Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-objective Optimization Configuration Scheme for Photovoltaic Energy Storage Charging Stations Considering Operational Efficiency

Abstract—The operational efficiency of photovoltaic energy storage charging stations affects their economic benefits and grid-side power quality. To address the problem of non-essential losses due to insufficient consideration of operational efficiency in the current capacity allocation optimization, the paper proposes a multi-objective capacity optimization method for photovoltaic energy storage charging stations. This method analyzes equipment loss on operational efficiency and defines an assessment index for it. Additionally, it constructs a capacity allocation method considering economic benefits, operational efficiency, and grid-side power quality as primary objectives. The NSGA-II algorithm is adapted to efficiently achieve the optimization goals, resulting in an optimized capacity allocation scheme. The paper validates the effectiveness of our proposed method through its application to a typical photovoltaic energy storage charging stations.
Semantic data augmentation for meaning maintenance on Task-Oriented Conversation with Large-size Language Model

This paper presents our approach to building a generalized model for Track 5 in DSTC11: “Task-oriented Conversational Modeling with Subjective Knowledge” which addresses the challenge of generating responses to users’ utterances based on a variety of factual and subjective knowledge. To tackle this challenge, we first augmented the training data by leveraging contextual word embedding and back translation, thereby increasing the quantity of available data. Then, we utilized a large-size language model to enhance the acceptability of the augmented data and fine-tuned the model using augmented data. Specifically, we applied the DeBERTa-v3-large model for knowledge detection and selection, and the BART-large model for response generation. Our best model achieved the seventh rank in the objective evaluation and the second rank in the final official human evaluation. These outcomes serve as solid evidence that data augmentation and using a large-size model were highly effective for developing a conversational model system that incorporates objective and subjective knowledge.
Biological and hydrogeological interactions affect the persistence of 17β‐estradiol in an agricultural watershed

17β‐estradiol (E2), one of the natural estrogen compounds, is an endocrine disruptor, and low levels in natural waters can impair the reproductive health of aquatic organisms. Its presence has been reported in animal faecal wastes and some aquatic habitats, including surface waters impacted by intense animal agriculture or sewage contamination. Little is known about its transport in hydrological systems or its persistence in water supplies. We routinely sampled stream and soil water over the growing season in an instrumented 1.2‐km2 agricultural watershed in central Virginia. E2 concentrations in stream water ranged from 0.01 to 0.12 ng mL−1; soil‐water values ranged from 0.03 to 0.18 ng mL−1. The highest concentrations were observed early in the growing season shortly after application of composted poultry litter to the cropped fields, and values decreased both with hydrological transport distance from the cropped field and over the course of the summer. Given the known application rate, E2 must be lost from the soil solution, and we explored biodegradation as a mechanism for this loss. A bacterial consortium cultured from the poultry compost biodegraded E2 in laboratory flasks amended with solutions of 1 : 1 acetate : glucose at concentrations ranging from 0.1 to 10 g L−1 dissolved organic carbon (DOC), spiked with 1.8 ng mL−1 E2, and incubated at different temperatures. A loss of 97–98% of the initial E2 occurred within 180 h in experiments at 22 °C and 28 °C with 1.0 or 0.1 g L−1 DOC amendments. Higher DOC concentrations and lower temperatures slowed the rates of reaction, suggesting that more readily available carbon inhibits use of the E2 by degrading micro‐organisms. The rapid rates of biodegradation in the laboratory incubations are inconsistent with the persistence of E2 in the watershed. This suggests that either the rates of biodegradation are reduced compared with the laboratory experiments or that E2 probably interacts with the components of the natural environment through complexation, sorption or abiotic transformation in the ageing process that leads to diminished bioavailability.
A Complete Framework for Offline and Counterfactual Evaluations of Interactive Recommendation Systems

Interactive recommendation has been recognized as a Multi-Armed Bandit (MAB) problem. Items are arms to be pulled (i.e., recommended) and the user’s satisfaction is the reward to be maximized. Despite the advances, there is still a lack of consensus on the best practices to evaluate such solutions. Recently, two complementary frameworks were proposed to evaluate bandit solutions more accurately: iRec and OBP. The first one has a complete set of offline metrics and bandit models that allows us to perform an comparisons with several evaluation policies. The second one provides a huge set of bandit models to be evaluated through several counterfactual estimators. However, there is a room to be explored when joining these two frameworks. We propose and evaluate an integration between both, demonstrating the potential and richness of such combination.
Review of North Sea Basin development

The large amount of data now available on the North Sea stratigraphy, and on the history of the positive structures which form the sites of the oilfields, provides a basis for a review of basin development. The area was initially occupied by two inter-cratonic basins: the Northern Basin had a Devonian ancestor; the Southern Basin dates back at least to the Carboniferous. During the Permian and Trias broad intercratonic subsidence continued in the south, but rifting developed across the separating Mid North Sea High and in the Northern Basin. Rifting (taphrogenic) control of subsidence became widely dominant through the Jurassic and lower Cretaceous, dying away in the upper Cretaceous, with major subsidence in the Viking Graben in the Northern Basin and in a series of narrow troughs in the south. No opening of the crust was associated with the central rifting: deep penetrations have encountered Devonian or Precambdan in the rift floor. For the most part the upper Cretaceous sagged into the earlier depressions with minor faulting only, but in the south inversion of the troughs took place in late Cretaceous and early Tertiary times; this may have had a compressional (orogenic) cause. The Tertiary basin developed as a single, relatively simple synclinal subsidence of the whole North Sea area, centred on the main rift system but showing an absence of fault-control. Interruptions in deposition on the individual structural high areas show wide correspondence in the North Sea. Except for a major late Triassic movement limited to the Netherlands and adjoining areas they show also close relation to those in Britain. The most notable are early middle Jurassic, early upper Jurassic and end Jurassic/lower Cretaceous. These produced erosional phases even in the deeper parts of the basin. They were entirely of epeirogenic type. Halokinetic movements of Permian salt produced many local complications (especially in the Southern North Sea Basin) with consequent structural and stratigraphical anomalies from the middle Trias onwards.
Seismological Studies at Parkfield IX: Fault-Zone Imaging Using Guided Wave Attenuation

Numerical modeling studies and a growing number of observations have argued for the propagation of fault-zone guided waves (FZGWs) within a San Andreas Fault (SAF) zone that is 100-200 m wide at seismogenic depths and with 20%-40% lower shear-wave velocity than the adjacent unfaulted rock. Thousands of microearthquakes have been recorded since 1987 by the borehole High-Resolution Seismic Network at Parkfield, California, and they provide a comprehensive data set for characterizing wave propagation in the SAF zone. Using microearthquakes we confirm that FZGWs at Parkfield are generated within the fault zone (FZ) and that they are most prominent late in the coda of S . Numerical waveform modeling and guided-wave amplitude tomographic inversion show clearly that FZGWs are significantly less attenuated in a well-defined region of the FZ. This region plunges to the northwest along the northwest boundary of the region of highest moment release and separates locked and slipping sections of the SAF at depth, as determined independently from geodesy, seismicity, and the recurrence rates of characteristically repeating microearthquakes. We interpret this localized zone of strong FZGW propagation to be the northwest edge of the M 6 asperity at Parkfield. The mechanism for low FZGW attenuation in the zone is possibly due to dewatering by fracture closure and/or fault-normal compression or changes in fracture orientation due to a complex stress or strain field at the boundary between creeping and locked zones of the SAF. Manuscript received 23 April 2002.
Integrated single-cell multiome analysis reveals muscle fiber-type gene regulatory circuitry modulated by endurance exercise

Endurance exercise is an important health modifier. We studied cell-type specific adaptations of human skeletal muscle to acute endurance exercise using single-nucleus (sn) multiome sequencing in human vastus lateralis samples collected before and 3.5 hours after 40 min exercise at 70% VO2max in four subjects, as well as in matched time of day samples from two supine resting circadian controls. High quality same-cell RNA-seq and ATAC-seq data were obtained from 37,154 nuclei comprising 14 cell types. Among muscle fiber types, both shared and fiber-type specific regulatory programs were identified. Single-cell circuit analysis identified distinct adaptations in fast, slow and intermediate fibers as well as LUM-expressing FAP cells, involving a total of 328 transcription factors (TFs) acting at altered accessibility sites regulating 2,025 genes. These data and circuit mapping provide single-cell insight into the processes underlying tissue and metabolic remodeling responses to exercise.
Sensorimotor Integration Supporting Perception Requires Syngap1 Expression in Cortex

Perception, a cognitive construct, emerges through sensorimotor integration (SMI). The molecular and cellular mechanisms that shape SMI within circuits that promote cognition are poorly understood. Here, we demonstrate that expression of the autism/intellectual disability gene, Syngap1, in mouse cortical excitatory neurons promotes touch sensitivity required to elicit perceptual behaviors. Cortical Syngap1 expression enabled touch-induced feedback signals within sensorimotor loops by assembling circuits that support tactile sensitivity. These circuits also encoded correlates of attention that promoted self-generated whisker movements underlying purposeful and sustained object exploration. As Syngap1 deficient animals explored objects with whiskers, relatively weak touch signals were integrated with relatively strong motor signals. This produced a signal-to-noise deficit consistent with impaired tactile sensitivity, reduced tactile exploration, and weak tactile learning. Thus, Syngap1 expression in cortex promotes tactile perception by assembling circuits that integrate touch and whisker motor signals. Deficient Syngap1 expression likely contributes to cognitive impairment through abnormal top-down SMI.
Interpretable Metric Learning in Comparative Metagenomics: The Adaptive Haar-like Distance

Random forests have emerged as a promising tool in comparative metagenomics because they can predict environmental characteristics based on microbial composition in datasets where β-diversity metrics fall short of revealing meaningful relationships between samples. Nevertheless, despite this efficacy, they lack biological insight in tandem with their predictions, potentially hindering scientific advancement. To overcome this limitation, we leverage a geometric characterization of random forests to introduce a data-driven phylogenetic β-diversity metric, the adaptive Haar-like distance. This new metric assigns a weight to each internal node (i.e., split or bifurcation) of a reference phylogeny, indicating the relative importance of that node in discerning environmental samples based on their microbial composition. Alongside this, a weighted nearest-neighbors classifier, constructed using the adaptive metric, can be used as a proxy for the random forest while maintaining accuracy on par with that of the original forest and another state-of-the-art classifier, CoDaCoRe. As shown in datasets from diverse microbial environments, however, the new metric and classifier significantly enhance the biological interpretability and visualization of high-dimensional metagenomic samples. Author summary Traditional phylogenetic β-diversity metrics, particularly weighted and unweighted UniFrac, have had great success in comparing and visualizing high-dimensional metagenomic samples. Nonetheless, these metrics rely upon pre-established biological assumptions that might not capture key microbial players or relationships between some samples. On the contrary, supervised machine learning algorithms, such as random forests, can often capture intricate relationships between microbial samples; however, unveiling these relationships is often challenging due to the intricate inner mechanisms inherent to these algorithms. The adaptive Haar-like distance integrates the merits of β-diversity metrics and random forests, allowing for precise, intuitive, and visual comparison of metagenomic samples, offering valuable scientific insight into the distinctions and associations among microbial environments.
SARS-CoV-2 NSP14 governs mutational instability and assists in making new SARS-CoV-2 variants

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the rapidly evolving RNA virus behind the COVID-19 pandemic, has spawned numerous variants since its 2019 emergence. The multifunctional NSP14 enzyme, possessing exonuclease and mRNA capping capabilities, serves as a key player. Notably, single and co-occurring mutations within NSP14 significantly influence replication fidelity and drive variant diversification. This study comprehensively examines 120 co-mutations, 68 unique mutations, and 160 conserved residues across NSP14 homologs, shedding light on their implications for phylogenetic patterns, pathogenicity, and residue interactions. Quantitative physicochemical analysis categorizes 3953 NSP14 variants into three clusters, revealing genetic diversity. This research underscores the dynamic nature of SARS-CoV-2 evolution, primarily governed by NSP14 mutations. Understanding these genetic dynamics provides valuable insights for therapeutic and vaccine development.
inDrops-2: a flexible, versatile and cost-efficient droplet microfluidics approach for high-throughput scRNA-seq of fresh and preserved clinical samples

The development of a large variety of single-cell analytical techniques has empowered researchers to explore diverse biological questions at the level of individual cells. Among these, droplet-based single-cell RNA sequencing (scRNA-seq) methods have been particularly prevalent owing to their high-throughput capabilities and reduced reaction volumes. While commercial systems have contributed to the widespread adoption of droplet-based scRNA-seq, the relatively high cost impose limitations for profiling large numbers of samples. Moreover, as the scope and scale of single cell sequencing methods keeps expanding, the possibility to accommodate diverse molecular biology workflows and inexpensively profile multiple biospecimens simultaneously becomes highly relevant. Herein, we present inDrops-2: an open-source scRNA-seq platform designed to profile fresh or preserved clinical samples with a sensitivity matching that of state-of-the-art commercial systems, yet at a few folds lower cost. Using inDrops-2, we conducted a comparative analysis of two prominent scRNA-seq protocols – those based on exponential and linear amplification of cDNA – and provide useful insights about the pros and cons inherited to each approach. We showcase the utility of inDrops-2 by simultaneously profiling 18 human lung carcinoma samples, all in one run, following cell preservation, long-term storage and multiplexing, to obtain a multiregional cellular profile of tumor microenvironment. The scalability, experimental flexibility and cost-efficiency offered by inDrops-2 should make it appealing for various single-cell transcriptomic studies.
skDER: microbial genome dereplication approaches for comparative and metagenomic applications

skDER (https://github.com/raufs/skDER) combines recent advances to efficiently estimate average nucleotide identity (ANI) between thousands of microbial genomes by skani1 with two low-memory methods for genomic dereplication. The first method implements a dynamic algorithm to determine a concise set of representative genomes. This approach is well-suited for selecting reference genomes to align metagenomic reads onto for tracking strain presence across related microbiome samples. This is because fewer representative genomes should alleviate the concern that reads belonging to the same strain get falsely partitioned across closely related genomes. The other method, which uses a greedy approach, is better suited for use in comparative genomics, where users might be overwhelmed with the high number of genomes available for certain taxa and aim to reduce redundancy and, therefore, computational requirements for downstream analytics. This method selects a larger number of representative genomes to comprehensively sample the pangenome space for the taxon of interest. To further aid usage for comparative genomics studies, skDER also features an option to automatically download genomes classified as a particular species or genus in the Genome Taxonomy Database2–4 and we provide precomputed representative genomes for commonly studied bacterial taxa5.
QuePaxa: Escaping the tyranny of timeouts in consensus

Leader-based consensus algorithms are fast and efficient under normal conditions, but lack robustness to adverse conditions due to their reliance on timeouts for liveness. We present QuePaxa, the first protocol offering state-of-the-art normal-case efficiency without depending on timeouts. QuePaxa uses a novel randomized asynchronous consensus core to tolerate adverse conditions such as denial-of-service (DoS) attacks, while a one-round-trip fast path preserves the normal-case efficiency of Multi-Paxos or Raft. By allowing simultaneous proposers without destructive interference, and using short hedging delays instead of conservative timeouts to limit redundant effort, QuePaxa permits rapid recovery after leader failure without risking costly view changes due to false timeouts. By treating leader choice and hedging delay as a multi-armed-bandit optimization, QuePaxa achieves responsiveness to prevalent conditions, and can choose the best leader even if the current one has not failed. Experiments with a prototype confirm that QuePaxa achieves normal-case LAN and WAN performance of 584k and 250k cmd/sec in throughput, respectively, comparable to Multi-Paxos. Under conditions such as DoS attacks, misconfigurations, or slow leaders that severely impact existing protocols, we find that QuePaxa remains live with median latency under 380ms in WAN experiments.
Reducing tokenizer’s tokens per word ratio in Financial domain with T-MuFin BERT Tokenizer

Most domain-specific BERT models are designed to work with short sentences and do not deal with the limitation of 512 tokens in the default BERT tokenizer. This limitation is further exacerbated if the tokenizer has high number of tokens per word ratio (fertility) and thus splits words into several tokens. A term-based multilingual Financial (T-MuFin) BERT tokenizer has been proposed to reduce the fertility of the default BERT tokenizer by extending the base dictionary with the most common financial terms instead of word pieces. One key factor of this proposal is to introduce multiword domain-specific terms without affecting the performance of the BERT models. T-MuFin BERT tokenizer reduces at least 40% of the fertility of long text sequences. T-MuFin BERT improves the fine-tuning of a downstream task by approximately 4% compared to a default fine-tuned model. Hence, by reducing the tokenizer’s fertility, the results of explainable methods are more user-friendly.
Language planning and policy in Africa: Vol I: Botswana, Malawi, Mozambique and South Africa.

This volume covers the language situation in Botswana, Malawi, Mozambique and South Africa explaining the linguistic diversity, the historical and political contexts and the current language situation, including language-in-education planning, the role of the media, the role of religion, and the roles of non-indigenous languages. The authors are indigenous and have been participants in the language planning context.
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes

Knowledge distillation (KD) is widely used for compressing a teacher model to reduce its inference cost and memory footprint, by training a smaller student model. However, current KD methods for auto-regressive sequence models suffer from distribution mismatch between output sequences seen during training and those generated by the student during inference. To address this issue, we introduce Generalized Knowledge Distillation (GKD). Instead of solely relying on a fixed set of output sequences, GKD trains the student on its self-generated output sequences by leveraging feedback from the teacher on such sequences. Unlike supervised KD approaches, GKD also offers the flexibility to employ alternative loss functions between the student and teacher, which can be useful when the student lacks the expressivity to mimic the teacher's distribution. Furthermore, GKD facilitates the seamless integration of distillation with RL fine-tuning (RLHF). We demonstrate the efficacy of GKD for distilling auto-regressive language models on summarization, translation, and arithmetic reasoning tasks, and task-agnostic distillation for instruction-tuning.
Reconstitution of Early Paclitaxel Biosynthetic Network

Paclitaxel is an anticancer therapeutic produced by the yew tree. Over the last two decades, a significant bottleneck in the reconstitution of early paclitaxel biosynthesis has been the propensity of heterologously expressed pathway cytochromes P450, including taxadiene 5α-hydroxylase (T5αH), to form multiple products. This diverts metabolic flux away from the paclitaxel precursor, taxadien-5α-ol, thus previous attempts of reconstitution have not yielded sufficient material for characterization, regardless of the heterologous host. Here, we structurally characterized four new products of T5αH, many of which appear to be over-oxidation of the primary mono-oxidized products. By tuning the promoter strength for T5αH expression, levels of these proposed byproducts decrease with a concomitant increase in the accumulation of taxadien-5α-ol by four-fold. This engineered system enabled the reconstitution of a six step biosynthetic pathway to produce isolatable 5α,10β-diacetoxy-taxadien-13α-ol. Furthermore, we showed that this pathway may function as a metabolic network rather than a linear pathway. The engineering of the paclitaxel biosynthetic network demonstrates that Taxus genes can coordinatively function for the biosynthetic production of key early stage paclitaxel intermediates and serves as a crucial platform for the discovery of the remaining biosynthetic genes.