Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics of Land Use Land Cover Change and Soil Erosion Rate in Chure Landscape of Sudurpaschim Province

Land use land cover, a dynamic process, has become a central component of current strategies in managing natural resources and monitoring environmental changes. This study adopts an integrated GIS and Remote Sensing approach to assess changes in land use, land cover (LU/LC), and soil erosion rates between 2001 and 2021. Land use maps were generated using supervised classification and the Maximum Likelihood algorithm, while RUSEL factors facilitated soil erosion mapping analysis. The findings underscore a remarkable decrease in 'agricultural land' and 'water,' accompanied by a simultaneous increase in 'settlement area,' 'forest area,' and 'bare land. ' Agricultural land witnessed an annual decline of 2.5 percent, while water bodies decreased by 0.65 percent in 2021. Forest areas exhibited a 0.09 percent increment from 2001 to 2021, and settlement areas expanded at a rate of 0.41 percent annually. Notably, agricultural land reduction primarily contributed to the growth of settlement areas. Additionally, soil erosion rates rose from 3.82 (t/ha/year) in 2001 to 4.090 (t/ha/year) in 2021, indicating a correlation between land use change and soil erosion. Factors such as increased settlement, population growth, infrastructure development, riverbank cutting, and erosion were associated with the decline in agricultural land. Effective land use planning in the Churia region is imperative to address these changes. Government intervention and stakeholder cooperation are crucial to curbing forest degradation, unchecked human settlement expansion, and riverbank cutting. Furthermore, the study advocates for the intensified use of GIS and Remote Sensing technologies in monitoring and managing natural resources.
Differential Roles of Nuclear and Cytoplasmic Cyclin-Dependent Kinase 5 in Apoptotic and Excitotoxic Neuronal Death

Cyclin-dependent kinase 5 (cdk5) is a member of the cyclin-dependent kinase family whose activity is localized mainly to postmitotic neurons attributable to the selective expression of its activating partners p35 and p39. Deregulation of cdk5, as a result of calpain cleavage of p35 to a smaller p25 form, has been suggested to be a central component of neuronal death underlying numerous neurodegenerative diseases. However, the relevance of cdk5 in apoptotic death that relies on the mitochondrial pathway is unknown. Furthermore, evidence that cdk5 can also promote neuronal survival has necessitated a more complex understanding of cdk5 in the control of neuronal fate. Here we explore each of these issues using apoptotic and excitotoxic death models. We find that apoptotic death induced by the DNA-damaging agent camptothecin is associated with early transcription-mediated loss of p35 and with late production of p25 that is dependent on Bax, Apaf1, and caspases. In contrast, during excitotoxic death induced by glutamate, neurons rapidly produce p25 independent of the mitochondrial pathway. Analysis of the localization of p35 and p25 revealed that p35 is mainly cytoplasmic, whereas p25 accumulates selectively in the nucleus. By targeting a dominant-negative cdk5 to either the cytoplasm or nucleus, we show that cdk5 has a death-promoting activity within the nucleus and that this activity is required in excitotoxic death but not apoptotic death. Moreover, we also find that cdk5 contributes to pro-survival signaling selectively within the cytoplasm, and manipulation of this signal can modify death induced by both excitotoxicity and DNA damage.
Overexpression of TIP30 inhibits the growth and invasion of glioma cells

Glioma is an aggressive malignancy with limited effective treatment and poor prognosis. Therefore, the identification of novel prognostic markers and effective therapeutic targets is important for the treatment of human glioma. TIP30 is a tumor suppressor involved in the regulation of numerous cellular processes, including tumor cell growth, metastasis, and angiogenesis in various human cancers. The present study investigated whether Tat-interacting protein (TIP)30 was able to regulate tumorigenesis and predict the clinical outcome of patients with glioma. A total of 92 human glioma tissue samples and 10 normal brain tissue samples were examined by immunostaining. The results indicated that the expression levels of TIP30 significantly decreased in glioma tissue samples. as compared with normal brain tissue samples. Furthermore, TIP30 expression was inversely correlated with tumor histological classification, pathological grade, tumor size, and epidermal growth factor receptor (EGFR) expression; however, no association was detected between TIP30 expression and patient age and gender. In addition, patients with positive TIP30 expression exhibited significantly longer median overall survival rates, as compared with those with negative TIP30 expression. In vitro experiments revealed that upregulation of TIP30 expression by lentiviral vector transfection inhibited cell growth and induced cell apoptosis, as determined by MTT assay and Annexin V-fluorescein isothiocyanate staining, respectively. In addition, TIP30 expression markedly attenuated cell migration and invasion, as determined by wound healing and transwell assays. Upregulation of TIP30 expression in glioma cells decreased the expression levels of EGFR and its associated downstream molecules phosphorylated extracellular signal-regulated kinases (ERK) and phosphorylated AKT, as determined by western blot analysis. The results of the present study indicated that TIP30 may suppress oncogenesis and glioma progression, thereby improving the prognosis of patients with glioma. Therefore, TIP30 may prove useful as a prognostic biomarker, and as a potential target for glioma therapy.
Improved AoA based localization of UHF RFID tags using spatial diversity

In a recent paper we presented new measurement results for an angle of arrival based approach to localize UHF RFID transponders in an indoor environment with off-the-shelf IDS R901G RFID reader ICs and UPM Raflatac DogBone RFID transponders. We were able to localize the transponders with a mean accuracy of 0.21 m using three antenna array positions for triangulation in a 3 m × 3 m room. In this paper we present an RSS based algorithm to select a special subset of antenna array positions for the estimation of the transponder position in a measurement setup with five different antenna array positions. With this algorithm a reduction of the mean measurement error by 50% was achieved for the considered test grid compared to the original approach in [1].
Spectral Methods for Correlated Topic Models

In this paper, we propose guaranteed spectral methods for learning a broad range of topic models, which generalize the popular Latent Dirichlet Allocation (LDA). We overcome the limitation of LDA to incorporate arbitrary topic correlations, by assuming that the hidden topic proportions are drawn from a flexible class of Normalized Infinitely Divisible (NID) distributions. NID distributions are generated through the process of normalizing a family of independent Infinitely Divisible (ID) random variables. The Dirichlet distribution is a special case obtained by normalizing a set of Gamma random variables. We prove that this flexible topic model class can be learned via spectral methods using only moments up to the third order, with (low order) polynomial sample and computational complexity. The proof is based on a key new technique derived here that allows us to diagonalize the moments of the NID distribution through an efficient procedure that requires evaluating only univariate integrals, despite the fact that we are handling high dimensional multivariate moments. In order to assess the performance of our proposed Latent NID topic model, we use two real datasets of articles collected from New York Times and Pubmed. Our experiments yield improved perplexity on both datasets compared with the baseline.
Improving Communication Performance and Scalability of Native Applications on Intel Xeon Phi Coprocessor Clusters

Intel Xeon Phi coprocessor-based clusters offer high compute and memory performance for parallel workloads and also support direct network access. Many real world applications are significantly impacted by network characteristics and to maximize the performance of such applications on these clusters, it is particularly important to effectively saturate network bandwidth and/or hide communications latency. We demonstrate how to do so using techniques such as pipelined DMAs for data transfer, dynamic chunk sizing, and better asynchronous progress. We also show a method for, and the impact of avoiding serialization and maximizing parallelism during application communication phases. Additionally, we apply application optimizations focused on balancing computation and communication in order to hide communication latency and improve utilization of cores and of network bandwidth. We demonstrate the impact of our techniques on three well known and highly optimized HPC kernels running natively on the Intel Xeon Phi coprocessor. For the Wilson-Dslash operator from Lattice QCD, we characterize the improvements from each of our optimizations for communication performance, apply our method for maximizing concurrency during communication phases, and show an overall 48% improvement from our previously best published result. For HPL/LINPACK, we show 68.5% efficiency with 97 TFLOPs on 128 Intel Xeon Phi coprocessors, the first ever reported native HPL efficiency on a coprocessor-based supercomputer. For FFT, we show 10.8 TFLOPs using 1024 Intel Xeon Phi coprocessors on the TACC Stampede cluster, the highest reported performance on any Intel Architecture-based cluster and the first such result to be reported on a coprocessor-based supercomputer.
Portlandemys gracilis n. sp., a New Coastal Marine Turtle from the Late Jurassic of Porrentruy (Switzerland) and a Reconsideration of Plesiochelyid Cranial Anatomy

Background Several groups of stem cryptodires became adapted to coastal marine environments as early as the Late Jurassic, 40 million years before the Pan-Chelonioidea. The Plesiochelyidae are a major component of this first radiation of crown-group turtles into marine habitats. They are abundant in many European localities, but their systematics is still greatly confused. Only three species are represented by cranial material: Plesiochelys etalloni, Plesiochelys planiceps, and Portlandemys mcdowelli. Methodology/Principal Findings In the present study, we describe a cranium and a mandible from the Kimmeridgian of Porrentruy (Switzerland), which we refer to a new species, Portlandemys gracilis n. sp. This new taxon differs from Portlandemys mcdowelli in several aspects of the cranium and mandible, notably in being generally more gracile, but the two species share a narrow skull, a more acute angle between the labial ridges on the mandible, and a unique configuration of the anterodorsal part of the basicranium. The cranial anatomy of plesiochelyid turtles is discussed in details based primarily on these new specimens and new cranial material of Plesiochelys etalloni from Solothurn, Switzerland. Conclusions/Significance Several characters (e.g., the contribution of the parietal to the foramen nervi trigemini, the configuration of the dorsum sellae and sella turcica, the presence of an infolding ridge on the posterior surface of the quadrate) appear as potential candidates to help elucidate plesiochelyid relationships. Some of these characters are included in a previously published phylogenetic dataset and help to stabilize the relationships of plesiochelyid turtles and closely related taxa. For the first time, our results suggest that plesiochelyids, 'Thalassemys' moseri, and Solnhofia parsonsi (representing the Eurysternidae) form a clade at the base of Eucryptodira.
Breaking up is hard to do: security and functionality in a commodity hypervisor

Cloud computing uses virtualization to lease small slices of large-scale datacenter facilities to individual paying customers. These multi-tenant environments, on which numerous large and popular web-based applications run today, are founded on the belief that the virtualization platform is sufficiently secure to prevent breaches of isolation between different users who are co-located on the same host. Hypervisors are believed to be trustworthy in this role because of their small size and narrow interfaces. We observe that despite the modest footprint of the hypervisor itself, these platforms have a large aggregate trusted computing base (TCB) that includes a monolithic control VM with numerous interfaces exposed to VMs. We present Xoar, a modified version of Xen that retrofits the modularity and isolation principles used in micro-kernels onto a mature virtualization platform. Xoar breaks the control VM into single-purpose components called service VMs. We show that this componentized abstraction brings a number of benefits: sharing of service components by guests is configurable and auditable, making exposure to risk explicit, and access to the hypervisor is restricted to the least privilege required for each component. Microrebooting components at configurable frequencies reduces the temporal attack surface of individual components. Our approach incurs little performance overhead, and does not require functionality to be sacrificed or components to be rewritten from scratch.
IC Packaging Material Identification via a Hybrid Deep Learning Framework with CNN–Transformer Bidirectional Interaction

With the advancement of micro- and nanomanufacturing technologies, electronic components and chips are increasingly being miniaturized. To automatically identify their packaging materials for ensuring the reliability of ICs, a hybrid deep learning framework termed as CNN–transformer interaction (CTI) model is designed on IC packaging images in this paper, in which several cascaded CTI blocks are designed to bidirectionally capture local and global features from the IC packaging image. Each CTI block involves a CNN branch with two designed convolutional neural networks (CNNs) for CNN local features and a transformer branch with two transformers for transformer global features and transformer local-window features. A bidirectional interaction mechanism is designed to interactively transfer the features in channel and spatial dimensions between the CNNs and transformers. Experimental results indicate that the hybrid framework can recognize three types of IC packaging materials with a good performance of 96.16% F1-score and 97.92% accuracy, which is superior to some existing deep learning methods.
Algorithms for Optimizing Bandwidth Costs on the Internet

Content delivery networks (CDNs) deliver Web content to end-users from a large distributed platform of Web servers hosted in data centers belonging to thousands of Internet service providers (ISPs) around the world. The bandwidth cost incurred by a CDN is the sum of the amounts it pays each ISP for routing traffic from its servers located in that ISP out to end-users. A large enterprise may also contract with multiple ISPs to provide redundant Internet access for its origin infrastructure using technologies such as multihoming and mirroring, thereby incurring a significant bandwidth cost across multiple ISPs. This paper initiates the formal study of bandwidth cost minimization in the context of a large enterprise or a CDN, a problem area that is both algorithmically rich and practically very important. First, we model different types of contracts that are used in practice by ISPs to charge for bandwidth usage, including average, maximum, and 95th-percentile contracts. Then, we devise an optimal offline algorithm that routes traffic to achieve the minimum bandwidth cost, when the network contracts charge either on a maximum or on an average basis. Next, we devise a deterministic (resp., randomized) online algorithm that achieves cost that is within a factor of 2 (resp., e/(e - 1)) of the optimal offline cost for maximum and average contracts. In addition, we prove that our online algorithms achieve the best possible competitive ratios in both the deterministic and the randomized cases. An interesting theoretical contribution of this work is that we show intriguing connections between the online bandwidth optimization problem and the seemingly-unrelated but well-studied ski rental problem where similar optimal competitive ratios are known to hold. Finally, we consider extensions for contracts with a committed amount of spend (CIRs) and contracts that charge on a 95th-percentile basis
The Seventh International Conference on the Genetics of Streptococci, Lactococci, and Enterococci

The Seventh International Conference on the Genetics of Streptococci, Lactococci, and Enterococci, sponsored by the American Society for Microbiology, was held 18 to 21 June 2006, overlooking the sea in Saint Malo, Bretagne, France. The conference began in 1981 as the Streptococcal Genetics meeting, at a time when the genera Enterococcus and Lactococcus were still classified as Streptococcus spp. Through the years, this meeting has served as a quadrennial platform to showcase cutting-edge advances in the dissection of the underlying genetics of growth control, metabolism, extrachromosomal elements, substrate utilization, competence development, antibiotic resistance, and virulence of this fascinating group of organisms. The penultimate meeting, held in 2002, focused heavily on then-recent advances made through bacterial and phage genome-sequencing projects. The meeting in Saint Malo highlighted the remarkable progress over the last 4 years in the areas of functional and comparative genomics that was spawned by the large-scale sequencing projects completed early this century. Some of these advances are summarized in this review of the oral sessions of the programs, and additional reports published in this issue of the Journal of Bacteriology provide a more detailed glimpse of the tremendous insights gained through genetic analysis of this unique group of eubacteria.
Biologically inspired methods in speech recognition and synthesis: closing the loop

Current state-of-the-art approaches to computational speech recognition and synthesis are based on statistical analyses of extremely large data sets. It is currently unknown how these methods relate to the methods that the human brain uses to perceive and produce speech. In this thesis, I present a conceptual model, Sermo, which describes some of the computations that the human brain uses to perceive and produce speech. I then implement three largescale brain models that accomplish tasks theorized to be required by Sermo, drawing upon techniques in automatic speech recognition, articulatory speech synthesis, and computational neuroscience. The first model extracts features from an audio signal by performing a frequency decomposition with an auditory periphery model, then decorrelating the information in that power spectrum with methods commonly used in audio and image compression. I show that the features produced by this model implemented with biologically plausible spiking neurons can be used to classify phones in pre-segmented speech with significantly better accuracy than the features typically used in automatic speech recognition systems. Additionally, I show that this model can be used to compare auditory periphery models in terms of their ability to support phone classification of pre-segmented speech. The second model uses a symbol-like neural representation of a sequence of syllables to generate a trajectory of premotor commands that can be used to control an articulatory synthesizer. I show that the model can produce trajectories up to several seconds in length from a static syllable sequence representation that result in intelligible synthesized speech. The trajectories reflect the high temporal variability of human speech, and smoothly transition between successive syllables, even in rapid utterances. The third model classifies syllables from a trajectory of premotor commands. I show that the model is able to classify syllables online despite high temporal variability, and can produce the same syllable representations used by the second model. These two models can be connected in future work in order to implement a closed-loop sensorimotor speech system. Unlike current computational approaches, all three of these models are implemented with biologically plausible spiking neurons, which can be simulated with neuromorphic hardware, and can interface naturally with artificial cochleas. All models are shown to scale to the level of adult human vocabularies in terms of the neural resources required, though limitations on their performance as a result of scaling will be discussed.
An improved fuzzy C-means clustering algorithm based on PSO

To deal with the problem of premature convergence of the fuzzy c-means clustering algorithm based on particle swarm optimization, which is sensitive to noise and less effective when handling the data set that dimensions greater than the number of samples, a novel fuzzy c-means clustering method based on the enhanced Particle Swarm Optimization algorithm is presented. Firstly, this approach distributes the memberships on the basis of the distance between the sample and cluster centers, making memberships meet the constraints of FCM. Then, optimization strategy is presented that the optimal particle can be guided to close the group effectively. The experimental results show the proposed method significantly improves the clustering effect of the PSO-based FCM that encoded in membership.
Diagnostic strategy for the assessment of rheumatoid vasculitis

Objective: To determine the clinical features associated with histologically proven rheumatoid vasculitis (HRV) and the additional diagnostic value of serological markers in an inception cohort of 81 patients with rheumatoid arthritis (RA) suspected of RV. Methods: The presence and number of recently developed extra-articular manifestations (EAMs) and a weighted EAM score, as well as the levels of serological markers, were compared between 31 patients with RA with histologically proven vasculitis and 50 patients with RA in whom vasculitis could not be documented histologically. The following markers were evaluated: circulating immune complexes, complement components C3 and C4, class-specific rheumatoid factors (IgM RF, IgG RF, IgA RF), antineutrophil cytoplasmic antibodies, antinuclear antibodies, antiendothelial antibodies, circulating intercellular adhesion molecule-1 and -3, circulating vascular cell adhesion molecule and E-selectin, cellular fibronectin, von Willebrand factor antigen, and C reactive protein. The diagnostic value of these markers, in addition to the clinical features, was evaluated with logistic regression analysis. Results: Peripheral neuropathy or purpura/petechiae, or both, were the most important clinical features to discriminate patients with RA with and without histologically proven RV. The presence of a high number of EAMs and a higher weighted EAM score in patients with RA suspected of vasculitis were also associated with an increased probability of histologically proven RV. After adjustment for EAMs, only the combination of an increased serum IgA RF level and a decreased serum C3 level appeared to make an additional contribution to the diagnosis histologically proven RV. Evidence of systemic vasculitis was found in a muscle biopsy of the rectus femoris in 9/14 (64%) patients with vasculitis with neuropathy and in 3/11 (27%) patients with purpura/petechiae and vasculitis of the skin. Conclusions: In the diagnostic process of RV the presence of peripheral neuropathy and/or purpura/petechiae or a high weighted EAM score will increase the probability of histologically proven RV. Of the circulating factors previously suggested to be markers for RV only IgA RF and C3 further increase the probability of histologically proven RV and may be useful to guide diagnostic decisions.