Miracle-dz/UniFAR-FTUs huggingface dataset:
I Can Grow as an In Utero if You Give Me Enough Nutrition: Premature ≤29 Weeks Gestation

Introduction: In general, everyone believe they should grow as “in utero” for the smallest premature babies but many thinks that it is not possible with today's nutrition “guidelines”. There is resistance to give enough nutrition for fear of "toxicity". We use volumes that some would consider high, although there are more and more groups that accept it. We chose the difference in Z-score between birth and a corrected discharge gestational age to assess postnatal growth in our unit. Material and methods: Between January 2018 and December 2020, we review all cases ≤ 29 weeks of GA at birth that survived to 36 weeks corrected gestational age, or that were discharged home if this occurred before. We had a protocol with extremely aggressive nutrition including parenteral as well as oral nutrition. We then separated the patients who had lost more than 1 Zscore point to see the weight trajectory plotted on the Fenton 2013 growth curve and to review the clinical characteristics of those patients. We also graphed the patients who had had a smaller Z-score difference to also see their trajectory in the Fenton curves. We calculate the number of cases, medians, and interquartile ranges for all groups. Results: We found 32 cases. The median ± IQR (interquartile range) of change in Z-score between birth and discharge of the whole group was -0.52 ± 0.53; 6/32 (19%) had more than 1 point drop; all had severe diseases listed in Figure 1 with plotting all cases. The median decline in Z score ± IQR of this group with poor growth was -1.24 ± 0.22. The group of cases with a fall of <1 point of Z score were 26 (81%), the median ± IQR of fall of Z score was -0.39 ± 0.55 (see figure 2). All cases had the initial fall in weight the first 1-2 weeks. No important complications secondary to the ingested volumes or parenteral nutrition were reported. Conclusion: The group of cases with a fall of > 1 point of Z score had severe pathologies. The other cases had an adequate growth parallel to normal growth charts in some cases with some catch up. Discussion: With this work we try to show that in many premature babies it is possible to grow like in utero if they are given enough nutrition. Randomized studies are necessary to confirm our findings.
Real time product price monitoring and analysis for E-commerce website.

Recommendation systems have emerged rapidly in the past decade. Recommendation of products to attract customers that meet their requirements is very important for the vendors to survive in the global market. The approach proposed in this paper is novel and serves as a better alternative to rate a product based on its technical speciﬁcation by analyzing large number of user reviews which are extracted dynamically from several top e-commerce websites. This avoids the need, for the user, to search for opinions and comments online before making a purchase. The proposed approach in this study extracts speciﬁcation list like battery, processor, camera etc. and customer reviews for a user speciﬁed product from different websites and identiﬁes crucial terms corresponding to the technical features of the product in the review to determine polarity of the feature and classifying it under the speciﬁcation list. Each speciﬁcation is assigned a score based on polarity i.e. positive/negative feedback. Overall product rate is calculated by aggregating the score speciﬁc to individual features. This approach is very useful for those customer swhotar get at speciﬁc features in a product.
OTT_A_296400 1997..2002

Department of Pathology, Shanghai General Hospital,Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People’s Republic of China Introduction: Primary tracheal adenoid cystic carcinoma (ACC) is a rare and heterogeneous group of neoplasms arising from the respiratory tract. The solid variant of ACC is a histologically distinct subtype with an unfavorable clinical course. We report on a case of tracheal ACC with immunohistochemical and molecular analysis together with a review of the literature. Case Report: We observed a case in which a 31-year-old male presented with a neoplasm bulging into the lumen and caused symptoms of tracheal obstruction and even hemoptysis. Cytological smears of an endobronchial fine needle aspiration revealed aggregates of basaloid cells with small to medium size, scant cytoplasm, and evenly hyperchromatic nuclei. Histologically, the tumor is characterized by a predominant compact sheet-like and nested pattern of rounded basaloid cells. Immunohistochemically, the tumor was diffusely positive for CK and CD117. CK7 and CK5/6 were focally positive in the genuine glandular structures. P63 was completely negative in the majority of neoplastic cells. Fluorescence in situ hybridization analysis revealed MYB gene rearrangement. Conclusion: The solid variant of ACC from trachea is rare and hence poses diagnostic difficulty. Computed tomography (CT) scan and bronchoscopy help assess the extent of the disease. Histological features combined with immunophenotypic and molecular analysis aid in distinguishing this uncommon type from other round cell neoplasms. The accurate diagnosis can help expedite treatment of this highly aggressive tumor.
Essay Content is Strongly Related to Household Income and SAT Scores: Evidence from 60,000 Undergraduate Applications

There is substantial evidence of the potential for class bias in the use of standardized tests to evaluate college applicants, yet little comparable inquiry considers the written essays typically required of applicants to selective US colleges and universities. We utilize a corpus of 240,000 admissions essays submitted by 60,000 applicants to the University of California in November 2016 to measure the relationship between the content of application essays, reported household income, and standardized test scores (SAT) at scale. We quantify essay content using correlated topic modeling (CTM) and the Linguistic Inquiry and Word Count (LIWC) software package. Results show that essays have a stronger correlation to reported household income than SAT scores. Essay content also explains much of the variance in SAT scores, suggesting that essays encode some of the same information as the SAT, though this relationship attenuates as household income increases. Efforts to realize more equitable college admissions protocols can be informed by attending to how social class is encoded in non-numerical components of applications.
BOSS: Bandwidth-Optimized Search Accelerator for Storage-Class Memory

Search is one of the most popular and important web services. The inverted index is the standard data structure adopted by most full-text search engines. Recently, custom hardware accelerators for inverted index search have emerged to demonstrate much higher throughput than the conventional CPU or GPU. However, less attention has been paid to addressing the memory capacity pressure with inverted index. The conventional DDRx DRAM memory system significantly increases the system cost to make a terabyte-scale main memory. Instead, a shared memory pool composed of storage-class memory (SCM) devices is a promising alternative for scaling memory capacity at a much lower cost. However, this SCM-based pooled memory poses new challenges caused by the limited bandwidth of both SCM devices and the shared interconnect to the host CPU. Thus, we propose BOSS, the first near-data processing (NDP) architecture for inverted index search on SCM-based pooled memory, which maintains high throughput of query processing in this bandwidth- constrained environment. BOSS mitigates the impact of low bandwidth of SCM devices by employing early-termination search algorithms, reducing the footprint of intermediate data, and introducing a programmable decompression module that can select the best compression scheme for a given inverted index. Furthermore, BOSS includes a top-k selection module in hardware to substantially reduce the host-accelerator bandwidth consumption. Compared to Apache Lucene, a production-grade search engine library, running on 8 CPU cores, BOSS achieves a geomean speedup of 8.1× on various complex query types, while reducing the average energy consumption by 189×.
Phased synthesis of divide and conquer programs

We propose a fully automated method that takes as input an iterative or recursive reference implementation and produces divide-and-conquer implementations that are functionally equivalent to the input. Three interdependent components have to be synthesized: a function that divides the original problem instance, a function that solves each sub-instance, and a function that combines the results of sub-computations. We propose a methodology that splits the synthesis problem into three successive phases, each with a substantially reduced state space compared to the original monolithic task, and therefore substantially more tractable. Our methodology is implemented as an addition to the existing synthesis tool Parsynt, and we demonstrate the efficacy of it by synthesizing highly nontrivial divide-and-conquer implementations of a set of benchmarks fully automatically.
Pulsatile Pattern of Growth Hormone Secretion in Females in Elele Adegoke

The study was carried out to determine the variations in growth hormone secretion in females between morning and evening. Blood samples were collected from 60 females at Elele, both in the morning and evening. The samples were assayed using ELISA-based Growth hormone assay to quantitate the growth hormone concentrations of these females between morning and evening. The result showed a significant increase in growth hormone secretion in the morning (4.70±0.54ng/ml) compared with (2.02±0.23ng/ml) obtained in the evening (P<0.05). Females less than 20 years of age (20 subjects) had the highest secretion in the morning (4.99±0.98) compared with the evening (2.35±0.35) while females within 2030 years had4.58±1.04 and1.54±0.33 for morning and evening respectively. Females within 30-40 age group had 4.53±0.80 and 2.18±0.50 for morning and evening respectively. The result of this study suggest that Growth hormone pulse amplitude increased significantly in the morning after the onset of a night sleep (4.70±0.54) and was significantly greater than the secretion rate in the evening(2.02±0.23). © 2015 Elixir All rights reserved. ARTICLE INFO Article history: Received: 7 February 2015; Received in revised form: 8 July 2015; Accepted: 18 July 2015;
The future of metabolomics in ELIXIR [version 2; peer review: 3 approved]

Metabolomics, the youngest of the major omics technologies, is supported by an active community of researchers and infrastructure developers across Europe. To coordinate and focus efforts around infrastructure building for metabolomics within Europe, a workshop on the “Future of metabolomics in ELIXIR” was organised at Frankfurt Airport in Germany. This one-day strategic workshop involved representatives of ELIXIR Nodes, members of the PhenoMeNal consortium developing an e-infrastructure that supports workflow-based metabolomics analysis pipelines, and experts from the international metabolomics community. The workshop established metabolite identification as the critical area, where a maximal impact of computational metabolomics and data management on other fields could be achieved. In particular, the existing four ELIXIR Use Cases, where the metabolomics community - both industry and academia - would benefit most, and which could be exhaustively mapped onto the current five ELIXIR Platforms were discussed. This opinion article is a call for support for a new ELIXIR metabolomics Use Case, which aligns with and complements the existing and planned ELIXIR Platforms and Use Cases.
Hamilton Equations on a Contact 5-Manifolds

It is well known that a dynamical system is a concept in mathematics where a fixed rule describes how a point in a geometrical space depends on time. A mathematical model is a precise representation of a system's dynamics used to answer questions via analysis and simulation. Mathematica models allow us to reason about a system and make predictions about who a system will behave. Contact geometry is the odd-dimensional analogue of symplectic geometry. It is close to symplectic geometry and like the latter it originated in questions of classical and analytical mechanics. If contact geometry is considered as a symplectic geometry, it has broad applications in mathematical physics, geometrical optics, classical mechanics, analytical mechanics, mechanical systems, thermodynamics, geometric quantization and applied mathematics such as control theory. It is well known fact that one way of solving problems in classical mechanics occur with the help of the Hamilton equations. Hamiltonian method is particularly important because of its utility in formulating quantum mechanics. In this study, Hamilton equations as representive the object motion were found on a contact 5-manifolds. Also, implicit solutions of the differential equations found in this study are solved by Maple computation program. © 2016 Elixir All rights reserved. Elixir Adv. Math. 92 (2016) 38743-38748 Advanced Mathematics Available online at www.elixirpublishers. com (Elixir International Journal) Zeki KASAP/ Elixir Adv. Math. 92 (2016) 38743-38748 38744 Dirac structures [10]. Manev and Ivanova examined that the canonical-type connection on the almost contact manifolds with B-metric is constructed [11]. Etnyre showed any almost contact structure on a 5-manifold is homotopic to a contact structure [12]. Sekiya introduced generalized almost contact structures which admit the B-field transformations on odd dimensional manifolds [13]. Dwivedi et al proved that a (k,μ)manifold with vanishing Endo curvature tensor is a Sasakian manifold [14]. Malek and Balgeshir introduced the notion of slant submanifold of an almost contact metric 3-structure manifold [15]. Davidov observed that the CR-structures on the twistor space are induced by almost contact metric structures [16]. Kasap and Tekkoyun examined Lagrangian and Hamiltonian formalism for mechanical systems using para/pseudo-Kähler manifolds, representing an interesting multidisciplinary field of research [17]. Kasap obtained that the Weyl-Euler-Lagrange and Weyl-Hamilton equations on R2nn such that is a model of tangent manifolds of constant Wsectional curvature [18]. Tekkoyun showed paracomplex analogue of Euler-Lagrange and Hamiltonian equations [19].
Implementation Analysis of Cellular Manufacturing System to Improve Cell Performance

This present paper explores the way in which the cellular manufacturing can help a selected manufacturing company for a selected Machining Center, a highly flexible shop with many different customers choice and products, achieve improved performance and customer satisfaction. The environment in which the product of selected company operates today is very different from the one in which it has historically succeeded. The decline in heavy commercial vehicle spending has increased the importance of cost or affordability in a decision process which previously emphasized the incorporation of state-of-the-art technology into new products in the heavy auto industry. In addition, the heavy vehicle industry consolidation is producing fewer companies competing fiercely for a piece of a decreasing pie. Therefore, Product of demand from master companies’ success depends on its ability to exceed customers’ expectations through superior performance, by delivering high quality products in a timely manner, with shorter leadtimes and lower costs. © 2017 Elixir All rights reserved. Elixir Mech. Engg. 105 (2017) 46117-46120 Mechanical Engineering Available online at www.elixirpublishers. com (Elixir International Journal)
Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion

In this paper, we propose a highly practical fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input. The proposed method is based on the Gaussian mixture probability hypothesis density (GMPHD) filter, a hierarchical data association (HDA), and a mask-based affinity fusion (MAF) model to achieve high-performance online tracking. The HDA consists of two associations: segment-to-track and track-to-track associations. One affinity, for position and motion, is computed by using the GMPHD filter, and the other affinity, for appearance is computed by using the responses from a single object tracker such as a kernalized correlation filter. These two affinities are simply fused by using a score-level fusion method such as min-max normalization referred to as MAF. In addition, to reduce the number of false positive segments, we adopt mask IoU-based merging (mask merging). The proposed MOTS framework with the key modules: HDA, MAF, and mask merging, is easily extensible to simultaneously track multiple types of objects with CPU only execution in parallel processing. In addition, the developed framework only requires simple parameter tuning unlike many existing MOTS methods that need intensive hyperparameter optimization. In the experiments on the two popular MOTS datasets, the key modules show some improvements. For instance, ID-switch decreases by more than half compared to a baseline method in the training sets. In conclusion, our tracker achieves state-of-the-art MOTS performance in the test sets.
Neural Network Modeling of Probabilities for Coding the Octree Representation of Point Clouds

This paper describes a novel lossless point cloud compression algorithm that uses a neural network for estimating the coding probabilities for the occupancy status of voxels, depending on wide three dimensional contexts around the voxel to be encoded. The point cloud is represented as an octree, with each resolution layer being sequentially encoded and decoded using arithmetic coding, starting from the lowest resolution, until the final resolution is reached. The occupancy probability of each voxel of the splitting pattern at each node of the octree is modeled by a neural network, having at its input the already encoded occupancy status of several octree nodes (belonging to the past and current resolutions), corresponding to a 3D context surrounding the node to be encoded. The algorithm has a fast and a slow version, the fast version selecting differently several voxels of the context, which allows an increased parallelization by sending larger batches of templates to be estimated by the neural network, at both encoder and decoder. The proposed algorithms yield state-of-the-art results on benchmark datasets. The implementation will be available at https://github.com/marmus12/nnctx
Variance Reduced Training with Stratified Sampling for Forecasting Models

In large-scale time series forecasting, one often encounters the situation where the temporal patterns of time series, while drifting over time, differ from one another in the same dataset. In this paper, we provably show under such heterogeneity, training a forecasting model with commonly used stochastic optimizers (e.g. SGD) potentially suffers large variance on gradient estimation, and thus incurs long-time training. We show that this issue can be efficiently alleviated via stratification, which allows the optimizer to sample from pre-grouped time series strata. For better trading-off gradient variance and computation complexity, we further propose SCott (Stochastic Stratified Control Variate Gradient Descent), a variance reduced SGD-style optimizer that utilizes stratified sampling via control variate. In theory, we provide the convergence guarantee of SCott on smooth non-convex objectives. Empirically, we evaluate SCott and other baseline optimizers on both synthetic and real-world time series forecasting problems, and demonstrate SCott converges faster with respect to both iterations and wall clock time.
Interactions of Ions and Ultracold Neutral Atom Ensembles in Composite Optical Dipole Traps: Developments and Perspectives

Ion–atom interactions are a comparatively recent field of research that has drawn considerable attention due to its applications in areas including quantum chemistry and quantum simulations. In first experiments, atomic ions and neutral atoms have been successfully overlapped by devising hybrid apparatuses combining established trapping methods, Paul traps for ions and optical or magneto-optical traps for neutral atoms, respectively. Since then, the field has seen considerable progress, but the inherent presence of radiofrequency (rf) fields in such hybrid traps was found to have a limiting impact on the achievable collision energies. Recently, it was shown that suitable combinations of optical dipole traps (ODTs) can be used for trapping both atoms and atomic ions alike, allowing to carry out experiments in absence of any rf fields. Here, we show that the expected cooling in such bichromatic traps is highly sensitive to relative position fluctuations between the two optical trapping beams, suggesting that this is the dominant mechanism limiting the currently observed cooling performance. We discuss strategies for mitigating these effects by using optimized setups featuring adapted ODT configurations. This includes proposed schemes that may mitigate three-body losses expected at very low temperatures, allowing to access the quantum dominated regime of interaction.
RNN with Particle Flow for Probabilistic Spatio-temporal Forecasting

Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks. Many classical statistical models often fall short in handling the complexity and high non-linearity present in time-series data. Recent advances in deep learning allow for better modelling of spatial and temporal dependencies. While most of these models focus on obtaining accurate point forecasts, they do not characterize the prediction uncertainty. In this work, we consider the time-series data as a random realization from a nonlinear state-space model and target Bayesian inference of the hidden states for probabilistic forecasting. We use particle flow as the tool for approximating the posterior distribution of the states, as it is shown to be highly effective in complex, high-dimensional settings. Thorough experimentation on several real world time-series datasets demonstrates that our approach provides better characterization of uncertainty while maintaining comparable accuracy to the state-of-the art point forecasting methods.
New challenges in covariance estimation: multiple structures and coarse quantization

In this self-contained chapter, we revisit a fundamental problem of multivariate statistics: estimating covariance matrices from finitely many independent samples. Based on massive Multiple-Input Multiple-Output (MIMO) systems we illustrate the necessity of leveraging structure and considering quantization of samples when estimating covariance matrices in practice. We then provide a selective survey of theoretical advances of the last decade focusing on the estimation of structured covariance matrices. This review is spiced up by some yet unpublished insights on how to benefit from combined structural constraints. Finally, we summarize the findings of our recently published preprint"Covariance estimation under one-bit quantization"to show how guaranteed covariance estimation is possible even under coarse quantization of the samples.