Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Planting Time and Spacing on Chincherinchee

The experiment on chincherinchee was conducted to find out optimum planting time and plant spacing for better vegetative growth, flowering and maximum bulb and bulblet production. Bulbs were planted in middle of October, November, December and January at spacings of 20 × 20 cm, 20 × 30 cm and 20 × 40 cm. It was observed that mid November was the best time to plant chincherinchee bulbs. The plant spacing of 20 × 20 cm resulted in optimum growth, flowering and bulb production over other spacings.
Evaluating the combustion process of methane fired cross draft ceramic kiln for efficiency and sustainability

Sustainable ceramic production is not only dependent on the adoption of renewable fuel, but also on combustion proficiency, efficient fuel utilization, and thermal energy released. This study reports on the thermal evaluation of a cross-draft ceramic kiln, with a view to determine the efficiency of the combustion process. The methodology involved firing the kiln to determine the equivalent ratio, stoichiometric air-methane ratio, enthalpy of formation of reactants and products, and the thermal energy liberated from the firing process. The results indicated that: 1000°C is attainable in 7 hours and 30 min at a firing rate of 136.97 sec/°C, fuel consumption rate of 1L/61.6 sec and average temperature of 613.4°C. The results also showed that, enthalpies of formation of products and reactants of -74,897 and -557,376.843 were generated at air-methane equivalent ratio of 1 : 5 and stoichiometric ratio of 9.818. This translated to 57.18% of air available for the combustion process and a 42.82% deficiency. The study concluded that though the combustion process of the methane fired kiln was weak, resulting in thermal energy loss of 39.9%, there was an energy utilization of up to 60.1%.
Incidence of blood culture-related sepsis in neonates and antibiotics sensitivity of implicated organisms in a secondary healthcare facility in Ghana

Summary Objective We determined the incidence of blood culture-related sepsis, causative bacteria, and antibiotics sensitivity among newborn babies with suggestive signs of sepsis admitted at the Upper East Regional Hospital in Bolgatanga, Ghana. Design Prospective cross-sectional study Setting Newborn Care Unit of the Upper East Regional Hospital, Bolgatanga Participants Neonates admitted to the Newborn Care Unit from August 2019 to August 2020 with signs of sepsis Main outcome measures Organisms isolated from blood cultures and sensitivity of isolated organisms to antibiotics. Results The study included two hundred and seventy-six (276) patients. Laboratory confirmed sepsis was 13.4% (37/276). Early onset sepsis was 3.3% (9/276), while late-onset sepsis was 10.1% (28/276). The most common clinical signs associated with positive culture cases were temperature instability (35.5%), poor feeding (14.5%), neonatal jaundice (11.3%), vomiting (9.7%), and respiratory distress (8.1%). Staphylococcus aureus and Staphylococcus epidermidis were the most common bacterial isolates (46% and 32%, respectively). There was no relationship between independent variables and blood culture confirmed sepsis. Antibiotics to which isolates were most resistant included flucloxacillin 4/4, penicillin 14/15, ampicillin 16/18, and tetracycline 23/28. Bacterial isolates were most sensitive to amikacin 16/16, levofloxacin 5/5, erythromycin 8/8, cefazolin 7/8, and ciprofloxacin 18/24. Conclusion Late-onset sepsis is a common sepsis category, and the implicated microorganisms are resistant to commonly prescribed antibiotics. Funding This work was funded by Upper East Regional Hospital, Bolgatanga.
An Efficient Goal-Oriented Adaptive Finite Element Method for Accurate Simulation of Complex Electromagnetic Radiation Problems

A goal-oriented adaptive frequency-domain finite element method (FEM) for solving electromagnetic radiation problems including complex structures is presented in this article. Compared with the traditional adaptive FEM, the goal-oriented method can flexibly control the refined regions according to the parameters of interest; therefore, it has better convergence and has made significant progress in scattering problems and eigenvalue problems. To simulate complex antennas, this article proposes an error indicator with high accuracy and low computational cost, and it uses the adjoint problem to weight element residuals without additional degrees of freedom (DoF). Moreover, high-quality mesh refinement algorithms adapted to this indicator are developed using a suitable point insertion strategy for multiscale structures. By simulating two practical antennas, comparisons with the traditional goal-oriented FEM and the well-developed $h$ -adaptive FEM in commercial software demonstrate the accuracy and efficiency of the proposed method.
Trace au bord des solutions positives d'équations elliptiques et paraboliques non linéaires. Résultats d'existence et d'unicité

Soient B la boule unite ouverte de R N de bord ∂B, S un sous-ensemble ferme de ∂B et μ une mesure de Radon positive sur R = ∂B\S. Nous demontrons des resultats d'existence et d'unicite de solutions positives de (EE): -Δu + u q = 0 dans B dont la trace au bord est (S, μ). Des resultats similaires concernant la trace initiale des solutions positives de (EP): u t - Δu + u q = 0 sont obtenus.
split-intein Gal4 provides intersectional genetic labeling that is repressible by Gal80

Significance The split-Gal4 system allows Drosophila researchers to drive transgene expression with extraordinary cell type specificity. However, the existing split-Gal4 system cannot be controlled temporally, and therefore cannot be applied to many important areas of research. Here, we present a split-Gal4 system based on a self-excising split-intein, which is controllable by Gal80, as well as a related drug-inducible split GeneSwitch system. This approach can both leverage and inform single-cell RNAseq datasets, and we introduce an algorithm to identify pairs of genes that precisely and narrowly mark a desired cell cluster. Our split-intein Gal4 system will be of value to the Drosophila research community, and allow for the creation of highly specific genetic drivers that are also inducible/repressible.
Operational Concurrency Control in the Face of Arbitrary Scale and Latency

We present for the first time a complete solution to the problem of proving the correctness of a concurrency control algorithm for collaborative text editors against the standard consistency model. The success of our approach stems from the use of comprehensive stringwise operational transformations, which appear to have escaped a formal treatment until now. Because these transformations sometimes lead to an increase in the number of operations as they are transformed, we cannot use inductive methods and adopt the novel idea of decreasing diagrams instead. We also base our algorithm on a client-server model rather than a peer-to-peer one, which leads to the correct application of operational transformations to both newly generated and pending operations. And lastly we solve the problem of latency, so that our algorithm works perfectly in practice. The result of these innovations is the first ever formally correct concurrency control algorithm for collaborative text editors together with a fast, fault tolerant and highly scalable implementation.
SLaM: Student-Label Mixing for Distillation with Unlabeled Examples

Knowledge distillation with unlabeled examples is a powerful training paradigm for generating compact and lightweight student models in applications where the amount of labeled data is limited but one has access to a large pool of unlabeled data. In this setting, a large teacher model generates ``soft'' pseudo-labels for the unlabeled dataset which are then used for training the student model. Despite its success in a wide variety of applications, a shortcoming of this approach is that the teacher's pseudo-labels are often noisy, leading to impaired student performance. In this paper, we present a principled method for knowledge distillation with unlabeled examples that we call Student-Label Mixing (SLaM) and we show that it consistently improves over prior approaches by evaluating it on several standard benchmarks. Finally, we show that SLaM comes with theoretical guarantees; along the way we give an algorithm improving the best-known sample complexity for learning halfspaces with margin under random classification noise, and provide the first convergence analysis for so-called ``forward loss-adjustment"methods.
Bayes optimal learning in high-dimensional linear regression with network side information

Supervised learning problems with side information in the form of a network arise frequently in applications in genomics, proteomics and neuroscience. For example, in genetic applications, the network side information can accurately capture background biological information on the intricate relations among the relevant genes. In this paper, we initiate a study of Bayes optimal learning in high-dimensional linear regression with network side information. To this end, we first introduce a simple generative model (called the Reg-Graph model) which posits a joint distribution for the supervised data and the observed network through a common set of latent parameters. Next, we introduce an iterative algorithm based on Approximate Message Passing (AMP) which is provably Bayes optimal under very general conditions. In addition, we characterize the limiting mutual information between the latent signal and the data observed, and thus precisely quantify the statistical impact of the network side information. Finally, supporting numerical experiments suggest that the introduced algorithm has excellent performance in finite samples.
Impact of starting measurement voltage relative to flat-band voltage position on the capacitance-voltage hysteresis and on the defect characterization of InGaAs/high-k metal-oxide-semiconductor stacks

In this work, we discuss how the position of the flat band voltage with respect to the starting voltage of the C-V measurement sweep can influence the estimation of the hysteresis in high-k/InGaAs MOS devices. We show that, with the support of experimental data and conceptual oxide defect band calculations, the interpretation and subsequent parameter extraction from flat-band voltage shifts observed in III-V MOS devices is more complex as compared to Si gate stacks. It is demonstrated that such complication arises due to the wider distribution of defect levels in the dielectric band gap in the case of InGaAs/high-k stack as compared to standard Si/SiO2/HfO2 MOS. In particular, for Al2O3 deposited on InGaAs, two wide, partially overlapping oxide defect bands are identified, centered ∼1.5 eV and ∼0.5 eV above and below the channel conduction band, respectively. Such defect levels are expected to affect the device operation and reliability.
Deep Learning for Day Forecasts from Sparse Observations

Deep neural networks offer an alternative paradigm for modeling weather conditions. The ability of neural models to make a prediction in less than a second once the data is available and to do so with very high temporal and spatial resolution, and the ability to learn directly from atmospheric observations, are just some of these models' unique advantages. Neural models trained using atmospheric observations, the highest fidelity and lowest latency data, have to date achieved good performance only up to twelve hours of lead time when compared with state-of-the-art probabilistic Numerical Weather Prediction models and only for the sole variable of precipitation. In this paper, we present MetNet-3 that extends significantly both the lead time range and the variables that an observation based neural model can predict well. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 introduces a key densification technique that implicitly captures data assimilation and produces spatially dense forecasts in spite of the network training on extremely sparse targets. MetNet-3 has a high temporal and spatial resolution of, respectively, up to 2 minutes and 1 km as well as a low operational latency. We find that MetNet-3 is able to outperform the best single- and multi-member NWPs such as HRRR and ENS over the CONUS region for up to 24 hours ahead setting a new performance milestone for observation based neural models. MetNet-3 is operational and its forecasts are served in Google Search in conjunction with other models.
DocAligner: Annotating Real-world Photographic Document Images by Simply Taking Pictures

Recently, there has been a growing interest in research concerning document image analysis and recognition in photographic scenarios. However, the lack of labeled datasets for this emerging challenge poses a significant obstacle, as manual annotation can be time-consuming and impractical. To tackle this issue, we present DocAligner, a novel method that streamlines the manual annotation process to a simple step of taking pictures. DocAligner achieves this by establishing dense correspondence between photographic document images and their clean counterparts. It enables the automatic transfer of existing annotations in clean document images to photographic ones and helps to automatically acquire labels that are unavailable through manual labeling. Considering the distinctive characteristics of document images, DocAligner incorporates several innovative features. First, we propose a non-rigid pre-alignment technique based on the document's edges, which effectively eliminates interference caused by significant global shifts and repetitive patterns present in document images. Second, to handle large shifts and ensure high accuracy, we introduce a hierarchical aligning approach that combines global and local correlation layers. Furthermore, considering the importance of fine-grained elements in document images, we present a details recurrent refinement module to enhance the output in a high-resolution space. To train DocAligner, we construct a synthetic dataset and introduce a self-supervised learning approach to enhance its robustness for real-world data. Through extensive experiments, we demonstrate the effectiveness of DocAligner and the acquired dataset. Datasets and codes will be publicly available.
FACTIFY3M: A Benchmark for Multimodal Fact Verification with Explainability through 5W Question-Answering

Combating disinformation is one of the burning societal crises -- about 67% of the American population believes that disinformation produces a lot of uncertainty, and 10% of them knowingly propagate disinformation. Evidence shows that disinformation can manipulate democratic processes and public opinion, causing disruption in the share market, panic and anxiety in society, and even death during crises. Therefore, disinformation should be identified promptly and, if possible, mitigated. With approximately 3.2 billion images and 720,000 hours of video shared online daily on social media platforms, scalable detection of multimodal disinformation requires efficient fact verification. Despite progress in automatic text-based fact verification (e.g., FEVER, LIAR), the research community lacks substantial effort in multimodal fact verification. To address this gap, we introduce FACTIFY 3M, a dataset of 3 million samples that pushes the boundaries of the domain of fact verification via a multimodal fake news dataset, in addition to offering explainability through the concept of 5W question-answering. Salient features of the dataset include: (i) textual claims, (ii) ChatGPT-generated paraphrased claims, (iii) associated images, (iv) stable diffusion-generated additional images (i.e., visual paraphrases), (v) pixel-level image heatmap to foster image-text explainability of the claim, (vi) 5W QA pairs, and (vii) adversarial fake news stories.
Detecting Check-Worthy Claims in Political Debates, Speeches, and Interviews Using Audio Data

Developing tools to automatically detect check-worthy claims in political debates and speeches can greatly help moderators of debates, journalists, and fact-checkers. While previous work on this problem has focused exclusively on the text modality, here we explore the utility of the audio modality as an additional input. We create a new multimodal dataset (text and audio in English) containing 48 hours of speech from past political debates in the USA. We then experimentally demonstrate that, in the case of multiple speakers, adding the audio modality yields sizable improvements over using the text modality alone; moreover, an audio-only model could outperform a text-only one for a single speaker. With the aim to enable future research, we make all our data and code publicly available at https://github.com/petar-iv/audio-checkworthiness-detection.
Can Large Language Models Infer Causation from Correlation?

Causal inference is one of the hallmarks of human intelligence. While the field of CausalNLP has attracted much interest in the recent years, existing causal inference datasets in NLP primarily rely on discovering causality from empirical knowledge (e.g., commonsense knowledge). In this work, we propose the first benchmark dataset to test the pure causal inference skills of large language models (LLMs). Specifically, we formulate a novel task Corr2Cause, which takes a set of correlational statements and determines the causal relationship between the variables. We curate a large-scale dataset of more than 200K samples, on which we evaluate seventeen existing LLMs. Through our experiments, we identify a key shortcoming of LLMs in terms of their causal inference skills, and show that these models achieve almost close to random performance on the task. This shortcoming is somewhat mitigated when we try to re-purpose LLMs for this skill via finetuning, but we find that these models still fail to generalize -- they can only perform causal inference in in-distribution settings when variable names and textual expressions used in the queries are similar to those in the training set, but fail in out-of-distribution settings generated by perturbing these queries. Corr2Cause is a challenging task for LLMs, and would be helpful in guiding future research on improving LLMs' pure reasoning skills and generalizability. Our data is at https://huggingface.co/datasets/causalnlp/corr2cause. Our code is at https://github.com/causalNLP/corr2cause.
Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding

On the one hand, the dehazing task is an ill-posedness problem, which means that no unique solution exists. On the other hand, the dehazing task should take into account the subjective factor, which is to give the user selectable dehazed images rather than a single result. Therefore, this paper proposes a multi-output dehazing network by introducing illumination controllable ability, called IC-Dehazing. The proposed IC-Dehazing can change the illumination intensity by adjusting the factor of the illumination controllable module, which is realized based on the interpretable Retinex model. Moreover, the backbone dehazing network of IC-Dehazing consists of a Transformer with double decoders for high-quality image restoration. Further, the prior-based loss function and unsupervised training strategy enable IC-Dehazing to complete the parameter learning process without the need for paired data. To demonstrate the effectiveness of the proposed IC-Dehazing, quantitative and qualitative experiments are conducted. Code is available at https://github.com/Xiaofeng-life/ICDehazing.
Posterior predictive checking for gravitational-wave detection with pulsar timing arrays. I. The optimal statistic

A gravitational-wave background can be detected in pulsar-timing-array data as Hellings--Downs correlations among the timing residuals measured for different pulsars. The optimal statistic implements this concept as a classical null-hypothesis statistical test: a null model with no correlations can be rejected if the observed value of the statistic is very unlikely under that model. To address the dependence of the statistic on the uncertain pulsar noise parameters, the pulsar-timing-array community has adopted a hybrid classical--Bayesian scheme (Vigeland et al. 2018) in which the posterior distribution of the noise parameters induces a posterior distribution for the statistic. In this article we propose a rigorous interpretation of the hybrid scheme as an instance of posterior predictive checking, and we introduce a new summary statistic (the Bayesian signal-to-noise ratio) that should be used to accurately quantify the statistical significance of an observation instead of the mean posterior signal-to-noise ratio, which does not support such a direct interpretation. In addition to falsifying the no-correlation hypothesis, the Bayesian signal-to-noise ratio can also provide evidence supporting the presence of Hellings--Downs correlations. We demonstrate our proposal with simulated datasets based on NANOGrav's 12.5-yr data release. We also establish a relation between the posterior distribution of the statistic and the Bayes factor in favor of correlations, thus calibrating the Bayes factor in terms of hypothesis-testing significance.
Iteratively Refined Behavior Regularization for Offline Reinforcement Learning

One of the fundamental challenges for offline reinforcement learning (RL) is ensuring robustness to data distribution. Whether the data originates from a near-optimal policy or not, we anticipate that an algorithm should demonstrate its ability to learn an effective control policy that seamlessly aligns with the inherent distribution of offline data. Unfortunately, behavior regularization, a simple yet effective offline RL algorithm, tends to struggle in this regard. In this paper, we propose a new algorithm that substantially enhances behavior-regularization based on conservative policy iteration. Our key observation is that by iteratively refining the reference policy used for behavior regularization, conservative policy update guarantees gradually improvement, while also implicitly avoiding querying out-of-sample actions to prevent catastrophic learning failures. We prove that in the tabular setting this algorithm is capable of learning the optimal policy covered by the offline dataset, commonly referred to as the in-sample optimal policy. We then explore several implementation details of the algorithm when function approximations are applied. The resulting algorithm is easy to implement, requiring only a few lines of code modification to existing methods. Experimental results on the D4RL benchmark indicate that our method outperforms previous state-of-the-art baselines in most tasks, clearly demonstrate its superiority over behavior regularization.
Designing Empathy Game: Case on Participatory Design Session with children within the Indian context

Empathy games are a promising yet new research avenue that explores how to design empathic game experiences that would help children to understand and address the emotions of other people. Research in this field was primarily done in the USA and there is a research gap in understanding how empathy game design can apply and differ from the contexts of other countries. Our study replicated a study earlier conducted in the USA, aiming to explore the dynamic of the PD process, and identify specifics and challenges for PD methodology related to empathy and game design in the Indian context. We conducted a series of participatory design (PD) sessions with 18 Indian children between 7 and 11 years old. This paper reports our preliminary findings, including the following: (1) it might be challenging for Indian children to discuss and design for empathy and emotions-related topics, (2) using the English language can be a barrier while working with Indian children of 8 years old and younger, (3) cultural context affects roles children play in the design process. This paper contributes insights on identifying areas for further methodological work in PD for the Indian context.