Miracle-dz/UniFAR-FTUs huggingface dataset:
ECOD domain classification of 48 whole proteomes from AlphaFold Structure Database using DPAM

Protein structure prediction has now been deployed widely across several different large protein sets. Large-scale domain annotation of these predictions can aid in the development of biological insights. Using our Evolutionary Classification of Protein Domains (ECOD) from experimental structures as a basis for classification, we describe the detection and cataloging of domains from 48 whole proteomes deposited in the AlphaFold Database. On average, we can provide positive classification (either of domains or other identifiable non-domain regions) for 90% of residues in all proteomes. We classified 746,349 domains from 536,808 proteins comprised of over 226,424,000 amino acid residues. We examine the varying populations of homologous groups in both eukaryotes and bacteria. In addition to containing a higher fraction of disordered regions and unassigned domains, eukaryotes show a higher proportion of repeated proteins, both globular and small repeats. We enumerate those highly populated domains that are shared in both eukaryotes and bacteria, such as the Rossmann domains, TIM barrels, and P-loop domains. Additionally, we compare the sampling of homologous groups from this whole proteome set against our stable ECOD reference and discuss groups that have been enriched by structure predictions. Finally, we discuss the implication of these results for protein target selection for future classification strategies for very large protein sets. Author Summary Proteins can contain one or more domains, regions that are evolutionary independent and convey fiction and function. Here we present our classification of proteins within 48 proteomes provided by the AlphaFold Structural Database. These proteomes span multiple model organisms used in research as a common ground for studying biological principles as well as organisms involved in prevalent human infectious diseases. We classify these domains by our AlphaFold-specific Domain Parser for AlphaFold Models (DPAM), which was previously tested on the human proteome. We find that eukaryotic and bacterial proteomes can be classified to different degrees, with significantly more disordered and low-confident regions in eukaryotic proteins.
Perceptual formant discrimination during speech movement planning

Evoked potential studies have shown that speech planning modulates auditory cortical responses. The phenomenon’s functional relevance is unknown. We tested whether, during this time window of cortical auditory modulation, there is an effect on speakers’ perceptual sensitivity for vowel formant discrimination. Participants made same/different judgments for pairs of stimuli consisting of a pre-recorded, self-produced vowel and a formant-shifted version of the same production. Stimuli were presented prior to a “go” signal for speaking, prior to passive listening, and during silent reading. The formant discrimination stimulus /uh/ was tested with a congruent productions list (words with /uh/) and an incongruent productions list (words without /uh/). Logistic curves were fitted to participants’ responses, and the just-noticeable difference (JND) served as a measure of discrimination sensitivity. We found a statistically significant effect of condition (worst discrimination before speaking) without congruency effect. Post-hoc pairwise comparisons revealed that JND was significantly greater before speaking than during silent reading. Thus, formant discrimination sensitivity was reduced during speech planning regardless of the congruence between discrimination stimulus and predicted acoustic consequences of the planned speech movements. This finding may inform ongoing efforts to determine the functional relevance of the previously reported modulation of auditory processing during speech planning.
Parental investment in growth and development: Cape Verdean migrants in a portuguese poor neighbourhood

Background Cape Verde has produced migrants over the centuries. Its history and geography have compelled males and females to leave their homeland in search of resources to invest in their family s survival and development. Literature on parental investment has evidenced the association between investment in embodied capital during infancy and early childhood and its outcomes at later stages. However, these studies seldom address migrant population. Aim This study aims to gain a better understanding of the relationship in a migratory context between parental investment in infancy and its outcomes in prepuberty embodied capital, among Cape Verdean children living in Cova da Moura, a deprived neighbourhood in Lisbon Metropolitan Area, Portugal. Methods A mixed method s approach combining quantitative with qualitative studies - is used. The prepubertal capital of the 221 schoolchildren attending the basic school located in Cova da Moura is assessed through Anthropometry and educational records analysis. The parental investment in infancy of 75 is analysed through interviews with parents and combined documentation (e.g. health booklets, reports, legislation). Results The key findings are: 1)Children are born and raised between 1997 and 2002, a time characterized by a favourable socioeconomic development in Portugal in general and Cova da Moura in particular. 2)In spite of living in a so called deprived neighbourhood , the school children linear growth falls into the healthy range of the III NHANES growth reference, and it is slightly better than the linear growth of other groups of children measured in Portugal in late 1980s and early 2000. School-oriented cognitive development is not adequate, however. A third of the students have not a regular school performance. 3)Parental investment in infancy is significantly associated to prepubertal physical growth and school-oriented cognitive development. The size effect is, however, small.
Competitive interactions and the spatial distribution of Carex lyngbyei and Scirpus americanus in a Pacific Northwest brackish tidal marsh

Zonal patterns of vegetation in brackish tidal marshes may be explained by Tilman's resource-ratio hypothesis, which Proposes that spatial changes in plant dominance on nutrient gradients are controlled by resource competition for soil nitrogen and light. This hypothesis was evaluated by analyzing abiotic and biotic field data collected from a brackish tidal marsh on the Fraser River delta, British Columbia, and by performing manipulative experiments in the field and in common garden conditions. The study site is dominated by two species which occupy distinct elevational domains in the upper intertidal zone. Elevations between -0.80 to 0.20 m (geodetic datum) are dominated by Scir~us americanus, while higher sites have communities dominated by Carex lvnabvei. Total soil nitrogen, standing crop, plant height, and shading of the soil surface were all found to be positively correlated to marsh platform elevation. These results suggest that Scir~us americanus may be dominant in the low marsh because it is a better competitor for soil nitrogen. Carex lvnabvei may be competitively dominant in the high marsh because its greater biomass and shoot height make it a superior competitor for light. Shoot standing crops of both species increased significantly with increasing additions of nitrogen in field fertilization trials. Transplantation of both species at six different locations along the platform gradient revealed that both species are being competitively restricted in their spatial distribution by the presence of the other. Additional field experiments indicated that light competition is important in excluding Scirgus americanus from the high marsh Carex community, while below-ground competition by Scirgus americanus impedes Carex from establishing itself in
A Built-in Self-repair Scheme for Multiport Rams

— Built-in self-repair (BISR) techniques have been widely used for enhancing the yield of embedded memories. This paper presents an efficient BISR scheme for multiport RAMs (MPRAMs). The BISR scheme has a defect-location module (DLM) executing a defect-location algorithm to locate inter-port defects. This enhances the fault-location capability of the applied test algorithm with only a few amount of cost of testing time. A built-in redundancy analyzer (BIRA) executing a proposed redundancy analysis algorithm is also proposed to allocate two-dimension redundancy of MPRAMs. Experimental results show that if a faulty MPRAM has 20% inter-port faults, the DLM can boost the increment of repair rate from 8.4% to 14.4% for different redundancy configurations. The area cost of the BIRA and DLM is small, it is only about 1% for a 4096×128-bit MPRAM with 1 spare row and 1 spare IO.
Détection de la controverse : une approche basée sur les réseaux de neurones, appliquée aux graphes et aux textes

Résumé Cet article propose une approche de détection de la contro-verse dans les réseaux sociaux, basée sur la structure d’une discussion et de ses caractéristiques textuelles. La méthode proposée s’appuie sur les réseaux de neurones graphiques (Graph Neural Networks ou GNN) pour encoder la repré-sentation graphique de la discussion (y compris les textes) dans un vecteur multidimensionnel. Ce dernier est utilisé pour classer les fils de discussions comme étant controver-sés ou non. Les expériences menées sur différents jeux de données montrent l’impact positif de la combinaison des caractéristiques textuelles et structurelles.
THE LINK BETWEEN MANIPULATION WITH ACCOUNTING: LEARNING FROM THE ECONOMIC EFFICIENCY COUPLED WITH THE SETTING OF FINANCIAL LEGISLATION

Piyasa etkinligini bozmak suretiyle finansal raporlama surecinde yipranmasina ciddi anlamda etki eden manipulasyon eylemi sucunun saglam muhasebe, finansal, hukuki ve ekonomik baglantilarinin oldugu ziyadesiyle bilinmektedir. Ancak bu baglantilar literaturde hakkettigi ilgiyi yeterince gorememistir. Bu calismanin temel amaci bahsi gecen disiplinler arasindaki baglantiyi teorik olarak irdelemek ve daha somut bir cerceveye oturtmaktir. Bunu yaparken, ekonomik etkinlik ve var olan finansal duzenlemelerden hareketle manipulasyonun anlam ve kapsami netlestirilerek muhasebe dunyasi ve global uygulamalari anlamak acisindan literaturdeki boslugun doldurulmasina bir nebzede olsa katkida bulunmak hedeflenmektedir. Bu calisma, iyi niyetli yatirimcilarin veya finansal bilgi kullanicilarinin manipulasyon sucunu isleyen (halka acik) sirketlerden zarar gormelerinin engellenmesi icin manipulasyon taniminin finansal tablolari da icine alacak sekilde gelistirilmesine ihtiyac oldugunu net bir sekilde gostermektedir. Diger bir deyisle, calisma gunumuz ihtiyaclarina daha iyi cevap verecek kapsamli finansal duzenlemelerin cok daha dogru muhasebe uygulamalarini ve daha iyi bir ekonomik ve finansal sistemi beraberinde getirecegini gostermektedir.
Shock metamorphism of quartz in nature and experiment: I. Basic observation and theory*

Abstract— Quartz, as a ubiquitous mineral constituent of the Earth's crust, displays the greatest variety of well-defined residual shock effects among all rock-forming minerals. It represents an important and most reliable shock barometer and thermometer for terrestrial impact formations. In this paper, the current status of knowledge about the nature, origin, and experimental pressure-temperature calibration of shock-induced deformations and phase transformations is reviewed for natural and experimental shock conditions.
Induced Gravitational Waves from Non-attractor Inflation and NANOGrav data

In this paper, we investigate the scalar-induced gravitational waves in single-field non-attractor inflation for the Pulsar Timing Arrays data. Our model comprises three phases of inflation: the first and third phases are slow-roll inflation, while the second phase is a period of non-attractor inflation. We analyze the model's predictions for various values of the sound speed $c_s$ and examine the sharp transitions to the final attractor phase. Furthermore, we study the model's predictions for NANOGrav observations and future gravitational wave observations. We also calculate the non-Gaussianity parameter $f_{NL}$ for the non-attractor setup with a general sound speed and the sharpness parameter.
Patch of Invisibility: Naturalistic Physical Black-Box Adversarial Attacks on Object Detectors

Adversarial attacks on deep-learning models have been receiving increased attention in recent years. Work in this area has mostly focused on gradient-based techniques, so-called ``white-box'' attacks, wherein the attacker has access to the targeted model's internal parameters; such an assumption is usually unrealistic in the real world. Some attacks additionally use the entire pixel space to fool a given model, which is neither practical nor physical (i.e., real-world). On the contrary, we propose herein a direct, black-box, gradient-free method that uses the learned image manifold of a pretrained generative adversarial network (GAN) to generate naturalistic physical adversarial patches for object detectors. To our knowledge this is the first and only method that performs black-box physical attacks directly on object-detection models, which results with a model-agnostic attack. We show that our proposed method works both digitally and physically. We compared our approach against four different black-box attacks with different configurations. Our approach outperformed all other approaches that were tested in our experiments by a large margin.
LPFormer: An Adaptive Graph Transformer for Link Prediction

Link prediction is a common task on graph-structured data that has seen applications in a variety of domains. Classically, hand-crafted heuristics were used for this task. Heuristic measures are chosen such that they correlate well with the underlying factors related to link formation. In recent years, a new class of methods has emerged that combines the advantages of message-passing neural networks (MPNN) and heuristics methods. These methods perform predictions by using the output of an MPNN in conjunction with a"pairwise encoding"that captures the relationship between nodes in the candidate link. They have been shown to achieve strong performance on numerous datasets. However, current pairwise encodings often contain a strong inductive bias, using the same underlying factors to classify all links. This limits the ability of existing methods to learn how to properly classify a variety of different links that may form from different factors. To address this limitation, we propose a new method, {\bf LPFormer}, which attempts to adaptively learn the pairwise encodings for each link. LPFormer models the link factors via an attention module that learns the pairwise encoding that exists between nodes by modeling multiple factors integral to link prediction. Extensive experiments demonstrate that LPFormer can achieve SOTA performance on numerous datasets while maintaining efficiency.
Remarks on effects of projective phase on eigenstate thermalization hypothesis

The existence of p-form symmetry in (d + 1)-dimensional quantum field is known to always lead to the breakdown of the eigenstate thermalization hypothesis (ETH) for certain (d − p)-dimensional operators other than symmetry operators under some assumptions. The assumptions include the mixing of symmetry sectors within a given energy shell, which is rather challenging to verify because it requires information on the eigenstates in the middle of the spectrum. We reconsider this assumption from the viewpoint of projective representations to avoid this difficulty. In the case of $\mathbb {Z}_N$ symmetries, we can circumvent the difficulty by considering $\mathbb {Z}_N\times \mathbb {Z}_N$-symmetric theories with nontrivial projective phases, and perturbing the Hamiltonian while preserving one of the $\mathbb {Z}_N$ symmetries of our interest. We also perform numerical analyses for (1 + 1)-dimensional spin chains and the (2 + 1)-dimensional $\mathbb {Z}_2$ lattice gauge theory.
Favorable and unfavorable many-body interactions for near-field radiative heat transfer in nanoparticle networks

Near-field radiative heat transfer (NFRHT) in nanoparticle networks is complicated due to the multiple scattering of thermally excited electromagnetic wave (namely, many-body interaction, MBI). The MBI regime is analyzed using the many-body radiative heat transfer theory at the particle scale for networks of a few nanoparticles. Effect of MBI on radiative heat diffusion in networks of a large number of nanoparticles is analyzed using the normal-diffusion radiative heat transfer theory at the continuum scale. An influencing factor $\psi$ is defined to numerically figure out the border of the different many-body interaction regimes. The whole space near the two nanoparticles can be divided into four zones, non-MBI zone, enhancement zone, inhibition zone and forbidden zone, respectively. Enhancement zone is relatively smaller than the inhibition zone, so many particles can lie in the inhibiting zone that the inhibition effect of many-body interaction on NFRHT in nanoparticle networks is common in literature. Analysis on the radiative thermal energy confirms that multiple scattering caused by the inserted scatter accounts for the enhancement and inhibition of NFRHT. By arranging the nanoparticle network in aspect of structures and optical properties, the MBI can be used to modulate radiative heat diffusion characterized by the radiative effective thermal conductivity ($k_{\rm eff}$) over a wide range, from inhibition (over 55% reduction) to amplification (30 times of magnitude). To achieve a notable MBI, it is necessary to introduce particles that have resonances well-matched with those of the particles of interest, irrespective of their match with the Planckian window. This work may help for the understanding of the thermal radiation in nanoparticle networks.
Revisiting Bellman Errors for Offline Model Selection

It is well-known that the empirical Bellman errors are poor predictors of value function estimation accuracy and policy performance. This has led researchers to abandon offline model selection procedures based on Bellman errors and instead focus on directly estimating the expected return under different policies of interest. The problem with this approach is that it can be very difficult to use an offline dataset generated by one policy to estimate the expected returns of a different policy. In contrast, we argue that Bellman errors can be useful for offline model selection, and that the discouraging results in past literature has been due to estimating and utilizing them incorrectly. We propose a new algorithm, Supervised Bellman Validation , that estimates the expected squared Bellman error better than the empirical Bellman errors. We demonstrate the relative merits of our method over competing methods through both theoretical results and empirical results on offline datasets from the Atari benchmark. We hope that our results will challenge current attitudes and spur future research into Bellman errors and their utility in offline model selection.
Leveraging Large Language Model for Automatic Evolving of Industrial Data-Centric R&D Cycle

In the wake of relentless digital transformation, data-driven solutions are emerging as powerful tools to address multifarious industrial tasks such as forecasting, anomaly detection, planning, and even complex decision-making. Although data-centric R&D has been pivotal in harnessing these solutions, it often comes with significant costs in terms of human, computational, and time resources. This paper delves into the potential of large language models (LLMs) to expedite the evolution cycle of data-centric R&D. Assessing the foundational elements of data-centric R&D, including heterogeneous task-related data, multi-facet domain knowledge, and diverse computing-functional tools, we explore how well LLMs can understand domain-specific requirements, generate professional ideas, utilize domain-specific tools to conduct experiments, interpret results, and incorporate knowledge from past endeavors to tackle new challenges. We take quantitative investment research as a typical example of industrial data-centric R&D scenario and verified our proposed framework upon our full-stack open-sourced quantitative research platform Qlib and obtained promising results which shed light on our vision of automatic evolving of industrial data-centric R&D cycle.
Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generation

Large language models (LLMs) have showcased remarkable prowess in code generation. However, automated code generation is still challenging since it requires a high-level semantic mapping between natural language requirements and codes. Most existing LLMs-based approaches for code generation rely on decoder-only causal language models often treate codes merely as plain text tokens, i.e., feeding the requirements as a prompt input, and outputing code as flat sequence of tokens, potentially missing the rich semantic features inherent in source code. To bridge this gap, this paper proposes the"Semantic Chain-of-Thought"approach to intruduce semantic information of code, named SeCoT. Our motivation is that the semantic information of the source code (\eg data flow and control flow) describes more precise program execution behavior, intention and function. By guiding LLM consider and integrate semantic information, we can achieve a more granular understanding and representation of code, enhancing code generation accuracy. Meanwhile, while traditional techniques leveraging such semantic information require complex static or dynamic code analysis to obtain features such as data flow and control flow, SeCoT demonstrates that this process can be fully automated via the intrinsic capabilities of LLMs (i.e., in-context learning), while being generalizable and applicable to challenging domains. While SeCoT can be applied with different LLMs, this paper focuses on the powerful GPT-style models: ChatGPT(close-source model) and WizardCoder(open-source model). The experimental study on three popular DL benchmarks (i.e., HumanEval, HumanEval-ET and MBPP) shows that SeCoT can achieves state-of-the-art performance, greatly improving the potential for large models and code generation.
Hillslope infiltration and lateral downslope unsaturated flow

Lateral downslope vadose flow occurs in hillslopes with deep homogeneous soils. When and why this happens has been partially answered by past experimental, numerical, and quasi-analytical studies. What has been overlooked, however, is that lateral downslope flow in unlayered soils is largely a drainage phenomenon driven by the changing character of the boundary condition at the soil surface. Numerical simulations show that when rainfall ceases, the near-surface head gradient in a hillslope becomes nearly parallel to the slope as a simple consequence of the surfaces becoming a no-flow boundary, neglecting evaporation. During rainfall, infiltration is nearly vertical for most soils except those with high constant anisotropy.