Miracle-dz/UniFAR-FTUs huggingface dataset:
Momentum características y estabilidad temporal. Resultados para la bolsa española(*) Momen tum: characteristics and time-stability. Some evidence for the Spanish stock market

This paper tests the momentum effect in the Spanish stock market for the period 19912004. Though evidence of momentum is found, it fades after the 1997 crisis. A link is observed between momentum profits and both size and turnover, neither of which proves determinant. The tuni of the year effect is also unable to explain either the presence or the disappearance of momentum in stock retunic. The behaviour of the winner portfolio, however, particularly when formed by small size, high turnover winners, plays a fundamental role in explaining this puzzle.
Automatic quality monitoring of two-photon printed devices based on deep learning

Two-photon 3D printing technology is an additive manufacturing technology that uses the two-photon absorption process of near-infrared radiation to create a three-dimensional micro-nano scale structure with extremely high resolution. However, in the preparation process of two-photon printing, the laser parameters for inducing photopolymerization have a huge impact on the quality of the polymer structure. Therefore, monitoring the quality of the device during the manufacturing process and rationally optimizing the laser parameters are of great significance to the field of additive manufacturing. In this study, we collected video data of different structural devices prepared by self-made photoresist materials under different laser parameters, and used a variety of convolutional neural network variant models to train and verify our collected datasets. The results show that the variant deep learning neural network model can classify the quality of polymer structures in milliseconds, and the test accuracy can reach 95%.
Facial expression recognition with convolutional neural network embedded with attention mechanism

Facial expression recognition has always been a complex problem in computer vision. In recent years, with the rapid development of deep learning, some methods based on convolutional neural networks have greatly improved the accuracy of expression recognition. To further improve the accuracy of facial expression recognition, this paper proposes a convolutional neural network facial expression recognition model embedded with attention mechanism. The convolutional layer is used to extract the shallow features of facial expressions, the SE attention module is embedded into the network model to realize the extraction of salient regional features of facial expressions. To effectively prevent gradient explosion and gradient disappearance, and speed up the training and convergence speed of the network, a BN layer is added after each convolutional layer. The convolutional layer is used instead of the fully connected layer, and 1×1 convolution is used to achieve classification, which reduces the number of parameters, avoids the overfitting problem caused by a large amount of calculation, and improves the generalization ability of the model. The experimental results show that the facial expression recognition accuracy of the proposed method on the FER2013 and CK+ public datasets has been improved to a certain extent, which verifies the method's effectiveness.
Struggle for safety: Adaptive responses of wintering waders to their avian predators

Overwintering Coots Fulica atra atra were observed in January 2006, in Keoladeo National Park, India. Here they fed on aquatic plants, which they exploited in strictlymaintained dense flotillas; these flocks behaved like a super-organism. This observation adds to a description of Ali & Ripley (1969), who do not explicitly discuss the cause of raft formation by Coots, but only mention the occurrence of Coot predation by Greater Spotted Eagle Aquila clanga. Having observed a Coot being drowned by a Ganges Softshell Turtle Aspideretus gangeticus, I suggest that predation risk by these turtles should be taken into account as a possible cause for this herding behaviour. When on the water, Coot flocks now and then suddenly scattered; this suggested disturbance by a hidden aquatic predator, all the more because no raptor attacks were witnessed on these occasions. Coots visited the water exclusively for foraging, and, in contrast to their usual behaviour, roosted on islands. This indicates that they considered the water as a dangerous environment, despite the fact that on land the risk of predation by eagles may be greater. My observations suggest that, by foraging in very dense groups, Coots and other waterbirds exploited the rich food resources in Keoladeo whilst minimising the risk
Characterizing Multi-Chip GPU Data Sharing

Multi-chip Graphics Processing Unit (GPU) systems are critical to scale performance beyond a single GPU chip for a wide variety of important emerging applications. A key challenge for multi-chip GPUs, though, is how to overcome the bandwidth gap between inter-chip and intra-chip communication. Accesses to shared data, i.e., data accessed by multiple chips, pose a major performance challenge as they incur remote memory accesses possibly congesting the inter-chip links and degrading overall system performance. This article characterizes the shared dataset in multi-chip GPUs in terms of (1) truly versus falsely shared data, (2) how the shared dataset scales with input size, (3) along which dimensions the shared dataset scales, and (4) how sensitive the shared dataset is with respect to the input’s characteristics, i.e., node degree and connectivity in graph workloads. We observe significant variety in scaling behavior across workloads: some workloads feature a shared dataset that scales linearly with input size, whereas others feature sublinear scaling (following a \(\sqrt {2}\) or \(\sqrt [3]{2}\) relationship). We further demonstrate how the shared dataset affects the optimum last-level cache organization (memory-side versus SM-side) in multi-chip GPUs, as well as optimum memory page allocation and thread scheduling policy. Sensitivity analyses demonstrate the insights across the broad design space.
REST: Drug-Drug Interaction Prediction via Reinforced Student-Teacher Curriculum Learning

Accurate prediction of drug-drug interaction (DDI) is crucial to achieving effective decision-making in medical treatment for both doctors and patients. Recently, many deep learning based methods have been proposed to learn from drug-related features and conduct DDI prediction. These works have achieved promising results. However, the extreme imbalance of medical data poses a serious problem to DDI prediction, where a small fraction of DDI types occupy the majority training data. A straightforward way is to develop an appropriate policy to sample the data. Due to the high complexity and speciality of medical science, a dynamic learnable policy is required instead of a heuristic, uniform or static one. Therefore, we propose a REinforced Student-Teacher curriculum learning model (REST) for effective sampling to tackle this imbalance problem. Specifically, REST consists of two interactive parts, which are a heterogeneous graph neural network as the student and a reinforced sampler as the teacher. In each interaction, the teacher model takes action to sample an appropriate batch to train the student model according to the student model state while the cumulated improvement in performance of the student model is treated as the reward for policy gradient of the teacher model. The experimental results on two benchmarking datasets have demonstrated the significant effectiveness of our proposed model in DDI prediction, especially for the DDI types with low frequency.
Thermodynamic and Radiative Impact of the Correction of Sounding Humidity Bias in the Tropics

Accurate measurements of atmospheric water vapor are crucial to many aspects of climate research and atmospheric science. This paper discusses some of the meteorological implications of a bias discovered in the measurement of water vapor in widely deployed radiosonde systems. This problem apparently arose in the early 1990s, and a correction scheme has been recently developed that intends to remove the bias. The correction scheme also includes improvements in the humidity measurements in the upper troposphere and near the surface. It has been applied to data taken during the Tropical Ocean and Global Atmosphere Coupled Ocean‐Atmosphere Response Experiment (TOGA COARE). The impact of the bias on the general stability of the tropical atmosphere to deep convection, as measured by the convective available potential energy (CAPE) and the convective inhibition (CIN), is quite large. On the basis of the uncorrected dataset, one might erroneously conclude that it is difficult to trigger deep convection over the region. When the correction is taken into account, the atmosphere over the tropical western Pacific becomes typically unstable to deep convection, with convective instability similar to that measured from aircraft in the vicinity of active convective systems. Radiative fluxes are also significantly modified. For clear sky conditions, it is found that on average, the net surface radiative flux increases b y4Wm 22, and the outgoing longwave flux decreases by more than 2 W m 22 due to the humidity correction. Under more realistic cloudy conditions, the differences are weaker but still significant. Changes in radiative fluxes are explained at first order by the precipitable water increase. It is likely that such a dry bias would hide any modifications of the atmospheric water vapor associated with the increase of greenhouse gases.
Thermal conductivity in porous media: Percolation‐based effective‐medium approximation

Knowledge of porosity and saturation‐dependent thermal conductivities is necessary to investigate heat and water transfer in natural porous media such as rocks and soils. Thermal conductivity in a porous medium is affected by the complicated relationship between the topology and geometry of the pore space and the solid matrix. However, as water content increases from completely dry to fully saturated, the effect of the liquid phase on thermal conductivity may increase substantially. Although various methods have been proposed to model the porosity and saturation dependence of thermal conductivity, most are empirical or quasiphysical. In this study, we present a theoretical upscaling framework from percolation theory and the effective‐medium approximation, which is called percolation‐based effective‐medium approximation (P‐EMA). The proposed model predicts the thermal conductivity in porous media from endmember properties (e.g., air, solid matrix, and saturating fluid thermal conductivities), a scaling exponent, and a percolation threshold. In order to evaluate our porosity and saturation‐dependent models, we compare our theory with 193 porosity‐dependent thermal conductivity measurements and 25 saturation‐dependent thermal conductivity data sets and find excellent match. We also find values for the scaling exponent different than the universal value of 2, in insulator‐conductor systems, and also different from 0.76, the exponent in conductor‐superconductor mixtures, in three dimensions. These results indicate that the thermal conductivity under fully and partially saturated conditions conforms to nonuniversal behavior. This means the value of the scaling exponent changes from medium to medium and depends not only on structural and geometrical properties of the medium but also characteristics (e.g., wetting or nonwetting) of the saturating fluid.
Simplex2vec Backward: From Vectors Back to Simplicial Complex

Simplicial neural networks (SNNs) were proposed to generate higher-order simplicial complex representations as vectors that encode not only pairwise relationships but also higher-order interactions between nodes. Although these vectors allowing us to consider richer data representations compared to typical graph convolution, most real-world graphs associated with molecule or human-related activities are often sensitive and might contain confidential information, e.g., molecular geometry or friend lists. However, little works investigate the potential threats for these simplicial complexes (higher-order interactions between nodes). We name this threat by Simplicial Complexes Reconstruction Attack (SCRA) and conduct this attack by studying whether the vectors can be inverted to (approximately) recover the simplicial complexes who used to generate them. Specifically, we first generate the vectors via a k-simplex2vec approach that extends the node2vec algorithm to simplices of higher dimensions to associate Euclidean vectors to simplicial complexes. We then present a Simplex2vec Backward algorithm to perform the SCRA on k-simplex2vec vectors by pointwise mutual information (PMI) matrix reconstruction.
Learning Sparse Lexical Representations Over Specified Vocabularies for Retrieval

A recent line of work in first-stage Neural Information Retrieval has focused on learning sparse lexical representations instead of dense embeddings. One such work is SPLADE, which has been shown to lead to state-of-the-art results in both the in-domain and zero-shot settings, can leverage inverted indices for efficient retrieval, and offers enhanced interpretability. However, existing SPLADE models are fundamentally limited to learning a sparse representation based on the native BERT WordPiece vocabulary. In this work, we extend SPLADE to support learning sparse representations over arbitrary sets of tokens to improve flexibility and aid integration with existing retrieval systems. As an illustrative example, we focus on learning a sparse representation over a large (300k) set of unigrams. We add an unsupervised pretraining task on C4 to learn internal representations for new tokens. Our experiments show that our Expanded-SPLADE model maintains the performance of WordPiece-SPLADE on both in-domain and zero-shot retrieval while allowing for custom output vocabularies.
ELTRA: An Embedding Method based on Learning-to-Rank to Preserve Asymmetric Information in Directed Graphs

Double-vector embedding methods capture the asymmetric information in directed graphs first, and then preserve them in the embedding space by providingtwo latent vectors, i.e., source and target, per node. Although these methods are known to besuperior to the single-vector ones (i.e., providing asingle latent vector per node), wepoint out their three drawbacks as inability to preserve asymmetry on NU-paths, inability to preserve global nodes similarity, and impairing in/out-degree distributions. To address these, we first proposeCRW, anovel similarity measure for graphs that considers contributions ofboth in-links and out-links in similarity computation,without ignoring their directions. Then, we proposeELTRA, aneffective double-vector embedding method to preserve asymmetric information in directed graphs. ELTRA computesasymmetry preserving proximity scores (AP-scores) by employing CRW in which the contribution of out-links and in-links in similarity computation isupgraded anddowngraded, respectively. Then, for every node u, ELTRA selects its top-tclosest nodes based on AP-scores andconforms theranks of their corresponding target vectors w.r. t u's source vector in the embedding space to theiroriginal ranks. Our extensive experimental results withseven real-world datasets andsixteen embedding methods show that (1) CRWsignificantly outperforms Katz and RWR in computing nodes similarity in graphs, (2) ELTRAoutperforms the existing state-of-the-art methods in graph reconstruction, link prediction, and node classification tasks.
Adaptive modeling and inference of higher-order coordination in neuronal assemblies: a dynamic greedy estimation approach

Central in the study of population codes, coordinated ensemble spiking activity is widely observable in neural recordings with hypothesized roles in robust stimulus representation, interareal communication, and learning and memory formation. Model-free measures of synchrony characterize coherent pairwise activity but not higher-order interactions, a limitation transcended by statistical models of ensemble spiking activity. However, existing model-based analyses often impose assumptions about the relevance of higher-order interactions and require repeated trials to characterize dynamics in the correlational structure of ensemble activity. To address these shortcomings, we propose an adaptive greedy filtering algorithm based on a discretized mark point-process model of ensemble spiking and a corresponding statistical inference framework to identify significant higher-order coordination. In the course of developing a precise statistical test, we show that confidence intervals can be constructed for greedily estimated parameters. We demonstrate the utility of our proposed methods on simulated neuronal assemblies. Applied to multi-electrode recordings from human and rat cortical assemblies, our proposed methods provide new insights into the dynamics underlying localized population activity during transitions between brain states. Author summary Simultaneous ensemble spiking is hypothesized to have important roles in neural encoding; however, neurons can also spike simultaneously by chance. In order to characterize the potentially time-varying higher-order correlational structure of ensemble spiking, we propose an adaptive greedy filtering algorithm that estimates the rate of all reliably-occurring simultaneous ensemble spiking events. Moreover, we propose an accompanying statistical inference framework to distinguish the chance occurrence of simultaneous spiking events from coordinated higher-order spiking. We demonstrate the proposed methods accurately differentiate coordinated simultaneous spiking from chance occurrences in simulated data. In application to human and rat cortical data, the proposed methods reveal time-varying dynamics in higher-order coordination that coincide with changing brain states.
Plasmid partitioning driven by collective migration of ParA between nucleoid lobes

The ParABS system is crucial for the faithful segregation and inheritance of many bacterial chromosomes and low-copy number plasmids. However, despite extensive research, the spatio-temporal dynamics of the ATPase ParA and its connection to the dynamics and positioning of the ParB-coated cargo has remained unclear. In this study, we utilise high-throughput imaging, quantitative data analysis, and computational modelling to explore the in vivo dynamics of ParA and its interaction with ParB-coated plasmids and the nucleoid. As previously observed, we find that F-plasmid ParA undergoes collective migrations (‘flips’) between cell halves multiple times per cell cycle. We reveal that a constricting nucleoid is required for these migrations and that they are triggered by a plasmid crossing into the cell half with greater ParA. Using simulations, we show that these dynamics can be explained by the combination of nucleoid constriction and cooperative ParA binding to the DNA, in line with the behaviour of other ParA proteins. We further show that these ParA flips act to equally partition plasmids between the two lobes of the constricted nucleoid and are therefore important for plasmid stability, especially in fast growth conditions for which the nucleoid constricts early in the cell cycle. Overall our work identifies a second mode of action of the ParABS system and deepens our understanding of how this important segregation system functions.
PD-L1 Restrains the PD-1hiNrp1loTGFβ+ Treg to block IL6+ Neutrophil tumor infiltration to Suppress Inflammation-driven Colorectal Cancer

PD-L1 functions as a suppressor of T cell activation and colonic inflammation. The consequence of and mechanism underlying these opposite functions of PD-L1 in colorectal cancer remains unknown. We report that global Cd274 deletion promotes inflammation-driven colorectal tumorigenesis. 16S rRNA and shotgun metagenomic sequencing revealed that loss of host PD-L1 leads to expansion of gut Ligilactobacillus murinus and activation of the AhR pathway in tumor-bearing mice. scRNA-seq analysis revealed that PD-L1 regulates PD-189Nrp11215 Treg, IL6+ neutrophils, and B cells in colorectal tumor. Treg expresses high level of TGFβ to recruit IL6+ neutrophils. IL6 inhibits activation of B and T cells. IL6 blockade or B cell activation via CD40 agonist increases CTL activation and suppresses colon tumor growth in vivo. Our findings determine that PD-L1 functions as a tumor suppressor in the context of inflammation-driven colorectal cancer and the PD-L1/L. murius/PD-189Nrp11215TGFβ+ Treg/IL6+ neutrophils pathway controls host cancer immunosurveillance and colorectal tumorigenesis. Key points Global deletion of Cd274 promotes inflammation-driven colorectal tumorigenesis. Loss of PD-L1 increases gut L. murinus and activates AhR pathway in colorectal tumor. PD-1hiTGFβ+Nrp1lo Treg recruits IL6+ neutrophils to inhibit B and T cell function in colorectal tumor. L. murinus and tryptophan metabolites connect PD-L1 and colorectal tumor immunosurveillance. Graphic summary
2-D wavepath migration

Prestack Kirchhoff migration (KM) is computationally intensive for iterative velocity analysis. This is partly because each time sample in a trace must be smeared along a quasi‐ellipsoid in the model. As a less costly alternative, we use the stationary phase approximation to the KM integral so that the time sample is smeared along a small Fresnel zone portion of the quasi‐ellipsoid. This is equivalent to smearing the time samples in a trace over a 1.5‐D fat ray (i.e., wavepath), so we call this “wavepath migration” (WM). This compares to standard KM, which smears the energy in a trace along a 3‐D volume of quasi‐concentric ellipsoids. In principle, single trace migration with WM has a computational count of O(N1.5) compared to KM, which has a computational count of O(N3), where N is the number of grid points along one side of a cubic velocity model. Our results with poststack data show that WM produces an image that in some places contains fewer migration artifacts and is about as well resolved as the KM .