Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the structural linkage between IT capability and organizational agility: A study on Indian financial enterprises

Purpose Information technology (IT) is normally regarded as an enabling factor for making firms agile. Usually, it has been realized that greater IT spending enhances a firm’s agility. However, the role of IT as an obstructing factor towards organizational agility cannot be overlooked. Taking this commonly perceived but less-studied IT-agility contradiction into account, the purpose of this paper is to investigate whether IT can augment or impede organizational agility. This research which is conducted in context to privately owned Indian financial enterprises proposes the premise that effective IT resource management is imperative for organizations to thrive for greater firm-wide IT capability for enhanced agility. Design/methodology/approach Primary data collected from 300 business and IT executives working in various privately owned financial enterprises across India are used for this study and a structural equation modelling is employed to assess the IT-agility link. Findings The findings of the study are two-folded. First, this study concludes that IT capability acts as an enabler for business process and market responsive organizational agility. Second, if IT spending is not properly translated into creating superior capability, huge and impudent IT investments will impede the overall organizational agility. Originality/value This paper investigates both exogenous variable (IT capability) and endogenous variable (organizational agility) in terms of second-order reflective measures and establishes a significant structural link between both the dimensions of IT capability (managerial and technical) and organizational agility (business process and market responsive). This analysis illustrates the moderating effect of IT spending on each of these relationships, thereby greatly contributes and extends the existing IT capability-agility related information systems literature.
Too Much to Lose, or More to Gain? Should Sweden Join the Euro?

This paper considers the costs and benefits of Sweden joining the European Economic and Monetary Union (EMU). We pay particular attention to the costs of abandoning the krona in terms of a loss of monetary policy independence. For this purpose, we apply a cointegrated VAR framework to examine the degree of monetary independence that the Sveriges Riksbank enjoys. Our results suggest that Sweden has in fact relatively little to lose from joining EMU, at least in terms of monetary independence. We complement our analysis by looking into other criteria affecting the cost-benefit calculus of monetary integration, which, by and large, support our positive assessment of Swedish EMU membership.
Individual Differences in Sentence Comprehension: A Functional Magnetic Resonance Imaging Investigation of Syntactic and Lexical Processing Demands

Language comprehension is neurally underpinned by a network of collaborating cortical processing centers; individual differences in comprehension must be related to some set of this network's properties. This study investigated the neural bases of individual differences during sentence comprehension by examining the network's response to two variations in processing demands: reading sentences containing words of high versus low lexical frequency and having simpler versus more complex syntax. In a functional magnetic resonance imaging study, readers who were independently identified as having high or low working memory capacity for language exhibited three differentiating properties of their language network, namely, neural efficiency, adaptability, and synchronization. First, greater efficiency (defined as a reduction in activation associated with improved performance) was manifested as less activation in the bilateral middle frontal and right lingual gyri in high-capacity readers. Second, increased adaptability was indexed by larger lexical frequency effects in high-capacity readers across bilateral middle frontal, bilateral inferior occipital, and right temporal regions. Third, greater synchronization was observed in high-capacity readers between left temporal and left inferior frontal, left parietal, and right occipital regions. Synchronization interacted with adaptability, such that functional connectivity remained constant or increased with increasing lexical and syntactic demands in high-capacity readers, whereas low-capacity readers either showed no reliable differentiation or a decrease in functional connectivity with increasing demands. These results are among the first to relate multiple cortical network properties to individual differences in reading capacity and suggest a more general framework for understanding the relation between neural function and individual differences in cognitive performance.
Localizing Objects in 3D from Egocentric Videos with Visual Queries

With the recent advances in video and 3D understanding, novel 4D spatio-temporal challenges fusing both concepts have emerged. Towards this direction, the Ego4D Episodic Memory Benchmark proposed a task for Visual Queries with 3D Localization (VQ3D). Given an egocentric video clip and an image crop depicting a query object, the goal is to localize the 3D position of the center of that query object with respect to the camera pose of a query frame. Current methods tackle the problem of VQ3D by lifting the 2D localization results of the sister task Visual Queries with 2D Localization (VQ2D) into a 3D reconstruction. Yet, we point out that the low number of Queries with Poses (QwP) from previous VQ3D methods severally hinders their overall success rate and highlights the need for further effort in 3D modeling to tackle the VQ3D task. In this work, we formalize a pipeline that better entangles 3D multiview geometry with 2D object retrieval from egocentric videos. We estimate more robust camera poses, leading to more successful object queries and substantially improved VQ3D performance. In practice, our method reaches a top-1 overall success rate of 86.36% on the Ego4D Episodic Memory Benchmark VQ3D, a 10 × improvement over the previous state-of-the-art. In addition, we provide a complete empirical study highlighting the remaining challenges in VQ3D.
Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data

A tacit assumption in linear regression is that (response, predictor)-pairs correspond to identical observational units. A series of recent works have studied scenarios in which this assumption is violated under terms such as “Unlabeled Sensing and “Regression with Unknown Permutation”. In this paper, we study the setup of multiple response variables and a notion of mismatches that generalizes permutations in order to allow for missing matches as well as for one-to-many matches. A two-stage method is proposed under the assumption that most pairs are correctly matched. In the first stage, the regression parameter is estimated by handling mismatches as contaminations, and subsequently the generalized permutation is estimated by a basic variant of matching. The approach is both computationally convenient and equipped with favorable statistical guarantees. Specifically, it is shown that the conditions for permutation recovery become considerably less stringent as the number of responses m per observation increase. Particularly, for m = Ω(log n), the required signal-to-noise ratio no longer depends on the sample size n. Numerical results on synthetic and real data are presented to support the main findings of our analysis.
Numerical simulations of the mantle lithosphere delamination

[1] Sudden uplift, extension, and increased igneous activity are often explained by rapid mechanical thinning of the lithospheric mantle. Two main thinning mechanisms have been proposed, convective removal of a thickened lithospheric root and delamination of the mantle lithosphere along the Moho. In the latter case, the whole mantle lithosphere peels away from the crust by the propagation of a localized shear zone and sinks into the mantle. To study this mechanism, we perform two-dimensional (2-D) numerical simulations of convection using a viscoplastic rheology with an effective viscosity depending strongly on temperature, depth, composition (crust/mantle), and stress. The simulations develop in four steps. (1) We first obtain “classical” sublithospheric convection for a long time period (∼300 Myr), yielding a slightly heterogeneous lithospheric temperature structure. (2) At some time, in some simulations, a strong thinning of the mantle occurs progressively in a small area (∼100 km wide). This process puts the asthenosphere in direct contact with the lower crust. (3) Large pieces of mantle lithosphere then quickly sink into the mantle by the horizontal propagation of a detachment level away from the “asthenospheric conduit” or by progressive erosion on the flanks of the delaminated area. (4) Delamination pauses or stops when the lithospheric mantle part detaches or when small-scale convection on the flanks of the delaminated area is counterbalanced by heat diffusion. We determine the parameters (crustal thicknesses, activation energies, and friction coefficients) leading to delamination initiation (step 2). We find that delamination initiates where the Moho temperature is the highest, as soon as the crust and mantle viscosities are sufficiently low. Delamination should occur on Earth when the Moho temperature exceeds ∼800°C. This condition can be reached by thermal relaxation in a thickened crust in orogenic setting or by corner flow lithospheric erosion in the overriding lithosphere of subduction zones.
Multisource remote sensing image fusion based on curvelet and wavelet transform

Aiming at limitations of existing multiresolution analysis (MRA) fusion methods, this paper proposes a new fusion method which combines curvelet and wavelet transform. Curvelet transform processes edges better than wavelet transform does. While wavelet transform handles smooth area better than curvelet transform does. As an image often includes more than one feature, the proposed method is conducted on the basis of region segmentation and use Àtrous wavelet transform (ATWT) to fuse smooth areas and fast discrete curvelet transform (FDCT) to fuse areas with edges. Furthermore, an optimal objective function defined based on a balance between spectral preservation and spatial resolution improvement is put forward to search optimal segmentation threshold. The optimal fusion result can be obtained by fusion processing through the optimal segmentation threshold. Landsat TM multispectral (MS) images and SPOT Panchromatic (Pan) image covering a region of Wuhan in Hubei province are tested to assess this proposed method. Visual evaluation and statistics analysis are employed to assess the quality of fused images of different methods. The proposed method demonstrates best results among methods being tested in this study. So by combining attributes of both transforms, it is possible to get better image fusion result than by using wavelet and curvelet individually.
Modal Bin Hybrid Model: A surface area consistent, triple‐moment sectional method for use in process‐oriented modeling of atmospheric aerosols

A triple‐moment sectional (TMS) aerosol dynamics model, Modal Bin Hybrid Model (MBHM), has been developed. In addition to number and mass (volume), surface area is predicted (and preserved), which is important for aerosol processes and properties such as gas‐to‐particle mass transfer, heterogeneous reaction, and light extinction cross section. The performance of MBHM was evaluated against double‐moment sectional (DMS) models with coarse (BIN4) to very fine (BIN256) size resolutions for simulating evolution of particles under simultaneously occurring nucleation, condensation, and coagulation processes (BINx resolution uses x sections to cover the 1 nm to 1 µm size range). Because MBHM gives a physically consistent form of the intrasectional distributions, errors and biases of MBHM at BIN4‐8 resolution were almost equivalent to those of DMS at BIN16–32 resolution for various important variables such as the moments Mk (k: 0, 2, 3), dMk/dt, and the number and volume of particles larger than a certain diameter. Another important feature of MBHM is that only a single bin is adequate to simulate full aerosol dynamics for particles whose size distribution can be approximated by a single lognormal mode. This flexibility is useful for process‐oriented (multicategory and/or mixing state) modeling: Primary aerosols whose size parameters would not differ substantially in time and space can be expressed by a single or a small number of modes, whereas secondary aerosols whose size changes drastically from 1 to several hundred nanometers can be expressed by a number of modes. Added dimensions can be applied to MBHM to represent mixing state or photochemical age for aerosol mixing state studies.
Estimating total horizontal aeolian flux within shrub‐invaded groundwater‐dependent meadows using empirical and mechanistic models

Wind erosion is a significant environmental problem that removes soil resources from sensitive ecosystems and contributes to air pollution. In regions of shallow groundwater, friable (puffy) soils are maintained through capillary action, surface evaporation of solute‐rich soil moisture, and protection from mobilization by groundwater‐dependent grasses and shrubs. When a reduction in vegetation cover occurs through any disturbance process, there is potential for aeolian transport and dust emission. We find that as mean gap size between vegetation elements scaled by vegetation height increases, total horizontal aeolian sediment flux increases and explains 58% of the variation in total horizontal aeolian sediment flux. We also test a probabilistic model of wind erosion based on gap size between vegetation elements scaled by vegetation height (the Okin model), which predicts measured total horizontal aeolian sediment flux more closely than another commonly used model based on the average plant area observed in profile (Raupach model). The threshold shear velocity of bare soil appears to increase as gap size between vegetation elements scaled by vegetation height increases, reflecting either surface armoring or reduced interaction between the groundwater capillary zone and surface sediments. This work advances understanding of the importance of measuring gap size between vegetation elements scaled by vegetation height for empirically estimating Q and for structuring process‐based models of desert wind erosion in groundwater‐dependent vegetation.
Suspended sediment load, bed load, and dissolved load yields from a semiarid drainage basin: A 15‐year study

A 15‐year record for the semiarid upland Eshtemoa catchment provides a basis for estimating the efflux of water, sediment, and solutes in a type of environment for which there is a dearth of information. Average annual runoff was 5.4 mm, giving a runoff coefficient of 2.1%. Average annual yields were 275 Mg km−2 for suspended sediment, 15.3 Mg km−2 for bed load, and 0.6 Mg km−2 for dissolved load, though distinct seasonal differences are identified and related to the prevalence of cellular, convectively enhanced storms in autumn and spring and frontal storms in winter. The majority of the average annual total sediment and solute yield of 291 Mg km−2 is generated by as few as 8 of the 74 flow events of the 15‐year record. The relative importance of bed load and the consequent ranking of the load components as suspended load ≫ bed load ≫ dissolved load distinguishes this dryland environment from its humid counterparts.
Electroencephalographic complexity markers explain neuropsychological test scores in Alzheimer's disease

We investigated the correlation of Alzheimer's disease (AD) severity as measured by the Mini-Mental State Examination (MMSE) to the signal complexity measures auto-mutual information, Shannon entropy and Tsallis entropy in 79 patients with probable AD from the multi-centric Prospective Dementia Database Austria (PRODEM). Using quadratic (linear) regressions, auto-mutual information explained up to 48% (43%), Shannon entropy up to 48% (37%) and Tsallis entropy up to 49% (35%) of the variations in MMSE scores, all at left temporal (T7) electrode site. The steepest slope of the linear regression was found for auto-mutual information (Δy/Δx = 36). For Shannon and Tsallis entropy, slopes were less steep. Comparing to traditional slowing measures, complexity measures yielded higher coefficients of determination. We conclude that auto-mutual information is well suited to characterize disease severity in mild to moderate AD.
Ensemble-based Offline-to-Online Reinforcement Learning: From Pessimistic Learning to Optimistic Exploration

Offline reinforcement learning (RL) is a learning paradigm where an agent learns from a fixed dataset of experience. However, learning solely from a static dataset can limit the performance due to the lack of exploration. To overcome it, offline-to-online RL combines offline pre-training with online fine-tuning, which enables the agent to further refine its policy by interacting with the environment in real-time. Despite its benefits, existing offline-to-online RL methods suffer from performance degradation and slow improvement during the online phase. To tackle these challenges, we propose a novel framework called E nsemble-based O ffline-to-O nline (E2O) RL. By increasing the number of Q-networks, we seamlessly bridge offline pre-training and online fine-tuning without degrading performance. Moreover, to expedite online performance enhancement, we appropriately loosen the pessimism of Q-value estimation and incorporate ensemble-based exploration mechanisms into our framework. Experimental results demonstrate that E2O can substantially improve the training stability, learning efficiency, and final performance of existing offline RL methods during online fine-tuning on a range of locomotion and navigation tasks, significantly outperforming existing offline-to-online RL methods.
Mean magnitude variations of earthquakes as a function of depth: Different crustal stress distribution depending on tectonic setting

The mean magnitude of earthquakes in the Gulf of Corinth is found to increase strongly with depth (b‐value decreases), whereas the dip of fault planes decreases. The b‐value difference of 0.25, between shallow and deep earthquake distributions, is based on about 7,000 events and therefore is statistically highly significant. The same is true in California, but opposite patterns are observed in southern Iceland and in western Nagano, Japan. Because large mean magnitudes (low b‐values) are indicative of relatively high stress levels, we propose that in the detachment layer at about 9 ± 2 km depth, earthquakes are generated at higher stresses than in the shallower parts of the crust. The correlation of low b‐values with low faulting dips can be taken as line of evidence that low b‐values map high stress regimes.
Elucidating the Altered Transcriptional Programs in Breast Cancer using Independent Component Analysis

The quantity of mRNA transcripts in a cell is determined by a complex interplay of cooperative and counteracting biological processes. Independent Component Analysis (ICA) is one of a few number of unsupervised algorithms that have been applied to microarray gene expression data in an attempt to understand phenotype differences in terms of changes in the activation/inhibition patterns of biological pathways. While the ICA model has been shown to outperform other linear representations of the data such as Principal Components Analysis (PCA), a validation using explicit pathway and regulatory element information has not yet been performed. We apply a range of popular ICA algorithms to six of the largest microarray cancer datasets and use pathway-knowledge and regulatory-element databases for validation. We show that ICA outperforms PCA and clustering-based methods in that ICA components map closer to known cancer-related pathways, regulatory modules, and cancer phenotypes. Furthermore, we identify cancer signalling and oncogenic pathways and regulatory modules that play a prominent role in breast cancer and relate the differential activation patterns of these to breast cancer phenotypes. Importantly, we find novel associations linking immune response and epithelial–mesenchymal transition pathways with estrogen receptor status and histological grade, respectively. In addition, we find associations linking the activity levels of biological pathways and transcription factors (NF1 and NFAT) with clinical outcome in breast cancer. ICA provides a framework for a more biologically relevant interpretation of genomewide transcriptomic data. Adopting ICA as the analysis tool of choice will help understand the phenotype–pathway relationship and thus help elucidate the molecular taxonomy of heterogeneous cancers and of other complex genetic diseases.
PX-NET: Simple, Efficient Pixel-Wise Training of Photometric Stereo Networks

Retrieving accurate 3D reconstructions of objects from the way they reflect light is a very challenging task in computer vision. Despite more than four decades since the definition of the Photometric Stereo problem, most of the literature has had limited success when global illumination effects such as cast shadows, self-reflections and ambient light come into play, especially for specular surfaces. Recent approaches have leveraged the power of deep learning in conjunction with computer graphics in order to cope with the need of a vast number of training data in order to invert the image irradiance equation and retrieve the geometry of the object. However, rendering global illumination effects is a slow process which can limit the amount of training data that can be generated. In this work we propose a novel pixel-wise training procedure for normal prediction by replacing the training data of globally rendered images with independent per-pixel renderings. We show that robustness to global physical effects can be achieved via data-augmentation which greatly simplifies and speeds up the data creation procedure. Our network, PX-NET, achieves the state-of-the-art performance on synthetic datasets, as well as the Diligent real dataset.
Can the Accuracy of Sea Surface Salinity Measurement be Improved by Incorporating Spaceborne GNSS-Reflectometry?

Sea surface salinity (SSS) can be measured by L-band (1.4 GHz) radiometry. However, the L-band brightness temperature is sensitive to ocean surface roughness, so that the precise knowledge of sea state can help to improve the accuracy of SSS retrievals. Cyclone Global Navigation Satellite System (CyGNSS) mission measures tropical ocean wind speeds, which can provide further knowledge about sea state. This letter, by investigating the sensitivity of brightness temperature derived from two L-band radiometry satellite missions [i.e., Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP)] to CyGNSS data, first explores the potential of using spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) to improve the accuracy of SSS measurements. The statistical results from two-year CyGNSS data show that the systematic uncertainties of the brightness temperature up to 0.5 K in the SMOS/SMAP can be minimized at low wind speed, which proves the concept that CyGNSS wind speed data can be used to improve the accuracy of SSS retrievals across the oceans. The findings strongly suggest that the spaceborne GNSS-R instruments could be utilized as cost-effective hosted payloads in designing future ocean remote sensing missions.