Miracle-dz/UniFAR-FTUs huggingface dataset:
Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning

Real-world graphs naturally exhibit hierarchical or cyclical structures that are unfit for the typical Euclidean space. While there exist graph neural networks that leverage hyperbolic or spherical spaces to learn representations that embed such structures more accurately, these methods are confined under the message-passing paradigm, making the models vulnerable against side-effects such as oversmoothing and oversquashing. More recent work have proposed global attention-based graph Transformers that can easily model long-range interactions, but their extensions towards non-Euclidean geometry are yet unexplored. To bridge this gap, we propose Fully Product-Stereographic Transformer, a generalization of Transformers towards operating entirely on the product of constant curvature spaces. When combined with tokenized graph Transformers, our model can learn the curvature appropriate for the input graph in an end-to-end fashion, without the need of additional tuning on different curvature initializations. We also provide a kernelized approach to non-Euclidean attention, which enables our model to run in time and memory cost linear to the number of nodes and edges while respecting the underlying geometry. Experiments on graph reconstruction and node classification demonstrate the benefits of generalizing Transformers to the non-Euclidean domain.
Depth Completion with Multiple Balanced Bases and Confidence for Dense Monocular SLAM

Dense SLAM based on monocular cameras does indeed have immense application value in the field of AR/VR, especially when it is performed on a mobile device. In this paper, we propose a novel method that integrates a light-weight depth completion network into a sparse SLAM system using a multi-basis depth representation, so that dense mapping can be performed online even on a mobile phone. Specifically, we present a specifically optimized multi-basis depth completion network, called BBC-Net, tailored to the characteristics of traditional sparse SLAM systems. BBC-Net can predict multiple balanced bases and a confidence map from a monocular image with sparse points generated by off-the-shelf keypoint-based SLAM systems. The final depth is a linear combination of predicted depth bases that can be optimized by tuning the corresponding weights. To seamlessly incorporate the weights into traditional SLAM optimization and ensure efficiency and robustness, we design a set of depth weight factors, which makes our network a versatile plug-in module, facilitating easy integration into various existing sparse SLAM systems and significantly enhancing global depth consistency through bundle adjustment. To verify the portability of our method, we integrate BBC-Net into two representative SLAM systems. The experimental results on various datasets show that the proposed method achieves better performance in monocular dense mapping than the state-of-the-art methods. We provide an online demo running on a mobile phone, which verifies the efficiency and mapping quality of the proposed method in real-world scenarios.
Enhancing Phosphorus Uptake in Sugarcane: A Critical Evaluation of Humic Acid and Phosphorus Fertilizers Effectiveness

Our research conducted in an area characterized by alkaline, lime-abundant soils investigated the potential of utilizing phosphorus fertilizer and humic acid to enhance phosphorus absorption in sugarcane crops. The results indicated that the application of phosphorus fertilizer significantly increased the total and bioavailable phosphorus in the rhizospheric soil, despite observing a decrease in phosphatase enzyme activity. An important observation was the considerable growth of active carbon, a crucial soil health indicator, under the influence of humic acid treatments. The findings also demonstrated an enhancement in phosphorus absorption by sugarcane due to the synergistic application of humic acid and phosphorus fertilizer at both harvest periods. Interestingly, humic acid treatments, when applied through immersion, were found to be more effective than soil applications, implying a greater impact on root absorption processes. The findings underline the potential of integrating humic acid into sugarcane cultivation for better phosphorus absorption. Our study offers valuable insights for improved soil management strategies, and could potentially pave the way towards more sustainable agricultural practices. However, we also recommend further investigation into alternative methods of humic acid application and its usage at different stages of plant growth. Such exploration could provide a comprehensive understanding of the potential benefits and most effective utilization of humic acid in agriculture, especially in regions with similar soil characteristics as West Azarbaijan, Iran
Almost partitioning every 2-edge-coloured complete k-graph into k monochromatic tight cycles

A $k$-uniform tight cycle is a $k$-graph with a cyclic order of its vertices such that every~$k$ consecutive vertices from an edge. We show that for $k\geq 3$, every red-blue edge-coloured complete $k$-graph on~$n$ vertices contains~$k$ vertex-disjoint monochromatic tight cycles that together cover $n - o(n)$ vertices.
Single-Molecule Time-Resolved Spectroscopy in a Tunable STM Nanocavity

Spontaneous fluorescence rates of single-molecule emitters are typically on the order of nanoseconds. However, coupling them with plasmonic nanostructures can substantially increase their fluorescence yields. The confinement between a tip and sample in a scanning tunneling microscope creates a tunable nanocavity, an ideal platform for exploring the yields and excitation decay rates of single-molecule emitters, depending on their coupling strength to the nanocavity. With such a setup, we determine the excitation lifetimes from the direct time-resolved measurements of phthalocyanine fluorescence decays, decoupled from the metal substrates by ultrathin NaCl layers. We find that when the tip is approached to single molecules, their lifetimes are reduced to the picosecond range due to the effect of coupling with the tip-sample nanocavity. On the other hand, ensembles of the adsorbed molecules measured without the nanocavity manifest nanosecond-range lifetimes. This approach overcomes the drawbacks associated with the estimation of lifetimes for single molecules from their respective emission line widths.
Riemannian Adaptive Regularized Newton Methods with H\"older Continuous Hessians

This paper presents strong worst-case iteration and operation complexity guarantees for Riemannian adaptive regularized Newton methods, a unified framework encompassing both Riemannian adaptive regularization (RAR) methods and Riemannian trust region (RTR) methods. We comprehensively characterize the sources of approximation in second-order manifold optimization methods: the objective function's smoothness, retraction's smoothness, and subproblem solver's inexactness. Specifically, for a function with a $\mu$-H\"older continuous Hessian, when equipped with a retraction featuring a $\nu$-H\"older continuous differential and a $\theta$-inexact subproblem solver, both RTR and RAR with $2+\alpha$ regularization (where $\alpha=\min\{\mu,\nu,\theta\}$) locate an $(\epsilon,\epsilon^{\alpha/(1+\alpha)})$-approximate second-order stationary point within at most $O(\epsilon^{-(2+\alpha)/(1+\alpha)})$ iterations and at most $\tilde{O}(\epsilon^{-(4+3\alpha)/(2(1+\alpha))})$ Hessian-vector products. These complexity results are novel and sharp, and reduce to an iteration complexity of $O(\epsilon^{-3/2})$ and an operation complexity of $\tilde{O}(\epsilon^{-7/4})$ when $\alpha=1$.
Total Variation Floodgate for Variable Importance Inference in Classification

Inferring variable importance is the key problem of many scientific studies, where researchers seek to learn the effect of a feature $X$ on the outcome $Y$ in the presence of confounding variables $Z$. Focusing on classification problems, we define the expected total variation (ETV), which is an intuitive and deterministic measure of variable importance that does not rely on any model context. We then introduce algorithms for statistical inference on the ETV under design-based/model-X assumptions. These algorithms build on the floodgate notion for regression problems (Zhang and Janson 2020). The algorithms we introduce can leverage any user-specified regression function and produce asymptotic lower confidence bounds for the ETV. We show the effectiveness of our algorithms with simulations and a case study in conjoint analysis on the US general election.
Capturing continuous, long timescale behavioral changes in Drosophila melanogaster postural data

Animal behavior spans many timescales, from short, seconds-scale actions to circadian rhythms over many hours to life-long changes during aging. Most quantitative behavior studies have focused on short-timescale behaviors such as locomotion and grooming. Analysis of these data suggests there exists a hierarchy of timescales; however, the limited duration of these experiments prevents the investigation of the full temporal structure. To access longer timescales of behavior, we continuously recorded individual Drosophila melanogaster at 100 frames per second for up to 7 days at a time in featureless arenas on sucrose-agarose media. We use the deep learning framework SLEAP to produce a full-body postural data set for 47 individuals resulting in nearly 2 billion pose instances. We identify stereotyped behaviors such as grooming, proboscis extension, and locomotion and use the resulting ethograms to explore how the flies’ behavior varies across time of day and days in the experiment. We find distinct circadian patterns in all of our stereotyped behavior and also see changes in behavior over the course of the experiment as the flies weaken and die.
New improvement to Falconer distance set problem in higher dimensions

We show that if a compact set $E\subset \mathbb{R}^d$ has Hausdorff dimension larger than $\frac{d}{2}+\frac{1}{4}-\frac{1}{8d+4}$, where $d\geq 3$, then there is a point $x\in E$ such that the pinned distance set $\Delta_x(E)$ has positive Lebesgue measure. This improves upon bounds of Du-Zhang and Du-Iosevich-Ou-Wang-Zhang in all dimensions $d \ge 3$. We also prove lower bounds for Hausdorff dimension of pinned distance sets when $\dim_H (E) \in (\frac{d}{2} - \frac{1}{4} - \frac{3}{8d+4}, \frac{d}{2}+\frac{1}{4}-\frac{1}{8d+4})$, which improves upon bounds of Harris and Wang-Zheng in dimensions $d \ge 3$.
Numerical simulation and analytical modelling of self-heating in FDSOI MOSFETs down to very deep cryogenic temperatures

Self-heating (SHE) TCAD numerical simulations have been performed, for the first time, on 30nm FDSOI MOS transistors at extremely low temperatures. The self-heating temperature rise dTmax and the thermal resistance Rth are computed as functions of the ambient temperature Ta and the dissipated electrical power (Pd), considering calibrated silicon and oxide thermal conductivities. The characteristics of the SHE temperature rise dTmax(Pd) display sub-linear behavior at sufficiently high levels of dissipated power, in line with standard FDSOI SHE experimental data. It has been observed that the SHE temperature rise dTmax can significantly exceed the ambient temperature more easily at very low temperatures. Furthermore, a detailed thermal analysis of the primary heat flows in the FDSOI device has been conducted, leading to the development of an analytical SHE model calibrated against TCAD simulation data. This SHE analytical model accurately describes the dTmax(Pd) and Rth(Ta) characteristics of an FDSOI MOS device operating at extreme low ambient temperatures. These TCAD simulations and analytical models hold great promise for predicting the SHE and electro-thermal performance of FDSOI MOS transistors against ambient temperature and dissipated power.
Common Firm-level Investor Fears: Evidence from Equity Options

We identify a new type of risk, common firm-level investor fears, from commonalities within the cross-sectional distribution of individual stock options. We define firm-level fears that link with upward price movements as good fears, and those relating to downward price movements as bad fears. Such information is different to market fears that we extract from index options. Stocks with high sensitivities to common firm-level investor fears earn lower returns, with investors demanding a higher compensation for exposure to common bad fears relative to common good fears. Risk premium estimates for common bad fears range from -5.63% to -4.92% per annum.
From Text to Mask: Localizing Entities Using the Attention of Text-to-Image Diffusion Models

Diffusion models have revolted the field of text-to-image generation recently. The unique way of fusing text and image information contributes to their remarkable capability of generating highly text-related images. From another perspective, these generative models imply clues about the precise correlation between words and pixels. In this work, a simple but effective method is proposed to utilize the attention mechanism in the denoising network of text-to-image diffusion models. Without re-training nor inference-time optimization, the semantic grounding of phrases can be attained directly. We evaluate our method on Pascal VOC 2012 and Microsoft COCO 2014 under weakly-supervised semantic segmentation setting and our method achieves superior performance to prior methods. In addition, the acquired word-pixel correlation is found to be generalizable for the learned text embedding of customized generation methods, requiring only a few modifications. To validate our discovery, we introduce a new practical task called"personalized referring image segmentation"with a new dataset. Experiments in various situations demonstrate the advantages of our method compared to strong baselines on this task. In summary, our work reveals a novel way to extract the rich multi-modal knowledge hidden in diffusion models for segmentation.
Counterfactual Explanations via Locally-guided Sequential Algorithmic Recourse

Counterfactuals operationalised through algorithmic recourse have become a powerful tool to make artificial intelligence systems explainable. Conceptually, given an individual classified as y -- the factual -- we seek actions such that their prediction becomes the desired class y' -- the counterfactual. This process offers algorithmic recourse that is (1) easy to customise and interpret, and (2) directly aligned with the goals of each individual. However, the properties of a"good"counterfactual are still largely debated; it remains an open challenge to effectively locate a counterfactual along with its corresponding recourse. Some strategies use gradient-driven methods, but these offer no guarantees on the feasibility of the recourse and are open to adversarial attacks on carefully created manifolds. This can lead to unfairness and lack of robustness. Other methods are data-driven, which mostly addresses the feasibility problem at the expense of privacy, security and secrecy as they require access to the entire training data set. Here, we introduce LocalFACE, a model-agnostic technique that composes feasible and actionable counterfactual explanations using locally-acquired information at each step of the algorithmic recourse. Our explainer preserves the privacy of users by only leveraging data that it specifically requires to construct actionable algorithmic recourse, and protects the model by offering transparency solely in the regions deemed necessary for the intervention.
A simple, analytic 3-dimensional downburst model based on boundary layer stagnation flow

A simple downburst model is developed for use in batch and real-time piloted simulation studies of guidance strategies for terminal area transport aircraft operations in wind shear conditions. The model represents an axisymmetric stagnation point flow, based on velocity profiles from the Terminal Area Simulation System (TASS) model developed by Proctor and satisfies the mass continuity equation in cylindrical coordinates. Altitude dependence, including boundary layer effects near the ground, closely matches real-world measurements, as do the increase, peak, and decay of outflow and downflow with increasing distance from the downburst center. Equations for horizontal and vertical winds were derived, and found to be infinitely differentiable, with no singular points existent in the flow field. In addition, a simple relationship exists among the ratio of maximum horizontal to vertical velocities, the downdraft radius, depth of outflow, and altitude of maximum outflow. In use, a microburst can be modeled by specifying four characteristic parameters, velocity components in the x, y and z directions, and the corresponding nine partial derivatives are obtained easily from the velocity equations.
Exploring the Potential “Brain-Cognition-Behavior” Relationship in Children With ADHD Based on Resting-State Brain Local Activation and Functional Connectivity

Objective: Exploring how abnormal brain function in children with ADHD affects executive function and ultimately leads to behavioral impairment provides a theoretical basis for clinically targeted neurotherapy and cognitive training. Method: Amplitude of low frequency fluctuations (ALFF), regional homogeneity (ReHo), and seed-based FC were analyzed in 53 ADHD and 52 healthy controls. The “brain-cognition-behavior” relationship was further explored using mediation analysis. Results: ADHD showed abnormal local activation in the middle temporal gyrus (MTG), inferior occipital gyrus and inferior frontal gyrus (IFG) and reduced FC between the IFG and the cerebellum. ADHD diagnosis may affect ALFF of MTG and further modulate shift and finally affect inattentive symptoms. It may also affect the total symptoms through the FC of the IFG with the cerebellum. Conclusion: ADHD showed extensive spontaneous activity abnormalities and frontal-cerebellar FC impairments. Localized functional abnormalities in the MTG may affect the shift in EF, resulting in attention deficit behavior.
chem16S: community-level chemical metrics for exploring genomic adaptation to environments

Abstract Summary The chem16S package combines taxonomic classifications of 16S rRNA gene sequences with amino acid compositions of prokaryotic reference proteomes to generate community reference proteomes. Taxonomic classifications from the RDP Classifier or data objects created by the phyloseq R package are supported. Users can calculate and visualize a variety of chemical metrics in order to explore the effects of redox, salinity, and other physicochemical variables on the genomic adaptation of protein sequences at the community level. Availability and implementation Development of chem16S is hosted at https://github.com/jedick/chem16S. Version 1.0. 0 is freely available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/package=chem16S.
Effector-specific motor simulation supplements core action recognition processes in adverse conditions

Abstract Observing other people acting activates imitative motor plans in the observer. Whether, and if so when and how, such ‘effector-specific motor simulation’ contributes to action recognition remains unclear. We report that individuals born without upper limbs (IDs)—who cannot covertly imitate upper-limb movements—are significantly less accurate at recognizing degraded (but not intact) upper-limb than lower-limb actions (i.e. point-light animations). This finding emphasizes the need to reframe the current controversy regarding the role of effector-specific motor simulation in action recognition: instead of focusing on the dichotomy between motor and non-motor theories, the field would benefit from new hypotheses specifying when and how effector-specific motor simulation may supplement core action recognition processes to accommodate the full variety of action stimuli that humans can recognize.
Whole genome assessment of a declining game bird reveals cryptic genetic structure and insights for population management

Population genomics applied to game species conservation can help delineate management units, ensure appropriate harvest levels and identify populations needing genetic rescue to safeguard their adaptive potential. The ruffed grouse (Bonasa umbellus) is rapidly declining in much of the eastern USA due to a combination of forest maturation and habitat fragmentation. More recently, mortality from West Nile Virus may have affected connectivity of local populations; however, genetic approaches have never explicitly investigated this issue. In this study, we sequenced 54 individual low‐coverage (~5X) grouse genomes to characterize population structure, assess migration rates across the landscape to detect potential barriers to gene flow and identify genomic regions with high differentiation. We identified two genomic clusters with no clear geographic correlation, with large blocks of genomic differentiation associated with chromosomes 4 and 20, likely due to chromosomal inversions. After excluding these putative inversions from the data set, we found weak but nonsignificant signals of population subdivision. Estimated gene flow revealed reduced rates of migration in areas with extensive habitat fragmentation and increased genetic connectivity in areas with less habitat fragmentation. Our findings provide a benchmark for wildlife managers to compare and scale the genetic diversity and structure of ruffed grouse populations in Pennsylvania and across the eastern USA, and we also reveal structural variation in the grouse genome that requires further study to understand its possible effects on individual fitness and population distribution.
Soil Tillage in Agroecosystems

Introduction, A. El Titi Techniques of Soil Tillage, K. Koller The Role of Soil Tillage for Soil Structure, B.Huwe The Soil Microbial Community and Soil Tillage, D. S. Andrade, A. Colozzi-Filho and K. E. Giller Soil Tillage for Sustainable Nutrient Management. C. Pekrun, H. P. Kaul and W. Claupein Implications of Soil Tillage for Crop and Weed Seeds, C. Pekrun, A. El Titi, and W. Claupein Implications of Soil Tillage for Weed Communities, A. El Titi Influence of Cultivation Practices on Arable Crop Diseases, V.W. L. Jordan and J. A. Hutcheon Influence of Soil Tillage on Slugs and Their Natural Enemies, D. M. Glen & W. O. C. Symondson Interactions Between Tillage and Earthworms in Agroecosystems, W. D. Shuster and C. A. Edwards Effects of Tillage on Invertebrates in Soil Ecosystems, A. El Titi Soil Tillage and Epigeal Predatory Arthropods, D. A. Kendall The Synthesis, A. El Titi Index