Miracle-dz/UniFAR-FTUs huggingface dataset:
Gastrointestinal barriers to levodopa transport and absorption in Parkinson's disease

Levodopa is the gold standard for the symptomatic treatment of Parkinson's disease (PD). There are well documented motor and non‐motor fluctuations, however, that occur almost inevitably once levodopa is started after a variable period in people with PD. Whilst brain neurodegenerative processes play a part in the pathogenesis of these fluctuations, a range of barriers across the gastrointestinal (GI) tract can alter levodopa pharmacokinetics, ultimately contributing to non‐optimal levodopa response and symptoms fluctuations. GI barriers to levodopa transport and absorption include dysphagia, delayed gastric emptying, constipation, Helicobacter pylori infection, small intestinal bacterial overgrowth and gut dysbiosis. In addition, a protein‐rich diet and concomitant medication intake can further alter levodopa pharmacokinetics. This can result in unpredictable or sub‐optimal levodopa response, ‘delayed on’ or ‘no on’ phenomena. In this narrative review, we provided an overview on the plethora of GI obstacles to levodopa transport and absorption in PD and their implications on levodopa pharmacokinetics and development of motor fluctuations. In addition, management strategies to address GI dysfunction in PD are highlighted, including use of non‐oral therapies to bypass the GI tract.
The Effects of Graded Levels of Calorie Restriction: XIX. Impact of Graded Calorie Restriction on Protein Expression in the Liver

Abstract Calorie restriction (CR) extends life span by modulating the mechanisms involved in aging. We quantified the hepatic proteome of male C57BL/6 mice exposed to graded levels of CR (0%–40% CR) for 3 months, and evaluated which signaling pathways were most affected. The metabolic pathways most significantly stimulated by the increase in CR, included the glycolysis/gluconeogenesis pathway, the pentose phosphate pathway, the fatty acid degradation pathway, the valine, leucine, and isoleucine degradation pathway, and the lysine degradation pathway. The metabolism of xenobiotics by cytochrome P450 pathway was activated and feminized by increased CR, while production in major urinary proteins (Mups) was strongly reduced, consistent with a reduced investment in reproduction as predicted by the disposable soma hypothesis. However, we found no evidence of increased somatic protection, and none of the 4 main pathways implied to be linked to the impact of CR on life span (insulin/insulin-like growth factor [IGF-1], nuclear factor-κB [NF-κB], mammalian Target of Rapamycin [mTOR], and sirtuins) as well as pathways in cancer, were significantly changed at the protein level in relation to the increase in CR level. This was despite previous work at the transcriptome level in the same individuals indicating such changes. On the other hand, we found Aldh2, Aldh3a2, and Aldh9a1 in carnitine biosynthesis and Acsl5 in carnitine shuttle system were up-regulated by increased CR, which are consistent with our previous work on metabolome of the same individuals. Overall, the patterns of protein expression were more consistent with a “clean cupboards” than a “disposable soma” interpretation.
Fast likelihood calculations for comparative analyses

1. Modern comparative approaches use model‐based methods to describe evolutionary processes. Generalised least squares calculations lie at the heart of many methods; however, they can be computationally intensive. This is because it is necessary to form a variance–covariance matrix, then to calculate the inverse and determinant of this.
Mass Spectrometry-Based Untargeted Metabolomics Reveals the Importance of Glycosylated Flavones in Patterned Lentil Seed Coats

Lentil seed coats are rich in antioxidant polyphenols that are important for plant defense and have potential as valorized byproducts. Although biochemical differences among lentil seed coat colors have been previously studied, differences among seed coat patterns remain largely unexplored. This study used mass spectrometry-based untargeted metabolomics to investigate polyphenol differences among lentil seed coat patterns to search for biochemical pathways potentially responsible for seed coat pattern differences. Comparing patterned with non-patterned green lentil seed coats, 28 significantly upregulated metabolites were found in patterned seed coats; 19 of them were identified as flavones. Flavones were virtually absent in non-patterned seed coats, thereby strongly suggesting a blockage in their flavone biosynthetic pathway. Although the black pattern is not readily discernible on black seed coats, many of the same flavones found in green marbled seed coats were also found in black seed coats, indicating that black seed coats likely have a marbled pattern.
Distributed Algorithms for Large-Scale Graphs

Motivated by the increasing need for fast processing of large-scale graphs, we study a number of fundamental graph problems in a message-passing model for distributed computing, called $k$-machine model, where we have $k$ machines that jointly perform computations on $n$-node graphs. The graph is assumed to be partitioned in a balanced fashion among the $k$ machines, a common implementation in many real-world systems. Communication is point-to-point via bandwidth-constrained links, and the goal is to minimize the round complexity, i.e., the number of communication rounds required to finish a computation. We present a generic methodology that allows to obtain efficient algorithms in the $k$-machine model using distributed algorithms for the classical CONGEST model of distributed computing. Using this methodology, we obtain algorithms for various fundamental graph problems such as connectivity, minimum spanning trees, shortest paths, maximal independent sets, and finding subgraphs, showing that many of these problems can be solved in $\tilde{O}(n/k)$ rounds; this shows that one can achieve speedup nearly linear in $k$. To complement our upper bounds, we present lower bounds on the round complexity that quantify the fundamental limitations of solving graph problems distributively. We first show a lower bound of $\Omega(n/k)$ rounds for computing a spanning tree of the input graph. This result implies the same bound for other fundamental problems such as computing a minimum spanning tree, breadth-first tree, or shortest paths tree. We also show a $\tilde \Omega(n/k^2)$ lower bound for connectivity, spanning tree verification and other related problems. The latter lower bounds follow from the development and application of novel results in a random-partition variant of the classical communication complexity model.
Principled and Efficient Motif Finding for Structure Learning in Lifted Graphical Models

Structure learning is a core problem in AI central to the fields of neuro-symbolic AI and statistical relational learning. It consists in automatically learning a logical theory from data. The basis for structure learning is mining repeating patterns in the data, known as structural motifs. Finding these patterns reduces the exponential search space and therefore guides the learning of formulas. Despite the importance of motif learning, it is still not well understood. We present the first principled approach for mining structural motifs in lifted graphical models, languages that blend first-order logic with probabilistic models, which uses a stochastic process to measure the similarity of entities in the data. Our first contribution is an algorithm, which depends on two intuitive hyperparameters: one controlling the uncertainty in the entity similarity measure, and one controlling the softness of the resulting rules. Our second contribution is a preprocessing step where we perform hierarchical clustering on the data to reduce the search space to the most relevant data. Our third contribution is to introduce an O(n ln(n)) (in the size of the entities in the data) algorithm for clustering structurally-related data. We evaluate our approach using standard benchmarks and show that we outperform state-of-the-art structure learning approaches by up to 6% in terms of accuracy and up to 80% in terms of runtime.
Sharp interface analysis of a diffuse interface model for cell blebbing with linker dynamics

We investigate the convergence of solutions of a recently proposed diffuse interface/phase field model for cell blebbing by means of matched asymptotic expansions. It is a biological phenomenon that increasingly attracts attention by both experimental and theoretical communities. Key to understanding the process of cell blebbing mechanically are proteins that link the cell cortex and the cell membrane. Another important model component is the bending energy of the cell membrane and cell cortex which accounts for differential equations up to sixth order. Both aspects pose interesting mathematical challenges that will be addressed in this work like showing non‐singularity formation for the pressure at boundary layers, deriving equations for asymptotic series coefficients of uncommonly high order, and dealing with a highly coupled system of equations.
Fuzzing Automatic Differentiation in Deep-Learning Libraries

Deep learning (DL) has attracted wide attention and has been widely deployed in recent years. As a result, more and more research efforts have been dedicated to testing DL libraries and frameworks. However, existing work largely overlooked one crucial component of any DL system, automatic differentiation (AD), which is the basis for the recent development of DL. To this end, we propose ∇Fuzz, the first general and practical approach specifically targeting the critical AD component in DL libraries. Our key insight is that each DL library API can be abstracted into a function processing tensors/vectors, which can be differentially tested under various execution scenarios (for computing outputs/gradients with different implementations). We have implemented $\nabla \text{Fuzz}$ as a fully automated API-level fuzzer targeting AD in DL libraries, which utilizes differential testing on different execution scenarios to test both first-order and high-order gradients, and also includes automated filtering strategies to remove false positives caused by numerical instability. We have performed an extensive study on four of the most popular and actively-maintained DL libraries, PyTorch, TensorFlow, JAX, and OneFlow. The result shows that $\nabla \text{Fuzz}$ substantially outperforms state-of-the-art fuzzers in terms of both code coverage and bug detection. To date, $\nabla \text{Fuzz}$ has detected 173 bugs for the studied DL libraries, with 144 already confirmed by developers (117 of which are previously unknown bugs and 107 are related to AD). Remarkably, $\nabla \text{Fuzz}$ contributed 58.3% (7/12) of all high-priority AD bugs for PyTorch and JAX during a two-month period. None of the confirmed AD bugs were detected by existing fuzzers.
An End-to-End Framework for Marketing Effectiveness Optimization under Budget Constraint

Online platforms often incentivize consumers to improve user engagement and platform revenue. Since different consumers might respond differently to incentives, individual-level budget allocation is an essential task in marketing campaigns. Recent advances in this field often address the budget allocation problem using a two-stage paradigm: the first stage estimates the individual-level treatment effects using causal inference algorithms, and the second stage invokes integer programming techniques to find the optimal budget allocation solution. Since the objectives of these two stages might not be perfectly aligned, such a two-stage paradigm could hurt the overall marketing effectiveness. In this paper, we propose a novel end-to-end framework to directly optimize the business goal under budget constraints. Our core idea is to construct a regularizer to represent the marketing goal and optimize it efficiently using gradient estimation techniques. As such, the obtained models can learn to maximize the marketing goal directly and precisely. We extensively evaluate our proposed method in both offline and online experiments, and experimental results demonstrate that our method outperforms current state-of-the-art methods. Our proposed method is currently deployed to allocate marketing budgets for hundreds of millions of users on a short video platform and achieves significant business goal improvements. Our code will be publicly available.
Importance Sampling Deterministic Annealing for Clustering

—A current assumption of most clustering methods is that the training data and future data are taken from the same distribution. However, this assumption may not hold in some real-world scenarios. In this paper, we propose an importance sampling based deterministic annealing approach (ISDA) for clustering problems which minimizes the worst case of expected distortions under the constraint of distribution deviation. The distribution deviation constraint can be converted to the constraint over a set of weight distributions centered on the uniform distribution derived from importance sampling. The objective of the proposed approach is to minimize the loss under maximum degradation hence the resulting problem is a constrained minimax optimization problem which can be reformulated to an unconstrained problem using the Lagrange method and be solved by the quasi-newton algorithm. Experiment results on synthetic datasets and a real-world load forecasting problem validate the effectiveness of the proposed ISDA. Furthermore, we show that fuzzy c-means is a special case of ISDA with the logarithmic distortion. This observation sheds a new light on the relationship between fuzzy c-means and deterministic annealing clustering algorithms and provides an interesting physical and information-theoretical interpretation for fuzzy exponent m .
Balanced Allocations in Batches: The Tower of Two Choices

In the balanced allocation framework, the goal is to allocate m balls into n bins, so as to minimize the gap (difference of maximum to average load). The One-Choice process allocates each ball to a bin sampled independently and uniformly at random. The Two-Choice process allocates balls sequentially, and each ball is placed in the least loaded of two sampled bins. Finally, the (1+β)-process mixes these processes, meaning each ball is allocated using Two-Choice with probability β in (0,1), and using One-Choice otherwise. Despite Two-Choice being optimal in the sequential setting, it has been observed in practice that it does not perform well in a parallel environment, where load information may be outdated. Following [BCEFN12], we study such a parallel setting where balls are allocated in batches of size b, and balls within the same batch are allocated with the same strategy and based on the same load information. For small batch sizes b in [n, n log n], it was shown in [LS22c] that Two-Choice achieves an asymptotically optimal gap among all allocation processes with two (or any constant number of) samples. In this work, we focus on larger batch sizes b in [n log n, n³]. It was proved in [LS22a] that Two-Choice leads to a gap of Θ(b/n). As our main result, we prove that the gap reduces to O(√((b/n) log n)), if one runs the (1+β)-process with an appropriately chosen β (in fact this result holds for a larger class of processes). This not only proves the phenomenon that Two-Choice is not the best (leading to the formation of "towers" over previously light bins), but also that mixing two processes (One-Choice and Two-Choice) leads to a process which achieves a gap that is asymptotically smaller than both. We also derive a matching lower bound of Ω(√((b/n) log n)) for any allocation process, which demonstrates that the above (1+β)-process is asymptotically optimal. Our analysis also works in the presence of randomly weighted balls, and also implies exponential tails for the number of bins above a certain load value.
Entanglement detection in arbitrary-dimensional bipartite quantum systems through partial realigned moments

Detection of entanglement through partial knowledge of the quantum state is a challenge to implement efficiently. Here we propose a separability criterion for detecting bipartite entanglement in arbitrary dimensional quantum states using partial moments of the realigned density matrix. Our approach enables detection of both distillable and bound entangled states through a common framework. We illustrate the efficiency of our method through examples of states belonging to both the above categories, which are not detectable using comparable other schemes relying on partial state information. The formalism of employing partial realigned moments proposed here is further shown to be effective for two-qubit systems too, with a slight modification of our separability criterion.
Contestable Camera Cars: A Speculative Design Exploration of Public AI That Is Open and Responsive to Dispute

Local governments increasingly use artificial intelligence (AI) for automated decision-making. Contestability, making systems responsive to dispute, is a way to ensure they respect human rights to autonomy and dignity. We investigate the design of public urban AI systems for contestability through the example of camera cars: human-driven vehicles equipped with image sensors. Applying a provisional framework for contestable AI, we use speculative design to create a concept video of a contestable camera car. Using this concept video, we then conduct semi-structured interviews with 17 civil servants who work with AI employed by a large northwestern European city. The resulting data is analyzed using reflexive thematic analysis to identify the main challenges facing the implementation of contestability in public AI. We describe how civic participation faces issues of representation, public AI systems should integrate with existing democratic practices, and cities must expand capacities for responsible AI development and operation.
Black hole and host galaxy properties of two 𝒛 (cid:39) 6 . 8 quasars from the NIRSpec IFU

Integral Field Spectroscopy (IFS) with JWST NIRSpec will signiﬁcantly improve our understanding of the ﬁrst quasars, by providing spatially resolved, infrared spectroscopic capabilities which cover key rest-frame optical emission lines that have been previously unobservable. Here we present our results from the ﬁrst two 𝑧 > 6 quasars observed as a part of the NIRSpec Galaxy Assembly IFS GTO program, DELS J0411 − 0907 at 𝑧 = 6 . 82 and VDES J0020 − 3653 at 𝑧 = 6 . 86. By observing the H 𝛽 , [O iii] 𝜆𝜆 4959 , 5007, and H 𝛼 emission lines in these high-𝑧 quasars for the ﬁrst time, we measure accurate black hole masses, 𝑀 BH = 1 . 7 + 2 − 0 . 8 × 10 9 M (cid:12) and 2 . 6 + 3 . 2 − 1 . 2 × 10 9 M (cid:12) , corresponding to Eddington ratios of 𝜆 Edd = 0 . 9 + 0 . 4 − 0 . 3 and 0 . 4 ± 0 . 2 for DELS J0411 − 0907 and VDES J0020 − 3653 respectively, providing a key comparison for existing estimates from the Mgii line which we show can be unreliable. We perform quasar—host decomposition using models of the quasars’ broad lines, to measure the underlying host galaxies. We also discover two emission line regions surrounding each of the host galaxies, which are likely companion galaxies undergoing mergers with these hosts. We measure the star formation rates, excitation mechanisms, and dynamical masses of the hosts and companions, giving the ﬁrst reliable 𝑀 BH / 𝑀 dyn ratios at high-𝑧 . DELS J0411 − 0907 and VDES J0020 − 3653 both lie above the local black hole—host mass relation, and are consistent with the existing observations of 𝑧 (cid:38) 6 quasar host galaxies with ALMA. We detect ionized outﬂows in [O iii] 𝜆𝜆 4959 , 5007 and H 𝛽 from both quasars, with mass outﬂow rates of 63 + 29 − 35 and 420 + 66 − 72 M (cid:12) yr − 1 for DELS J0411 − 0907 and VDES J0020 − 3653, much larger than their host star formation rates of < 39 . 3 and < 205 M (cid:12) /yr, respectively. This work highlights the exceptional capabilities of the JWST NIRSpec IFU for observing
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction

Understanding the landscape of potential harms from algorithmic systems enables practitioners to better anticipate consequences of the systems they build. It also supports the prospect of incorporating controls to help minimize harms that emerge from the interplay of technologies and social and cultural dynamics. A growing body of scholarship has identified a wide range of harms across different algorithmic technologies. However, computing research and practitioners lack a high level and synthesized overview of harms from algorithmic systems. Based on a scoping review of computing research (n=172), we present an applied taxonomy of sociotechnical harms to support a more systematic surfacing of potential harms in algorithmic systems. The final taxonomy builds on and refers to existing taxonomies, classifications, and terminologies. Five major themes related to sociotechnical harms — representational, allocative, quality-of-service, interpersonal harms, and social system/societal harms — and sub-themes are presented along with a description of these categories. We conclude with a discussion of challenges and opportunities for future research.
Machine Learning Detects Multiplicity of the First Stars in Stellar Archaeology Data

In unveiling the nature of the first stars, the main astronomical clue is the elemental compositions of the second generation of stars, observed as extremely metal-poor (EMP) stars, in the Milky Way. However, no observational constraint was available on their multiplicity, which is crucial for understanding early phases of galaxy formation. We develop a new data-driven method to classify observed EMP stars into mono- or multi-enriched stars with support vector machines. We also use our own nucleosynthesis yields of core-collapse supernovae with mixing fallback that can explain many of the observed EMP stars. Our method predicts, for the first time, that 31.8% ± 2.3% of 462 analyzed EMP stars are classified as mono-enriched. This means that the majority of EMP stars are likely multi-enriched, suggesting that the first stars were born in small clusters. Lower-metallicity stars are more likely to be enriched by a single supernova, most of which have high carbon enhancement. We also find that Fe, Mg. Ca, and C are the most informative elements for this classification. In addition, oxygen is very informative despite its low observability. Our data-driven method sheds a new light on solving the mystery of the first stars from the complex data set of Galactic archeology surveys.
Observation of the decay $D^+_s\to \omega\pi^+\eta$

Using 7.33 fb$^{-1}$ of $e^+e^-$ collision data collected by the BESIII detector at center-of-mass energies between 4.128 and 4.226~GeV, we observe for the first time the decay $D^{\pm}_s\to \omega\pi^{\pm}\eta$ with a statistical significance of 7.6$\sigma$. The measured branching fraction of this decay is $(0.54\pm0.12\pm0.04)\%$, where the first uncertainty is statistical and the second is systematic.