Miracle-dz/UniFAR-FTUs huggingface dataset:
The Fossilised Birth-Death Model is Identifiable

Time-dependent birth-death sampling models have been used in numerous studies for inferring past evolutionary dynamics in different areas, e.g. speciation and extinction rates in macroevolutionary studies, or effective reproductive number in epidemiological studies. These models are branching processes where lineages can bifurcate, die, or be sampled with time-dependent birth, death, and sampling rates and generate phylogenetic trees. It has recently been shown that in some subclasses of such models, different sets of rates can result in the same distributions of reconstructed phylogenetic trees, and therefore the rates become unidentifiable from the trees regardless of their size. Here we show that widely used time-dependent fossilised birth-death (FBD) models are identifiable. This subclass of models makes more realistic assumptions about the fossilisation process and certain infectious disease transmission processes than the unidentifiable birth-death sampling models. Namely, FBD models assume that sampled lineages stay in the process rather than being immediately removed upon sampling. Identifiability of the time-dependent FBD model ensures that statistical methods that implement this model infer the true underlying temporal diversification or epidemiological dynamics from phylogenetic trees or directly from molecular or other comparative data. We further show that the time-dependent birth-death model with an extra parameter, the removal after sampling probability, is unidentifiable. This implies that in scenarios where we do not know how sampling affects lineages we are unable to infer this extra parameter together with birth, death, and sampling rates solely from trees. Significance Statement Identifiability of a statistical model is crucial for consistent inference which guarantees that parameters can be estimated close to their true values with sufficient amount of data. Recent results on unidentifiability of birth-death sampling models have put into question many macroevolutionary and epidemiological studies and caused considerable concern among researchers using these models. A related class of models, fossilised birth-death (FBD) models, has recently become a major tool for inferring past speciation or infectious disease transmission dynamics and dated evolutionary (phylogenetic) trees. Our result showing identifiability of FBD models enables researchers to confidently use these models and ensures the large body of research in palaeontology, ecology, and epidemiology reliant on the previously estimated parameters remains valid.
Functional characterization and annotation of trait-associated genomic regions by transcriptome analysis

Functional genomics is the subject of studying biological data recorded in the complete state of a genomic system using high-throughput techniques, to describe the function of DNA and its interactions with intermediate RNA transcripts and functional protein products. One of the most crucial issues to deal with in genomics is the ambiguity arising from sequence homology. Duplicated DNA sequences of variable length commonly exist in most organisms, which impose a great challenge on the technologies used in genome research. As the two flagship high-throughput techniques used to characterize genomes and trnascriptomes and to quantify the level of various biological activities and redundancy, tiling array and next-generation sequencing both require careful handling of non-uniquely mapped features to ensure their accuracies. Thus many works have been done in the field of array probe design and in mapping sequencing reads back to reference or in de novo genome assembly. According to the recent result from the international collaboration effort, The ENCODE project, 80 percent of the human genome are either transcribed or biochemically functional, a number much higher than the known protein coding segments scientists used to believe even in 2003, when the human genome was fully sequenced. As a consequence of the growing availability of new high-throughput techniques, many of such novel functional fragments need to be identified and further functionally characterized. In this work, two novel implementations have been presented, which could assist in the design and data analysis of high-throughput genomic experiments. An efficient and flexible tiling probe selection pipeline utilizing the penalized uniqueness score has been implemented, which could be employed in the design of various types and scales of genome tiling task, with high coverage and resolution, while giving more control of the expected hybridization efficiency. A novel hidden semi-Markov model (HSMM) implementation is made available within the Bioconductor project, which provides a unified interface for segmenting genomic data in a wide range of research subjects. It was designed specifically for genomic data analysis, with flexible distributional assumption and optional prior learning using annotation or previous studies. The usages and performance of the two novel tools have been illustrated and evaluated using simulation and published datasets. Moreover, through an integrative and detailed case study, in which genome regions previously show to exhibit quantitative trait loci (QTL) should be characterized in terms of encoding differentially expressed genes, the two implementations have been utilized. The penalized uniqueness score was used to design 1M feature tiling arrays that covered a 18 Mb region of the porcine genome at a coverage of 49%. The HSMM was applied on the data from hybridization experiments of divergent animals enabled detecting candidate genes with trait-dependent expression.
Situations and Strategy of Sustained Development on Alpine Grassland-Livestock Industry in Headwater Region of Yangtze and Yellow Rivers

Human having come down and depredated immoderately on grassland resource had been badly threatening living environment and sustainable development in Yangtze and Yellow Rivers headwater region.Which resulted in a series of problems on grassland-livestock industry:contradiction of supply and demand between grassland and livestock being outstanding;overstocking,mouse and insect pest being bad,and rangeland badly degraded;drove structure being immoderate,and infrastructure of resisting disaster and protecting livestock being bad;theory study of alpine grassland-livestock industry being scarce and lacking good demonstration templets.Therefore,contraposing problems of grassland-livestock industry in this region,according to national demand strategies of western expending and sustainable developing,these would be done as follows:①to strengthen scientific management and reasonable utilization of natural rangeland resources;②to development seasonal livestock industry and quicken turnover of drove;③to plant and use planted artificial grassland,and to enhance supplementary in winter;④to optimize ecological structure and production model of household farmer;⑤to found and perfect supervising(including wild animal),forecasting,and all-round evaluating system for rangeland resources;⑥to establish optimal production models of livestock industry and demonstration region of technological system in the different eco-type grasslands;⑦to reinforce pastoral education and infrastructure build,and to erect ecological compensation mechanism.
One-sided communication on a non-cache-coherent many-core architecture (Einseitige Kommunikation auf einer nicht-cache-kohärenten Vielkern-Prozessorarchitektur)

Contemporary multi-core processors are parallel systems that also provide shared memory for programs running on them. Both the increasing number of cores in so-called many-core systems and the still growing computational power of the cores demand for memory systems that are able to deliver high bandwidths. Caches are essential components to satisfy this requirement. Nevertheless, hardware-based cache coherence in many-core chips faces practical limits to provide both coherence and high memory bandwidths. In addition, a shift away from global coherence can be observed. As a result, alternative architectures and suitable programming models need to be investigated. This thesis focuses on fast communication for non-cache-coherent many-core architectures. Experiments are conducted on the Single-Chip Cloud Computer (SCC), a non-cache-coherent many-core processor with 48 mesh-connected cores. Although originally designed for message passing, the results of this thesis show that shared memory can be efficiently used for one-sided communication on this kind of architecture. One-sided communication enables data exchanges between processes where the receiver is not required to know the details of the performed communication. In the notion of the Message Passing Interface (MPI) standard, this type of communication allows to access memory of remote processes. In order to support this communication scheme on non-cache-coherent architectures, both an efficient process synchronization and a communication scheme with software-managed cache coherence are designed and investigated. The process synchronization realizes the concept of the general active target synchronization scheme from the MPI standard. An existing classification of implementation approaches is extended and used to identify an appropriate class for the non-cachecoherent shared memory platform. Based on this classification, existing implementations are surveyed in order to find beneficial concepts, which are then used to design a lightweight synchronization protocol for the SCC that uses shared memory and uncached memory accesses. The proposed scheme is not prone to process skew and also enables direct communication as soon as both communication partners are ready. Experimental results show very good scaling properties and up to five times lower synchronization latency compared to a tuned message-based MPI implementation for the SCC. For the communication, SCOSCo, a shared memory approach with software-managed cache coherence, is presented. According requirements for the coherence that fulfill
Evaluation of SPOT panchromatic digital imagery for updating road locations in a harvested forest area

Maintaining and updating road inventory information for Geographic Information System (GIS) applications can be costly in large areas that are managed for timber production. We evaluated the positional accuracy of SPOT (Systeme Probatoire D'Observation de La Terre) 10-metre panchromatic imagery to update road locations in a forested area in northern Idaho. One 7.5-minute quadrangle of SPOT level 1B panchromatic data was precision rectified using a digital elevation model. A 3 by 3 high pass filter was used to enhance linear features such as roads. The data were then resampled from the original 10-metre pixels to 3.3-metre pixels to increase the precision of on-screen digitizing of roads. The digitized roads arcs were compared with reference road arcs that had been compiled using an analytical stereoplotter
Observation and Validation of Methane from FTIR Over Addis Ababa

Methane is the most abundant organic trace gas in the atmosphere. The concentration of this gas is increasing more than double since industrial era. Due to its diverse sources and high impact on climate change study of the global atmospheric methane concentration is of special concern. In this work the sources, impacts and sinks of Methane are explored on regional basis. The book can serve as an excellent spring board for students,researchers,policy makers and climate scientists who are concerned about global warming.
Misfolded Amyloid β-42 induced impairment of the endo-lysosomal pathway revealed by real-time optical monitoring

Misfolding and aggregation of proteins is strongly linked to several neurodegenerative diseases but how such species bring about their cytotoxic actions remains poorly understood. Here we used specifically-designed optical reporter probes and live fluorescence imaging in primary hippocampal neurons to characterise the mechanism by which prefibrillar, oligomeric forms of the Alzheimer’s-associated peptide, Aβ42, exert their detrimental effects. We used a pH-sensitive reporter, Aβ42-CypHer, to track Aβ internalisation in real time, demonstrating that oligomers are rapidly taken up into cells in a dynamin-dependent manner, and trafficked via the endo-lysosomal pathway resulting in accumulation in lysosomes. In contrast, a non-assembling variant of Aβ42 (vAβ42) assayed in the same way is not internalised. Tracking ovalbumin uptake into cells using CypHer or Alexa Fluor tags, shows that preincubation with Aβ42 significantly disrupts protein uptake. Our results identify a potential mechanism by which amyloidogenic aggregates directly impair cellular function through disruption of the endosomal-lysosomal pathway.
Eff-Large SNN: A Lightweight and Versatile Approach for One-Shot Image Classification in Face Recognition

The field of face recognition has undergone a revolutionary transformation due to the introduction of deep learning algorithms, resulting in significant advancements in accuracy, robustness, and real-world applicability. However, the performance of face recognition systems is still influenced by challenging scenarios such as pose variation, expression, illumination, and occlusions. This paper presents a novel approach for one-shot image classification in face recognition, utilizing a modified Siamese Neural Network (SNN) to improve both speed and accuracy, even when the dataset is limited. The embedding layer of the SNN incorporates the EfficientNetV2L and Xception neural networks, which are then compared to determine the model's convergence. The experimental results were tested and demonstrated using our dataset and LFW dataset and concluded that the Eff-Large SNN is a lightweight and promising solution for one-shot image classification. The result from this Eff-Large SNN makes an accuracy of 91.95%. It outperforms SNN with Xception in terms of efficiency, accuracy, and versatility, making it suitable for a wide range of applications. This model is deployed in real-time CCTV surveillance for face recognition applications.
Transfer-Learning of a LiDAR Detection Algorithm Pre-Trained on Autonomous Driving Data for Roadside Infrastructure Application

This study investigates the optimization of Light Detection and Ranging (LiDAR) object detection on roadside data for automated driving through transfer-learning. A State-of-the-art object detection algorithm, trained on an extensive dataset from Automated Vehicles (AVs), is evaluated for its applicability to roadside sensors. To address the scarcity of roadside data, a novel sensor setup was introduced, enabling simultaneous data collection from AVs and roadside poles. The performance of a pre-trained model on both AV and roadside data demonstrated a significant decline, with the roadside data yielding only 82% of the performance achieved on AV data. However, by fine-tuning the pre-trained model with a small subset of roadside data, a remarkable improvement to 94% of the AV data based performance was achieved. These findings highlight the necessity of transfer-learning and emphasize its potential to enhance object detection accuracy in roadside scenarios. This study contributes to the advancement of automated driving technology, paving the way for more safe and efficient transportation systems.
A Comparison of Control Strategies for Passivity-Based VSI in Microgrids Between Conventional Droop Control and Droop Control with Two Integrators

As energy demands in microgrids have expanded, parallel-connected distributed generators are needed to fulfill the load users' demand. By combining the frequency and voltage of inverter-based generators, this study analyzes two essential control systems in microgrids: conventional droop control and Droop Control with two Integrators. This work will give a comparative analysis of control techniques regarding stability, control flexibility, transient mode and implementation complexity in a passivity-based voltage source inverter (VSI) under various scenarios and load types. The effectiveness of these strategies is demonstrated using MATLAB Simulink.
A Knowledge Embedding and Multi-Head Selection Model for Nested NER

In existing research on nested named entity recognition, span-based methods are employed to treat named entity recognition problems as span classification tasks, wherein finetuned pretrained models are utilized to facilitate more efficient recognition of nested entities. However, issues such as domain knowledge deficiency and failure to realize multi-classification are still unsolved. To address these issues, this paper innovatively put forward a Multi-Head Model Based on Knowledge Embedding (MKE), which: (1) introduces domain-specific background knowledge in the form of entity matrices, allowing the background knowledge to be embedded without any loss, and (2) transforms named entity recognition into a multi-head selection process, followed by scoring the candidate spans using the attention score model. This method realizes entity multi-classification, while also ensuring accurate identification of nested entity boundaries. The experimental results show that the background knowledge embedding realized by the entity matrix method is able to not only elevate the recognition accuracy, but also achieve the state-of-theart performance on seven nested and flat named entity recognition datasets.
A Statistically Efficient Method for Fusion of Multimodal Images in NSCT Domain

Image fusion is a procedure that enhances the understanding of a scene by integrating information collected from various imaging sensors. This technique is extensively applied in the field of image processing due to its effectiveness. Multifocus image fusion is a technique that deals with the challenge of combining two or more images captured of the same scene but focused on different depths. In medical image fusion, the primary benefit is the ability to extract relevant data from input images captured by different devices for the same organ, which is very useful for disease diagnosis. This concept of image processing holds significant importance across various applications as it contributes to improved perception, decision-making, and overall system performance.In the present study, we propose an efficient algorithm based on the non-subsampled contourlet transform (NSCT). This method takes into consideration the strong linear dependency between high-frequency coefficients in the input images. The low-frequency coefficients are fused using different fusion rules based on principal component analysis and a salience match measure. We conducted experiments using three sets of multimodal images, and the performance of our algorithm is compared to various spatial and transform domain image fusion methods presented in existing literature. We employed quantitative evaluation techniques based on diverse image fusion quality criteria to assess the fusion results. Additionally, we conducted subjective assessments to gauge the performance of the resulting fused images.
Performance Enhancement of CZTS/ZTO based Solar Cell through Sequential Optimization of Parameters

Copper zinc tin sulfide (CZTS) is a very promising semiconductor material for use as an absorber layer in thin film solar cells because of its low cost, high earthly availability, and non-toxicity. Recently CZTS based solar cell received increased attention because of their useful band-gap as well as high absorption coefficient. Zinc tin oxide (ZTO) is another nontoxic and widely available semiconductor material with strong potential to replace toxic Cadmium. sulfide (CdS) in the buffer. layer. In this paper, CZTS/ZTO structure based solar cell is studied for performance maximization through sequential optimization of parameters. Variational effect of CZTS and ZTO layer thickness, CZTS shallow acceptor doping density, and temperature are considered for proper tuning. Optimally tuned solar cell performance is evaluated using SCAPS-1D simulation software and obtained a maximum efficiency of 16.475 % at 300K, which surpasses the past records of 13.23%. Substituting the back contact metal with a higher work function (WF) one led to an improvement in the efficiency of this particular cell. The FTO/ZnO/ZTO/CZTS/Pt structure has achieved the highest efficiency of approximately 31%. The outcomes of this study can be a valuable guidelines for researchers aiming to develop CZTS/ZTO based solar cells that are cost-effective, environmentally friendly, and possess high levels of efficiency.
Mixed-traffic Intersection Management using Traffic-load-responsive Reservation and V2X -enabled Speed Coordination

Vehicle-to-everything (V2X) communication enables connected autonomous vehicles (CAVs) to share information and generate optimal decisions. The networking abilities of CAVs have led to the development of unsignalized autonomous intersection management (AIM) methods that leverage CAVs to significantly improve traffic flows. However, AIM methods assume 100% CAV market penetration, which is currently unrealistic owing to the gradual adoption of CAVs. Therefore, CAVs must share road usage with nonconnected vehicles (NCVs). Thus, we propose a mixed-traffic intersection management method that considers NCVs while ensuring high traffic flow, called traffic-load-responsive reservation for intersection management (TLRRIM). In TLRRIM, the roadside unit (RSU) first classifies vehicles and groups them into clusters before selecting a reservation cluster to cross an intersection. The reservation cluster selection considers both traffic load and crossing urgency. In addition, the RSU utilizes V2X-enabled speed coordination (VESC) for CAVs within the reservation cluster to further improve traffic flow, while utilizing traffic lights to guide NCVs. Simulation-based experiments using OpenCDA and CARLA showed that TLRRIM can increase throughput and reduce waiting time by up to 89.63% and 60.71 %, respectively, compared with the fixed-time signaling method. Moreover, adding VESC can increase throughput by 12.21 % and reduce waiting time by 10.80%, further enhancing traffic flow.
Elephant: LLM System for Accurate Recantations

This paper investigates the crossroads between Large Language Models (LLMs), Information Retrieval (IR), and the growing issue of misinformation in this current age of the Internet. Large Language Models, including the GPT and LLaMA series (both implemented in this paper), have given us profound insight into how we interact on the Internet and in real life. They have also innovated the way we interact with information. However, these innovations also pose significant shortfalls, particularly in the domain of misinformation from the text. The main contribution of this paper lies in developing a proposed strategy to mitigate the risks of misinformation seen on the Internet and generated from LLMs with a focus on public individuals by recanting statements they have made and the retrieval of said statements. We propose a multi-faceted approach that includes utilizing GPT3.5 and the open source LLaMA2 LLMs, finetuning data curation, and integrating accuracy mechanisms to ensure the most relevant and accurate information is retrieved. The efficacy of this methodology is measured using a cosine similarity metric. Considering that the recanting of this model must be at or as close to the original statement as possible, this metric is deemed most fitting. Findings later in this paper deemed a similarity recall of 90.42% on average with the GPT3.5 variant and 88.29% on average with the LLaMA2 variant, both in zero-shot examples, indicating the core semantic meanings were retrieved with variations on the format of illustration.