Miracle-dz/UniFAR-FTUs huggingface dataset:
gNOMO2: a comprehensive and modular pipeline for integrated multi-omics analyses of microbiomes

Background Over the past few years, the rise of omics technologies has offered an exceptional chance to gain a deeper insight into the structural and functional characteristics of microbial communities. As a result, there is a growing demand for user friendly, reproducible, and versatile bioinformatic tools that can effectively harness multi-omics data to offer a holistic understanding of microbiomes. Previously, we introduced gNOMO, a bioinformatic pipeline specifically tailored to analyze microbiome multi-omics data in an integrative manner. In response to the evolving demands within the microbiome field and the growing necessity for integrated multi-omics data analysis, we have implemented substantial enhancements to the gNOMO pipeline. Results Here, we present gNOMO2, a comprehensive and modular pipeline that can seamlessly manage various omics combinations, ranging from two to four distinct omics data types including 16S rRNA gene amplicon sequencing, metagenomics, metatranscriptomics, and metaproteomics. Furthermore, gNOMO2 features a specialized module for processing 16S rRNA gene amplicon sequencing data to create a protein database suitable for metaproteomics investigations. Moreover, it incorporates new differential abundance, integration and visualization approaches, all aimed at providing a more comprehensive toolkit and insightful analysis of microbiomes. The functionality of these new features is showcased through the use of four microbiome multi-omics datasets encompassing various ecosystems and omics combinations. gNOMO2 not only replicated most of the primary findings from these studies but also offered further valuable perspectives. Conclusions gNOMO2 enables the thorough integration of taxonomic and functional analyses in microbiome multi-omics data, opening up avenues for novel insights in the field of both host associated and free-living microbiome research. gNOMO2 is available freely at https://github.com/muzafferarikan/gNOMO2.
Adaptive algorithms for shaping behavior

Dogs and laboratory mice are commonly trained to perform complex tasks by guiding them through a curriculum of simpler tasks (‘shaping’). What are the principles behind effective shaping strategies? Here, we propose a machine learning framework for shaping animal behavior, where an autonomous teacher agent decides its student’s task based on the student’s transcript of successes and failures on previously assigned tasks. Using autonomous teachers that plan a curriculum in a common sequence learning task, we show that near-optimal shaping algorithms adaptively alternate between simpler and harder tasks to carefully balance reinforcement and extinction. Based on this intuition, we derive an adaptive shaping heuristic with minimal parameters, which we show is near-optimal on the sequence learning task and robustly trains deep reinforcement learning agents on navigation tasks that involve sparse, delayed rewards. Extensions to continuous curricula are explored. Our work provides a starting point towards a general computational framework for shaping animal behavior.
Synthetic Lethality Screening with Recursive Feature Machines

Synthetic lethality refers to a genetic interaction where the simultaneous perturbation of gene pairs leads to cell death. Synthetically lethal gene pairs (SL pairs) provide a potential avenue for selectively targeting cancer cells based on genetic vulnerabilities. The rise of large-scale gene perturbation screens such as the Cancer Dependency Map (DepMap) offers the opportunity to identify SL pairs automatically using machine learning. We build on a recently developed class of feature learning kernel machines known as Recursive Feature Machines (RFMs) to develop a pipeline for identifying SL pairs based on CRISPR viability data from DepMap. In particular, we first train RFMs to predict viability scores for a given CRISPR gene knockout from cell line embeddings consisting of gene expression and mutation features. After training, RFMs use a statistical operator known as average gradient outer product to provide weights for each feature indicating the importance of each feature in predicting cellular viability. We subsequently apply correlation-based filters to re-weight RFM feature importances and identify those features that are most indicative of low cellular viability. Our resulting pipeline is computationally efficient, taking under 3 minutes for analyzing all 17, 453 knockouts from DepMap for candidate SL pairs. We show that our pipeline more accurately recovers experimentally verified SL pairs than prior approaches. Moreover, our pipeline finds new candidate SL pairs, thereby opening novel avenues for identifying genetic vulnerabilities in cancer.
AI and Jobs: Has the Inflection Point Arrived? Evidence from an Online Labor Platform

Artificial intelligence (AI) refers to the ability of machines or software to mimic or even surpass human intelligence in a given cognitive task. While humans learn by both induction and deduction, the success of current AI is rooted in induction, relying on its ability to detect statistical regularities in task input -- an ability learnt from a vast amount of training data using enormous computation resources. We examine the performance of such a statistical AI in a human task through the lens of four factors, including task learnability, statistical resource, computation resource, and learning techniques, and then propose a three-phase visual framework to understand the evolving relation between AI and jobs. Based on this conceptual framework, we develop a simple economic model of competition to show the existence of an inflection point for each occupation. Before AI performance crosses the inflection point, human workers always benefit from an improvement in AI performance, but after the inflection point, human workers become worse off whenever such an improvement occurs. To offer empirical evidence, we first argue that AI performance has passed the inflection point for the occupation of translation but not for the occupation of web development. We then study how the launch of ChatGPT, which led to significant improvement of AI performance on many tasks, has affected workers in these two occupations on a large online labor platform. Consistent with the inflection point conjecture, we find that translators are negatively affected by the shock both in terms of the number of accepted jobs and the earnings from those jobs, while web developers are positively affected by the very same shock. Given the potentially large disruption of AI on employment, more studies on more occupations using data from different platforms are urgently needed.
Emergent self-adaptation in an integrated photonic neural network for backpropagation-free learning

Plastic self-adaptation, nonlinear recurrent dynamics and multi-scale memory are desired features in hardware implementations of neural networks, because they enable them to learn, adapt and process information similarly to the way biological brains do. In this work, we experimentally demonstrate these properties occurring in arrays of photonic neurons. Importantly, this is realised autonomously in an emergent fashion, without the need for an external controller setting weights and without explicit feedback of a global reward signal. Using a hierarchy of such arrays coupled to a backpropagation-free training algorithm based on simple logistic regression, we are able to achieve a performance of 98.2% on the MNIST task, a popular benchmark task looking at classification of written digits. The plastic nodes consist of silicon photonics microring resonators covered by a patch of phase-change material that implements nonvolatile memory. The system is compact, robust, and straightforward to scale up through the use of multiple wavelengths. Moreover, it constitutes a unique platform to test and efficiently implement biologically plausible learning schemes at a high processing speed.
Archaeological Evidence of Medieval Sugar Production in Sicily: A Reassessment

SINCE MEDIEVAL TIMES, the impact of sugar production and consumption on European social, cultural, and economic development has been huge. The introduction of sugar cultivation entailed knowledge transfer and new technological requirements, such as the manufacture of sugar pots used to crystallise sugar, which is often the only archaeological evidence of its production and consumption. Sicily was one of the major sugar producers in medieval and post-medieval times, and many studies based on written sources are devoted to this phenomenon. However, the archaeological evidence seems comparatively scarce. This paper reviews the contexts and materials already known in Sicily, as well as research conducted under the EU-funded Marie Skłodowska-Curie SPotEU project. A typological classification based on sugar-pot shape and size is proposed here. This study allows the reconsideration of the archaeological evidence on the island and discusses changes in the design of sugar pots over phases, contexts and sites.
Safety-Enhanced Self-Learning for Optimal Power Converter Control

Data-driven learning-based control methods such as reinforcement learning (RL) have become increasingly popular with recent proliferation of the machine learning paradigm. These methods address the parameter sensitiveness and unmodeled dynamics in model-based controllers, such as finite control-set model predictive control. RL agents are typically utilized in simulation environments, where they are allowed to explore multiple"unsafe"actions during the learning process. However, this type of learning is not applicable to online self-learning of controllers in physical power converters, because unsafe actions would damage them. To address this, this letter proposes a safe online RL-based control framework to autonomously find the optimal switching strategy for the power converters, while ensuring system safety during the entire self-learning process. The proposed safe online RL-based control is validated in a practical testbed on a two-level voltage source converter system, and the results confirm the effectiveness of the proposed method.
On the Absolute-Value Integral of a Brownian Motion with Drift: Exact and Asymptotic Formulae

The present paper is concerned with the integral of the absolute value of a Brownian motion with drift. By establishing an asymptotic expansion of the space Laplace transform, we obtain series representations for the probability density function and cumulative distribution function of the integral, making use of Meijer's G-function. A functional recursive formula is derived for the moments, which is shown to yield only exponentials and Gauss' error function up to arbitrary orders, permitting exact computations. To obtain sharp asymptotic estimates for small- and large-deviation probabilities, we employ a marginal space-time Laplace transform and apply a newly developed generalization of Laplace's method to exponential Airy integrals. The impact of drift on the complete distribution of the integral is explored in depth. The resultant new formulae complement existing ones in the standard Brownian motion case to great extent in terms of both theoretical generality and modeling capacity and have been presented for easy implementation, which numerical experiments demonstrate.
EfNST: A composite scaling network of EfficientNet for improving spatial domain identification performance

Spatial Transcriptomics (ST) leverages Gene Expression Profiling while preserving Spatial Location and Histological Images, enabling it to provide new insights into tissue structure, tumor microenvironment, and biological development. The identification of spatial domains serves as not only the foundation for ST research but also a crucial step in various downstream analyses. However, accurately identifying spatial domains using computational methods remains a tremendous challenge due to the poor computational performance of many existing algorithms. Here, we propose EfNST, a deep learning algorithm based on a composite scaling network of the EfficientNet Network, designed specifically for the analysis of 10X Visium spatial transcriptomics data. We applied EfNST to three different datasets: human Dorsolateral Prefrontal Cortex, human breast cancer and mouse brain anterior. EfNST outperforms five advanced competing algorithms, achieving the best Adjusted Rand Index (ARI) scores of 0.554, 0.607, and 0.466, respectively. Notably, EfNST demonstrated high accuracy in identifying fine tissue structure and discovering corresponding marker genes with an improved running speed. In conclusion, EfNST offers a novel approach for inferring spatial organization of cells from discrete datapoints, facilitating the exploration of new insights in this field.
simona: a Comprehensive R package for Semantic Similarity Analysis on Bio-Ontologies

Bio-ontologies are keys in structuring complex biological information for effective data integration and knowledge representation. In this paper, we present simona, a novel R package for semantic similarity analysis on general bio-ontolgies. Simona implements infrastructures for ontology analysis by offering efficient data structures, fast ontology traversal methods, and elegant visualizations. Moreover, it provides a robust toolbox supporting over 70 methods for semantic similarity analysis. With simona, we conduct a benchmark against current semantic similarity methods. The results demonstrate methods are clustered based on their mathematical methodologies, providing guidance for researchers in the selection of appropriate methods. Additionally, we explore annotation-based versus topology-based methods, revealing that semantic similarities solely based on ontology topology can efficiently reveal semantic similarity structures, facilitating analysis on less-studied organisms and other ontologies. Simona is freely available from https://bioconductor.org/packages/simona/.
CHANGES OF GLACIERS IN GLACIER BAY, ALASKA, USING GROUND AND SATELLITE MEASUREMENTS

The deglaciation of Glacier Bay, Alaska is studied using historical maps, field measurements, and satellite imagery. In general, both tidewater and non-tidewater glaciers in the area have receded during the last century, but the rates of recession are quite variable, especially in the case of the tidewater glaciers. Available average air temperature data from nearby meteorological stations show a general warming trend, especially over the last 20 years. Such a warming trend may help to explain recession of the non-tidewater glaciers of the area. Comparison of September Landsat multispectral scanner imagery from 1973 and 1986 shows that the amount of vegetation increased significantly and rapidly as glaciers receded. The vegetation within a Landsat subscene covering Muir Inlet increased about 86% during the 13-year period. The once-impressive Muir Glacier, a tidewater glacier, has receded dramatically since the late 1800s. Satellite measurements show that the terminus retreated >7.3 km between 1973 and 199.
Nouveaux Microtrachelizini Zimmerman, 1994, du Népal et de Sumatra (Coleoptera, Brentidae)

Trois nouvelles especes de Microtrachelizini appartenant aux genres Entomopisthius Muizon, 1959, Microtrachelizus Senna, 1893, et Aneorhachis Kleine, 1923, sont decrites du Nepal et de Sumatra. Leurs affinites phylogenetiques sont discutees. Pour le genre Aneorhachis, une analyse cladistique est presentee, basee sur 26 caracteres morphologiques de l''adulte, et une nouvelle donnee geographique est apportee pour A. hirta (Kabakov, 2001).
Graph Metanetworks for Processing Diverse Neural Architectures

Neural networks efficiently encode learned information within their parameters. Consequently, many tasks can be unified by treating neural networks themselves as input data. When doing so, recent studies demonstrated the importance of accounting for the symmetries and geometry of parameter spaces. However, those works developed architectures tailored to specific networks such as MLPs and CNNs without normalization layers, and generalizing such architectures to other types of networks can be challenging. In this work, we overcome these challenges by building new metanetworks - neural networks that take weights from other neural networks as input. Put simply, we carefully build graphs representing the input neural networks and process the graphs using graph neural networks. Our approach, Graph Metanetworks (GMNs), generalizes to neural architectures where competing methods struggle, such as multi-head attention layers, normalization layers, convolutional layers, ResNet blocks, and group-equivariant linear layers. We prove that GMNs are expressive and equivariant to parameter permutation symmetries that leave the input neural network functions unchanged. We validate the effectiveness of our method on several metanetwork tasks over diverse neural network architectures.
Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers

This paper presents an in-depth analysis of Large Language Models (LLMs), focusing on LLaMA, a prominent open-source foundational model in natural language processing. Instead of assessing LLaMA through its generative output, we design multiple-choice tasks to probe its intrinsic understanding in high-order tasks such as reasoning and computation. We examine the model horizontally, comparing different sizes, and vertically, assessing different layers. We unveil several key and uncommon findings based on the designed probing tasks: (1) Horizontally, enlarging model sizes almost could not automatically impart additional knowledge or computational prowess. Instead, it can enhance reasoning abilities, especially in math problem solving, and helps reduce hallucinations, but only beyond certain size thresholds; (2) In vertical analysis, the lower layers of LLaMA lack substantial arithmetic and factual knowledge, showcasing logical thinking, multilingual and recognitive abilities, with top layers housing most computational power and real-world knowledge.
Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning

Due to the availability of only a few labeled instances for the novel target prediction task and the significant domain shift between the well annotated source domain and the target domain, cross-domain few-shot learning (CDFSL) induces a very challenging adaptation problem. In this paper, we propose a simple Adaptive Weighted Co-Learning (AWCoL) method to address the CDFSL challenge by adapting two independently trained source prototypical classification models to the target task in a weighted co-learning manner. The proposed method deploys a weighted moving average prediction strategy to generate probabilistic predictions from each model, and then conducts adaptive co-learning by jointly fine-tuning the two models in an alternating manner based on the pseudo-labels and instance weights produced from the predictions. Moreover, a negative pseudo-labeling regularizer is further deployed to improve the fine-tuning process by penalizing false predictions. Comprehensive experiments are conducted on multiple benchmark datasets and the empirical results demonstrate that the proposed method produces state-of-the-art CDFSL performance.
SmoothQuant+: Accurate and Efficient 4-bit Post-Training WeightQuantization for LLM

Large language models (LLMs) have shown remarkable capabilities in various tasks. However their huge model size and the consequent demand for computational and memory resources also pose challenges to model deployment. Currently, 4-bit post-training quantization (PTQ) has achieved some success in LLMs, reducing the memory footprint by approximately 75% compared to FP16 models, albeit with some accuracy loss. In this paper, we propose SmoothQuant+, an accurate and efficient 4-bit weight-only PTQ that requires no additional training, which enables lossless in accuracy for LLMs for the first time. Based on the fact that the loss of weight quantization is amplified by the activation outliers, SmoothQuant+ smoothes the activation outliers by channel before quantization, while adjusting the corresponding weights for mathematical equivalence, and then performs group-wise 4-bit weight quantization for linear layers. We have integrated SmoothQuant+ into the vLLM framework, an advanced high-throughput inference engine specially developed for LLMs, and equipped it with an efficient W4A16 CUDA kernels, so that vLLM can seamlessly support SmoothQuant+ 4-bit weight quantization. Our results show that, with SmoothQuant+, the Code Llama-34B model can be quantized and deployed on a A100 40GB GPU, achieving lossless accuracy and a throughput increase of 1.9 to 4.0 times compared to the FP16 model deployed on two A100 40GB GPUs. Moreover, the latency per token is only 68% of the FP16 model deployed on two A100 40GB GPUs. This is the state-of-the-art 4-bit weight quantization for LLMs as we know.
Universal readout error mitigation scheme characterized on superconducting qubits

Quantum technologies rely heavily on accurate control and reliable readout of quantum systems. Current experiments are limited by numerous sources of noise that can only be partially captured by simple analytical models and additional characterization of the noise sources is required. We test the ability of readout error mitigation to correct realistic noise found in systems composed of quantum two-level objects (qubits). To probe the limit of such methods, we designed a universal readout error mitigation protocol based on quantum state tomography (QST), which estimates the density matrix of a quantum system, and quantum detector tomography (QDT), which characterizes the measurement procedure. By treating readout error mitigation in the context of state tomography the method becomes largely device-, architecture-, noise source-, and quantum state-independent. We implement this method on a superconducting qubit and benchmark the increase in reconstruction fidelity for QST. We characterize the performance of the method by varying important noise sources, such as suboptimal readout signal amplification, insufficient resonator photon population, off-resonant qubit drive, and effectively shortened $T_1$ and $T_2$ decay times. As a result, we identified noise sources for which readout error mitigation worked well, and observed decreases in readout infidelity by a factor of up to 30.
Energetic production by soft and hard mast foods of American black bears in the Smoky Mountains

We measured caloric production by 19 species of vegetation used as food by American black bears (Ursus americanus) in Great Smoky Mountains National Park to determine the significance of production by mast type, season, and species. Mean annual production by all species was 351,209 cal/ha. Hard mast produced 74.5% (16.0 billion cal) of total calories available on the study area; soft mast produced 25.5% (5.5 billion cal). Gross energetic content of soft and hard mast did not differ (P = 0.488, n = 19). Mid-summer was the lowest period of production. Northern red oak (Quercus rubra) produced 65.7% of calories; squawroot (Conopholis americana) produced 15.8%, and huckleberries (Gaylussacia spp.) produced 5.1 %. A white oak (Q. alba and Q. prinus) mast failure occurred, and white oaks produced only 5.1% of calories. Oaks are likely the single most influential genera affecting bear ecology in the southern Appalachians. However, availability of soft mast likely has a substantial impact on bear populations because of the timing of production, nutrients available, and its function as a surrogate during hard mast failure. Further study is needed to determine the effects of soft mast abundance on age of primiparity, litter interval, recruitment, and density. Once the roles of major foods are well understood, appropriate habitat compositions and silvicultural prescriptions may be defined. Ursus 13:57-68 (2002)