Miracle-dz/UniFAR-FTUs huggingface dataset:
CLR-Face: Conditional Latent Refinement for Blind Face Restoration Using Score-Based Diffusion Models

Recent generative-prior-based methods have shown promising blind face restoration performance. They usually project the degraded images to the latent space and then decode high-quality faces either by single-stage latent optimization or directly from the encoding. Generating fine-grained facial details faithful to inputs remains a challenging problem. Most existing methods produce either overly smooth outputs or alter the identity as they attempt to balance between generation and reconstruction. This may be attributed to the typical trade-off between quality and resolution in the latent space. If the latent space is highly compressed, the decoded output is more robust to degradations but shows worse fidelity. On the other hand, a more flexible latent space can capture intricate facial details better, but is extremely difficult to optimize for highly degraded faces using existing techniques. To address these issues, we introduce a diffusion-based-prior inside a VQGAN architecture that focuses on learning the distribution over uncorrupted latent embeddings. With such knowledge, we iteratively recover the clean embedding conditioning on the degraded counterpart. Furthermore, to ensure the reverse diffusion trajectory does not deviate from the underlying identity, we train a separate Identity Recovery Network and use its output to constrain the reverse diffusion process. Specifically, using a learnable latent mask, we add gradients from a face-recognition network to a subset of latent features that correlates with the finer identity-related details in the pixel space, leaving the other features untouched. Disentanglement between perception and fidelity in the latent space allows us to achieve the best of both worlds. We perform extensive evaluations on multiple real and synthetic datasets to validate the superiority of our approach.
Unsaturated flow modeling for exposed and covered tailings dams

Seepage through mining dams involves saturated and unsaturated flow. The latter is often neglected but should also be considered to accurately estimate the seepage flux and the corresponding position of the water table. Also, in some instances, the tailings may be reactive and produce acid mine drainage. In such cases, a layered cover can be installed on tailings dams (and on tailings in the pond) to control both water and oxygen fluxes. Again, to understand and predict the hydraulic behaviour of covered tailings dams, unsaturated flow has to be included in the analyses. This paper presents different calculations performed using the finite element method, to evaluate water motion and distribution in tailings dams, with and without covers. For exposed dams, different heights (from 5 to 50 m) were analysed numerically with a 2D FE code, for typical geometries. From the numerical results, simple mathematical expressions to predict the total seepage flow rate through dams are developed. In the case of dams with an inclined layered cover, numerical modeling was used to estimate water distribution in the cover, as a function of the system geometry. The influence of different parameters associated with the dam and the cover were investigated, including slope length and inclination, drainage time, and cover materials properties. The relationship between those parameters and the ability of a cover to limit gas migration is expressed through a simple equation.
Impact on Public Health Decision Making by Utilizing Big Data Without Domain Knowledge

New data sources, and artificial intelligence (AI) methods to extract information from them are becoming plentiful, and relevant to decision making in many societal applications. An important example is street view imagery, available in over 100 countries, and considered for applications such as assessing built environment aspects in relation to community health outcomes. Relevant to such uses, important examples of bias in the use of AI are evident when decision-making based on data fails to account for the robustness of the data, or predictions are based on spurious correlations. To study this risk, we utilize 2.02 million GSV images along with health, demographic, and socioeconomic data from New York City. Initially, we demonstrate that built environment characteristics inferred from GSV labels at the intra-city level may exhibit inadequate alignment with the ground truth. We also find that the average individual-level behavior of physical inactivity significantly mediates the impact of built environment features by census tract, as measured through GSV. Finally, using a causal framework which accounts for these mediators of environmental impacts on health, we find that altering 10% of samples in the two lowest tertiles would result in a 4.17 (95% CI 3.84 to 4.55) or 17.2 (95% CI 14.4 to 21.3) times bigger decrease on the prevalence of obesity or diabetes, than the same proportional intervention on the number of crosswalks by census tract. This work illustrates important issues of robustness and model specification for informing effective allocation of interventions using new data sources.
FaBERT: Pre-training BERT on Persian Blogs

We introduce FaBERT, a Persian BERT-base model pre-trained on the HmBlogs corpus, encompassing both informal and formal Persian texts. FaBERT is designed to excel in traditional Natural Language Understanding (NLU) tasks, addressing the intricacies of diverse sentence structures and linguistic styles prevalent in the Persian language. In our comprehensive evaluation of FaBERT on 12 datasets in various downstream tasks, encompassing Sentiment Analysis (SA), Named Entity Recognition (NER), Natural Language Inference (NLI), Question Answering (QA), and Question Paraphrasing (QP), it consistently demonstrated improved performance, all achieved within a compact model size. The findings highlight the importance of utilizing diverse and cleaned corpora, such as HmBlogs, to enhance the performance of language models like BERT in Persian Natural Language Processing (NLP) applications. FaBERT is openly accessible at https://huggingface.co/sbunlp/fabert
Manifold-valued models for analysis of EEG time series data

We propose a model for time series taking values on a Riemannian manifold and fit it to time series of covariance matrices derived from EEG data for patients suffering from epilepsy. The aim of the study is two-fold: to develop a model with interpretable parameters for different possible modes of EEG dynamics, and to explore the extent to which modelling results are affected by the choice of manifold and its associated geometry. The model specifies a distribution for the tangent direction vector at any time point, combining an autoregressive term, a mean reverting term and a form of Gaussian noise. Parameter inference is carried out by maximum likelihood estimation, and we compare modelling results obtained using the standard Euclidean geometry on covariance matrices and the affine invariant geometry. Results distinguish between epileptic seizures and interictal periods between seizures in patients: between seizures the dynamics have a strong mean reverting component and the autoregressive component is missing, while for the majority of seizures there is a significant autoregressive component and the mean reverting effect is weak. The fitted models are also used to compare seizures within and between patients. The affine invariant geometry is advantageous and it provides a better fit to the data.
The In-Out Formalism for In-In Correlators

Cosmological correlators, the natural observables of the primordial universe, have been extensively studied in the past two decades using the in-in formalism pioneered by Schwinger and Keldysh for the study of dissipative open systems. Ironically, most applications in cosmology have focused on non-dissipative closed systems. We show that, for non-dissipative systems, correlators can be equivalently computed using the in-out formalism with the familiar Feynman rules. In particular, the myriad of in-in propagators is reduced to a single (Feynman) time-ordered propagator and no sum over the labelling of vertices is required. In de Sitter spacetime, this requires extending the expanding Poincar\'e patch with a contracting patch, which prepares the bra from the future. Our results are valid for fields of any mass and spin but assuming the absence of infrared divergences. We present three applications of the in-out formalism: a representation of correlators in terms of a sum over residues of Feynman propagators in the energy-momentum domain; an algebraic recursion relation that computes Minkowski correlators in terms of lower order ones; and the derivation of cutting rules from Veltman's largest time equation, which we explicitly develop and exemplify for two-vertex diagrams to all loop orders. The in-out formalism leads to a natural definition of a de Sitter scattering matrix, which we discuss in simple examples. Remarkably, we show that our scattering matrix satisfies the standard optical theorem and the positivity that follows from it in the forward limit.
ContPhy: Continuum Physical Concept Learning and Reasoning from Videos

We introduce the Continuum Physical Dataset (ContPhy), a novel benchmark for assessing machine physical commonsense. ContPhy complements existing physical reasoning benchmarks by encompassing the inference of diverse physical properties, such as mass and density, across various scenarios and predicting corresponding dynamics. We evaluated a range of AI models and found that they still struggle to achieve satisfactory performance on ContPhy, which shows that the current AI models still lack physical commonsense for the continuum, especially soft-bodies, and illustrates the value of the proposed dataset. We also introduce an oracle model (ContPRO) that marries the particle-based physical dynamic models with the recent large language models, which enjoy the advantages of both models, precise dynamic predictions, and interpretable reasoning. ContPhy aims to spur progress in perception and reasoning within diverse physical settings, narrowing the divide between human and machine intelligence in understanding the physical world. Project page: https://physical-reasoning-project.github. io.
Hagedorn temperature from the thermal scalar in AdS and pp-wave backgrounds

We propose a thermal scalar equation of motion (EOM) that takes into account curvature corrections for backgrounds supported by Ramond-Ramond fluxes. This can be used to obtain the Hagedorn temperature for type II string theory on AdS and pp-wave backgrounds. For Ramond-Ramond flux supported pp-waves we show that the proposed thermal scalar EOM reproduces the leading curvature correction in the Hagedorn temperature equation obtained from the type II string theory spectrum. Furthermore, we use the thermal scalar EOM to obtain curvature corrections to the Hagedorn temperature for the AdS$_5 \times S^5$ and AdS$_4 \times \mathbb{C} P^3$ backgrounds. These corrections match with strong coupling results of the integrable dual field theories, recently obtained by the Quantum Spectral Curve technique.
Randomness Is All You Need: Semantic Traversal of Problem-Solution Spaces with Large Language Models

We present a novel approach to exploring innovation problem and solution domains using LLM fine-tuning with a custom idea database. By semantically traversing the bi-directional problem and solution tree at different temperature levels we achieve high diversity in solution edit distance while still remaining close to the original problem statement semantically. In addition to finding a variety of solutions to a given problem, this method can also be used to refine and clarify the original problem statement. As further validation of the approach, we implemented a proof-of-concept Slack bot to serve as an innovation assistant.
SQT - std Q-target

Std $Q$-target is a conservative, actor-critic, ensemble, $Q$-learning-based algorithm, which is based on a single key $Q$-formula: $Q$-networks standard deviation, which is an"uncertainty penalty", and, serves as a minimalistic solution to the problem of overestimation bias. We implement SQT on top of TD3/TD7 code and test it against the state-of-the-art (SOTA) actor-critic algorithms, DDPG, TD3 and TD7 on seven popular MuJoCo and Bullet tasks. Our results demonstrate SQT's $Q$-target formula superiority over TD3's $Q$-target formula as a conservative solution to overestimation bias in RL, while SQT shows a clear performance advantage on a wide margin over DDPG, TD3, and TD7 on all tasks.
Elastohydrodynamic mechanisms govern beat pattern transitions in eukaryotic flagella

Eukaryotic cilia and flagella exhibit complex beating patterns that vary depending on environmental conditions such as fluid viscosity1. These transitions are thought to arise from changes in the internal forcing provided by the axoneme, although the mechanism remains unclear2,3. We demonstrate with simulations of Kirchhoff rods driven internally by active bending moments that a single elastohydrodynamic instability universally explains transitions between planar, quasiplanar, helical, and complex beating patterns due to changes in either the internal forcing, flagellar stiffness and length, or due to changes in the hydrodynamic resistance, either due to the viscosity of the ambient medium or the presence of a plane wall. The beat patterns and transitions are comparable to those exhibited by bull sperm and sea urchin sperm in our experiments and elsewhere3–5. Our results point to a general model that can describe flagellar and ciliary beating across all species. We further show that internal dynein forces can be estimated by comparing simulation results with experimental observations of transitional viscosities. This can potentially lead to diagnostic assays to measure the health of sperm cells based on their beating pattern.
Degradation of PET Plastics by Wastewater Bacteria Engineered via Conjugation

Microplastics are contaminants of global concern that pose risks to ecosystems and human health. Focusing on PET plastics, we present a proof-of-concept for reduction of microplastic pollution: in situ engineering of bacteria in wastewater to degrade PET. Using a broad-host-range conjugative plasmid, we enabled various bacterial species from a municipal wastewater sample to express FAST-PETase, which was released into the extracellular environment. We found that FAST-PETase purified from some isolates could degrade about 40% of a 0.25 mm thick PET film within four days at 50 °C. We then demonstrate partial degradation of post-consumer PET over 5-7 days by exposure to conditioned media from isolates. These results have broad implications for addressing the global plastic pollution problem by enabling environmental bacteria to degrade PET plastics in situ.
Morroniside promotes skin wound re-epithelialization by facilitating epidermal stem cell proliferation through GLP-1R-mediated upregulation of β-catenin expression

Epidermal stem cells (EpSCs) play a vital role in skin wound healing through re-epithelialization. Identifying chemicals that can promote EpSC proliferation is helpful for treating skin wounds. This study investigates the effect of morroniside on cutaneous wound healing in mice and explores the underlying mechanisms. Application of 10-50 μg/mL of morroniside to the skin wound promotes wound healing in mice. In vitro studies demonstrate that morroniside stimulates the proliferation of mouse and human EpSCs in a time- and dose-dependent manner. Mechanistic studies reveal that morroniside promotes the proliferation of EpSCs by facilitating the cell cycle transition from the G1 to S phase. Morroniside increases the expression of β-catenin via the glucagon-like peptide-1 receptor (GLP-1R)-mediated PKA, PKA/PI3K/AKT and PKA/ERK signalling pathways, resulting in the increase of cyclin D1 and cyclin E1 expression, either directly or by upregulating c-Myc expression. This process ultimately leads to EpSC proliferation. Administration of morroniside to mouse skin wounds increases the phosphorylation of AKT and ERK, the expression of β-catenin, c-Myc, cyclin D1, and cyclin E1, as well as the proliferation of EpSCs, in periwound skin tissue, and accelerates wound re-epithelialization. These effects of morroniside are mediated by the GLP-1R. Overall, these results indicate that morroniside promotes skin wound healing by stimulating the proliferation of EpSCs via increasing β-catenin expression and subsequent upregulation of c-Myc, cyclin D1, and cyclin E1 expression through GLP-1R signalling pathways. Morroniside has clinical potential for treating skin wounds.
Digital WPI Major Qualifying Projects ( All Years ) Major Qualifying Projects January 2008 ACTIVATION OF MACROPHAGES IN VIVO BY GLYCAN LNFPIII

1 TABLE OF CONTENTS . 2 ACKNOWLEDGEMENTS . 3 BACKGROUND . 4 PROJECT PURPOSE . 12 METHODS . 13 DISCUSSION . 26 BIBLIOGRAPHY . 31
Faster and Better: Detecting Vulnerabilities in Linux-based IoT Firmware with Optimized Reaching Definition Analysis

—IoT devices are often found vulnerable, i.e., un-trusted inputs may trigger potential vulnerabilities and flow to sensitive operations in the firmware, which could cause severe damage. As such vulnerabilities are in general taint-style, a promising solution to find them is static taint analysis. However, existing solutions have limited e ffi ciency and e ff ectiveness. In this paper, we propose a new e ffi cient and e ff ective taint analysis solution, namely HermeScan, to discover such vulnerabilities, which utilizes reaching definition analysis (RDA) to conduct taint analysis and gets much fewer false negatives, false positives, and time costs. We have implemented a prototype of HermeScan and conducted a thorough evaluation on two datasets, i.e., one 0-day dataset with 30 latest firmware and one N-day dataset with 98 older firmware, and compared with two state-of-the-art (SOTA) solutions, i.e., KARONTE and SaTC. In terms of e ff ectiveness, HermeScan, SaTC, and KARONTE find 163, 32, and 0 vulnerabilities in the 0-day dataset respectively. In terms of accuracy, the true positive rates of HermeScan, SaTC, and KARONTE are 81%, 42%, and 0% in the 0-day dataset. In terms of e ffi ciency, HermeScan is 7.5X and 3.8X faster than SaTC and KARONTE on average in finding 0-day vulnerabilities.
A framework of spatiotemporal fuzzy clustering for land-cover change detection using SAR time series

ABSTRACT Compared with optical satellite images, synthetic aperture radar (SAR) images are less influenced by weather conditions such as cloud and haze. With the support of SAR image time series, a framework of change detection based on spatiotemporal fuzzy clustering is presented. This framework mainly consists of three components: (1) pixel-level SAR image time-series modelling, based on scale invariant feature transform (SIFT); (2) probability analysis of change node based on iterative binary partition-mean square error model of the series is calculated to ascertain change nodes; (3) spatiotemporal fuzzy clustering is used to determine the types of change detection. To validate the method, 26 SAR images of the study area between 2004 and 2010 are utilized to monitor annual changes of cultivated land to construction land, and comparative experiments are conducted to evaluate the detection accuracy. Experimental results showed that the proposed framework could effectively extract the change nodes and change pixels, with correctness of 84.52% and completeness of 82.64%, outperforming the traditional fuzzy clustering method, as well as traditional classification methods.
Unambiguous Bearing Estimation of Coherent Signals Using Acoustic Vector Sensor

This paper makes full use of the characteristics of acoustic vector sensor (AVS), which can simultaneously measure both acoustic pressure scalar and particle velocity vector at single point in acoustic field. A new method is introduced by extending modified multiple signal classification (MMUSIC) to AVS, named as V-MMUSIC, which can be used to bearing estimation for coherent signals. Simulation results show that this method can achieve bearing estimation of coherent or unrelated signals and remove the port/starboard indistinct phenomenon. The proposed method can accomplish full spatial, unambiguous and small error bearing estimation in the low SNR.
The perceptions of relationships between district climate and the development of professional learning communities in schools

ii DEDICATION . iv ACKNOWLEDGMENTS . vi LIST OF TABLES . xii LIST OF FIGURES . xiii CHAPTER I: INTRODUCTION . 1 Conceptual Framework . 3 Statement of the Problem . 7 Research Questions . 7 Methods.8 Definitions of Terms . 9 Sample.11 Limitations, Delimitations, and Assumptions . 11 Summary . 13 CHAPTER II: REVIEW OF THE LITERATURE . 15 Introduction . 15 Conceptual Framework . 15 The Evolution of the School District . 16 District Climate . 21 Integrated Leadership of the Superintendent . 24
BGP-iSec: Improved Security of Internet Routing Against Post-ROV Attacks

—We present BGP-iSec, an enhancement of the BGP-sec protocol for securing BGP, the Internet’s inter-domain routing protocol. BGP-iSec ensures additional and stronger security properties, compared to BGPsec, without significant extra overhead. The main improvements are: (i) Security for partial adoption: BGP-iSec provides significant security benefits for early adopters, in contrast to BGPsec, which requires universal adoption. (ii) Defense against route leakage: BGP-iSec defends against route leakage, a common cause of misrouting that is not prevented by BGPsec. (iii) Integrity of attributes: BGP-iSec ensures the integrity of integrity-protected attributes , thereby preventing announcement manipulation attacks not prevented by BGPsec. We argue that BGP-iSec achieves these goals using extensive simulations as well as security analysis. The BGP-iSec design conforms, where possible, with the BGPsec design, modifying it only where necessary to improve security or ease deployment. By providing stronger security guarantees, especially for partial adoption, we hope BGP-iSec will be a step towards finally protecting inter-domain routing, which remains, for many years, a vulnerability of the Internet’s infrastructure.
Rupture process of the 2011 off the Pacific coast of Tohoku earthquake derived from strong-motion data

2011年 3月 11日 14時 46分に発生した東北地方太平洋沖地震は、我が国の観測史上最大の M9. 0を記録し、東北か ら関東地方にかけて最大震度 7に達する強い揺れをもたらしたほか、東日本の太平洋沿岸に甚大な被害を引き起こした 大津波をともなった。 この超巨大地震の発生メカニズムの解明に向けた第一歩として、本研究では防災科学技術研究所 の K-NETと KiK-netで観測された強震記録を用いて震源破壊過程の推定を行った。 プレート境界面の形状を参考に走向 195度、傾斜 13度、余震分布の広がりを考慮して長さ 510 km、幅 210 kmの矩形断層面を設定した。 青森県から千葉県 にかけての K-NET 11観測点での地表記録と KiK-net 22観測点での地中記録について、周波数帯域 0.02-0.125 Hz速度波 形の S波部分 240秒間を解析に用いた。 これらの観測点でのグリーン関数の計算には、観測点ごとに構築した地下構造 モデルを使用した。 解析手法はマルチタイムウィンドウ線型波形インバージョン法に基づき、断層面を 30 km× 30 km の小断層に分割し、各小断層のすべり履歴を 6秒幅の基底関数を 3秒ずらして 25個並べることにより表現した。 これに より各小断層は気象庁の震源位置とした破壊開始点との距離を、第一タイムウィンドウトリガリング速度で除して求ま る時刻から、78秒間すべることができる。 本解析より、破壊開始点から日本海溝軸に沿う断層面の浅い領域にかけての宮城県はるか沖にすべりの大きい領域が 推定され、最大すべり量は 33 mとなった。 また宮城県沖のみ、断層面の陸側の深い領域まで 5 m以上のすべりが推定さ れた。 推定された震源モデルの地震モーメントは 4.24× 10 Nmで、モーメントマグニチュードは 9. 0である。 破壊の 時間発展過程を見ると、初めの 20秒間はすべりが小さく、その後、宮城県はるか沖のすべりの大きい領域で約 25秒間 のすべりが生じ、更に 65秒から 95秒後にかけて再び宮城県はるか沖の領域で大きなすべりが生じている。 モーメント レート関数にはこの 2つの破壊に対応する 2つの高まりが見られ、65秒から 95秒のモーメント解放の方が規模が大き い。 強震波形を南北に並べたペーストアップ図では、宮城県の観測点を先頭として伝播する波群が約 40秒の間をおいて 2つ存在しており、この 2回の破壊イベントに対応していると考えられる。 また、ペーストアップ図には福島県と茨城県 の県境付近の観測点を先頭として伝播する波群も見られ、このフェーズについては破壊開始後 95秒から 120秒後にすべ りの見られる福島県沖の領域付近から生じている可能性がある。 宮城県沖の破壊開始点付近から海溝軸にかけての領域 ですべりが大きいという結果は、津波記録を用いた震源過程解析結果とも整合的である。 この領域で 2回の破壊イベン トが見られるという結果は、大津波を引き起こした可能性のある浅い領域での大きなすべりの要因を理解する上で重要 であろうと考えられる。