Miracle-dz/UniFAR-FTUs huggingface dataset:
Black Sea nitrogen cycling and the preservation of phytoplankton δ15N signals during the Holocene

The stable isotopic compositions of bulk, clay‐bound, organic, and compound‐specific nitrogen were determined for mid to late Holocene Black Sea sediments from a set of box and gravity cores. The data demonstrate that cyanobacterial N2fixation provided ∼55% of phytoplankton‐derived N preserved in the top 1–2 cm of the sediments. Prior to widespread agricultural and industrial development in the catchment, N2fixation was more prominent, providing 70–80% of phytoplankton N. Organic and clay‐bound nitrogen fractions record different down‐coreδ15N trends that reflect phytoplankton and detrital sources, respectively, and in samples with low organic matter content, the clay‐bound fraction comprises up to 38% of bulk nitrogen. Compared with bulk samples, pyropheophytina (Pphe a), which is a chlorophyll a (Chl a) degradation product, provides a more accurate record of changing phytoplankton δ15N values during the Holocene. An examination of the δ15NPphe a values in light of published and new estimates of the isotopic difference between biomass and Chl a suggests that most of the preserved Pphe a was derived from eukaryotic algae, not cyanobacteria. We infer from these data that cyanobacterial biomass is rapidly recycled in the photic zone, with 15N‐depleted NH4+ released during heterotrophy and assimilated by other phytoplankton. A conceptual model for N2 fixation in the Black Sea is presented, drawing upon water column nutrient and hydrographic data as well as regional climate variability to explain the proposed temporal variability in N2 fixation.
On Galerkin approximations of the Navier–Stokes equations in the limit of large Grashof numbers

We examine how stationary solutions to Galerkin approximations of the Navier--Stokes equations behave in the limit as the Grashof number $G$ tends to $\infty$. An appropriate scaling is used to place the Grashof number as a new coefficient of the nonlinear term, while the body force is fixed. A new type of asymptotic expansion, as $G\to\infty$, for a family of solutions is introduced. Relations among the terms in the expansion are obtained by following a procedure that compares and totally orders positive sequences generated by the expansion. The same methodology applies to the case of perturbed body forces and similar results are obtained. We demonstrate with a class of forces and solutions that have convergent asymptotic expansions in $G$. All the results hold in both two and three dimensions, as well as for both no-slip and periodic boundary conditions.
The emergence of the nicotinamide riboside kinases in the regulation of NAD+ metabolism

The concept of replenishing or elevating NAD+ availability to combat metabolic disease and ageing is an area of intense research. This has led to a need to define the endogenous regulatory pathways and mechanisms cells and tissues utilise to maximise NAD+ availability such that strategies to intervene in the clinical setting are able to be fully realised. This review discusses the importance of different salvage pathways involved in metabolising the vitamin B3 class of NAD+ precursor molecules, with a particular focus on the recently identified nicotinamide riboside kinase pathway at both a tissue-specific and systemic level. NAD+ as a redox cofactor and signalling molecule The vital role of NAD+ in redox metabolism was first described in 1906 by Arthur Harden as being a required cofactor for alcohol fermentation (Harden & Young 1906). It has since been well established as a redox coenzyme necessary for many redox reactions (EulerChelpin 1966). During these redox events NAD+ can be reduced by accepting electrons from donor molecules or, in reverse, NADH can be oxidised to NAD+ by donating electrons. This interchange of electrons allows catalysis of reversible transhydrogenase reactions (Warburg & Christian 1936). Notably, NAD+ redox reactions are vital in energy production pathways such as glycolysis, oxidative phosphorylation and fatty acid oxidation. Typically NAD+ is predominantly found in its oxidised form with NAD/NADH ratios varying from 10/1 to 700/1 and a total cellular concentration thought to be estimated between 0.3–1.0 mM in mammalian cells (Veech et al. 1972, Casazza & Veech 1986). Alternatively, NAD+ can be phosphorylated to NADP+ by NAD kinase and likewise this also exists in its reduced form NADPH. NADP(H) concentrations are estimated at 10% of total NAD(H) levels (Veech et al. 1972, Casazza & Veech 1986). NADPH acts as an essential reducing agent aiding many anabolic pathways including nucleic acid and lipid biosynthesis (Berger et al. 2004, Tedeschi et al. 2016). More recently, NAD+ has been recognised as an important signalling molecule that is consumed upon the activity of several enzymes including sirtuins (SIRTs), polyADP-ribose polymerases (PARPs) and cyclic ADP-ribose synthases (cADPRSs) (Houtkooper et al. 2010, Dolle et al. 2013). This new found purpose highlighted that NAD+, previously thought of as a stable molecule, is actually in continuous turnover and tightly regulated to maintain metabolic homeostasis. In fact, with approximately 2.5 ATP molecules formed per pair of electrons donated by NADH in the electron transport chain, and an estimated 80 kg of ATP turned over by an adult male each day, it is predicted as much as 30 kg NADH is generated every day, reinforcing the need for regulated maintenance of NAD(H) biosynthetic pathways (Kornberg 1991, Walsh et al. 2018). Journal of Molecular Endocrinology (2018) 61, R107–R121 Key Words
The DeepZen Speech Synthesis System for Blizzard Challenge 2023

This paper describes the DeepZen text to speech (TTS) system for Blizzard Challenge 2023. The goal of this challenge is to synthesise natural and high-quality speech in French, from a large monospeaker dataset (hub task) and from a smaller dataset by speaker adaptation (spoke task). We participated to both tasks with the same model architecture. Our approach has been to use an auto-regressive model, which retains an advantage for generating natural sounding speech but to improve prosodic control in several ways. Similarly to non-attentive Tacotron, the model uses a duration predictor and gaussian upsampling at inference, but with a simpler unsupervised training. We also model the speaking style at both sentence and word levels by extracting global and local style tokens from the reference speech. At inference, the global and local style tokens are predicted from a BERT model run on text. This BERT model is also used to predict specific pronunciation features like schwa elision and optional liaisons. Finally, a modified version of HifiGAN trained on a large public dataset and fine-tuned on the target voices is used to generate speech waveform. Our team is identified as O in the the Blizzard evaluation and MUSHRA test results show that our system performs second ex aequo in both hub task (median score of 0.75) and spoke task (median score of 0.68), over 18 and 14 participants, respectively.
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks

As powerful tools for representation learning on graphs, graph neural networks (GNNs) have played an important role in applications including social networks, recommendation systems, and online web services. However, GNNs have been shown to be vulnerable to adversarial attacks, which can significantly degrade their effectiveness. Recent state-of-the-art approaches in adversarial attacks rely on gradient-based meta-learning to selectively perturb a single edge with the highest attack score until they reach the budget constraint. While effective in identifying vulnerable links, these methods are plagued by high computational costs. By leveraging continuous relaxation and parameterization of the graph structure, we propose a novel attack method called Differentiable Graph Attack (DGA) to efficiently generate effective attacks and meanwhile eliminate the need for costly retraining. Compared to the state-of-the-art, DGA achieves nearly equivalent attack performance with 6 times less training time and 11 times smaller GPU memory footprint on different benchmark datasets. Additionally, we provide extensive experimental analyses of the transferability of the DGA among different graph models, as well as its robustness against widely-used defense mechanisms.
Widely Separated MIMO Radar Using Matrix Completion

We present a low-complexity widely separated multiple-input-multiple-output (WS-MIMO) radar that samples the signals at each of its multiple receivers at reduced rates. We process the low-rate samples of all transmit-receive chains at each receiver as data matrices. We demonstrate that each of these matrices is low rank as long as the target moves slowly within a coherent processing interval. We leverage matrix completion (MC) to recover the missing samples of each receiver signal matrix at the common fusion center. Subsequently, we estimate the targets’ positions and Doppler velocities via the maximum likelihood method. Our MC-based WS-MIMO (MC-WS-MIMO) approach recovers missing samples and thereafter target parameters at reduced rates without discretization of the parameter space. Our analysis using ambiguity functions shows that antenna geometry affects the performance of MC-WS-MIMO. Numerical experiments demonstrate reasonably accurate target localization at SNR of 20 dB and sampling rate reduction to 20%.
Seroprevalence of hepatitis B and C virus infections: Results from the 2007 to 2009 and 2009 to 2011 Canadian Health Measures Survey.

BACKGROUND Chronic hepatitis B (HBV) and C (HCV) virus infections can lead to liver failure, liver cancer, and death. In Canada, prevalence studies of HBV and HCV have been limited to regional and special populations. DATA AND METHODS Data are from cycles 1 (2007 to 2009) and 2 (2009 to 2011) of the Canadian Health Measures. Socio-demographic, health and lifestyle information was obtained via a household questionnaire; blood samples collected at mobile examination centres were used to identify present and resolved HBV infections, vaccine-induced HBV immunity, and HCV infections. RESULTS The seroprevalence of present HBV infection among the population aged 14 to 79 was 0.4%, representing an estimated 111,800 individuals. Another 4.2% had evidence of a previous HBV infection. Nearly 30% had vaccine-induced HBV immunity. The seroprevalence of HCV infection was 0.5%, representing an estimated 138,600. More than half of people with laboratory-confirmed HBV and 70% with laboratory-confirmed HCV were unaware of their infections. INTERPRETATION This is the first Canadian study to report laboratory-confirmed seroprevalence of HBV and HCV infections based on a nationally representative household sample. Substantial percentages of younger Canadians have vaccine-induced HBV immunity.
Automatic assessment of text-based responses in post-secondary education: A systematic review

Text-based open-ended questions in academic formative and summative assessments help students become deep learners and prepare them to understand concepts for a subsequent conceptual assessment. However, grading text-based questions, especially in large courses, is tedious and time-consuming for instructors. Text processing models continue progressing with the rapid development of Artificial Intelligence (AI) tools and Natural Language Processing (NLP) algorithms. Especially after breakthroughs in Large Language Models (LLM), there is immense potential to automate rapid assessment and feedback of text-based responses in education. This systematic review adopts a scientific and reproducible literature search strategy based on the PRISMA process using explicit inclusion and exclusion criteria to study text-based automatic assessment systems in post-secondary education, screening 838 papers and synthesizing 93 studies. To understand how text-based automatic assessment systems have been developed and applied in education in recent years, three research questions are considered. All included studies are summarized and categorized according to a proposed comprehensive framework, including the input and output of the system, research motivation, and research outcomes, aiming to answer the research questions accordingly. Additionally, the typical studies of automated assessment systems, research methods, and application domains in these studies are investigated and summarized. This systematic review provides an overview of recent educational applications of text-based assessment systems for understanding the latest AI/NLP developments assisting in text-based assessments in higher education. Findings will particularly benefit researchers and educators incorporating LLMs such as ChatGPT into their educational activities.
Higher order interpolative geometries and gradient regularity in evolutionary obstacle problems

We prove new optimal $C^{1,\alpha}$ regularity results for obstacle problems involving evolutionary $p$-Laplace type operators in the degenerate regime $p>2$. Our main results include the optimal regularity improvement at free boundary points in intrinsic backward $p$-paraboloids, up to the critical exponent, $\alpha \leq 2/(p-2)$, and the optimal regularity across the free boundaries in the full cylinders up to a universal threshold. Moreover, we provide an intrinsic criterion by which the optimal regularity improvement at free boundaries can be extended to the entire cylinders. An important feature of our analysis is that we do not impose any assumption on the time derivative of the obstacle. Our results are formulated in function spaces associated to what we refer to as higher order or $C^{1,\alpha}$ intrinsic interpolative geometries.
BRDF Approximation of maize and canopy parameter retrieval by ProSail inversion

There is a general consent about the necessity of radiometric calibration of airborne scanner data to normalize for sensor, atmospheric, topographic and cross track effects. Less investigated are diurnal effects due to changing illumination to stand structure geometries which are superimposing the before mentioned alterations. The presentation reports from a study conducted to quantify reflection changes for predefined view directions over the day and over the vegetation period of maize by the help of multidirectional field spectroscopic measurements. Morning, midday and afternoon measurement series are used to approximate the BRDF at different phenologic stages during the vegetation period 2000. By the help of ProSail canopy parameters are calculated for five particular times of the day. The results show significant differences in parameter retrieval leading to the conclusion that both complexes, the phase angle related correction approaches as well as the adaptation of physical models to the specific crop structure should further be improved.
Random Shortening of Linear Codes and Applications

Random linear codes (RLCs) are well known to have nice combinatorial properties and near-optimal parameters in many different settings. However, getting explicit constructions matching the parameters of RLCs is challenging, and RLCs are hard to decode efficiently. This motivated several previous works to study the problem of partially derandomizing RLCs, by applying certain operations to an explicit mother code. Among them, one of the most well studied operations is random puncturing, where a series of works culminated in the work of Guruswami and Mosheiff (FOCS' 22), which showed that a random puncturing of a low-biased code is likely to possess almost all interesting local properties of RLCs. In this work, we provide an in-depth study of another, dual operation of random puncturing, known as random shortening, which can be viewed equivalently as random puncturing on the dual code. Our main results show that for any small $\varepsilon$, by starting from a mother code with certain weaker conditions (e.g., having a large distance) and performing a random (or even pseudorandom) shortening, the new code is $\varepsilon$-biased with high probability. Our results hold for any field size and yield a shortened code with constant rate. This can be viewed as a complement to random puncturing, and together, we can obtain codes with properties like RLCs from weaker initial conditions. Our proofs involve several non-trivial methods of estimating the weight distribution of codewords, which may be of independent interest.
Exploring Deep Learning for Full-disk Solar Flare Prediction with Empirical Insights from Guided Grad-CAM Explanations

This study progresses solar flare prediction research by presenting a full-disk deep-learning model to forecast $\geq\mathrm{M}$-class solar flares and evaluating its efficacy on both central (within ±70°) and near-limb beyond ±70°) events, showcasing qualitative assessment of post hoc explanations for the model’s predictions, and providing empirical findings fro human-centered quantitative assessments of these explanations. Our model is trained using hourly full-disk line-of-sight magnetogram images to predict $\geq{\mathrm{M}}$-class solar flares within the subsequent 24-hour prediction window. Additionally, we apply the Guided Gradient-weighted Class Activation Mapping (Guided Grad-CAM) attribution method to interpret our model’s predictions and evaluate the explanations. Our analysis unveils that full-disk solar flare predictions correspond with active region characteristics. The following points represent the most important findings of our study: ❨1❩ Our deep learning models achieved an average true skill statistic (TSS) of $\sim 0.51$ and a Heidke skill score (HSS) of $\sim.38$, exhibiting skill to predict solar flares where for central locations the average recall is $\sim 0.75$ (recall values for X- and M-class are 0.95 and 0.73 respectively) and for the near-limb flares the average recall is $\sim 0.52$ (recall values for X- and M- class are 0.74 and 0.50 respectively); ❨2❩ qualitative examination of the model’s explanations reveals that it discerns and leverages features linked to active regions in both central and near-limb locations within full-disk magnetograms to produce respective predictions. In essence, our models grasp the shape and texture-based properties of flaring active regions, even in proximity to limb areas—a novel and essential capability with considerable significance for operational forecasting systems.
DeFTAN-II: Efficient Multichannel Speech Enhancement with Subgroup Processing

In this work, we present DeFTAN-II, an efficient multichannel speech enhancement model based on transformer architecture and subgroup processing. Despite the success of transformers in speech enhancement, they face challenges in capturing local relations, reducing the high computational complexity, and lowering memory usage. To address these limitations, we introduce subgroup processing in our model, combining subgroups of locally emphasized features with other subgroups containing original features. The subgroup processing is implemented in several blocks of the proposed network. In the proposed split dense blocks extracting spatial features, a pair of subgroups is sequentially concatenated and processed by convolution layers to effectively reduce the computational complexity and memory usage. For the F- and T-transformers extracting temporal and spectral relations, we introduce cross-attention between subgroups to identify relationships between locally emphasized and non-emphasized features. The dual-path feedforward network then aggregates attended features in terms of the gating of local features processed by dilated convolutions. Through extensive comparisons with state-of-the-art multichannel speech enhancement models, we demonstrate that DeFTAN-II with subgroup processing outperforms existing methods at significantly lower computational complexity. Moreover, we evaluate the model's generalization capability on real-world data without fine-tuning, which further demonstrates its effectiveness in practical scenarios.
Optimizing Factual Accuracy in Text Generation through Dynamic Knowledge Selection

Language models (LMs) have revolutionized the way we interact with information, but they often generate nonfactual text, raising concerns about their reliability. Previous methods use external knowledge as references for text generation to enhance factuality but often struggle with the knowledge mix-up(e.g., entity mismatch) of irrelevant references. Besides,as the length of the output text grows, the randomness of sampling can escalate, detrimentally impacting the factual accuracy of the generated text. In this paper, we present DKGen, which divide the text generation process into an iterative process. In each iteration, DKGen takes the input query, the previously generated text and a subset of the reference passages as input to generate short text. During the process, the subset is dynamically selected from the full passage set based on their relevance to the previously generated text and the query, largely eliminating the irrelevant references from input. To further enhance DKGen's ability to correctly use these external knowledge, DKGen distills the relevance order of reference passages to the cross-attention distribution of decoder. We train and evaluate DKGen on a large-scale benchmark dataset. Experiment results show that DKGen outperforms all baseline models.
The extremal number of cycles with all diagonals

In 1975, Erd\H{o}s asked the following natural question: What is the maximum number of edges that an $n$-vertex graph can have without containing a cycle with all diagonals? Erd\H{o}s observed that the upper bound $O(n^{5/3})$ holds since the complete bipartite graph $K_{3,3}$ can be viewed as a cycle of length six with all diagonals. In this paper, we resolve this old problem. We prove that there exists a constant $C$ such that every $n$-vertex with $Cn^{3/2}$ edges contains a cycle with all diagonals. Since any cycle with all diagonals contains cycles of length four, this bound is best possible using well-known constructions of graphs without a four-cycle based on finite geometry. Among other ideas, our proof involves a novel lemma about finding an `almost-spanning' robust expander which might be of independent interest.
IDVT: Interest-aware Denoising and View-guided Tuning for Social Recommendation

In the information age, recommendation systems are vital for efficiently filtering information and identifying user preferences. Online social platforms have enriched these systems by providing valuable auxiliary information. Socially connected users are assumed to share similar preferences, enhancing recommendation accuracy and addressing cold start issues. However, empirical findings challenge the assumption, revealing that certain social connections can actually harm system performance. Our statistical analysis indicates a significant amount of noise in the social network, where many socially connected users do not share common interests. To address this issue, we propose an innovative \underline{I}nterest-aware \underline{D}enoising and \underline{V}iew-guided \underline{T}uning (IDVT) method for the social recommendation. The first ID part effectively denoises social connections. Specifically, the denoising process considers both social network structure and user interaction interests in a global view. Moreover, in this global view, we also integrate denoised social information (social domain) into the propagation of the user-item interactions (collaborative domain) and aggregate user representations from two domains using a gating mechanism. To tackle potential user interest loss and enhance model robustness within the global view, our second VT part introduces two additional views (local view and dropout-enhanced view) for fine-tuning user representations in the global view through contrastive learning. Extensive evaluations on real-world datasets with varying noise ratios demonstrate the superiority of IDVT over state-of-the-art social recommendation methods.