Miracle-dz/UniFAR-FTUs huggingface dataset:
Palaeoceanography and sedimentology of a mid-Cretaceous Greensand

﻿ The Upper Albian (mid-Cretaceous) sediments of the Anglo-Paris Basin display a range of condensation phenomena, including glauconitic and phosphoritic sands, glauconitic intraformational pebble beds, and mineralised nodular hardgrounds. These are interpreted as the result of sedimentation rate fluctuations controlled by small-scale relative sea-level changes of at least regional nature. The regional signal was modified by local tectonics, including occasional synsedimentary faulting and the development of thinned successions and complex condensation horizons over broad submarine highs. Candidate sequence boundaries, marine flooding surfaces and systems tracts are proposed. Episodic, high-energy storm events record a spectrum of storm intensities and periodicities in the Upper Greensand Formation. Storm processes interacted with early submarine lithification to produce a suite of pebble-shell beds, simple and amalgamated coquinas, and storm-scoured hardgrounds. Analysis of shell bed fabrics and taphonomy indicates the important role of high-energy storms in generating a distinct event stratigraphy. Stable isotope analysis has detected a positive secular change in carbon-isotopic ratios within carbonates of dispar zone age, resolved as two smaller positive shifts across nodular hardgrounds. This correlates with the development of organic-rich sediments in a range of settings world-wide. Analytical problems, including silica diagenesis, have been addressed by a series of parallel control studies. Geochemical and petrographic analysis has confirmed the widespread development of glauconitic minerals throughout the Upper Greensand, and has been used to investigate grain evolution in a range of lithologies. A review of the global development of the Cretaceous glauconitic facies has been used to compile depositional models for a range of settings. Volumetric calculations of Cretaceous glauconite production rates have shown secular changes, with peak Aptian to Santonian values linked to transgressions across the broad shelf seas which developed globally during this time of rising sea-levels and sea-level highstand.
linc00152 promotes the growth and invasion of oral squamous cell carcinoma by regulating mir-139-5p

biological significance of LINC00152 and its potential role in oral squamous cell carcinoma (OSCC) remain to be determined. In the present study, we investigated the role of LINC00152 and the underlying mechanism of its oncogenic activity in OSCC. Materials and methods: The expression of LINC00152 in OSCC tissues and cell lines was detected using qRT-PCR. Cell proliferation, colony formation, migration, and invasion were measured using a cell counting kit, colony formation assay, wound healing, and transwell invasion assays, respectively. The target gene of LINC00152 was confirmed using a dual-luciferase reporter assay and qRT-PCR. A nude mouse model was established to analyze the function of LINC00152 in vivo. Results: LINC00152 expression was significantly upregulated in OSCC tissues and cell lines compared with that in normal counterparts. Upregulated LINC00152 served as an independent prognostic predictor in patients with OSCC. Moreover, knockdown of LINC00152 inhibited cell proliferation, colony formation, migration, and invasion, and suppressed the epithelial to mesenchymal transition in vitro, as well as impairing tumor growth in vivo. A mechanistic investigation indicated that LINC00152 could directly bind to miR-139-5p in OSCC. LINC00152 expression was inversely correlated with miR-139 expression in OSCC tissues. Conclusion: Taken together, these results suggested that LINC00152 may function as oncogene in OSCC and could be a potential therapeutic target in patients with OSCC.
Fast approximation algorithms for knapsack problems

Fully polynomial approximation algorithms for knapsack problems are presented. These algorithms are based on ideas of Ibarra and Kim, with modifications which yield better time and space bounds, and also tend to improve the practicality of the procedures. Among the principal improvements are the introduction of a more efficient method of scaling and the use of a median-finding routine to eliminate sorting. The 0-1 knapsack problem, for n items and accuracy ε ≫ 0, is solved in (n log(1/ε) + 1/ε4) time and 0(n + 1/ε3) space. The time bound is reduced to 0(n + 1/ε3) for the "unbounded" knapsack problem. For the "subset-sum" problem, 0(n + 1/ε3) time and 0(n + 1/ε2) space, or 0(n + 1/ε2 log (1/ε)) time and space, are achieved. The "multiple choice" problem, with m equivalence classes, is solved in 0(nm2/ε) time and space.
Fault-Tolerant Multiparty Session Types

Multiparty session types are designed to abstractly capture the structure of communication protocols and verify behavioural properties. One important such property is progress, i.e., the absence of deadlock. Distributed algorithms often resemble multiparty communication protocols. But proving their properties, in particular termination that is closely related to progress, can be elaborate. Since distributed algorithms are often designed to cope with faults, a first step towards using session types to verify distributed algorithms is to integrate fault-tolerance. We extend multiparty session types to cope with system failures such as unreliable communication and process crashes. Moreover, we augment the semantics of processes by failure patterns that can be used to represent system requirements (as, e.g., failure detectors). To illustrate our approach we analyse a variant of the well-known rotating coordinator algorithm by Chandra and Toueg.
Semi-Supervised Learning in the Few-Shot Zero-Shot Scenario

Semi-Supervised Learning (SSL) is a framework that utilizes both labeled and unlabeled data to enhance model performance. Conventional SSL methods operate under the assumption that labeled and unlabeled data share the same label space. However, in practical real-world scenarios, especially when the labeled training dataset is limited in size, some classes may be totally absent from the labeled set. To address this broader context, we propose a general approach to augment existing SSL methods, enabling them to effectively handle situations where certain classes are missing. This is achieved by introducing an additional term into their objective function, which penalizes the KL-divergence between the probability vectors of the true class frequencies and the inferred class frequencies. Our experimental results reveal significant improvements in accuracy when compared to state-of-the-art SSL, open-set SSL, and open-world SSL methods. We conducted these experiments on two benchmark image classification datasets, CIFAR-100 and STL-10, with the most remarkable improvements observed when the labeled data is severely limited, with only a few labeled examples per class
Low gestational age at birth and difficulties in school

Objectives Several studies suggest a relationship between gestational age at birth and risk of school difficulties. Our study aimed to investigate the association between the entire range of gestational ages and significant school difficulties measured as 1) More than nine hours per week special educational support and 2) Failing to complete compulsory school. Methods A population-based register study including all children attending the Danish compulsory school in 2015/2016 and all live-born infants born in Denmark from 1992 to 1997. Data were collected and linked using multiple registers held by Statistic Denmark. Multiple logistic regression analyses were used to estimate the association between gestational age and significant school difficulties, adjusted for explanatory variables.
Unsupervised Domain Adaptation via Domain-Adaptive Diffusion

Unsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain. Inspired by diffusion models which have strong capability to gradually convert data distributions across a large gap, we consider to explore the diffusion technique to handle the challenging UDA task. However, using diffusion models to convert data distribution across different domains is a non-trivial problem as the standard diffusion models generally perform conversion from the Gaussian distribution instead of from a specific domain distribution. Besides, during the conversion, the semantics of the source-domain data needs to be preserved for classification in the target domain. To tackle these problems, we propose a novel Domain-Adaptive Diffusion (DAD) module accompanied by a Mutual Learning Strategy (MLS), which can gradually convert data distribution from the source domain to the target domain while enabling the classification model to learn along the domain transition process. Consequently, our method successfully eases the challenge of UDA by decomposing the large domain gap into small ones and gradually enhancing the capacity of classification model to finally adapt to the target domain. Our method outperforms the current state-of-the-arts by a large margin on three widely used UDA datasets.
The new era of nanotechnology, an alternative to change cancer treatment

: In the last few years, nanostructures have gained considerable interest for the safe delivery of therapeutic agents. Several therapeutic approaches have been reported, such as molecular diagnosis, disease detection, nanoscale immunotherapy and anticancer drug delivery that could be integrated into clinical use. The current paper aims to highlight the background that supports the use of nanoparticles conjugated with different types of therapeutic agents, applicable in targeted therapy and cancer research, with a special emphasis on hematological malignancies. A particular key point is the functional characterization of nonviral delivery systems, such as gold nanoparticles, liposomes and dendrimers. The paper also presents relevant published data related to microRNA and RNA interference delivery using nanoparticles in cancer therapy.
Broadcast Channels with Heterogeneous Arrival and Decoding Deadlines: Second-Order Achievability

A standard assumption in the design of ultra-reliable low-latency communication systems is that the duration between message arrivals is larger than the number of channel uses before the decoding deadline. Nevertheless, this assumption fails when messages arrive rapidly and reliability constraints require that the number of channel uses exceed the time between arrivals. In this paper, we consider a broadcast setting in which a transmitter wishes to send two different messages to two receivers over Gaussian channels. Messages have different arrival times and decoding deadlines such that their transmission windows overlap. For this setting, we propose a coding scheme that exploits Marton's coding strategy. We derive rigorous bounds on the achievable rate regions. Those bounds can be easily employed in point-to-point settings with one or multiple parallel channels. In the point-to-point setting with one or multiple parallel channels, the proposed achievability scheme outperforms the Normal Approximation, especially when the number of channel uses is smaller than $200$. In the broadcast setting, our scheme agrees with Marton's strategy for sufficiently large numbers of channel uses and shows significant performance improvements over standard approaches based on time sharing for transmission of short packets.
Experimental simulation of quantum superchannels

Simulating quantum physical processes has been one of the major motivations for quantum information science. Quantum channels, which are completely positive and trace preserving processes, are the standard mathematical language to describe quantum evolution, while in recent years quantum superchannels have emerged as the substantial extension. Superchannels capture effects of quantum memory and non-Markovianality more precisely, and have found broad applications in universal models, algorithm, metrology, discrimination tasks, as examples. Here, we report an experimental simulation of qubit superchannels in a nuclear magnetic resonance (NMR) system with high accuracy, based on a recently developed quantum algorithm for superchannel simulation. Our algorithm applies to arbitrary target superchannels, and our experiment shows the high quality of NMR simulators for near-term usage. Our approach can also be adapted to other experimental systems and demonstrates prospects for more applications of superchannels.
Reputation Effects with Endogenous Records

A patient firm interacts with a sequence of consumers. The firm is either an honest type who supplies high quality and never erases its records, or an opportunistic type who chooses what quality to supply and may erase its records at a low cost. We show that in every equilibrium, the firm has an incentive to build a reputation for supplying high quality until its continuation value exceeds its commitment payoff, but its ex ante payoff must be close to its minmax value when it has a sufficiently long lifespan. Therefore, even a small fraction of opportunistic types can wipe out the firm's returns from building reputations. Even if the honest type can commit to reveal information about its history according to any disclosure policy, the opportunistic type's payoff cannot exceed its equilibrium payoff when the consumers receive no information.
Prevalence and Risk Factors of Low Back Pain Among the Office Workers of King Edward Medical University Lahore, Pakistan

Low back pain is a destructive health problem of present era. It is affecting over a large population with huge economical loss. Office workers has a unique life style, they work in static body position with poor body posture for long periods of time. The musculoskeletal problems can lead to uneasiness or pain which can have a bad impact on quality of life. A study was done to find the prevalence and risk factors of low back pain (LBP) among the office workers of King Edward Medical University (KEMU), LahorePakistan. After the ethical approval from Institutional Review Board (IRB), KEMU -Lahore, a cross sectional study with sample size of 250 office workers calculated by using proportion formula of sample size estimation with 5% margin of error from KEMU with effect from Jan 2015 to Sep 2015 aged between 18-60 with at least 1 year work experience filled the validated questionnaires. Results showed that Point & life time prevalence of LBP among office workers of King Edward Medical University, Lahore-Pakistan was 29.20% and 69.20% respectively and increased with increase in age ,work experience, low education and decrease in physical activity, sleep disturbance, smoking habit, more sitting and standing time, computer use and with low job satisfaction. On the basis of this study, different individual, ergonomic, and psychosocial factors are associated with LBP.Because of high prevalence of low back pain (LBP) among office workers of this university, better ergonomic facilities, awareness about sitting posture, regular exercise, good sleep and psychological support to the workers were recommended to decrease the effects of predisposing risk factors of low back pain.
Prevalence and risk factors of low back pain among workers in a health facility in South-South Nigeria.

Background: Low back pain (LBP) has been documented to be a common occupational health problem among health care workers. A cross sectional study was carried out in a health facility in South – South Nigeria to determine the prevalence and risk factors of low back pain among the workers. Materials and Methods: Data was collected using a self designed, interviewer assisted, semistructured questionnaire. Information obtained included socio-demographic characteristics, work history of respondents, prevalence and factors predisposing to low back pain. The tool was administered during the different shifts of the health workers. Frequencies were calculated and Fishers exact test was used to test the significance of association between different variables. Level of significance was set at 0.05. Results: A total of 50 out of 53 workers participated in the study. The mean age of the respondents was 36.59±8.6 years with a male to female ratio of 2:3. The overall prevalence of LBP Original Research Article Johnson and Edward; BJMMR, 11(8): 1-8, 2016; Article no.BJMMR. 20785 2 over the preceding 12 months was 28%, while the prevalence among males was 35% and females, 23.3%, respectively. The proportion of those with LBP among the doctors, pharmacists and nurses was 33.3% each. No history of back pain was recorded among the administrative staff. Prolonged standing/sitting (25.0%), age above 35 years (38.9%), being overweight or obese (50.0%), lifting of heavy objects (35.7%) and frequent stooping (33.3%) were common risk factors associated with LBP. The associations were however not statistically significant (p>0.05). Up to 42.8% of those with LBP had sleep disturbance and 21.4% needed to obtain sick leave as a result of the pain. Conclusion: LBP was a common problem among staff in the health facility. Also, prolonged standing/ sitting, being overweight or obese, and lifting of heavy objects were among risk factors reported by those with LBP. Use of ergonomically designed chairs and equipments in the workplace, better lifting techniques and encouragement of mobility among the workers may help reduce the risk of LBP and thus improve workers’ productivity and wellbeing.
Endothelial Nitric-oxide Synthase

Bovine endothelial nitric-oxide synthase (eNOS) expressed in Escherichia coli does not have the post-translational modifications found in the native enzyme and is free of tetrahydrobiopterin (BH). In the presence of BH, eNOS has an absorption maximum at 400 nm that shifts to 395 nm when the substrate L-arginine is added. The low-spin component of the spectrum of the BH-free protein is decreased by the addition of BH without a corresponding increase in the high-spin component. Addition of BH decreases the low-spin population of eNOS even in the presence of excess L-arginine. These results indicate that BH directly modulates the heme environment. BH-free eNOS is completely inactive, but catalytic activity is recovered when BH (EC 200 nM) is added. The spectroscopically determined binding constants for L-arginine are 1.9 μM in the presence and 4.0 μM in the absence of BH. The BH-supplemented enzyme has an activity of 90-120 nmol of citrulline•min•mg and K values of 3 and 14 μM for L-arginine and N-hydroxy-L-arginine, respectively. Of particular interest is the finding by SDS-polyacrylamide gel electrophoresis that BH-free eNOS exists in a monomer-dimer equilibrium very similar to that observed with the BH-reconstituted protein. Addition of BH increases the percent of the dimer by only 5%. The results establish that BH influences the heme environment and stabilizes the protein with respect to heme loss but is not required for dimer formation.
Comparing AutoML and Deep Learning Methods for Condition Monitoring using Realistic Validation Scenarios

This study extensively compares conventional machine learning methods and deep learning for condition monitoring tasks using an AutoML toolbox. The experiments reveal consistent high accuracy in random K-fold cross-validation scenarios across all tested models. However, when employing leave-one-group-out (LOGO) cross-validation on the same datasets, no clear winner emerges, indicating the presence of domain shift in real-world scenarios. Additionally, the study assesses the scalability and interpretability of conventional methods and neural networks. Conventional methods offer explainability with their modular structure aiding feature identification. In contrast, neural networks require specialized interpretation techniques like occlusion maps to visualize important regions in the input data. Finally, the paper highlights the significance of feature selection, particularly in condition monitoring tasks with limited class variations. Low-complexity models prove sufficient for such tasks, as only a few features from the input signal are typically needed. In summary, these findings offer crucial insights into the strengths and limitations of various approaches, providing valuable benchmarks and identifying the most suitable methods for condition monitoring applications, thereby enhancing their applicability in real-world scenarios.
Multipartite Entanglement in Quantum Networks Using Subgraph Complementations

Quantum networks are important for quantum communication and consist of entangled states that are essential for many tasks such as quantum teleportation, quantum key distribution, quantum sensing and quantum error correction. Graph states are a specific class of multipartite entangled states that can be represented by graphs. We propose a novel approach for distributing graph states across a quantum network. We show that the distribution of graph states can be characterised by $\boldsymbol{a}$ system of subgraph complementations, which we also relate to the minimum rank of the underlying graph and the degree of entanglement quantified by the Schmidt-rank of the quantum state. We analyse resource usage for our algorithm and show it to match or be improved in the number of qubits, bits for classical communication and EPR pairs utilised, as compared to prior work. The number of local operations is efficient, and the resource consumption for our approach scales linearly in the number of vertices. This presents a quadratic improvement in completion time for several classes of graph states represented by dense graphs, and implies a potential for improved fidelity in the presence of noise. Common classes of graph states are classified along with the optimal time for their distribution using subgraph complementations. We also provide a framework to similarly find the optimal sequence of operations to distribute an arbitrary graph state, and prove upper bounds along with providing approximate greedy algorithms.
Evaluating the Robustness to Instructions of Large Language Models

Recently, Instruction fine-tuning has risen to prominence as a potential method for enhancing the zero-shot capabilities of Large Language Models (LLMs) on novel tasks. This technique has shown an exceptional ability to boost the performance of moderately sized LLMs, sometimes even reaching performance levels comparable to those of much larger model variants. The focus is on the robustness of instruction-tuned LLMs to seen and unseen tasks. We conducted an exploration of six models including Alpaca, Vicuna, WizardLM, and Traditional Task-oriented Models(Flan-T5-XL/XXL, T0++) using real-world relation extraction datasets as case studies. We carried out a comprehensive evaluation of these instruction-following LLMs which have been tuned based on open-domain instructions and task-oriented instructions. The main discussion is their performance and robustness towards instructions. We have observed that in most cases, the model's performance in dealing with unfamiliar instructions tends to worsen significantly, and the robustness of the model for RE instructions deteriorates compared to QA. Further, we discovered that up until a certain parameter size threshold (3B), the performance of the FLAN-T5 model improves as the parameter count increases. The robustness of different scales of FLAN-T5 models to RE instruction is worse than the robustness to QA instruction.