Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimating the population size of Steneotarsonemus concavuscutum in coconut fruits using diagrammatic damage scales

Abstract Predictive models based on diagrammatic scales of damage have been developed to estimate the population density of Aceria guerreronis, an important coconut pest. Steneotarsonemus concavuscutum colonizes the same habitat as A. guerreronis and causes similar lesions on fruits. The present study aims to evaluate the possibility of using predictive models developed for A. guerreronis to estimate S. concavuscutum populations. Fruits infested by S. concavuscutum were collected and evaluated according to the diagrammatic scales developed by Galvão et al. (2008) and by Sousa et al. (2017). All active forms of S. concavuscutum were accounted for. Graphical and statistical analyzes were performed to evaluate the adequacy of the predictive models. Both the models proved to be inadequate to estimate the population of S. concavuscutum, both statistically and graphically. New predictive models were proposed using the grades of the diagrammatic scales and the mean numbers of S. concavuscutum/fruit. To validate the models, new fruits were collected and evaluated by 10 examiners with both the diagrammatic scales. All active forms of S. concavuscutum were accounted for. The new predictive model based on the grades of Galvão et al. (2008) underestimated the populations of S. concavuscutum, while the model obtained from the grades of Sousa et al. (2017) generated values close to the expected, proving itself to be adequate statistically and graphically, to estimate the populations of S. concavuscutum.
Continuum percolation on nonorientable surfaces: the problem of permeable disks on a Klein bottle

The percolation threshold and wrapping probability (R∞) for the two-dimensional problem of continuum percolation on the surface of a Klein bottle have been calculated by the Monte Carlo method with the Newman–Ziff algorithm for completely permeable disks. It has been shown that the percolation threshold of disks on the Klein bottle coincides with the percolation threshold of disks on the surface of a torus, indicating that this threshold is topologically invariant. The scaling exponents determining corrections to the wrapping probability and critical concentration owing to the finite-size effects are also topologically invariant. At the same time, the quantities R∞ are different for percolation on the torus and Klein bottle and are apparently determined by the topology of the surface. Furthermore, the difference between the R∞ values for the torus and Klein bottle means that at least one of the percolation clusters is degenerate.
Awareness, Attitudes and Perception of Green Building Practices and Principles in the Zambian Construction Industry: A Qualitative Descriptive Cross Sectional Study

Awareness of green buildings technology is essential if practitioners in the building industry are to contribute in reducing negative impacts of building on the environment. Green building demonstration projects have been used as a way of sharing this knowledge to both practitioners as well as communities. Different strategies have been employed to share green building technology, however there is little research to demonstrate awareness attained from green building sites in Zambia. There are very few green buildings under construction and these few buildings could be a source of awareness. Understanding the level of awareness, perceptions and attitudes of artisans, communities and professionals on green building practices provides a good baseline on which to disseminate information on green building technology. This paper investigates the level of awareness, attitudes and perceptions of green building practices amongst participants in a green buildings housing project in North-Western and Lusaka provinces of Zambia. A qualitative descriptive cross sectional study was used and all the people who were directly involved in the project were interviewed. The findings suggest that the ones who had participated in other sustainability projects showed greater awareness than those who did not. The professionals had attained the knowledge from workshops, media, green building demonstration houses and institutions of higher learning while the artisans had attained it from previous project on renewable energy. Only two (2) community members showed great awareness which they attained from reading and watching documentaries on national television. The results suggest that professionals who were stationed on site demonstrated greater awareness than the artisans who spent an equal amount of time on the same project. The professionals showed the highest positive perception and attitude on the Likert scale. All the groups agreed that green building materials could only be affordable if they were locally produced. The study concluded that there are various ways of training practitioners on green building practices and no one method is sufficient. The sufficiency of the training depends on whether participants were made aware on the knowledge was being imparted. This should done at the beginning of the project. The demonstration houses showed that they could be a reliable source of training. Based on what the community said publications can be a good source of knowledge. For the professions, knowledge was acquired from university. This knowledge influenced their level of awareness and perceptions. The artisans who had worked on previous green building projects had gained knowledge. However knowledge gained was dependent on education levels. Training in green building practices should therefore be introduced in institutions of higher learning and trade schools.
Tofersen in adults with SOD1-ALS: phase 3 VALOR trial and open-label extension results

VALOR was a Phase 3, placebo-controlled trial to evaluate the clinical efficacy and safety of tofersen in adults with ALS and a confirmed SOD1 mutation (SOD1-ALS). Participants were randomised 2:1 to tofersen 100 mg (3 doses ~2 weeks apart, then 5 doses every 4 weeks) or placebo administered intrathecally. The primary endpoint was change from baseline to Week 28 in the ALS Functional Rating Scale-Revised (ALSFRS-R) total score. Key secondary endpoints included CSF total SOD1 concentration and other clinical outcome measures. Participants could continue in an open-label extension (OLE) upon completion of VALOR. 108 total participants were enrolled (tofersen [n=72], placebo [n=36]). VALOR did not achieve statistical significance on the ALSFRS-R at Week 28, the primary endpoint. Tofersen administration led to robust reductions in CSF total SOD1 protein and plasma neurofilament light chain (NfL) at Week 28 compared to baseline. Trends favoring tofersen were seen across clinical outcome measures of respira- tory function, muscle strength, and quality of life. These effects became more apparent with longer-term follow-up in the OLE, particularly with earlier tofersen initiation. Most adverse events were mild to moderate in severity. Serious neurologic events, including myelitis (2%), were seen in tofersen-treated participants. Study sponsored by Biogen.
DynamAP: Architectural Support for Dynamic Graph Traversal on the Automata Processor

Dynamic graph traversals (DGTs) currently are widely used in many important application domains, especially in this big-data era that urgently demands high-performance graph processing and analysis. Unlike static graph traversals, DGTs in real-world application scenarios require not only fast traversal acceleration itself but also, more importantly, a runtime strategy that can effectively accommodate the ever-evolving nature of the graph structure updates followed by a diverse range of graph traversal algorithms. Because of these special features, state-of-the-art designs on conventional compute-centric architectures (e.g., CPU and GPU) struggle to provide sufficient acceleration for DGT processing due to the dominating irregular memory access patterns in graph traversal algorithms and inefficient platform-specific update mechanisms. In this article, we explore the algorithmic features and runtime requirements of real-world DGTs and identify their unique opportunities of acceleration on the recent Micron Automata Processor (AP), an in-situ memory-centric pattern-matching architecture. These features include the natural mapping between traversal algorithms’ path exploration pattern to classic non-deterministic finite automata processing, AP’s architectural and compilation support for DGTs’ evolving traversal operations, and its inherent hardware fitness. However, despite these benefits, enabling highly efficient DGT execution on AP is non-trivial and faces several major challenges. To tackle them, we propose DynamAP, the first AP framework design that enables fast processing for general DGTs. DynamAP is oblivious to periodical traversal algorithm changes and can address the significant overhead caused by frequent graph updates and AP recompilation through our novel hybrid macro designs and associated efficient updating strategies. We evaluate DynamAP against the current DGT designs on a CPU, GPU, and AP with a range of widely adopted DGT algorithms and real-world graphs. For a single update request, our DynamAP achieves an average speedup of 21.3x (up to 39.2x) over the state-of-the-art implementation on host-AP architecture; an average speedup of 9.2x (up to 14.7x) and 1.7x (up to 2.8x) over two highly optimized DGT design frameworks on a 64-GB Intel(R) Xeon CPU and a 32-GB NVIDIA Tesla V100 GPU. DynamAP also maintains high performance and resource utilization for high graph update ratios, and can significantly benefit natural graphs that present a high average vertex degree.
Downscaling Earth System Models with Deep Learning

Modern climate models offer simulation results that provide unprecedented details at the local level. However, even with powerful supercomputing facilities, their computational complexity and associated costs pose a limit on simulation resolution that is needed for agile planning of resource allocation, parameter calibration, and model reproduction. As regional information is vital for policymakers, data from coarse-grained resolution simulations undergo the process of "statistical downscaling" to generate higher-resolution projection at a local level. We present a new method for downscaling climate simulations called GINE (Geospatial INformation Encoded statistical downscaling). To preserve the characteristics of climate simulation data during this process, our model applies the latest computer vision techniques over topography-driven spatial and local-level information. The comprehensive evaluations on 2x, 4x, and 8x resolution factors show that our model substantially improves performance in terms of RMSE and the visual quality of downscaled data.
SIPF: Sampling Method for Inverse Protein Folding

Protein engineering has important applications in drug discovery. Among others, inverse protein folding is a fundamental task in protein design, which aims at generating protein's amino acid sequence given a 3D graph structure. However, most existing methods for inverse protein folding are based on sequential generative models and therefore limited in uncertainty quantification and exploration ability to the entire protein space. To address the issues, we propose a sampling method for inverse protein folding (SIPF). Specifically, we formulate inverse protein folding as a sampling problem and design two pretrained neural networks as Markov Chain Monte Carlo (MCMC) proposal distribution. To ensure sampling efficiency, we further design (i) an adaptive sampling scheme to select variables for sampling and (ii) an approximate target distribution as a surrogate of the unavailable target distribution. Empirical studies have been conducted to validate the effectiveness of SIPF, achieving 7.4% relative improvement on recovery rate and 6.4% relative reduction in perplexity compared to the best baseline.
TARNet: Task-Aware Reconstruction for Time-Series Transformer

Time-series data contains temporal order information that can guide representation learning for predictive end tasks (e.g., classification, regression). Recently, there are some attempts to leverage such order information to first pre-train time-series models by reconstructing time-series values of randomly masked time segments, followed by an end-task fine-tuning on the same dataset, demonstrating improved end-task performance. However, this learning paradigm decouples data reconstruction from the end task. We argue that the representations learnt in this way are not informed by the end task and may, therefore, be sub-optimal for the end-task performance. In fact, the importance of different timestamps can vary significantly in different end tasks. We believe that representations learnt by reconstructing important timestamps would be a better strategy for improving end-task performance. In this work, we propose TARNet, Task-Aware Reconstruction Network, a new model using Transformers to learn task-aware data reconstruction that augments end-task performance. Specifically, we design a data-driven masking strategy that uses self-attention score distribution from end-task training to sample timestamps deemed important by the end task. Then, we mask out data at those timestamps and reconstruct them, thereby making the reconstruction task-aware. This reconstruction task is trained alternately with the end task at every epoch, sharing parameters in a single model, allowing the representation learnt through reconstruction to improve end-task performance. Extensive experiments on tens of classification and regression datasets show that TARNet significantly outperforms state-of-the-art baseline models across all evaluation metrics.
XDAI: A Tuning-free Framework for Exploiting Pre-trained Language Models in Knowledge Grounded Dialogue Generation

Large-scale pre-trained language models (PLMs) have shown promising advances on various downstream tasks, among which dialogue is one of the most concerned. However, there remain challenges for individual developers to create a knowledge-grounded dialogue system upon such big models because of the expensive cost of collecting the knowledge resources for supporting the system as well as tuning these large models for the task. To tackle these obstacles, we propose XDAI, a knowledge-grounded dialogue system that is equipped with the prompt-aware tuning-free PLM exploitation and supported by the ready-to-use open-domain external knowledge resources plus the easy-to-change domain-specific mechanism. With XDAI, the developers can leverage the PLMs without any fine-tuning cost to quickly create the open-domain dialogue systems as well as easily customize their own domain-specific systems. Extensive experiments including human evaluation, Turing test, and online evaluation have demonstrated the competitive performance of XDAI compared with the state-of-the-art general PLMs and specific PLMs for dialogue. XDAI pilots studies on the exploitation of PLMs and made intriguing findings which could be inspiring for the future research on other PLM-based applications. Developers and related researchers can get access to our repository at https://github.com/THUDM/XDAI, which presents a series of APIs, incremental toolkits and chatbot service of XDAI platform.
SagDRE: Sequence-Aware Graph-Based Document-Level Relation Extraction with Adaptive Margin Loss

Relation extraction (RE) is an important task for many natural language processing applications. Document-level relation extraction task aims to extract the relations within a document and poses many challenges to the RE tasks as it requires reasoning across sentences and handling multiple relations expressed in the same document. Existing state-of-the-art document-level RE models use the graph structure to better connect long-distance correlations. In this work, we propose SagDRE model, which further considers and captures the original sequential information from the text. The proposed model learns sentence-level directional edges to capture the information flow in the document and uses the token-level sequential information to encode the shortest paths from one entity to the other. In addition, we propose an adaptive margin loss to address the long-tailed multi-label problem of document-level RE tasks, where multiple relations can be expressed in a document for an entity pair and there are a few popular relations. The loss function aims to encourage separations between positive and negative classes. The experimental results on datasets from various domains demonstrate the effectiveness of the proposed methods.
Generalizable Floorplanner through Corner Block List Representation and Hypergraph Embedding

In the recent years, the deep reinforcement learning community has achieved impressive success to tackle real-world challenges. In this work, we propose a novel deep reinforcement learning agent to perform floorplanning, one of the early stages of VLSI physical design. Traditional methods to solve floorplanning problem are intractable for large circuit netlists and impossible to learn from past experience. We adopt the domain knowledge of floorplanning representation and propose a learning-based method that directly predicts block id and location through an RL framework. The resulting solutions are platform-independent and can be converted into layout within $O(n)$ time. We encode the hypernet information in the circuit netlist in a one-to-one mapping through hypergraph neural networks. Furthermore, We deploy transformer-like action selection to allow for transferability and generalization across netlist circuits with different sizes and handle the large discrete action space. This allows the parameter space of our model to remain the same regardless of the number of blocks. Our RL agent is able to transfer previously learnt knowledge to quickly optimize a new design with different size and purpose. To our knowledge, this is the first work to select both id and block position with an entirely end-to-end learning-based framework that can generalize. Results on publicly available benchmarks of GSRC and MCNC demonstrate that our method can outperform the baselines while being able to generalize.
Synthesising Audio Adversarial Examples for Automatic Speech Recognition

Adversarial examples in automatic speech recognition (ASR) are naturally sounded by humans yet capable of fooling well trained ASR models to transcribe incorrectly. Existing audio adversarial examples are typically constructed by adding constrained perturbations on benign audio inputs. Such attacks are therefore generated with an audio dependent assumption. For the first time, we propose the Speech Synthesising based Attack (SSA), a novel threat model that constructs audio adversarial examples entirely from scratch, i.e., without depending on any existing audio to fool cutting-edge ASR models. To this end, we introduce a conditional variational auto-encoder (CVAE) as the speech synthesiser. Meanwhile, an adaptive sign gradient descent algorithm is proposed to solve the adversarial audio synthesis task. Experiments on three datasets (i.e., Audio Mnist, Common Voice, and Librispeech) show that our method could synthesise naturally sounded audio adversarial examples to mislead the start-of-the-art ASR models. Our web-page containing generated audio demos is at https://sites.google. com/view/ssa-asr/home.
Physics-Guided Graph Meta Learning for Predicting Water Temperature and Streamflow in Stream Networks

This paper proposes a graph-based meta learning approach to separately predict water quantity and quality variables for river segments in stream networks. Given the heterogeneous water dynamic patterns in large-scale basins, we introduce an additional meta-learning condition based on physical characteristics of stream segments, which allows learning different sets of initial parameters for different stream segments. Specifically, we develop a representation learning method that leverages physical simulations to embed the physical characteristics of each segment. The obtained embeddings are then used to cluster river segments and add the condition for the meta-learning process. We have tested the performance of the proposed method for predicting daily water temperature and streamflow for the Delaware River Basin (DRB) over a 14 year period. The results confirm the effectiveness of our method in predicting target variables even using sparse training samples. We also show that our method can achieve robust performance with different numbers of clusterings.
Proposed improvement of coastal habitat resilience: The case study of Pantano forest of Policoro in southern Italy

In the last decades, climate change and the rapid urbanization due to the development of the coastal economy have led to biodiversity loss and the fragmentation of habitat in many coastal zones. The presence of protected areas cannot prevent the progress of land degradation. However, these areas are very important because they provide significant ecosystem services and affect local tourism. With regard to increasing adaptation strategies to human pressures and climate change, the present study proposes a detailed monitoring activity and an ecological restoration plan which could improve the resilience of a protected coastal zone in the Pantano forest of Policoro, located on the Ionian coast (southern Italy). In this area, continuous phenomena of intensive deforestation, hydraulic reclamation actions, and fires have reduced the native species of particular naturalistic value, favouring the advancement of desertification, coastal erosion, and saltwater intrusion. The proposed actions are derived from a preliminary analysis on maps, UAV-images, climate data and from meetings with the local community. The operative process detailed in this article could be applied to other protected areas which are subjected to the same phenomena and problems.
Genetic variations in GBA1 and LRRK2 genes: Biochemical and clinical consequences in Parkinson disease

Variants in the GBA1 and LRRK2 genes are the most common genetic risk factors associated with Parkinson disease (PD). Both genes are associated with lysosomal and autophagic pathways, with the GBA1 gene encoding for the lysosomal enzyme, glucocerebrosidase (GCase) and the LRRK2 gene encoding for the leucine-rich repeat kinase 2 enzyme. GBA1-associated PD is characterized by earlier age at onset and more severe non-motor symptoms compared to sporadic PD. Mutations in the GBA1 gene can be stratified into severe, mild and risk variants depending on the clinical presentation of disease. Both a loss- and gain- of function hypothesis has been proposed for GBA1 variants and the functional consequences associated with each variant is often linked to mutation severity. On the other hand, LRRK2-associated PD is similar to sporadic PD, but with a more benign disease course. Mutations in the LRRK2 gene occur in several structural domains and affect phosphorylation of GTPases. Biochemical studies suggest a possible convergence of GBA1 and LRRK2 pathways, with double mutant carriers showing a milder phenotype compared to GBA1-associated PD. This review compares GBA1 and LRRK2-associated PD, and highlights possible genotype-phenotype associations for GBA1 and LRRK2 separately, based on biochemical consequences of single variants.