Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability of the Combination of Ceftazidime and Cephazolin in Icodextrin or pH Neutral Peritoneal Dialysis Solution

♦ Introduction: The objective of this study was to investigate the stability of ceftazidime and cephazolin in a 7.5% icodextrin or pH neutral peritoneal dialysis (PD) solution. ♦ Methods: Ceftazidime and cephazolin were injected into either a 7.5% icodextrin or pH neutral PD bag to obtain the concentration of 125 mg/L of each antibiotic. A total of nine 7.5% icodextrin or pH neutral PD bags containing ceftazidime and cephazolin were prepared and stored at 1 of 3 different temperatures: 4°C in a domestic refrigerator; 25°C at room temperature; or 37°C (body temperature) in an incubator. An aliquot was withdrawn immediately before (0 hour) or after 12, 24, 48, 96, 120, 144, 168 and 336 hours of storage. Each sample was analyzed in duplicate for the concentration of ceftazidime and cephazolin using a stability-indicating high-performance liquid chromatography technique. Ceftazidime and cephazolin were considered stable if they retained more than 90% of their initial concentration. Samples were also assessed for pH, colour changes and evidence of precipitation immediately after preparation and on each day of analysis. ♦ Results: Ceftazidime and cephazolin in both types of PD solution retained more than 90% of their initial concentration for 168 and 336 hours respectively when stored at 4°C. Both of the antibiotics lost more than 10% of the initial concentration after 24 hours of storage at 25 or 37°C. There was no evidence of precipitation at any time under the tested storage conditions. Change in the pH and color was observed at 25 and 37°C, but not at 4°C. ♦ Conclusion: Premixed ceftazidime and cephazolin in a 7.5% icodextrin or pH neutral PD solution is stable for at least 168 hours when refrigerated. This allows the preparation of PD bags in advance, avoiding the necessity for daily preparation. Both the antibiotics are stable for at least 24 hours at 25 and 37°C, permitting storage at room temperature and pre-warming of PD bags to body temperature prior to its administration.
Contention Resolution in Hashing Based Shared Memory Simulations

In this paper we study the problem of simulating shared memory on the distributed memory machine (DMM). Our approach uses multiple copies of shared memory cells, distributed among the memory modules of the DMM via universal hashing. The main aim is to design strategies that resolve contention at the memory modules. Extending results and methods from random graphs and very fast randomized algorithms, we present new simulation techniques that enable us to improve the previously best results exponentially. In particular, we show that an $n$-processor CRCW PRAM can be simulated by an n-processor DMM with delay $\O(\log\log\log n \log^*n)$, with high probability. Next we describe a general technique that can be used to turn these simulations into time-processor optimal ones, in the case of EREW PRAMs to be simulated. We obtain a time-processor optimal simulation of an (n log log log n log*n)-processor EREW PRAM on an n-processor DMM with delay $\O(\log\log\log n \log^*n)$, with high probability. When an (n log log log n log*n)-processor CRCW PRAM is simulated, the delay is only by a log*n factor larger. We further demonstrate that the simulations presented can not be significantly improved using our techniques. We show an $\Omega(\log\log\log n / \log\log\log\log n)$ lower bound on the expected delay for a class of PRAM simulations, called topological simulations, that covers all previously known simulations as well as the simulations presented in the paper.
MRC-based Nested Medical NER with Co-prediction and Adaptive Pre-training

In medical information extraction, medical Named Entity Recognition (NER) is indispensable, playing a crucial role in developing medical knowledge graphs, enhancing medical question-answering systems, and analyzing electronic medical records. The challenge in medical NER arises from the complex nested structures and sophisticated medical terminologies, distinguishing it from its counterparts in traditional domains. In response to these complexities, we propose a medical NER model based on Machine Reading Comprehension (MRC), which uses a task-adaptive pre-training strategy to improve the model's capability in the medical field. Meanwhile, our model introduces multiple word-pair embeddings and multi-granularity dilated convolution to enhance the model's representation ability and uses a combined predictor of Biaffine and MLP to improve the model's recognition performance. Experimental evaluations conducted on the CMeEE, a benchmark for Chinese nested medical NER, demonstrate that our proposed model outperforms the compared state-of-the-art (SOTA) models.
HT-LIP Model based Robust Control of Quadrupedal Robot Locomotion under Unknown Vertical Ground Motion

This paper presents a hierarchical control framework that enables robust quadrupedal locomotion on a dynamic rigid surface (DRS) with general and unknown vertical motions. The key novelty of the framework lies in its higher layer, which is a discrete-time, provably stabilizing footstep controller. The basis of the footstep controller is a new hybrid, time-varying, linear inverted pendulum (HT-LIP) model that is low-dimensional and accurately captures the essential robot dynamics during DRS locomotion. A new set of sufficient stability conditions are then derived to directly guide the controller design for ensuring the asymptotic stability of the HT-LIP model under general, unknown, vertical DRS motions. Further, the footstep controller is cast as a computationally efficient quadratic program that incorporates the proposed HT-LIP model and stability conditions. The middle layer takes the desired footstep locations generated by the higher layer as input to produce kinematically feasible full-body reference trajectories, which are then accurately tracked by a lower-layer torque controller. Hardware experiments on a Unitree Go1 quadrupedal robot confirm the robustness of the proposed framework under various unknown, aperiodic, vertical DRS motions and uncertainties (e.g., slippery and uneven surfaces, solid and liquid loads, and sudden pushes).
INPC: Implicit Neural Point Clouds for Radiance Field Rendering

We introduce a new approach for reconstruction and novel-view synthesis of unbounded real-world scenes. In contrast to previous methods using either volumetric fields, grid-based models, or discrete point cloud proxies, we propose a hybrid scene representation, which implicitly encodes a point cloud in a continuous octree-based probability field and a multi-resolution hash grid. In doing so, we combine the benefits of both worlds by retaining favorable behavior during optimization: Our novel implicit point cloud representation and differentiable bilinear rasterizer enable fast rendering while preserving fine geometric detail without depending on initial priors like structure-from-motion point clouds. Our method achieves state-of-the-art image quality on several common benchmark datasets. Furthermore, we achieve fast inference at interactive frame rates, and can extract explicit point clouds to further enhance performance.
Synergistic Effects of Human Milk Nutrients in the Support of Infant Recognition Memory: An Observational Study

The aim was to explore the relation of human milk lutein; choline; and docosahexaenoic acid (DHA) with recognition memory abilities of six-month-olds. Milk samples obtained three to four months postpartum were analyzed for fatty acids, lutein, and choline. At six months, participants were invited to an electrophysiology session. Recognition memory was tested with a 70–30 oddball paradigm in a high-density 128-lead event-related potential (ERP) paradigm. Complete data were available for 55 participants. Data were averaged at six groupings (Frontal Right; Frontal Central; Frontal Left; Central; Midline; and Parietal) for latency to peak, peak amplitude, and mean amplitude. Difference scores were calculated as familiar minus novel. Final regression models revealed the lutein X free choline interaction was significant for the difference in latency scores at frontal and central areas (p < 0.05 and p < 0.001; respectively). Higher choline levels with higher lutein levels were related to better recognition memory. The DHA X free choline interaction was also significant for the difference in latency scores at frontal, central, and midline areas (p < 0.01; p < 0.001; p < 0.05 respectively). Higher choline with higher DHA was related to better recognition memory. Interactions between human milk nutrients appear important in predicting infant cognition, and there may be a benefit to specific nutrient combinations.
Does the Yield Spread Predict Real Economic Activity?

This article evaluates the ability of the yield spread to forecast real economic activity in 11 industrial countries. The first section of this article defines the yield spread and explains why the spread may be a useful predictor of real economic activity. The second section describes the data and criteria used to evaluate the predictive power of the yield spread. The third section examines whether yield spreads have reliably forecast real economic activity in the 11 countries, using several measures of real economic activity and alternative forecast horizons. The empirical results indicate the yield spread is a statistically and economically significant predictor of real economic activity in several industrial countries besides the United States. In addition, the yield spread forecasting model generally outperforms two alternative forecasting models in predicting future real GDP growth.
Target Speech Extraction with Pre-trained AV-HuBERT and Mask-And-Recover Strategy

Audio-visual target speech extraction (AV-TSE) is one of the enabling technologies in robotics and many audio-visual applications. One of the challenges of AV-TSE is how to effectively utilize audio-visual synchronization information in the process. AV-HuBERT can be a useful pre-trained model for lip-reading, which has not been adopted by AV-TSE. In this paper, we would like to explore the way to integrate a pre-trained AV-HuBERT into our AV-TSE system. We have good reasons to expect an improved performance. To benefit from the inter and intra-modality correlations, we also propose a novel Mask-And-Recover (MAR) strategy for self-supervised learning. The experimental results on the VoxCeleb2 dataset show that our proposed model outperforms the baselines both in terms of subjective and objective metrics, suggesting that the pre-trained AV-HuBERT model provides more informative visual cues for target speech extraction. Furthermore, through a comparative study, we confirm that the proposed Mask-And-Recover strategy is significantly effective.
Convergence analysis of OT-Flow for sample generation

Deep generative models aim to learn the underlying distribution of data and generate new ones. Despite the diversity of generative models and their high-quality generation performance in practice, most of them lack rigorous theoretical convergence proofs. In this work, we aim to establish some convergence results for OT-Flow, one of the deep generative models. First, by reformulating the framework of OT-Flow model, we establish the $\Gamma$-convergence of the formulation of OT-flow to the corresponding optimal transport (OT) problem as the regularization term parameter $\alpha$ goes to infinity. Second, since the loss function will be approximated by Monte Carlo method in training, we established the convergence between the discrete loss function and the continuous one when the sample number $N$ goes to infinity as well. Meanwhile, the approximation capability of the neural network provides an upper bound for the discrete loss function of the minimizers. The proofs in both aspects provide convincing assurances for OT-Flow.
Detection of Genital Chlamydia trachomatis Infection using PCR and Immunofluorescence Methods within Women Infertility in Iran

For long time infertility was one of the most sequels in medical science. Microbial agents are one of its causes. Possible etiologic role of Chlamydia trachomatis in infertility has been suggested for years ago but it has not been approved completely yet. This study was a case-control type of investigation done over 150 infertile women without physiological deficiency for infertility and control group was 200 fertile safe and impregnated women. The presences of Chlamydia trachomatis in two groups examined by direct, indirect immunofluorescence tests and PCR. Chlamydia trachomatis was detected by direct immunofluorescence method in 23(15.3%) of infertile women compare with 7(3.5%) of control population. Using indirect immunofluorescence tests a positive test titer of 1:16 and above was detected in 34(22.6%) of infertile cases and in 9(4.5%) of control group. Chlamydia trachomatis was detected by PCR method in 48(32%) of infertile women compare with 13(8.7%) of control population. Thus, the results of our study suggest that there is a significant association between C. trachomatis infections and female infertility
Chase Termination Beyond Polynomial Time

The chase is a widely implemented approach to reason with tuple-generating dependencies (tgds), used in data exchange, data integration, and ontology-based query answering. However, it is merely a semi-decision procedure, which may fail to terminate. Many decidable conditions have been proposed for tgds to ensure chase termination, typically by forbidding some kind of"cycle"in the chase process. We propose a new criterion that explicitly allows some such cycles, and yet ensures termination of the standard chase under reasonable conditions. This leads to new decidable fragments of tgds that are not only syntactically more general but also strictly more expressive than the fragments defined by prior acyclicity conditions. Indeed, while known terminating fragments are restricted to PTime data complexity, our conditions yield decidable languages for any k-ExpTime. We further refine our syntactic conditions to obtain fragments of tgds for which an optimised chase procedure decides query entailment in PSpace or k-ExpSpace, respectively.
Supporting Vision-Language Model Inference with Confounder-pruning Knowledge Prompt

Vision-language models are pre-trained by aligning image-text pairs in a common space to deal with open-set visual concepts. To boost the transferability of the pre-trained models, recent works adopt fixed or learnable prompts, i.e., classification weights are synthesized from natural language describing task-relevant categories, to reduce the gap between tasks in the training and test phases. However, how and what prompts can improve inference performance remains unclear. In this paper, we explicitly clarify the importance of including semantic information in prompts, while existing prompting methods generate prompts without exploring the semantic information of textual labels. Manually constructing prompts with rich semantics requires domain expertise and is extremely time-consuming. To cope with this issue, we propose a semantic-aware prompt learning method, namely CPKP, which retrieves an ontological knowledge graph by treating the textual label as a query to extract task-relevant semantic information. CPKP further introduces a double-tier confounder-pruning procedure to refine the derived semantic information. The graph-tier confounders are gradually identified and phased out, inspired by the principle of Granger causality. The feature-tier confounders are demolished by following the maximum entropy principle in information theory. Empirically, the evaluations demonstrate the effectiveness of CPKP, e.g., with two shots, CPKP outperforms the manual-prompt method by 4.64% and the learnable-prompt method by 1.09% on average, and the superiority of CPKP in domain generalization compared to benchmark approaches. Our implementation is available at https://github.com/Mowenyii/CPKP.
Empirical Analysis of Performance Problems on Code Level

Performance problems are well known on architecture level. On code level their occurrences have not been systematically researched so far. Since a lot of everyday work of software developers is done on code level, methods and tools with focus on frequent performance problems are relevant. In the presented thesis, a method for systematically evaluating the occurrence and the frequency of performance problems on code level is presented and applied to repositories. The results of this empirical research will be a classification of performance problems and a quantification of their frequency. This will raise the awareness on certain problem classes for developers and will provide a basis for the development of new performance tools for preventing performance problems.
On Stability Behaviors of 5D M-theory Black Objects

Using $N = 2$ supergravity formalism, we investigate certain behaviors of five dimensional black objects from the compactification of M-theory on a Calabi-Yau three-fold. The manifold has been constructed as the intersection of two homogeneous polynomials of degrees $ (\omega+2,1)$ and $ (2,1) $ in a product of two weighted projective spaces given by $ \mathbb{WP}^{4}(\omega,1,1,1,1) \times\mathbb{P}^{1}$. First, we determine the allowed electric charge regions of the BPS and non BPS black holes obtained by wrapping M2-branes on appropriate two cycles in such a proposed Calabi-Yau three-fold. After that, we calculate the entropy of these solutions which takes a maximal value corresponding to $\omega=1$ defining the ordinary projective space $\mathbb{P}^{4}$. For generic values of $\omega$, we show that the non BPS states are unstable. Then, we conduct a similar study of five dimensional black strings. Concerning the allowed magnetic charge regions of the BPS and non BPS black stringy solutions derived from M5-branes on dual divisors, we calculate the tension taking a minimal value for $\mathbb{P}^{4}$. By determining the recombination factor, we show that the non-BPS black string states are stable in the allowed regions in the magnetic charge space.
PEaCE: A Chemistry-Oriented Dataset for Optical Character Recognition on Scientific Documents

Optical Character Recognition (OCR) is an established task with the objective of identifying the text present in an image. While many off-the-shelf OCR models exist, they are often trained for either scientific (e.g., formulae) or generic printed English text. Extracting text from chemistry publications requires an OCR model that is capable in both realms. Nougat, a recent tool, exhibits strong ability to parse academic documents, but is unable to parse tables in PubMed articles, which comprises a significant part of the academic community and is the focus of this work. To mitigate this gap, we present the Printed English and Chemical Equations (PEaCE) dataset, containing both synthetic and real-world records, and evaluate the efficacy of transformer-based OCR models when trained on this resource. Given that real-world records contain artifacts not present in synthetic records, we propose transformations that mimic such qualities. We perform a suite of experiments to explore the impact of patch size, multi-domain training, and our proposed transformations, ultimately finding that models with a small patch size trained on multiple domains using the proposed transformations yield the best performance. Our dataset and code is available at https://github.com/ZN1010/PEaCE.
Towards Algorithmic Fidelity: Mental Health Representation across Demographics in Synthetic vs. Human-generated Data (preprint)

Synthetic data generation has the potential to impact applications and domains with scarce data. However, before such data is used for sensitive tasks such as mental health, we need an understanding of how different demographics are represented in it. In our paper, we analyze the potential of producing synthetic data using GPT-3 by exploring the various stressors it attributes to different race and gender combinations, to provide insight for future researchers looking into using LLMs for data generation. Using GPT-3, we develop HEADROOM, a synthetic dataset of 3,120 posts about depression-triggering stressors, by controlling for race, gender, and time frame (before and after COVID-19). Using this dataset, we conduct semantic and lexical analyses to (1) identify the predominant stressors for each demographic group; and (2) compare our synthetic data to a human-generated dataset. We present the procedures to generate queries to develop depression data using GPT-3, and conduct analyzes to uncover the types of stressors it assigns to demographic groups, which could be used to test the limitations of LLMs for synthetic data generation for depression data. Our findings show that synthetic data mimics some of the human-generated data distribution for the predominant depression stressors across diverse demographics.
Antilisterial Effects of Ziziphora clinopodioides Essential Oil and Nisin in Milk

The aim of the present study was to evaluate the antibacterial effect of Ziziphora clinopodioides essential oil (0.1 and 0.2%) separately and in combination with nisin (250 and 500 IU/ml) on survival of Listeria monocytogenes inoculated in pasteurized milk sample during storage under refrigerated temperature for 9 days. Chemical composition of the essential oil was analyzed by gas chromatography coupled with mass spectrometer detector (GC-MS). Carvacrol (64.22%), followed by thymol (19.22%) and p-cymene (4.86%) were the most abundant components of the essential oil. Based on our findings, there was no significant difference (p>0.05) between samples treated with the essential oil at 0.1 and 0.2% and also nisin at 250 and 500 IU/ml. Our samples treated with the essential oil in combination with nisin showed the populations of pathogen significantly (p<0.05) lower than the untreated samples. Moreover, samples treated with the combination of the essential oil at 0.2% and nisin at 500 IU/ml, showed populations of L. monocytogenes significantly (p<0.05) lower than those of samples treated with other groups. According to the results of this work, Z. clinopodioides essential oil and nisin could be applied in food industries as alternative food preservatives and to control the growth of L. monocytogenes.
Drag-and-drop refactoring: Intuitive and efficient program transformation

Refactoring is a disciplined technique for restructuring code to improve its readability and maintainability. Almost all modern integrated development environments (IDEs) offer built-in support for automated refactoring tools. However, the user interface for refactoring tools has remained largely unchanged from the menu and dialog approach introduced in the Smalltalk Refactoring Browser, the first automated refactoring tool, more than a decade ago. As the number of supported refactorings and their options increase, invoking and configuring these tools through the traditional methods have become increasingly unintuitive and inefficient. The contribution of this paper is a novel approach that eliminates the use of menus and dialogs altogether. We streamline the invocation and configuration process through direct manipulation of program elements via drag-and-drop. We implemented and evaluated this approach in our tool, Drag-and-Drop Refactoring (DNDRefactoring), which supports up to 12 of 23 refactorings in the Eclipse IDE. Empirical evaluation through surveys and controlled user studies demonstrates that our approach is intuitive, more efficient, and less error-prone compared to traditional methods available in IDEs today. Our results bolster the need for researchers and tool developers to rethink the design of future refactoring tools.