Miracle-dz/UniFAR-FTUs huggingface dataset:
Hot Jupiters Have Giant Companions: Evidence for Coplanar High-eccentricity Migration

This study considers the characteristics of planetary systems with giant planets based on a population-level analysis of the California Legacy Survey planet catalog. We identified three characteristics common to hot Jupiters (HJs). First, while not all HJs have a detected outer giant planet companion ( Msini=0.3–30MJup ), such companions are ubiquitous when survey completeness corrections are applied for orbital periods out to 40,000 days. Giant-harboring systems without an HJ also host at least one outer giant planet companion per system. Second, the mass distributions of HJs and other giant planets are indistinguishable. However, within a planetary system that includes an HJ, the outer giant planet companions are at least 3× more massive than the inner HJs. Third, the eccentricity distribution of the outer companions in HJ systems (with an average model eccentricity of 〈e〉 = 0.34 ± 0.05) is different from the corresponding outer planets in planetary systems without HJs (〈e〉 = 0.19 ± 0.02). We conclude that the existence of two gas giants, where the outermost planet has an eccentricity ≥0.2 and is 3× more massive, are key factors in the production of an HJ. Our simple model based on these factors predicts that ∼10% of warm and cold Jupiter systems will by chance meet these assembly criteria, which is consistent with our measurement of a 16% ± 6% relative occurrence of HJ systems to all giant-harboring systems. We find that these three features favor coplanar high-eccentricity migration as the dominant mechanism for HJ formation.
A systems biology approach and in vitro experiment indicated Rapamycin targets key cancer and cell cycle‐related genes and miRNAs in triple‐negative breast cancer cells

An anticancer drug known as Rapamycin acts by inhibiting the mammalian target of the Rapamycin pathway. This agent has recently been investigated for its potential therapeutic benefits in sensitizing drug‐resistant breast cancer (BC) treatment. The molecular mechanism underlying these effects, however, is still a mystery. Using a systems biology method and in vitro experiment, this study sought to discover essential genes and microRNAs (miRNAs) targeted by Rapamycin in triple‐negative BC (TNBC) cells to aid prospective new medications with less adverse effects in BC treatment. We developed the transcription factor‐miRNA‐gene and protein−protein interaction networks using the freely accessible microarray data sets. FANMOD and MCODE were utilized to identify critical regulatory motifs, clusters, and seeds. Then, functional enrichment analyses were conducted. Using topological analysis and motif detection, the most important genes and miRNAs were discovered. We used quantitative real‐time polymerase chain reaction (qRT‐PCR) to examine the effect of Rapamycin on the expression of the selected genes and miRNAs to verify our findings. We performed flow cytometry to investigate Rapamycin's impact on cell cycle and apoptosis. Furthermore, wound healing and migration assays were done. Three downregulated (PTGS2, EGFR, VEGFA) and three upregulated (c‐MYC, MAPK1, PIK3R1) genes were chosen as candidates for additional experimental verification. There were also three upregulated miRNAs (miR‐92a, miR‐16, miR‐20a) and three downregulated miRNAs (miR‐146a, miR‐145, miR‐27a) among the six selected miRNAs. The qRT‐PCR findings in MDA‐MB‐231 cells indicated that c‐MYC, MAPK1, PIK3R1, miR‐92a, miR‐16, and miR‐20a expression levels were considerably elevated following Rapamycin treatment, whereas PTGS2, EGFR, VEGFA, miR‐146a, and miR‐145 expression levels were dramatically lowered (p < 0.05). These genes are engaged in cancer pathways, transcriptional dysregulation in cancer, and cell cycle, according to the top pathway enrichment findings. Migration and wound healing abilities of the cells declined after Rapamycin treatment, and the number of apoptotic cells increased. We demonstrated that Rapamycin suppresses cell migration and metastasis in the TNBC cell line. In addition, our data indicated that Rapamycin induces apoptosis in this cell line. The discovered vital genes and miRNAs affected by Rapamycin are anticipated to have crucial roles in the pathogenesis of TNBC and its therapeutic resistance.
Silica sand resources in the Netherlands

Abstract Silica sand, (almost) pure quartz sand, is a valuable and scarce mineral resource within the shallow Dutch subsurface. High-grade deposits are exploited in the southeasternmost part of the country, as raw material for the glass, ceramic, chemical and other process industries. Dutch land-use policy requires that scarce mineral resources (including silica sand) are taken into consideration in spatial planning and when preparing for largescale engineering or construction works. For this purpose, and in order to review the long-term possibilities for home production of silica sand, we determined resource potential nationally. Our approach was (1) to establish the relevant conditions and processes associated with the deposition of the currently exploited sands, (2) identify lithostratigraphic units that are genetically similar or are otherwise known to contain quartz-rich sands, and (3) query the Dutch geological survey's borehole database for potential silica sand occurrences within those units. As we have to rely on non-dedicated data, the latter step was undertaken using a largely qualitative set of lithological search parameters. Finally, a limited number of available chemical analyses was used for preliminary verification purposes. Using this approach, we identified three prospective areas: one in the north of the province of Limburg and east of the province of Noord-Brabant(~750 km2), one in the central south of Noord-Brabant (~45 km2), and one in the east of the Gelderland and Overijssel provinces (~1,200 km2). For each area, first-order characteristics of possible silica sand resources are presented (type of deposit, depth, approximate thickness). In the terms of current reporting conventions, we resolved silica sand occurrence to the level of ‘reconnaissance mineral resource’ or ‘exploration result’, and our results do not constitute a formal resource declaration. Available chemical data suggest that the resources in the first two areas could be or become economic, although the grades are lower than those of the currently exploited resources. The third area is less promising in that respect, but available data is too limited to reject the area in this stage. Even so, we tentatively conclude that home production of silica sand can probably be maintained after the reserves in Limburg are depleted.
Measurements of new particle formation and ultrafine particle growth rates at a clean continental site

Simultaneous measurements of aerosol particles and their expected gas phase precursors were made at Idaho Hill, Colorado, a remote continental site. This study used apparatus and techniques similar to those employed in an earlier study at the Mauna Loa Observatory, Hawaii [Weber et al., 1995]. New particle formation, identified by the presence of ultrafine particles (nominally 3 to 4 nm diameter), was commonly observed in downslope (westerly) air and was correlated with high sulfuric acid (H2SO4) concentrations, low relative humidity and low particle surface area concentrations. The data point to H2SO4 as a principle nucleation precursor species with typical daytime concentrations between 106 and 107 molecules cm−3. Particle production was observed at H2SO4 concentrations that are well below predicted values for binary nucleation of H2O and H2SO4, suggesting that another species participated. Particle growth rates were estimated from the data with two independent approaches and in both cases were ∼5 to 10 times higher than can be explained by condensation of H2SO4 and its associated water. This suggests that species in addition to H2S04 were also making large contributions to ultrafine particle growth. Finally, calculated steady-state H2SO4 concentrations were found to be in good agreement with measured values if the mass accommodation coefficient for H2SO4 on aerosol surfaces was assumed equal to ∼1.
On the fine structure of thunderstorms leading to the generation of sprites and elves: Fractal analysis

[1] The fine structure of convective clouds leading to the generation of sprites and elves has been studied using radar images. Sprites and elves were observed on two days, 19 December 1998 and 27 January 1999, in lightning during winter thunderstorms on the Sea of Japan. Fractal analyses of the radar images (as an ensemble and as separate clusters) for those two days have been performed with respect to the occurrence of sprites and elves. We have found that a sprite and/or an elve is triggered when and just after the image cluster maximum size increases to a certain value (∼150 km) and the ensemble fractal dimension approaches a threshold (i.e., percolation threshold).
Auxiliary Field Quantum Monte Carlo for Nuclear Physics on the Lattice

We employ constrained path Auxiliary Field Quantum Monte Carlo (AFQMC) in the pursuit of studying physical nuclear systems using a lattice formalism. Since AFQMC has been widely used in the study of condensed-matter systems such as the Hubbard model, we benchmark our method against published results for both one- and two-dimensional Hubbard model calculations. We then turn our attention to cold-atomic and nuclear systems. We use an onsite contact interaction that can be tuned in order to reproduce the known scattering length and effective range of a given interaction. Developing this machinery allows us to extend our calculations to study nuclear systems within a lattice formalism. We perform initial calculations for a range of nuclear systems from two- to few-body neutron systems.
Reactive dissolution instability driven by chemical diffusion with applications to harzburgite reactive dissolution

The morphological stability of a mineralogically sharp and planar dissolution front separating rock A from rock B during reactive dissolution in a multicomponent system was examined using a linear stability analysis. In the absence of melt flow, the underlining cause for the instability is chemical diffusion in the melt. The destabilizing chemical diffusion gives rise to a new class of morphological instability in which the maximum growth rate of the perturbation corresponds to the minimum wavelength of the porous system that is on the order of a few grain sizes. On the geological outcrop scale a chemical diffusion induced unstable dissolution front separating two rock units would appear diffuse. The diffuse boundaries between the harzburgite and dunite observed in ophiolites may be a result of such dissolution instability. When chemical diffusion is stabilizing the wavelength of the instability is larger and sharp lithological boundary can be preserved.
Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs

In this study, we introduce adaptive KV cache compression, a plug-and-play method that reduces the memory footprint of generative inference for Large Language Models (LLMs). Different from the conventional KV cache that retains key and value vectors for all context tokens, we conduct targeted profiling to discern the intrinsic structure of attention modules. Based on the recognized structure, we then construct the KV cache in an adaptive manner: evicting long-range contexts on attention heads emphasizing local contexts, discarding non-special tokens on attention heads centered on special tokens, and only employing the standard KV cache for attention heads that broadly attend to all tokens. Moreover, with the lightweight attention profiling used to guide the construction of the adaptive KV cache, FastGen can be deployed without resource-intensive fine-tuning or re-training. In our experiments across various asks, FastGen demonstrates substantial reduction on GPU memory consumption with negligible generation quality loss. We will release our code and the compatible CUDA kernel for reproducibility.
Discrete anisotropic curve shortening flow in higher codimension

We introduce a novel formulation for the evolution of parametric curves by anisotropic curve shortening flow in ${\mathbb R}^d$, $d\geq2$. The reformulation hinges on a suitable manipulation of the parameterization's tangential velocity, leading to a strictly parabolic differential equation. Moreover, the derived equation is in divergence form, giving rise to a natural variational numerical method. For a fully discrete finite element approximation based on piecewise linear elements we prove optimal error estimates. Numerical simulations confirm the theoretical results and demonstrate the practicality of the method.
Intelligent Reflecting Surface Aided MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer

An intelligent reflecting surface (IRS) is invoked for enhancing the energy harvesting performance of a simultaneous wireless information and power transfer (SWIPT) aided system. Specifically, an IRS-assisted SWIPT system is considered, where a multi-antenna aided base station (BS) communicates with several multi-antenna assisted information receivers (IRs), while guaranteeing the energy harvesting requirement of the energy receivers (ERs). To maximize the weighted sum rate (WSR) of IRs, the transmit precoding (TPC) matrices of the BS and passive phase shift matrix of the IRS should be jointly optimized. To tackle this challenging optimization problem, we first adopt the classic block coordinate descent (BCD) algorithm for decoupling the original optimization problem into several subproblems and alternately optimize the TPC matrices and the phase shift matrix. For each subproblem, we provide a low-complexity iterative algorithm, which is guaranteed to converge to the Karush-Kuhn-Tucker (KKT) point of each subproblem. The BCD algorithm is rigorously proved to converge to the KKT point of the original problem. We also conceive a feasibility checking method to study its feasibility. Our extensive simulation results confirm that employing IRSs in SWIPT beneficially enhances the system performance and the proposed BCD algorithm converges rapidly, which is appealing for practical applications.
Intractability of Learning the Discrete Logarithm with Gradient-Based Methods

The discrete logarithm problem is a fundamental challenge in number theory with significant implications for cryptographic protocols. In this paper, we investigate the limitations of gradient-based methods for learning the parity bit of the discrete logarithm in finite cyclic groups of prime order. Our main result, supported by theoretical analysis and empirical verification, reveals the concentration of the gradient of the loss function around a fixed point, independent of the logarithm's base used. This concentration property leads to a restricted ability to learn the parity bit efficiently using gradient-based methods, irrespective of the complexity of the network architecture being trained. Our proof relies on Boas-Bellman inequality in inner product spaces and it involves establishing approximate orthogonality of discrete logarithm's parity bit functions through the spectral norm of certain matrices. Empirical experiments using a neural network-based approach further verify the limitations of gradient-based learning, demonstrating the decreasing success rate in predicting the parity bit as the group order increases.
Hierarchical Generation of Human-Object Interactions with Diffusion Probabilistic Models

This paper presents a novel approach to generating the 3D motion of a human interacting with a target object, with a focus on solving the challenge of synthesizing long-range and diverse motions, which could not be fulfilled by existing auto-regressive models or path planning-based methods. We propose a hierarchical generation framework to solve this challenge. Specifically, our framework first generates a set of milestones and then synthesizes the motion along them. Therefore, the long-range motion generation could be reduced to synthesizing several short motion sequences guided by milestones. The experiments on the NSM, COUCH, and SAMP datasets show that our approach outperforms previous methods by a large margin in both quality and diversity. The source code is available on our project page https://zju3dv.github. io/hghoi.
Large Language Models Cannot Self-Correct Reasoning Yet

Large Language Models (LLMs) have emerged as a groundbreaking technology with their unparalleled text generation capabilities across various applications. Nevertheless, concerns persist regarding the accuracy and appropriateness of their generated content. A contemporary methodology, self-correction, has been proposed as a remedy to these issues. Building upon this premise, this paper critically examines the role and efficacy of self-correction within LLMs, shedding light on its true potential and limitations. Central to our investigation is the notion of intrinsic self-correction, whereby an LLM attempts to correct its initial responses based solely on its inherent capabilities, without the crutch of external feedback. In the context of reasoning, our research indicates that LLMs struggle to self-correct their responses without external feedback, and at times, their performance even degrades after self-correction. Drawing from these insights, we offer suggestions for future research and practical applications in this field.
A Unified Taxonomy and Evaluation of IoT Security Guidelines

Cybersecurity concerns about Internet of Things (IoT) devices and infrastructure are growing each year. In response, organizations worldwide have published IoT cybersecurity guidelines to protect their citizens and customers. These guidelines constrain the development of IoT systems, which include substantial software components both on-device and in the Cloud. While these guidelines are being widely adopted, e.g. by US federal contractors, their content and merits have not been critically examined. Two notable gaps are: (1) We do not know how these guidelines differ by the topics and details of their recommendations; and (2) We do not know how effective they are at mitigating real-world IoT failures. In this paper, we address these questions through an exploratory sequential mixed-method study of IoT cybersecurity guidelines. We collected a corpus of 142 general IoT cybersecurity guidelines, sampling them for recommendations until saturation was reached. From the resulting 958 unique recommendations, we iteratively developed a hierarchical taxonomy following grounded theory coding principles. We measured the guidelines' usefulness by asking novice engineers about the actionability of each recommendation, and by matching cybersecurity recommendations to the root causes of failures (CVEs and news stories). We report that: (1) Comparing guidelines to one another, each guideline has gaps in its topic coverage and comprehensiveness; and (2) Although 87.2% recommendations are actionable and the union of the guidelines mitigates all 17 of the failures from news stories, 21% of the CVEs apparently evade the guidelines. In summary, we report shortcomings in every guideline's depth and breadth, but as a whole they are capable of preventing security issues. Our results will help software engineers determine which and how many guidelines to study as they implement IoT systems.
The Ideal of Vanishing Polynomials and the Ring of Polynomial Functions

Vanishing polynomials are polynomials over a ring which output $0$ for all elements in the ring. In this paper, we study the ideal of vanishing polynomials over specific types of rings, along with the closely related ring of polynomial functions. In particular, we provide several results on generating vanishing polynomials. We first analyze the ideal of vanishing polynomial over $\mathbb{Z}_n$, the ring of the integers modulo $n$. We then establish an isomorphism between the vanishing polynomials of a ring and the vanishing polynomials of the constituent rings in its decomposition. Lastly, we generalize our results to study the ideal of vanishing polynomials over arbitrary commutative rings.
Indication of Sharp and Strong Phase-Transitions from NICER Observations

In this letter, we present a new method that quantitatively identifies the occurrence probability of equations of state (EoS) beyond"standard"EoS models that disfavor sharp and strong phase-transitions, based on neutron star mass and radius observations. The radii of two neutron stars with different masses are naturally correlated, in part because both of them are sensitive to the symmetry energy of the EoS. We show the radii of two neutron stars observed by NICER (PSR J0740+6620 and PSR 0030+0451) are correlated if these two neutron stars are built upon EoSs with no sharp and strong first-order phase transitions. We further show that the linear correlation of the neutron star radii can be significantly weakened, when strong and sharp first-order phase transitions occur. We propose a new quantity, ${D}_{\mathrm{L}}$, which measures the extent to which the linear correlation of the radii of two neutron stars is weakened. Our method gives a 48% identification probability (with a 5% false alarm rate) that the NICER observations indicate the necessity for a sharp and strong phase transition. Future observations can confirm or rule out this identification. Our method is generalizable to any pair of neutron star masses and can be employed with other sets of observations in the future.
methyl bromide loss rate constants in the north Pacific Ocean

The degradation rate constant of CH3Br in the North Pacific Ocean was measured in surface seawater between September and October 1999, using a stable isotope (13CH3Br) incubation technique. Total degradation rate constants ranged from 0.02 to 0.43 d−1, decreasing in colder waters as a result of the temperature‐dependence of chemical losses. Biological rate constants ranged from 0.01 to 0.20 d−1. In subtropical waters (13‐20°N), biological loss rate constants were small compared to chemical loss rate constants. North of Hawaii, biological processes played an increasingly significant role in CH3Br degradation. In subpolar waters (40‐58°N), biological losses dominated the removal of methyl bromide. Comparison of the measured loss rate constants with surface water CH3Br concentrations suggest that the CH3Br production rate is higher in warm, low latitude waters than in cold subpolar waters at this time of year. Diel studies revealed a midday maximum in biological degradation of methyl bromide.