Miracle-dz/UniFAR-FTUs huggingface dataset:
On Improving Summarization Factual Consistency from Natural Language Feedback

Despite the recent progress in language generation models, their outputs may not always meet user expectations. In this work, we study whether informational feedback in natural language can be leveraged to improve generation quality and user preference alignment. To this end, we consider factual consistency in summarization, the quality that the summary should only contain information supported by the input documents, as the user-expected preference. We collect a high-quality dataset, DeFacto, containing human demonstrations and informational natural language feedback consisting of corrective instructions, edited summaries, and explanations with respect to the factual consistency of the summary. Using our dataset, we study three natural language generation tasks: (1) editing a summary by following the human feedback, (2) generating human feedback for editing the original summary, and (3) revising the initial summary to correct factual errors by generating both the human feedback and edited summary. We show that DeFacto can provide factually consistent human-edited summaries and further insights into summarization factual consistency thanks to its informational natural language feedback. We further demonstrate that fine-tuned language models can leverage our dataset to improve the summary factual consistency, while large language models lack the zero-shot learning ability in our proposed tasks that require controllable text generation.
Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation

One of the major challenges of machine translation (MT) is ambiguity, which can in some cases be resolved by accompanying context such as images. However, recent work in multimodal MT (MMT) has shown that obtaining improvements from images is challenging, limited not only by the difficulty of building effective cross-modal representations, but also by the lack of specific evaluation and training data. We present a new MMT approach based on a strong text-only MT model, which uses neural adapters, a novel guided self-attention mechanism and which is jointly trained on both visually-conditioned masking and MMT. We also introduce CoMMuTE, a Contrastive Multilingual Multimodal Translation Evaluation set of ambiguous sentences and their possible translations, accompanied by disambiguating images corresponding to each translation. Our approach obtains competitive results compared to strong text-only models on standard English→French, English→German and English→Czech benchmarks and outperforms baselines and state-of-the-art MMT systems by a large margin on our contrastive test set. Our code and CoMMuTE are freely available.
A multicriteria approach to timeout collaboration protocol

This paper presents a multicriteria approach to exploring the properties of timeout collaboration protocol with different timeout thresholds in general testing environments. This is formulated as a discrete multiple criteria optimisation problem by choosing five representative timeout thresholds as alternatives with five common performance measures of production systems. The PROMETHEE method is adopted to deal with this multicriteria problem. The divide-and-label algorithm is developed to rank all the alternatives with the overall intensity of their performance, by using multiple valued outranking graphs from the PROMETHEE with multiple replications. It is shown that two extreme timeout thresholds, T 0 = 0 and ∞, are efficient over multiple criteria in almost all cases. The divide-and-label algorithm is a very efficient approach to overcome the limitations of the PROMETHEE algorithm and Belz and Mertens's procedure with multiple criteria and replications.
On the Applicability of Synthetic Data for Re-Identification

This contribution demonstrates the feasibility of applying Generative Adversarial Networks (GANs) on images of EPAL pallet blocks for dataset enhancement in the context of re-identification. For many industrial applications of re-identification methods, datasets of sufficient volume would otherwise be unattainable in non-laboratory settings. Using a state-of-the-art GAN architecture, namely CycleGAN, images of pallet blocks rotated to their left-hand side were generated from images of visually centered pallet blocks, based on images of rotated pallet blocks that were recorded as part of a previously recorded and published dataset. In this process, the unique chipwood pattern of the pallet block surface structure was retained, only changing the orientation of the pallet block itself. By doing so, synthetic data for re-identification testing and training purposes was generated, in a manner that is distinct from ordinary data augmentation. In total, 1,004 new images of pallet blocks were generated. The quality of the generated images was gauged using a perspective classifier that was trained on the original images and then applied to the synthetic ones, comparing the accuracy between the two sets of images. The classification accuracy was 98% for the original images and 92% for the synthetic images. In addition, the generated images were also used in a re-identification task, in order to re-identify original images based on synthetic ones. The accuracy in this scenario was up to 88% for synthetic images, compared to 96% for original images. Through this evaluation, it is established, whether or not a generated pallet block image closely resembles its original counterpart.
Elucidation of reactive oxygen species scavenging pathways of norbergenin utilizing DFT approaches

Bergenin is a polyphenolic compound that contains isocoumarin skeletal derived from C-glycosylated 4-O-methylgallic acid. The biological activities of this compound and its derivatives are quite diverse. Recent studies reveal neuroprotective effects in vitro and in vivo in Alzheimer's. Norbergenin is a demethylated form of bergenin, known for better antioxidant capacity and associated with neuroprotective properties through oxidative stress inhibition. This study focused on investigating the scavenging mechanism of norbergenin with the •OH, •OOH, and O2∙− as a radical model under physiological and lipid environments. The thermodynamic and kinetic parameters of the hydrogen transfer (HT), single electron transfer (SET), sequential proton lost-electron transfer (SPLET) and radical adduct formation (RAF) mechanisms were determined theoretically by the density functional theory (DFT) at M06-2X/6-311 + + G(d,p) level of theory. Based on the computational results, this compound has proved as an excellent •OOH and •OH scavenger under physiological conditions better than Trolox and vitamin C, whereas its radical demonstrated as an efficient O2∙− scavenger.
Shifts in predator behaviour following climate induced disturbance on coral reefs

Coral reefs are increasingly ecologically destabilized across the globe due to climate change. Behavioural plasticity in corallivore behaviour and short-term trophic ecology in response to bleaching events may influence the extent and severity of coral bleaching and subsequent recovery potential, yet our understanding of these interactions in situ remains unclear. Here, we investigated interactions between corallivory and coral bleaching during a severe high thermal event (10.3-degree heating weeks) in Belize. We found that parrotfish changed their grazing behaviour in response to bleaching by selectively avoiding bleached Orbicella spp. colonies regardless of bleaching severity or coral size. For bleached corals, we hypothesize that this short-term respite from corallivory may temporarily buffer coral energy budgets by not redirecting energetic resources to wound healing, and may therefore enable compensatory nutrient acquisition. However, colonies that had previously been heavily grazed were also more susceptible to bleaching, which is likely to increase mortality risk. Thus, short-term respite from corallivory during bleaching may not be sufficient to functionally rescue corals during prolonged bleaching. Such pairwise interactions and behavioural shifts in response to disturbance may appear small scale and short term, but have the potential to fundamentally alter ecological outcomes, especially in already-degraded ecosystems that are vulnerable and sensitive to change.
On the geometry of broad-line regions in BL Lac objects

The geometry of broad-line regions (BLRs) in active galactic nuclei (AGNs) is still controversial. We use a sample of BL Lac objects, of which the black hole masses Mbh are estimated from their host galaxy absolute magnitude at R-band, MR, by using the empirical relation between MR and black hole mass Mbh. The sizes of the broad-line regions for MgII are derived from the widths of MgII lines and the black hole masses. Compared with the empirical relation between BLR size RBLR and MgII line luminosity LMgII, it is found the BLR sizes in the BL Lac objects derived in this paper are 2–3 orders of magnitude higher. If the BLR geometry of these sources is disklike, then the viewing angle between the axis and the line of sight is in the range of ~2°−15°, which is consistent with the unification scheme.
E. coli Toxin YjjJ (HipH) Is a Ser/Thr Protein Kinase That Impacts Cell Division, Carbon Metabolism, and Ribosome Assembly

Adaptation to growth condition is the key for bacterial survival, and protein phosphorylation is one of the strategies adopted to transduce extracellular signal in physiological response. In a previous work, we identified YjjJ, a putative kinase, as target of the persistence-related HipA kinase. ABSTRACT Protein Ser/Thr kinases are posttranslational regulators of key molecular processes in bacteria, such as cell division and antibiotic tolerance. Here, we characterize the E. coli toxin YjjJ (HipH), a putative protein kinase annotated as a member of the family of HipA-like Ser/Thr kinases, which are involved in antibiotic tolerance. Using SILAC-based phosphoproteomics we provide experimental evidence that YjjJ is a Ser/Thr protein kinase and its primary protein substrates are the ribosomal protein RpmE (L31) and the carbon storage regulator CsrA. YjjJ activity impacts ribosome assembly, cell division, and central carbon metabolism but it does not increase antibiotic tolerance as does its homologue HipA. Intriguingly, overproduction of YjjJ and its kinase-deficient variant can activate HipA and other kinases, pointing to a cross talk between Ser/Thr kinases in E. coli. IMPORTANCE Adaptation to growth condition is the key for bacterial survival, and protein phosphorylation is one of the strategies adopted to transduce extracellular signal in physiological response. In a previous work, we identified YjjJ, a putative kinase, as target of the persistence-related HipA kinase. Here, we performed the characterization of this putative kinase, complementing phenotypical analysis with SILAC-based phosphoproteomics and proteomics. We provide the first experimental evidence that YjjJ is a Ser/Thr protein kinase, having as primary protein substrates the ribosomal protein RpmE (L31) and the carbon storage regulator CsrA. We show that overproduction of YjjJ has a major influence on bacterial physiology, impacting DNA segregation, cell division, glycogen production, and ribosome assembly.
The Midsummer Dry Spell’s Impact on Vegetation in Jamaica

Abstract The annual rainfall pattern of the intra-Americas sea reveals a bimodal feature with a minimum during the midsummer known as the midsummer dry spell (MSD). A first attempt is made to examine the impact of the MSD on vegetation through a normalized difference vegetation index (NDVI) analysis in Jamaica. Tropical Rainfall Measuring Mission rainfall estimates and NDVI derived from the Terra Moderate Resolution Imaging Spectroradiometer highlight a consistent MSD feature in both rainfall and vegetative vigor. Spatial variation of this MSD NDVI response is evident throughout Jamaica, with the strongest relationship between the rainfall reduction and NDVI decline throughout the southern portions of Jamaica including the area of major domestic food production. In all years except 2005 there is a notable reduction from early-summer NDVI to midsummer NDVI in this agricultural region. However, the lagged vegetative response undergoes clear interannual variation and is affected by other forcings besides rai.
The magnetic field effect on the transport and efficiency of group III tris(8-hydroxyquinoline) organic light emitting diodes

Magnetoresistance and efficiency measurements of organic light emitting diode structures based on the group III hydroxyquinolates (Mq3) have been made as a function of magnetic field and Mq3 thickness, where M=Al, Ga, and In. For all quinolates, independent of thickness, we observed very similar behavior for the efficiency of the devices, with large increases in efficiency occurring at low values of applied field, which rapidly saturate as the field is increased. The current through these devices is found to be a strong function of both the device thickness and the metal ion. For Alq3 based devices, the current changes appear to correlate strongly with the triplet population in the devices. For Gaq3 and Inq3 devices, the magnetoresistance is found not to correlate with the triplet concentration and this may be evidence that there is little energetic barrier for carrier trapping in these materials. For all materials, a further dependence of the magnetoresistance on applied field was observed, which needs c.
Efficient Parallel Computation Of 3D Model Deformation Based On Conformal Mapping

In general, the animation between three-dimensional models uses linear interpolation between models to calculate the intermediate state. Before interpolation, the mapping relationship between the source model and the target model should be calculated to find the one-to-one correspondence of vertices. It is often necessary to traverse all vertices of the mesh in interpolation calculation, it can be difficult to implement parallel operation due to the irregularity of triangular mesh. This paper is aimed to form a regular representation of the 3D model in the conformal parameterization and set up efficient parallel computing under the regular structure so as to improve the efficiency of deformation computing of 3D models.
Research on Strawberry Positioning Technology including Maturity Classification

In the development of agricultural picking equipment, the identification and positioning of the target fruit is one of the key technologies. There are three sequence steps: finding the target fruit, determining whether the target fruit is mature and needs to be picked, and determining the XYZ coordinate point of the target fruit. In this study, we modified a series of convolution neural networks to detect strawberries on XY scale and judge their maturity. In order to improve the precision of small object detection, we innovatively proposed a new structure called small feature extraction layers. On a moderate GPU, small feature extraction layers improved the positioning precision from 80.53% to 89.96% and small objects precision from 41.58% to 86.14% at a 110 fps object detection speed. When using binocular ranging to measure Z-axis distance, we achieved 15 fps XYZ coordinate determination speed on a 4.5W ultra low-power CPU, and the maximum Z-axis ranging error within 60 cm was less than 2.83%.
MetaCNN: A New Hybrid Deep Learning Image-based Approach for Vehicle Classification Using Transformer-like Framework

Abstract—With the development of vehicles and traffic system in the early 21st century, the need for a monitored traffic system and vehicle classification is enlarging. Together with the development of deep learning, computer vision realm has emerged versatile models that is able to fulfill the need of classification. Those popular models include CNN, Vision Trans- former, Metaformer and so on. However, these models handle the problem based on different data processing techniques, they either lacks efficiency or effectiveness. In particular, CNN is shortcoming in global data while ViT is lack of extraction of local information. Therefore, based on this research gap, we proposed a model called MetaCNN, which combines CNN and Poolformer – a specific metaformer structure, which takes the strength of the two models and compensate for both models’ deficiencies. Finally, in order to verify the feasibility of our model, we tested our model on a real-world remote sensing datasets of vehicle images in six different regions with different weather conditions. Our model MetaCNN has demonstrated better recognition performance compared to other baseline models. The results further prove that our model MetaCNN is adept at vehicle classification of remote sensing images though under complex scenarios
Multi-objective software test case selection based on density analysis

Software test case selection is committed to select the fewest test cases from test suites to perform a complete test at the least cost. Machine learning and multi-objective optimization techniques have developed rapidly in recent years, and they have been successfully applied to test case selection. In this paper, we present a method called DB-NSGA2, which uses the density clustering algorithm in machine learning combined with the non-dominated ranking algorithm (NSGA2) for test case selection, which can better select the test cases required for testing. In particular, we apply some of the clustering results generated by the clustering algorithm to the crossover and mutation operations of the NSGA2 to improve diversity progeny populations and ensure the transmission of good individuals. Extensive experiments show that the test cases selected by our method can produce a better set of Pareto solutions and can detect more faults at a lower cost than other methods.
Enhanced UNet++ model for brain glioma MRI image segmentation

MRI technology can obtain the brain avatar of patients from multiple modes and angles, which greatly improves the diagnostic efficiency of brain tumor patients. However, the MRI data of brain tumor patients is usually not enough to train the deep learning based segmentation model for sufficient performance. This paper proposes a simple and effective method to augment the data to improve the image segmentation effect of UNet++ model on brain glioma. To enhance the illumination robust of MRI image, we bring in the max filter and min filter for image augmentation, and evaluate the segmentation effect of the UNet++ model using the original dataset and the augmented dataset on glioma, and evaluate the experimental results. We used the public datasets MICCAI_ BraTS_20 to evaluate, The WT PPV values of the two models were 0.9233 and 0.9427 respectively, and other evaluation indicators were also improved to varying degrees. In summary, it is concluded that our proposed simple augmentation method could effective augment the brain glioma MRI segmentation performance of UNet++ model on glioma, especially in the limited data.
Ad-Hoc Monitoring of COVID-19 Global Research Trends for Well-Informed Policy Making

The COVID-19 pandemic has affected millions of people worldwide with severe health, economic, social, and political implications. Healthcare Policy Makers (HPMs) and medical experts are at the core of responding to this continuously evolving pandemic situation and are working hard to contain the spread and severity of this relatively unknown virus. Biomedical researchers are continually discovering new information about this virus and communicating the findings through scientific articles. As such, it is crucial for HPMs and funding agencies to monitor the COVID-19 research trend globally on a regular basis. However, given the influx of biomedical research articles, monitoring COVID-19 research trends has become more challenging than ever, especially when HPMs want on-demand guided search techniques with a set of topics of interest in mind. Unfortunately, existing topic trend modeling techniques are unable to serve this purpose as (1) traditional topic models are unsupervised, and (2) HPMs in different regions may have different topics of interest that they want to track. To address this problem, we introduce a novel computational task in this article called Ad-Hoc Topic Tracking, which is essentially a combination of zero-shot topic categorization and the spatio-temporal analysis task. We then propose multiple zero-shot classification methods to solve this task by building on state-of-the-art language understanding techniques. Next, we picked the best-performing method based on its accuracy on a separate validation dataset and then applied it to a corpus of recent biomedical research articles to track COVID-19 research endeavors across the globe using a spatio-temporal analysis. A demo website has also been developed for HPMs to create custom spatio-temporal visualizations of COVID-19 research trends. The research outcomes demonstrate that the proposed zero-shot classification methods can potentially facilitate further research on this important subject matter. At the same time, the spatio-temporal visualization tool will greatly assist HPMs and funding agencies in making well-informed policy decisions for advancing scientific research efforts.
Load prediction model based on LSTM and attention mechanism

load forecasting is an important research direction, which has always been the concern of academia and industry. Accurate prediction results can provide effective decisions for resource allocation of the system. However, the change of application load is very complex. How to accurately predict the change trend of load is a challenging task. Traditional prediction algorithms such as Arima algorithm based on statistical theory and neural network algorithm predict the target load only through the historical sequence of a single load index, ignoring the interaction between different load indexes. Therefore, this paper proposes a load prediction model based on long-term and short-term memory network and attention mechanism lstmda. The model successively uses convolutional neural network and channel attention mechanism to extract the local dependence characteristics between loads. The bidirectional LSTM network with attention mechanism is used to predict the load, and the data at different times are given different degrees of importance. The model proposed in this paper achieves better performance than existing prediction algorithms on real load data sets.