Miracle-dz/UniFAR-FTUs huggingface dataset:
Initial shock and long-term stand development following thinning in a Douglas-fir plantation.

Responses following the application of six precommercial thinning treatments to a 27-year-old Douglas-fir plantation (2.4-m spacing, height at age 100 = 24 m) have been monitored for 25 years. Spacing after thinning ranged from 3.4 m to 8. Im. Immediately following thinning, trees exhibited thinning shock; that is, substantial height growth reductions. The severity and duration of the shock effect were partially related to the severity of thinning. From 15 to 25 years following thinning, however, height growth was positively related to growing space with the best height growth at the wider spacings. Diameter growth increased following thinning, with the range in diameter growth rates between spacings increasing over time. Basal area and volume growth per hectare were reduced by treatment, but the differences among spacings are decreasing. FOREST Sol. 29:33--46.
Partial Information Breeds Systemic Risk

This paper considers finitely many investors who perform mean-variance portfolio selection under a relative performance criterion. That is, each investor is concerned about not only her terminal wealth, but how it compares to the average terminal wealth of all investors (i.e., the mean field). At the inter-personal level, each investor selects a trading strategy in response to others' strategies (which affect the mean field). The selected strategy additionally needs to yield an equilibrium intra-personally, so as to resolve time inconsistency among the investor's current and future selves (triggered by the mean-variance objective). A Nash equilibrium we look for is thus a tuple of trading strategies under which every investor achieves her intra-personal equilibrium simultaneously. We derive such a Nash equilibrium explicitly in the idealized case of full information (i.e., the dynamics of the underlying stock is perfectly known), and semi-explicitly in the realistic case of partial information (i.e., the stock evolution is observed, but the expected return of the stock is not precisely known). The formula under partial information involves an additional state process that serves to filter the true state of the expected return. Its effect on trading is captured by two degenerate Cauchy problems, one of which depends on the other, whose solutions are constructed by elliptic regularization and a stability analysis of the state process. Our results indicate that partial information alone can reduce investors' wealth significantly, thereby causing or aggravating systemic risk. Intriguingly, in two different scenarios of the expected return (i.e., it is constant or alternating between two values), our Nash equilibrium formula spells out two distinct manners systemic risk materializes.
Refined analysis of $\Omega^{-} \bar{\Omega}^{+}$ polarization in electron-positron annihilation process

We investigate the production of spin-3/2 hyperon pairs, $\Omega^- \bar{\Omega}^+$, in electron-positron annihilation within the helicity amplitude formalism. A refined selection of helicity basis matrices is proposed to relate polarization expansion coefficients and spin density matrix elements and to illuminate their inherent physical interpretations and symmetrical properties. With a novel parametrization scheme of helicity amplitudes, we perform an analysis of polarization correlation coefficients for double-tag $\Omega^- \bar{\Omega}^+$ pairs. We present three sets of expressions to describe the decay of $\Omega^{-}$ hyperons, and further address the existing tension in the measurements of its decay parameters, particularly $\phi_{\Omega}$. The method and the framework developed in this paper can also be applied to studies of the production and decay mechanisms of other spin-3/2 particles.
Slepian Beamforming: Broadband Beamforming using Streaming Least Squares

In this paper we revisit the classical problem of estimating a signal as it impinges on a multi-sensor array. We focus on the case where the impinging signal's bandwidth is appreciable and is operating in a broadband regime. Estimating broadband signals, often termed broadband (or wideband) beamforming, is traditionally done through filter and summation, true time delay, or a coupling of the two. Our proposed method deviates substantially from these paradigms in that it requires no notion of filtering or true time delay. We use blocks of samples taken directly from the sensor outputs to fit a robust Slepian subspace model using a least squares approach. We then leverage this model to estimate uniformly spaced samples of the impinging signal. Alongside a careful discussion of this model and how to choose its parameters we show how to fit the model to new blocks of samples as they are received, producing a streaming output. We then go on to show how this method naturally extends to adaptive beamforming scenarios, where we leverage signal statistics to attenuate interfering sources. Finally, we discuss how to use our model to estimate from dimensionality reducing measurements. Accompanying these discussions are extensive numerical experiments establishing that our method outperforms existing filter based approaches while being comparable in terms of computational complexity.
Plant Archives

Wheat ( Triticum aestivum L.) is regarded as an important staple food crop globally and is sensitive to high temperature (HT) during reproductive stages in comparison to vegetative stages. High-temperature stress is a major abiotic constraint and is projected to reduce crop yield there by threatening food security. A field investigation entitled “Effect of high-temperature stress on yield of various wheat varieties at different growth stages” was conducted at the Students Instructional Farm of the Acharya Narendra Deva University of Agriculture and Technology Kumarganj, Ayodhya (UP) during Rabi season of 2021-22. The experiment was planned under SPD (Split plot Design) with three replications and treatment consisting of three dates of sowing viz; D 1 (15th November), D 2 (30th November), and D 3 (15th December). Results indicated that growth attributes such as plant height, number of tillers plant -1 , dry matter plant -1 , and membrane stability index were significantly affected by the date of sowing. When late sown variety was planted timely all the growth attributes showed a drastic reduction with respect to late sowing (15th December) However, V 1 and V 2 showed comparatively good performance in terms of growth attributes when sown early D 1 and D 2 . Performance of V 3 was very good as compared to V 1 and V 2 when sown delayed D 3 i.e. facing high temperature stress. Time of sowing decreased substantially almost in all the yield components measured viz; the number of ear bearing tillers -1 , number of grains ear -1 , ear length (cm), grain yield plant -1 (gm), biological yield plant -1 (g), and test weight (g) and harvest index which caused severe reduction of yield in V 1 and V 2 . Overall growth attributes physiological traits and yield, as well as yield components of the wheat crop, was adversely affected by the time of sowing due to the onset of high temperature during crop growth and particularly grain filling. Variety i.e., V 3 (Kundan) reduced the detrimental effect of heat stress by improving physiological traits which ultimately helped in obtaining higher yield.
On the Role of Charge Transfer Excitations in Non-Fullerene Acceptors for Organic Photovoltaics

Through the development of new non-fullerene electron acceptor (NFA) materials, such as Y6 and its molecular derivatives, the power conversion efficiencies of organic photovoltaics (OPVs) have now exceeded 19%. However, despite this rapid progress, our fundamental understanding of the unique optical and electronic properties of these Y-series NFAs is lacking, and this currently limits progress in material design. In this work, we provide a detailed computational-experimental characterisation of the archetypal NFA, Y6. To explain the significant broadening and red shift of the absorption spectrum observed when moving from the solution phase to the solid state, we first rule out more typical causes, such as J-aggregation. Instead, by considering the role of charge transfer (CT) excitations and their mixing with Frenkel exciton (FE) states, we can computationally reproduce the experimental absorption spectra of Y6 with excellent accuracy. Using transient absorption spectroscopy, we provide evidence for this dense manifold of FE-CT hybrid electronic excitations in Y6 through the prominent sub-picosecond relaxation events following supra band gap excitation. Furthermore, through sub band gap excitation, we also find states with polaronic character in Y6 that are in a dynamic equilibrium with the FE-CT hybrid states. Magnetic resonance spectroscopies reveal that these polaronic states are polaron pairs, most likely located on neighbouring Y6 molecules, not free charge carriers, as has been previously suggested. Thus, this new understanding of how the solid-state packing motif directly controls the optical and electronic properties of Y-series NFAs opens the door to intelligently design NFA materials to further increase OPV performance.
High Pileup Particle Tracking with Object Condensation

Recent work has demonstrated that graph neural networks (GNNs) can match the performance of traditional algorithms for charged particle tracking while improving scalability to meet the computing challenges posed by the HL-LHC. Most GNN tracking algorithms are based on edge classification and identify tracks as connected components from an initial graph containing spurious connections. In this talk, we consider an alternative based on object condensation (OC), a multi-objective learning framework designed to cluster points (hits) belonging to an arbitrary number of objects (tracks) and regress the properties of each object. Building on our previous results, we present a streamlined model and show progress toward a one-shot OC tracking algorithm in a high-pileup environment.
Demonstration of nanosized high-impedance superconducting NbRe Dayem bridges

Here we demonstrate nano-sized superconducting Dayem-bridge weak-links made of different stoichiometric compositions of NbRe. Our devices possess a relatively high critical temperature, normal-state resistance, and kinetic inductance. In particular, the high kinetic inductance makes this material a good alternative to more conventional niobium-based superconductors (e.g., NbN or NbTiN) for the realization of superinductors and high-quality factor resonators, whereas the high normal-state resistance yields a large output voltage in superconducting switches and logic elements realized upon this compound. Moreover, out-of-plane critical magnetic fields exceeding 2 T ensure that possible applications requiring high magnetic fields can also be envisaged. Altogether, these features make this material appealing for a number of applications in the framework of quantum technologies.
PhysHOI: Physics-Based Imitation of Dynamic Human-Object Interaction

Humans interact with objects all the time. Enabling a humanoid to learn human-object interaction (HOI) is a key step for future smart animation and intelligent robotics systems. However, recent progress in physics-based HOI requires carefully designed task-specific rewards, making the system unscalable and labor-intensive. This work focuses on dynamic HOI imitation: teaching humanoid dynamic interaction skills through imitating kinematic HOI demonstrations. It is quite challenging because of the complexity of the interaction between body parts and objects and the lack of dynamic HOI data. To handle the above issues, we present PhysHOI, the first physics-based whole-body HOI imitation approach without task-specific reward designs. Except for the kinematic HOI representations of humans and objects, we introduce the contact graph to model the contact relations between body parts and objects explicitly. A contact graph reward is also designed, which proved to be critical for precise HOI imitation. Based on the key designs, PhysHOI can imitate diverse HOI tasks simply yet effectively without prior knowledge. To make up for the lack of dynamic HOI scenarios in this area, we introduce the BallPlay dataset that contains eight whole-body basketball skills. We validate PhysHOI on diverse HOI tasks, including whole-body grasping and basketball skills.
Ang1/Tie2/VE-Cadherin Signaling Regulates DPSCs in Vascular Maturation

Adding dental pulp stem cells (DPSCs) to vascular endothelial cell–formed vessel-like structures can increase the longevity of these vessel networks. DPSCs display pericyte-like cell functions and closely assemble endothelial cells (ECs). However, the mechanisms of DPSC-derived pericyte-like cells in stabilizing the vessel networks are not fully understood. In this study, we investigated the functions of E-DPSCs, which were DPSCs isolated from the direct coculture of human umbilical vein endothelial cells (HUVECs) and DPSCs, and T-DPSCs, which were DPSCs treated by transforming growth factor beta 1 (TGF-β1), in stabilizing blood vessels in vitro and in vivo. A 3-dimensional coculture spheroid sprouting assay was conducted to compare the functions of E-DPSCs and T-DPSCs in vitro. Dental pulp angiogenesis in the severe combined immunodeficiency (SCID) mouse model was used to explore the roles of E-DPSCs and T-DPSCs in vascularization in vivo. The results demonstrated that both E-DPSCs and T-DPSCs possess smooth muscle cell–like cell properties, exhibiting higher expression of the mural cell–specific markers and the suppression of HUVEC sprouting. E-DPSCs and T-DPSCs inhibited HUVEC sprouting by activating TEK tyrosine kinase (Tie2) signaling, upregulating vascular endothelial (VE)–cadherin, and downregulating vascular endothelial growth factor receptor 2 (VEGFR2). In vivo study revealed more perfused and total blood vessels in the HUVEC + E-DPSC group, HUVEC + T-DPSC group, angiopoietin 1 (Ang1) pretreated group, and vascular endothelial protein tyrosine phosphatase (VE-PTP) inhibitor pretreated group, compared to HUVEC + DPSC group. In conclusion, these data indicated that E-DPSCs and T-DPSCs could stabilize the newly formed blood vessels and accelerate their perfusion. The critical regulating pathways are Ang1/Tie2/VE-cadherin and VEGF/VEGFR2 signaling.
The pragmatics of characters’ mental perspectives in pronominal reference resolution

To date, cognitive models of pronoun resolution have primarily focused on how fairly shallow discourse-level and lexical cues yield the appropriate interpretation, despite classic work in computational linguistics emphasizing the importance of situation-specific pragmatic reasoning. We explore the latter in two studies of human judgments, which highlight the striking robustness of these pragmatic processes.
Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testing

Machine learning (ML) models used in prediction and classification tasks may display performance disparities across population groups determined by sensitive attributes (e.g., race, sex, age). We consider the problem of evaluating the performance of a fixed ML model across population groups defined by multiple sensitive attributes (e.g., race and sex and age). Here, the sample complexity for estimating the worst-case performance gap across groups (e.g., the largest difference in error rates) increases exponentially with the number of group-denoting sensitive attributes. To address this issue, we propose an approach to test for performance disparities based on Conditional Value-at-Risk (CVaR). By allowing a small probabilistic slack on the groups over which a model has approximately equal performance, we show that the sample complexity required for discovering performance violations is reduced exponentially to be at most upper bounded by the square root of the number of groups. As a byproduct of our analysis, when the groups are weighted by a specific prior distribution, we show that R\'enyi entropy of order $2/3$ of the prior distribution captures the sample complexity of the proposed CVaR test algorithm. Finally, we also show that there exists a non-i.i. d. data collection strategy that results in a sample complexity independent of the number of groups.
Combined latissimus dorsi transfer and deltoid flap for irreparable rotator cuff tear: A retrospective analysis

Purpose: This study will evaluate the outcomes of the combined latissimus dorsi tendon transfer and deltoid flap for the management of irreparable posterosuperior rotator cuff tears. Methods: This is a retrospective study of 15 patients who have undergone double transfer surgery for their irreparable posterosuperior rotator cuff tears. These patients were followed up in three periods. Functional outcomes such as the constant score (CS), subjective shoulder value (SSV), and range of motion were assessed as well as radiographic outcomes such as the acromio–humeral distance. Results: Fifteen patients were included in the early and intermediate follow-up while only 8 remained at the latest follow-up. At the early follow-up the SSV (p = 0.001), CS (p = 0.021), and A–H distance (p = 0.008) showed a statistically significant improvement from their pre-operative values. At the latest follow-up, only the external rotation decreased from its value at the intermediate follow-up (p = 0.027). Conclusion: The double transfer technique showed sustainable improvement of the functional outcomes except for the external rotation. However, the loss of external rotation did not affect the remaining outcomes. Trials comparing this technique to other surgical managements or MIRCTs are needed to confirm these results.
Effects of Industrial Specialization on Income Smoothing with Company Size and Leverage as Control Variables

The characteristic of industrial specialist auditors is that they have more information obtained from experience when auditing clients of industry specialists so that they can detect errors and irregularities that occur in financial statements.Companies that are audited by industrial specialist auditors tend not to perform income smoothing. The purpose of this study was to examine the effects of industrial specialization on income smoothing with leverage and company size as control variables. The data used were Food and Beverage companies listed on the Indonesia Stock Exchange of 2013-2018. The purposive sampling gave 78 data used in this study. The data analysis method was the multiple linear regression test. The result of this studyshowsthat industrial specialization does not affect income smoothing. For the control variables, leverage and company size, theyshow no effect on income smoothing either.
Prompt Highlighter: Interactive Control for Multi-Modal LLMs

This study targets a critical aspect of multi-modal LLMs' (LLMs&VLMs) inference: explicit controllable text generation. Multi-modal LLMs empower multi-modality understanding with the capability of semantic generation yet bring less explainability and heavier reliance on prompt contents due to their autoregressive generative nature. While manipulating prompt formats could improve outputs, designing specific and precise prompts per task can be challenging and ineffective. To tackle this issue, we introduce a novel inference method, Prompt Highlighter, which enables users to highlight specific prompt spans to interactively control the focus during generation. Motivated by the classifier-free diffusion guidance, we form regular and unconditional context pairs based on highlighted tokens, demonstrating that the autoregressive generation in models can be guided in a classifier-free way. Notably, we find that, during inference, guiding the models with highlighted tokens through the attention weights leads to more desired outputs. Our approach is compatible with current LLMs and VLMs, achieving impressive customized generation results without training. Experiments confirm its effectiveness in focusing on input contexts and generating reliable content. Without tuning on LLaVA-v1.5, our method secured 70.7 in the MMBench test and 1552.5 in MME-perception. The code is available at: https://github.com/dvlab-research/Prompt-Highlighter/
Cardiorespiratory fitness is associated with cortical thickness of medial temporal brain areas associated with spatial cognition in young but not older adults

Cardiorespiratory fitness has a potent effect on neurocognitive health, especially regarding the hippocampal memory system. However, less is known about the impact of cardiorespiratory fitness on medial temporal lobe extrahippocampal neocortical regions. Specifically, it is unclear how cardiorespiratory fitness modulates these brain regions in young adulthood and if these regions are differentially related to cardiorespiratory fitness in young versus older adults. The primary goal of this study was to investigate if cardiorespiratory fitness predicted medial temporal lobe cortical thickness which, with the hippocampus, are critical for spatial learning and memory. Additionally, given the established role of these cortices in spatial navigation, we sought to determine if cardiorespiratory fitness and medial temporal lobe cortical thickness would predict greater subjective sense of direction in both young and older adults. Cross‐sectional data from 56 young adults (20–35 years) and 44 older adults (55–85 years) were included. FreeSurfer 6.0 was used to automatically segment participants' 3T T1‐weighted images. Using hierarchical multiple regression analyses, we confirmed significant associations between greater cardiorespiratory fitness and greater left entorhinal, left parahippocampal, and left perirhinal cortical thickness in young, but not older, adults. Left parahippocampal cortical thickness interacted with age group to differentially predict subjective sense of direction in young and older adults. Young adults displayed a positive, and older adults a negative, correlation between left parahippocampal cortical thickness and sense of direction. Our findings extend previous work on the association between cardiorespiratory fitness and hippocampal subfield structure in young adults to left medial temporal lobe neocortical regions.
Horizon-Free and Instance-Dependent Regret Bounds for Reinforcement Learning with General Function Approximation

To tackle long planning horizon problems in reinforcement learning with general function approximation, we propose the first algorithm, termed as UCRL-WVTR, that achieves both \emph{horizon-free} and \emph{instance-dependent}, since it eliminates the polynomial dependency on the planning horizon. The derived regret bound is deemed \emph{sharp}, as it matches the minimax lower bound when specialized to linear mixture MDPs up to logarithmic factors. Furthermore, UCRL-WVTR is \emph{computationally efficient} with access to a regression oracle. The achievement of such a horizon-free, instance-dependent, and sharp regret bound hinges upon (i) novel algorithm designs: weighted value-targeted regression and a high-order moment estimator in the context of general function approximation; and (ii) fine-grained analyses: a novel concentration bound of weighted non-linear least squares and a refined analysis which leads to the tight instance-dependent bound. We also conduct comprehensive experiments to corroborate our theoretical findings.