipulator towards Non-Cooperative Object

Recent years have seen the emergence of non-cooperative objects in space, like failed satellites and space junk. These objects are usually operated or collected by free-float dual-arm space manipulators. Thanks to eliminating the difficulties of modeling and manual parameter-tuning, reinforcement learning (RL) methods have shown a more promising sign in the trajectory planning of space manipulators. Although previous studies demonstrate their effectiveness, they cannot be applied in tracking dynamic targets with unknown rotation (non-cooperative objects). In this paper, we proposed a learning system for motion planning of free-float dual-arm space manipulator (FFDASM) towards non-cooperative objects. Specifically, our method consists of two modules. Module I realizes the multi-target trajectory planning for two end-effectors within a large target space. Next, Module II takes as input the point clouds of the non-cooperative object to estimate the motional property, and then can predict the position of target points on an non-cooperative object. We leveraged the combination of Module I and Module II to track target points on a spinning object with unknown regularity successfully. Furthermore, the experiments also demonstrate the scalability and generalization of our learning system.
X-Ray Spectroscopy in the Microcalorimeter Era 4: Optical Depth Effects on the Soft X-Rays Studied with Cloudy

In this paper, we discuss atomic processes modifying the soft X-ray spectra from optical depth effects like photoelectric absorption and electron scattering suppressing the soft X-ray lines. We also show the enhancement in soft X-ray line intensities in a photoionized environment via continuum pumping. We quantify the suppression/enhancement by introducing a “line modification factor (f mod). ” If 0 ≤ f mod ≤ 1, the line is suppressed, which could be the case in both collisionally ionized and photoionized systems. If f mod ≥ 1, the line is enhanced, which occurs in photoionized systems. Hybrid astrophysical sources are also very common, where the environment is partly photoionized and partly collisionally ionized. Such a system is V1223 Sgr, an Intermediate Polar binary. We show the application of our theory by fitting the first-order Chandra Medium Energy Grating (MEG) spectrum of V1223 Sgr with a combination of Cloudy-simulated additive cooling-flow and photoionized models. In particular, we account for the excess flux for O vii, O viii, Ne ix, Ne x, and Mg xi lines in the spectrum found in a recent study, which could not be explained with an absorbed cooling-flow model.
BioTABQA: Instruction Learning for Biomedical Table Question Answering

Table Question Answering (TQA) is an important but under-explored task. Most of the existing QA datasets are in unstructured text format and only few of them use tables as the context. To the best of our knowledge, none of TQA datasets exist in the biomedical domain where tables are frequently used to present information. In this paper, we first curate a table question answering dataset, BioTABQA, using 22 templates and the context from a biomedical textbook on differential diagnosis. BioTABQA can not only be used to teach a model how to answer questions from tables but also evaluate how a model generalizes to unseen questions, an important scenario for biomedical applications. To achieve the generalization evaluation, we divide the templates into 17 training and 5 cross-task evaluations. Then, we develop two baselines using single and multi-tasks learning on BioTABQA. Furthermore, we explore instructional learning, a recent technique showing impressive generalizing performance. Experimental results show that our instruction-tuned model outperforms single and multi-task baselines on an average by ~23% and ~6% across various evaluation settings, and more importantly, instruction-tuned model outperforms baselines by ~5% on cross-tasks.
From algorithms to connectivity and back: finding a giant component in random k-SAT

We take an algorithmic approach to studying the solution space geometry of relatively sparse random and bounded degree $k$-CNFs for large $k$. In the course of doing so, we establish that with high probability, a random $k$-CNF $\Phi$ with $n$ variables and clause density $\alpha = m/n \lesssim 2^{k/6}$ has a giant component of solutions that are connected in a graph where solutions are adjacent if they have Hamming distance $O_k(\log n)$ and that a similar result holds for bounded degree $k$-CNFs at similar densities. We are also able to deduce looseness results for random and bounded degree $k$-CNFs in a similar regime. Although our main motivation was understanding the geometry of the solution space, our methods have algorithmic implications. Towards that end, we construct an idealized block dynamics that samples solutions from a random $k$-CNF $\Phi$ with density $\alpha = m/n \lesssim 2^{k/52}$. We show this Markov chain can with high probability be implemented in polynomial time and by leveraging spectral independence, we also observe that it mixes relatively fast, giving a polynomial time algorithm to with high probability sample a uniformly random solution to a random $k$-CNF. Our work suggests that the natural route to pinning down when a giant component exists is to develop sharper algorithms for sampling solutions in random $k$-CNFs.
The Role of Complex NLP in Transformers for Text Ranking

Even though term-based methods such as BM25 provide strong baselines in ranking, under certain conditions they are dominated by large pre-trained masked language models (MLMs) such as BERT. To date, the source of their effectiveness remains unclear. Is it their ability to truly understand the meaning through modeling syntactic aspects? We answer this by manipulating the input order and position information in a way that destroys the natural sequence order of query and passage and shows that the model still achieves comparable performance. Overall, our results highlight that syntactic aspects do not play a critical role in the effectiveness of re-ranking with BERT. We point to other mechanisms such as query-passage cross-attention and richer embeddings that capture word meanings based on aggregated context regardless of the word order for being the main attributions for its superior performance.
Learning Task Embeddings for Teamwork Adaptation in Multi-Agent Reinforcement Learning

Successful deployment of multi-agent reinforcement learning often requires agents to adapt their behaviour. In this work, we discuss the problem of teamwork adaptation in which a team of agents needs to adapt their policies to solve novel tasks with limited fine-tuning. Motivated by the intuition that agents need to be able to identify and distinguish tasks in order to adapt their behaviour to the current task, we propose to learn multi-agent task embeddings (MATE). These task embeddings are trained using an encoder-decoder architecture optimised for reconstruction of the transition and reward functions which uniquely identify tasks. We show that a team of agents is able to adapt to novel tasks when provided with task embeddings. We propose three MATE training paradigms: independent MATE, centralised MATE, and mixed MATE which vary in the information used for the task encoding. We show that the embeddings learned by MATE identify tasks and provide useful information which agents leverage during adaptation to novel tasks.
Vortex merging and splitting events in viscoelastic Taylor–Couette flow

Abstract Recent experiments have reported a novel transition to elasto-inertial turbulence in the Taylor–Couette flow of a dilute polymer solution. Unlike previously reported transitions, this newly discovered scenario, dubbed vortex merging and splitting (VMS) transition, occurs in the centrifugally unstable regime and the mechanisms underlying it are two-dimensional: the flow becomes chaotic due to the proliferation of events where axisymmetric vortex pairs may be either created (vortex splitting) or annihilated (vortex merging). In this paper, we present direct numerical simulations, using the finitely extensible nonlinear elastic-Peterlin (FENE-P) constitutive equation to model the polymer dynamics, which reproduce the experimental observations with great accuracy and elucidate the reasons for the onset of this surprising dynamics. Starting from the Newtonian limit and increasing progressively the fluid's elasticity, we demonstrate that the VMS dynamics is not associated with the well-known Taylor vortices, but with a steady pattern of elastically induced axisymmetric vortex pairs known as diwhirls. The amount of angular momentum carried by these elastic vortices becomes increasingly small as the fluid's elasticity increases and it eventually reaches a marginal level. When this occurs, the diwhirls become dynamically disconnected from the rest of the system and move independently from each other in the axial direction. It is shown that vortex merging and splitting events, along with local transient chaotic dynamics, result from the interactions among diwhirls, and that this complex spatio-temporal dynamics persists even at elasticity levels twice as large as those investigated experimentally.
Stochastic model for the alignment and tumbling of rigid fibers in two-dimensional turbulent shear flow

Non-spherical particles transported by an anisotropic turbulent ﬂow preferentially align with the mean shear and intermittently tumble when the local strain ﬂuctuates. Such an intricate behaviour is here studied for inertialess, rod-shaped particles embedded in a two-dimensional turbulent ﬂow with homogeneous shear. A Lagrangian stochastic model for the rods angular dynamics is introduced and compared to the results of direct numerical simulations. The model consists in superposing a short-correlated random component to the steady large-scale mean shear, and can thereby be integrated analytically. To reproduce the single-time orientation statistics obtained numerically, it is found that one has to properly account for the combined eﬀect of the mean shear, for anisotropic velocity gradient ﬂuctuations, and for the presence of persistent rotating structures in the ﬂow that bias Lagrangian statistics. The model is then used to address two-time statistics. The notion of tumbling rate is extended to diﬀusive dynamics by introducing the stationary probability ﬂux of the rods unfolded angle. The model is found to reproduce the long-term eﬀects of an average shear on the mean and the variance of the ﬁbres angular increment. Still, it does not reproduce an intricate behaviour observed in numerics for intermediate times: the unfolded angle is there very similar to a L´evy walk with distributions of increments displaying intermediate power-law tails.
Dimension formulas for Siegel modular forms of level 4

We prove several dimension formulas for spaces of scalar‐valued Siegel modular forms of degree 2 with respect to certain congruence subgroups of level 4. In case of cusp forms, all modular forms considered originate from cuspidal automorphic representations of GSp(4,A)${\rm GSp}(4,{\mathbb {A}})$ whose local component at p=2$p=2$ admits nonzero fixed vectors under the principal congruence subgroup of level 2. Using known dimension formulas combined with dimensions of spaces of fixed vectors in local representations at p=2$p=2$ , we obtain formulas for the number of relevant automorphic representations. These, in turn, lead to new dimension formulas, in particular for Siegel modular forms with respect to the Klingen congruence subgroup of level 4.
Blink: Lightweight Sample Runs for Cost Optimization of Big Data Applications

Distributed in-memory data processing engines accelerate iterative applications by caching substantial datasets in memory rather than recomputing them in each iteration. Selecting a suitable cluster size for caching these datasets plays an essential role in achieving optimal performance. In practice, this is a tedious and hard task for end users, who are typically not aware of cluster specifications, workload semantics and sizes of intermediate data. We present Blink, an autonomous sampling-based framework, which predicts sizes of cached datasets and selects optimal cluster size without relying on historical runs. We evaluate Blink on a variety of iterative, real-world, machine learning applications. With an average sample runs cost of 4.6% compared to the cost of optimal runs, Blink selects the optimal cluster size in 15 out of 16 cases, saving up to 47.4% of execution cost compared to average costs.
When does SGD favor flat minima? A quantitative characterization via linear stability

The observation that stochastic gradient descent (SGD) favors ﬂat minima has played a fundamental role in understanding implicit regularization of SGD and guiding the tuning of hyperparameters. In this paper, we provide a quantitative explanation of this striking phenomenon by relating the particular noise structure of SGD to its linear stability (Wu et al., 2018). Speciﬁcally, we consider training over-parameterized models with square loss. We prove that if a global minimum θ ∗ is linearly stable for SGD, then it must satisfy (cid:107) H ( θ ∗ ) (cid:107) F (cid:54) O ( √ B/η ) , where (cid:107) H ( θ ∗ ) (cid:107) F , B, η denote the Frobenius norm of Hessian at θ ∗ , batch size, and learning rate, respectively. Otherwise, SGD will escape from that minimum exponentially fast. Hence, for minima accessible to SGD, the ﬂatness—as measured by the Frobenius norm of the Hessian—is bounded independently of the model size and sample size. The key to obtaining these results is exploiting the particular geometry awareness of SGD noise: 1) the noise magnitude is proportional to loss value; 2) the noise directions concentrate in the sharp directions of local landscape. This property of SGD noise provably holds for linear networks and random feature models (RFMs) and is empirically veriﬁed for nonlinear networks. Moreover, the validity and practical relevance of our theoretical ﬁndings are justiﬁed by extensive numerical experiments.
Kaggle Competition: Cantonese Audio-Visual Speech Recognition for In-car Commands

With the rise of deep learning and intelligent vehicles, the smart assistant has become an essential in-car component to facilitate driving and provide extra functionalities. In-car smart assistants should be able to process general as well as car-related commands and perform corresponding actions, which eases driving and improves safety. However, in this research field, most datasets are in major languages, such as English and Chinese. There is a huge data scarcity issue for low-resource languages, hindering the development of research and applications for broader communities. Therefore, it is crucial to have more benchmarks to raise awareness and motivate the research in low-resource languages. To mitigate this problem, we collect a new dataset, namely Cantonese In-car Audio-Visual Speech Recognition (CI-AVSR), for in-car speech recognition in the Cantonese language with video and audio data. Together with it, we propose Cantonese Audio-Visual Speech Recognition for In-car Commands as a new challenge for the community to tackle low-resource speech recognition under in-car scenarios.
Handling Nonmonotone Missing Data with Available Complete-Case Missing Value Assumption

Nonmonotone missing data is a common problem in scientiﬁc studies. The conventional ignorability and missing-at-random (MAR) conditions are unlikely to hold for nonmonotone missing data and data analysis can be very challenging with few complete data. In this paper, we introduce the available complete-case missing value (ACCMV) assumption for handling nonmonotone and missing-not-at-random (MNAR) problems. Our ACCMV assumption is applicable to data set with a small set of complete observations and we show that the ACCMV assumption leads to nonparametric identiﬁcation of the distribution for the variables of interest. We further propose an inverse probability weighting estimator, a regression adjustment estimator, and a multiply-robust estimator for estimating a parameter of interest. We studied the underlying asymptotic and eﬃciency theories of the proposed estimators. We show the validity of our method with simulation studies and further illustrate the applicability of our method by applying it to a diabetes data set from electronic health records.