Miracle-dz/UniFAR-FTUs huggingface dataset:
IPA: Inference Pipeline Adaptation to Achieve High Accuracy and Cost-Efficiency

Efficiently optimizing multi-model inference pipelines for fast, accurate, and cost-effective inference is a crucial challenge in machine learning production systems, given their tight end-to-end latency requirements. To simplify the exploration of the vast and intricate trade-off space of latency, accuracy, and cost in inference pipelines, providers frequently opt to consider one of them. However, the challenge lies in reconciling latency, accuracy, and cost trade-offs. To address this challenge and propose a solution to efficiently manage model variants in inference pipelines, we present IPA, an online deep learning Inference Pipeline Adaptation system that efficiently leverages model variants for each deep learning task. Model variants are different versions of pre-trained models for the same deep learning task with variations in resource requirements, latency, and accuracy. IPA dynamically configures batch size, replication, and model variants to optimize accuracy, minimize costs, and meet user-defined latency Service Level Agreements (SLAs) using Integer Programming. It supports multi-objective settings for achieving different trade-offs between accuracy and cost objectives while remaining adaptable to varying workloads and dynamic traffic patterns. Navigating a wider variety of configurations allows \namex{} to achieve better trade-offs between cost and accuracy objectives compared to existing methods. Extensive experiments in a Kubernetes implementation with five real-world inference pipelines demonstrate that IPA improves end-to-end accuracy by up to 21% with a minimal cost increase. The code and data for replications are available at https://github.com/reconfigurable-ml-pipeline/ipa.
Solvability near the characteristic set for a class of planar vector fields of infinite type

On etudie la resolubilite des equations associees a un champ de vecteurs complexe L dans R 2 a coefficients de classe C ∞ ou C ω . On suppose que L est partout elliptique, sauf le long d'une courbe simple etfermee Σ. Sur Σ, on suppose que L est de type infini et que L A L s'annule a un ordre constant. Les equations considerees sont de la forme Lu = pu + f, ou f satisfait des conditions de compatibilite. On prouve, en particulier, que lorsque l'ordre d'annulation de L L est > 1, l'equation Lu = f est resoluble dans la categorie C ∞ mais pas dans la categorie C ω .
A Quantum G\"odelian Hunch

What if the paradoxical nature of quantum theory could find its source in some undecidability analog to that of Godel's incompleteness theorem ? This essay aims at arguing for such Godelian hunch via two case studies. Firstly, using a narrative based on the Newcomb problem, the theological motivational origin of quantum contextuality is introduced in order to show how this result might be related to a Liar-like undecidability. A topological generalization of contextuality by Abramsky et al. in which the logical structure of quantum contextuality is compared with "Liar cycles" is also presented. Secondly, the measurement problem is analyzed as emerging from a logical error. A personal analysis of the related Wigner's friend thought experiment and and a recent paradox by Frauchiger and Renner is presented, by introducing the notion of "meta-contextuality" as a Liar-like feature underlying the neo-Copenhagen interpretations of quantum theory. Finally, this quantum Godelian hunch opens a discussion of the paradoxical nature of quantum physics and the emergence of time itself from self-contradiction.
Three decades of machine learning with neural networks in computer-aided architectural design (1990–2021)

Abstract Over the past years, computational methods based on deep learning—that is, machine learning with multilayered neural networks—have become state-of-the-art in main research areas in computer-aided architectural design (CAAD). To understand current trends of CAAD with deep learning, to situate them in a broader historical context, and to identify future research challenges, this article presents a systematic review of publications that apply neural networks to CAAD problems. Research papers employing neural networks were collected, in particular, from CumInCad a major open-access repository of the CAAD community and categorized into different types of research problems. Upon analyzing the distribution of the papers in these categories, namely, the composition of research subjects, data types, and neural network models, this article suggests and discusses several historical and technical trends. Moreover, it identifies that the publications analyzed typically provide limited access to important research components used as part of their deep learning methods. The article points out the importance of sharing training experiments and data, of describing the dataset, dataset parameters, dataset samples, model, learning parameters, and learning results to support reproducibility. It proposes a guideline that aims at increasing the quality and availability of CAAD research with machine learning.
Anticoncentration in Ramsey graphs and a proof of the Erdős–McKay conjecture

Abstract An n-vertex graph is called C-Ramsey if it has no clique or independent set of size $C\log _2 n$ (i.e., if it has near-optimal Ramsey behavior). In this paper, we study edge statistics in Ramsey graphs, in particular obtaining very precise control of the distribution of the number of edges in a random vertex subset of a C-Ramsey graph. This brings together two ongoing lines of research: the study of ‘random-like’ properties of Ramsey graphs and the study of small-ball probability for low-degree polynomials of independent random variables. The proof proceeds via an ‘additive structure’ dichotomy on the degree sequence and involves a wide range of different tools from Fourier analysis, random matrix theory, the theory of Boolean functions, probabilistic combinatorics and low-rank approximation. In particular, a key ingredient is a new sharpened version of the quadratic Carbery–Wright theorem on small-ball probability for polynomials of Gaussians, which we believe is of independent interest. One of the consequences of our result is the resolution of an old conjecture of Erdős and McKay, for which Erdős reiterated in several of his open problem collections and for which he offered one of his notorious monetary prizes.
Sources and spatial variation of the chemical composition of snow in the Tien Shan, China

Research on the sources and spatial variation of the chemical content of snow was conducted in four headwater basins of the eastern Tien Shan, China. The mean SO4 2- concentration of 16.2 μeq L-1 was three-fold greater than background concentrations from other remote areas of the world. About 80% of the SO4 2- in snow was deposited in association with Ca2+ and Mg2+ , partially accounting for the relatively high pH of 6.34 in new snow. The variance of individualions in the snowpack ranged from 24 to 46%, measured as the standard deviation divided by the mean and based on 59 samples. Analysis of variance tests showed that solute concentrations varied significantly among sites within an individual basin and that the mean solute concentrations did not vary significantly among basins. Aeolian dust in the snowpack resulted in significant post-depositional alterations in the solute content of snow. Mineral weathering of the dust in the snowpack of the Tien Shan mproduced alkalinity and Ca2+, increasing conductance and buffering H+ .
Gastrointestinal syndromes preceding a diagnosis of Parkinson’s disease: testing Braak’s hypothesis using a nationwide database for comparison with Alzheimer’s disease and cerebrovascular diseases

Objective Braak’s hypothesis states that Parkinson’s disease (PD) originates in the gastrointestinal (GI) tract, and similar associations have been established for Alzheimer’s disease (AD) and cerebrovascular diseases (CVD). We aimed to determine the incidence of GI syndromes and interventions preceding PD compared with negative controls (NCs), AD and CVD. Design We performed a combined case-control and cohort study using TriNetX, a US based nationwide medical record network. Firstly, we compared subjects with new onset idiopathic PD with matched NCs and patients with contemporary diagnoses of AD and CVD, to investigate preceding GI syndromes, appendectomy and vagotomy. Secondly, we compared cohorts with these exposures to matched NCs for the development of PD, AD and CVD within 5 years. Results We identified 24 624 PD patients in the case-control analysis and matched 18 cohorts with each exposure to their NCs. Gastroparesis, dysphagia, irritable bowel syndrome (IBS) without diarrhoea and constipation showed specific associations with PD (vs NCs, AD and CVD) in both the case-control (odds ratios (ORs) vs NCs 4.64, 3.58, 3.53 and 3.32, respectively, all p<0.0001) and cohort analyses (relative risks (RRs) vs NCs 2.43, 2.27, 1.17 and 2.38, respectively, all p<0.05). While functional dyspepsia, IBS with diarrhoea, diarrhoea and faecal incontinence were not PD specific, IBS with constipation and intestinal pseudo-obstruction showed PD specificity in the case-control (OR 4.11) and cohort analysis (RR 1.84), respectively. Appendectomy decreased the risk of PD in the cohort analysis (RR 0.48). Neither inflammatory bowel disease nor vagotomy were associated with PD. Conclusion Dysphagia, gastroparesis, IBS without diarrhoea and constipation might specifically predict Parkinson’s disease.
Flood Elevation Limits in the Rocky Mountains

An analysis of 77,987 station-years of streamflow-gaging station data from 3,748 stations in the Rocky Mountains indicates that there is a latitude-dependent elevation limit to substantial rainfall-produced flooding. The elevation limit ranges from about 1,650 m in Montana to about 2,350 m in New Mexico. Above this elevation limit, large rainfall-produced floods occur very infrequently and maximum unit discharge is 1.7 m 3 /s/km 2 or less. Below this elevation limit, large-magnitude flooding is more common and maximum unit discharge ranges from to 30 m 3 /s/km 2 in Idaho and Montana to 59 m 3 /s/km 2 in New Mexico. These results emphasize the critical need for additional research to increase our knowledge of floods, and have important implications in water-resources investigations in the Rocky Mountains.
Minimizing Stack Memory for Hard Real-Time Applications on Multicore Platforms with Partitioned Fixed-Priority or EDF Scheduling

Multicore processors are increasingly adopted in resource-constrained real-time embedded applications. In the development of such applications, efficient use of RAM memory is as important as the effective scheduling of software tasks. Preemption Threshold Scheduling (PTS) is a well-known technique for controlling the degree of preemption, possibly improving system schedulability, and to reduce system stack usage. In this paper, we consider partitioned multi-processor scheduling on a multicore processor with either Fixed-Priority or Earliest Deadline First scheduling algorithms with PTS and address the design optimization problem of mapping tasks to processor cores and assignment of task priorities and preemption thresholds with the optimization objective of minimizing system stack usage. We present both optimal solution techniques based on Mixed Integer Linear Programming and efficient heuristic algorithms that can achieve high-quality results. We perform extensive performance evaluations using both synthetic tasksets and industrial case studies.
Deep screening of proximal and distal splicing-regulatory elements in a native sequence context

Pre-mRNA splicing, a key process in gene expression, can be therapeutically modulated using various drug modalities, including antisense oligonucleotides (ASOs). However, determining promising targets is impeded by the challenge of systematically mapping splicing-regulatory elements (SREs) in their native sequence context. Here, we use the catalytically dead CRISPR-RfxCas13d RNA-targeting system (dCas13d/gRNA) as a programmable platform to bind SREs and modulate splicing by competing against endogenous splicing factors. SpliceRUSH, a high-throughput screening method, was developed to map SREs in any gene of interest using a lentivirus gRNA library that tiles the genetic region, including distal intronic sequences. When applied to SMN2, a therapeutic target for spinal muscular atrophy, SpliceRUSH robustly identified not only known SREs, but also a novel distal intronic splicing enhancer, which can be targeted to alter exon 7 splicing using either dCas13d/gRNA or ASOs. This technology enables a deeper understanding of splicing regulation with applications for RNA-based drug discovery.
Phosphorylation-controlled cohesion of a nuclear condensate regulates mRNA retention

Nuclear speckles are membraneless organelles that associate with active transcription sites and participate in post-transcriptional mRNA processing. During the cell cycle, nuclear speckles dissolve following phosphorylation of their protein components. Here, we identify the PP1 family as the phosphatases that counteract kinase-mediated dissolution. PP1 overexpression increases speckle cohesion and leads to retention of polyadenylated RNA within speckles and the nucleus. Using APEX2 proximity labeling combined with RNA-sequencing, we characterized the relationship between the cohesion of nuclear speckles and the recruitment of specific RNAs. We find that many transcripts are preferentially enriched within nuclear speckles compared to the nucleoplasm, particularly chromatin- and nucleus-associated transcripts. While total polyadenylated RNA retention increased with nuclear speckle cohesion, the ratios of most mRNA species to each other were constant, indicating non-selective, or proportional, retention. We then explored whether nuclear speckle cohesion changes in response to environmental perturbations associated with changes in kinase or phosphatase activity. We found that cellular responses to heat shock, oxidative stress, and hypoxia include changes to the cohesion of nuclear speckles and mRNA retention. Our results demonstrate that tuning the material properties of nuclear speckles provides a mechanism for the acute control of mRNA localization.
Inferring laminar origins of MEG signals with optically pumped magnetometers (OPMs): a simulation study

We explore the potential of optically-pumped magnetometers (OPMs) to infer the laminar origins of neural activity non-invasively. OPM sensors can be positioned closer to the scalp than conventional cryogenic MEG sensors, opening an avenue to higher spatial resolution when combined with high-precision forward modelling. By simulating the forward model projection of single dipole sources onto OPM sensor arrays with varying sensor densities and measurement axes, and employing sparse source reconstruction approaches, we find that laminar inference with OPM arrays is possible at relatively low sensor counts at moderate to high signal-to-noise ratios (SNR). We observe improvements in laminar inference with increasing spatial sampling densities and number of measurement axes. Surprisingly, moving sensors closer to the scalp is less advantageous than anticipated - and even detrimental at high SNRs. Biases towards both the superficial and deep surfaces at very low SNRs and a notable bias towards the deep surface when combining empirical Bayesian beamformer (EBB) source reconstruction with a whole-brain analysis pose further challenges. Adequate SNR through appropriate trial numbers and shielding, as well as precise co-registration, is crucial for reliable laminar inference with OPMs.
Human EEG and artificial neural networks reveal disentangled representations of object real-world size in natural images

Remarkably, human brains have the ability to accurately perceive and process the real-world size of objects, despite vast differences in distance and perspective. While previous studies have delved into this phenomenon, distinguishing this ability from other visual perceptions, like depth, has been challenging. Using the THINGS EEG2 dataset with high time-resolution human brain recordings and more ecologically valid naturalistic stimuli, our study uses an innovative approach to disentangle neural representations of object real-world size from retinal size and perceived real-world depth in a way that was not previously possible. Leveraging this state-of-the-art dataset, our EEG representational similarity results reveal a pure representation of object real-world size in human brains. We report a representational timeline of visual object processing: object real-world depth appeared first, then retinal size, and finally, real-world size. Additionally, we input both these naturalistic images and object-only images without natural background into artificial neural networks. Consistent with the human EEG findings, we also successfully disentangled representation of object real-world size from retinal size and real-world depth in all three types of artificial neural networks (visual-only ResNet, visual-language CLIP, and language-only Word2Vec). Moreover, our multi-modal representational comparison framework across human EEG and artificial neural networks reveals real-world size as a stable and higher-level dimension in object space incorporating both visual and semantic information. Our research provides a detailed and clear characterization of the object processing process, which offers further advances and insights into our understanding of object space and the construction of more brain-like visual models.
Editorial board publication strategy and acceptance rates in Turkish national journals

Abstract Purpose This study takes advantage of newly released journal metrics to investigate whether local journals with more qualified boards have lower acceptance rates, based on data from 219 Turkish national journals and 2,367 editorial board members. Design/methodology/approach This study argues that journal editors can signal their scholarly quality by publishing in reputable journals. Conversely, editors publishing inside articles in affiliated national journals would send negative signals. The research predicts that high (low) quality editorial boards will conduct more (less) selective evaluation and their journals will have lower (higher) acceptance rates. Based on the publication strategy of editors, four measures of board quality are defined: Number of board inside publications per editor (INSIDER), number of board Social Sciences Citation Index publications per editor (SSCI), inside-to-SSCI article ratio (ISRA), and board citation per editor (CITATION). Predictions are tested by correlation and regression analysis. Findings Low-quality board proxies (INSIDER, ISRA) are positively, and high-quality board proxies (SSCI, CITATION) are negatively associated with acceptance rates. Further, we find that receiving a larger number of submissions, greater women representation on boards, and Web of Science and Scopus (WOSS) coverage are associated with lower acceptance rates. Acceptance rates for journals range from 12% to 91%, with an average of 54% and a median of 53%. Law journals have significantly higher average acceptance rate (68%) than other journals, while WOSS journals have the lowest (43%). Findings indicate some of the highest acceptance rates in Social Sciences literature, including competitive Business and Economics journals that traditionally have low acceptance rates. Limitations Research relies on local context to define publication strategy of editors. Findings may not be generalizable to mainstream journals and core science countries where emphasis on research quality is stronger and editorial selection is based on scientific merit. Practical implications Results offer useful insights into editorial management of national journals and allow us to make sense of local editorial practices. The importance of scientific merit for selection to national journal editorial boards is particularly highlighted for sound editorial evaluation of submitted manuscripts. Originality/value This is the first attempt to document a significant relation between acceptance rates and editorial board publication behavior.
Assessment of aquifer intercommunication in the B Pond-Gable Mountain Pond area of the Hanford Site

This report describes a hydrogeologic investigation of aquifer intercommunication in the area surrounding two if the waste disposal ponds, B Pond and Gable Mountain Pond, and encompassing the 200-East Area where subsurface liquid-waste disposal facilities are located. The investigation was focused in this area because previous work indicated areas of erosion of the confining bed and the presence of contamination in the confined aquifer. Also, large increases in waste disposal activities are planned for the near future in this area. The objectives of this investigation were: (1) to establish the geologic framework controlling ground-water flow in the aquifers, (2) to determine the ground-water flow characteristics of the Rattlesnake Ridge aquifer, (3) to quantify the mixing of unconfined aquifer waters in the Rattlesnake Ridge aquifer resulting from aquifer intercommunication, (4) to delineate the area where aquifer intercommunication has occurred, and (5) to determine the levels of contamination in the Rattlesnake Ridge aquifer. 70 refs., 34 figs., 9 tabs.
A Procedure for Inferring a Minimalist Lexicon from an SMT Model of a Language Acquisition Device

We introduce a constraint-based procedure for inferring a Minimalist Grammar (MG) that falls within the “Logic Grammar” framework. The procedure, implemented as a working computer program, takes as input an MG lexicon and a sequence of sentences paired with their semantic representation, and outputs an MG lexicon that is a superset of the input lexicon and that yields for each input sentence a syntatic structure encoding the associated semantic representation. The procedure operates by first constructing an SMT model of a language acquisition device that is constrained by the input lexicon and the (sentence, semantic-representation) pairs, and then using an SMT-solver to identify a model-solution in which the lexicon is optimized for parsimony. We show how the procedure can be used to form a computational model of a child language learner, presenting two experiments in which the procedure is used for instantaneous and incremental acquisition of an MG lexicon, and find that the optimal MG lexicons inferred by the procedure yield derivations that agree with the prescriptions of contemporary theories of minimalist syntax.
What's my Daily Value? Interpretation of network performance metrics in broadband consumer labels

Despite the importance of residential Internet, choosing Internet plans is often an opaque and frustrating process for consumers. To address this, United States regulatory authorities have proposed "broadband consumer labels", akin to nutrition labels, to improve transparency and empower consumers to make informed decisions, but to achieve their goal, such labels must both capture information relevant to consumers while being interpretable. In this paper, we present the results of a qualitative interview study with Internet consumers in rural/suburban communities in the Appalachian region of the United States. We find that network metrics typically used to characterize Internet service plans in proposed broadband labels, such as speeds and latency, are difficult to interpret and do not capture valued attributes of Internet service such as reliability. As a result, consumers to adopt a variety of social strategies to map their perceived needs to advertised offerings. We conclude with implications for proposed broadband consumer labels.