Miracle-dz/UniFAR-FTUs huggingface dataset:
The Cost of Misspecifying Price Impact

Portfolio managers' orders trade off return and trading cost predictions. Return predictions rely on alpha models, whereas price impact models quantify trading costs. This paper studies what happens when trades are based on an incorrect price impact model, so that the portfolio either over- or under-trades its alpha signal. We derive tractable formulas for these misspecification costs and illustrate them on proprietary trading data. The misspecification costs are naturally asymmetric: underestimating impact concavity or impact decay shrinks profits, but overestimating concavity or impact decay can even turn profits into losses.
Modes of the Sakai-Sugimoto soliton

The instanton in the Sakai-Sugimoto model corresponds to the Skyrmion on the holographic boundary - which is asymptotically flat - and is fundamentally different from the flat Minkowski space Yang-Mills instanton. We use the Atiyah-Patodi-Singer index theorem and a series of transformations to show that there are 6k zeromodes - or moduli - in the limit of infinite 't Hooft coupling of the Sakai-Sugimoto model. The implications for the low-energy baryons - the Skyrmions - on the holographic boundary, is a scale separation between 2k"heavy"massive modes and 6k-9"light"massive modes for k>1; the 9 global transformations that correspond to translations, rotations and isorotations remain as zeromodes. For k=1 there are 2"heavy"modes and 6 zeromodes due to degeneracy between rotations and isorotations.
Building Rearticulable Models for Arbitrary 3D Objects from 4D Point Clouds

We build rearticulable models for arbitrary everyday man-made objects containing an arbitrary number of parts that are connected together in arbitrary ways via 1 degree-of-freedom joints. Given point cloud videos of such everyday objects, our method identifies the distinct object parts, what parts are connected to what other parts, and the properties of the joints connecting each part pair. We do this by jointly optimizing the part segmentation, transformation, and kinematics using a novel energy minimization frame-work. Our inferred animatable models, enables retargeting to novel poses with sparse point correspondences guidance. We test our method on a new articulating robot dataset, and the Sapiens dataset with common daily objects. Experiments show that our method outperforms two leading prior works on various metrics.
Transfer Learning for Underrepresented Music Generation

This paper investigates a combinational creativity approach to transfer learning to improve the performance of deep neural network-based models for music generation on out-of-distribution (OOD) genres. We identify Iranian folk music as an example of such an OOD genre for MusicVAE, a large generative music model. We find that a combinational creativity transfer learning approach can efficiently adapt MusicVAE to an Iranian folk music dataset, indicating potential for generating underrepresented music genres in the future.
SafeDiffuser: Safe Planning with Diffusion Probabilistic Models

Diffusion model-based approaches have shown promise in data-driven planning, but there are no safety guarantees, thus making it hard to be applied for safety-critical applications. To address these challenges, we propose a new method, called SafeDiffuser, to ensure diffusion probabilistic models satisfy specifications by using a class of control barrier functions. The key idea of our approach is to embed the proposed finite-time diffusion invariance into the denoising diffusion procedure, which enables trustworthy diffusion data generation. Moreover, we demonstrate that our finite-time diffusion invariance method through generative models not only maintains generalization performance but also creates robustness in safe data generation. We test our method on a series of safe planning tasks, including maze path generation, legged robot locomotion, and 3D space manipulation, with results showing the advantages of robustness and guarantees over vanilla diffusion models.
Autism Disease Detection Using Transfer Learning Techniques: Performance Comparison Between Central Processing Unit vs Graphics Processing Unit Functions for Neural Networks

Neural network approaches are machine learning methods that are widely used in various domains, such as healthcare and cybersecurity. Neural networks are especially renowned for their ability to deal with image datasets. During the training process with images, various fundamental mathematical operations are performed in the neural network. These operations include several algebraic and mathematical functions, such as derivatives, convolutions, and matrix inversions and transpositions. Such operations demand higher processing power than what is typically required for regular computer usage. Since CPUs are built with serial processing, they are not appropriate for handling large image datasets. On the other hand, GPUs have parallel processing capabilities and can provide higher speed. This paper utilizes advanced neural network techniques, such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models, to compare CPU and GPU resources. We implemented a system for classifying Autism disease using face images of autistic and non-autistic children to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It was observed that GPU outperformed CPU in all tests conducted. Moreover, the performance of the neural network models in terms of accuracy increased on GPU compared to CPU.
Initial Guessing Bias: How Untrained Networks Favor Some Classes

Understanding and controlling biasing effects in neural networks is crucial for ensuring accurate and fair model performance. In the context of classification problems, we provide a theoretical analysis demonstrating that the structure of a deep neural network (DNN) can condition the model to assign all predictions to the same class, even before the beginning of training, and in the absence of explicit biases. We prove that, besides dataset properties, the presence of this phenomenon, which we call \textit{Initial Guessing Bias} (IGB), is influenced by model choices including dataset preprocessing methods, and architectural decisions, such as activation functions, max-pooling layers, and network depth. Our analysis of IGB provides information for architecture selection and model initialization. We also highlight theoretical consequences, such as the breakdown of node-permutation symmetry, the violation of self-averaging and the non-trivial effects that depth has on the phenomenon.
DCREATE: Stata module to create efficient designs for discrete choice experiments

dcreate creates efficient designs for discrete choice experiments using the modified Fedorov algorithm (Cook and Nachtsheim, 1980; Zwerina et al., 1996; Carlsson and Martinsson, 2003). The algorithm maximises the D-efficiency of the design based on the covariance matrix of the conditional logit model.
NeuroGF: A Neural Representation for Fast Geodesic Distance and Path Queries

Geodesics are essential in many geometry processing applications. However, traditional algorithms for computing geodesic distances and paths on 3D mesh models are often inefficient and slow. This makes them impractical for scenarios that require extensive querying of arbitrary point-to-point geodesics. Although neural implicit representations have emerged as a popular way of representing 3D shape geometries, there is still no research on representing geodesics with deep implicit functions. To bridge this gap, this paper presents the first attempt to represent geodesics on 3D mesh models using neural implicit functions. Specifically, we introduce neural geodesic fields (NeuroGFs), which are learned to represent the all-pairs geodesics of a given mesh. By using NeuroGFs, we can efficiently and accurately answer queries of arbitrary point-to-point geodesic distances and paths, overcoming the limitations of traditional algorithms. Evaluations on common 3D models show that NeuroGFs exhibit exceptional performance in solving the single-source all-destination (SSAD) and point-to-point geodesics, and achieve high accuracy consistently. Besides, NeuroGFs also offer the unique advantage of encoding both 3D geometry and geodesics in a unified representation. Moreover, we further extend generalizable learning frameworks of NeuroGFs by adding shape feature encoders, which also show satisfactory performances for unseen shapes and categories. Code is made available at https://github.com/keeganhk/NeuroGF/tree/master.
Phonon-mediated superconductivity in ternary silicides $X_4$CoSi ($X=$ Nb, Ta)

The superconducting properties of two recently synthesized ternary silicides with unit formula $X_4$CoSi ($X=$ Nb, Ta) are investigated through \textit{ab initio} calculations combined with Eliashberg theory. Interestingly, their crystal structure comprises interlocking honeycomb networks of Nb/Ta atoms. Nb$_4$CoSi is found to harbor better conditions for phonon-mediated superconductivity, as it possesses a higher density of states at the Fermi level, fostering stronger electron-phonon coupling. The superconducting critical temperatures ($T_c$) follow the same trend, with Nb$_4$CoSi having a twice higher value than Ta$_4$CoSi. Furthermore, the calculated $T_c$ values (6.0 K vs. 3.2 K) agree excellently with the experimentally obtained ones, establishing superconductivity in this new materials class as mediated by the electron-phonon coupling. Furthermore, our calculations show that the superconducting properties of these compounds do not simply correlate with the parameters of their honeycomb networks, contrary to proposals raised in the literature. Rather, their complete fermiology and phonon spectrum should be taken into account in order to explain their respective superconducting properties.
How Do ConvNets Understand Image Intensity?

Convolutional Neural Networks (ConvNets) usually rely on edge/shape information to classify images. Visualization methods developed over the last decade confirm that ConvNets rely on edge information. We investigate situations where the ConvNet needs to rely on image intensity in addition to shape. We show that the ConvNet relies on image intensity information using visualization.
Synthesis and thermoelectric properties of CezFe4−xCoxSb12 skutterudites

P-type Ce-filled skutterudites CezFe4−xCoxSb12 (z = 0.3–1, x = 1–3) were prepared, and their thermoelectric properties were examined at temperatures between 500 K and 800 K. Specimens were synthesized by encapsulated melting at 1323 K for 10 h and were hot pressed after annealing at 873 K for 24 h. Stable skutterudite phases could be obtained by the incorporation of Ce filling and charge compensation with Co for Fe. The electrical conductivity decreased slightly with increasing temperature and Co content. The Seebeck coefficient had positive values (p-type conductivity), increased with increasing temperature until a specific temperature, and then decreased thereafter. The thermal conductivity decreased through the further lattice scattering induced by substituting Co at Fe sites and by Ce filling. The thermoelectric performance of Fe-rich skutterudites was superior to that of Co-rich skutterudites. A dimensionless figure of merit of ZTmax = 0.7 was achieved at 823 K for CeFe4Sb12.
Investigation into mechanism of coalescence in vortex field

Based on the analysis of multiphase flow in vortex field,including the analysis of forces exerted on(droplets) and the movement of droplets,the mechanism of vortex field coalescence was investigated.The existence of coalescence in cyclonic flow field was verified with tests in laboratory.The optimum feed flow rate at peak(coalescence) effect was obtained.
Synthesis and in vivo evaluation of three fluid spray dried hybrid ciprofloxacin microparticles in Sprague Dawley rats

Abstract The aim of this study is to prepare and characterise mucoadhesive silica-coated silver nanoparticles loaded with ciprofloxacin (S-AgNPs-CSCFX), and investigate serum biochemical, haematological, and histopathological effects in Sprague Dawley rats upon oral administration. S-AgNPs-CSCFX microparticles were prepared using three fluid nozzle spray drying and characterised by scanning electron microscopy (SEM), X-ray dispersive spectrometry (EDX), transmission electron microscopy (TEM), Fourier transform infrared (FTIR), zeta potential and particles size measurements and X-ray powder diffraction (XRPD). Adult male Sprague Dawley rats were randomly divided between six-treated groups, including blank S-AgNPs and S-AgNPs-CSCFX (LD: Low dose; MD: Median Dose; HD: High Dose) and control group. Each group was treated daily to evaluate the effect of the prepared particles on the lipid profile, serum biochemical, hormonal level, haemogram, and vital organ histopathology. The results showed successful encapsulation of silver nanoparticles which resulted in spherical-shaped S-AgNPs-CSCFX with an average size of 1–5 μm and surface charge of 25.2 ± 5.52 mv. The in-vivo results showed that different doses of blank S-AgNPs and S-AgNPs-CSCFX had no significant toxic effects on the physiological, biochemical, and haematological parameters. There were no marked histopathological alterations in the vital organs of the treated rats with blank and loaded particles.
A multidisciplinary approach to estimating wolf population size for long‐term conservation

The wolf (Canis lupus) is among the most controversial of wildlife species. Abundance estimates are required to inform public debate and policy decisions, but obtaining them at biologically relevant scales is challenging. We developed a system for comprehensive population estimation across the Italian alpine region (100,000 km2), involving 1513 trained operators representing 160 institutions. This extensive network allowed for coordinated genetic sample collection and landscape‐level spatial capture–recapture analyses that transcended administrative boundaries to produce the first estimates of key parameters for wolf population status assessment. Wolf abundance was estimated at 952 individuals (95% credible interval 816–1120) and 135 reproductive units (i.e., packs) (95% credible interval 112–165). We also estimated that mature individuals accounted for 33–45% of the entire population. The monitoring effort was spatially estimated thereby overcoming an important limitation of citizen science data. This is an important approach for promoting wolf–human coexistence based on wolf abundance monitoring and an endorsement of large‐scale harmonized conservation practices.
Neurofilament light chain levels in cerebrospinal fluid as a sensitive biomarker for cerebral adrenoleukodystrophy

Adrenoleukodystrophy (ALD) has a poor prognosis when it progresses to the cerebral form (CALD). The aim of this study is to investigate whether cerebrospinal fluid (CSF) neurofilament light chain (cNfL) is a sensitive biomarker for detecting CALD and assessing response to hematopoietic stem cell transplantation (HSCT).
GABA treatment does not induce neogenesis of new endocrine cells from pancreatic ductal cells

ABSTRACT Previous studies indicated that ductal cells can contribute to endocrine neogenesis in adult rodents after alpha cells convert into beta cells. This can occur through Pax4 mis-expression in alpha cells or through long-term administration of gamma-aminobutyric acid (GABA) to healthy mice. GABA has also been reported to increase the number of beta cells through direct effects on their proliferation, but only in specific genetic mouse backgrounds. To test whether GABA induces neogenesis of beta cells from ductal cells or affects pancreatic cell proliferation, we administered GABA or saline over 2 or 6 months to Sox9CreER;R26RYFP mice in which 60–80% of large or small ducts were efficiently lineage labeled. We did not observe any increases in islet neogenesis from ductal cells between 1 and 2 months of age in saline treated mice, nor between 2 and 6 months of saline treatment, supporting previous studies indicating that adult ductal cells do not give rise to new endocrine cells during homeostasis. Unlike previous reports, we did not observe an increase in beta cell neogenesis after 2 or 6 months of GABA administration. Nor did we observe a significant increase in the pancreatic islet area, the number of insulin and glucagon double positive cells, or cell proliferation in the pancreas. This indicates that the effect of long term GABA administration on the pancreas is minimal or highly context dependent. GRAPHICAL ABSTRACT
Femtosecond laser irradiation as a novel method for nanosheet growth and defect generation in g-C3N4

Among the many recently developed photo-catalytic materials, graphitic carbon nitride (g-C3N4) shows great promise as a catalytic material for water splitting, hydrogen generation, and related catalytic applications. Herein, synthesized bulk g-C3N4 is simply irradiated under a 35 fs pulse at mixed photon energies (800 nm and its second harmonic). g-C3N4 was synthesized from melamine following a facile thermal polymerization procedure. The prepared material was introduced, in an aqueous environment, to the femtosecond laser for various lengths of time. The treated material demonstrates a significant increase in surface area, relative to the untreated samples, indicating that irradiation is a successful method for exfoliation. The subsequent characterization reveals that the mixed irradiation process drives significant defect generation and sheet growth, which is not seen under 800 nm irradiation. Extended mixed irradiation results in 4 nm thick nanosheets with lateral dimensions 4× that of the bulk material. The treated material shows improved dye absorption/removal. This novel method of defect generation and nanosheet growth shows great potential as a g-C3N4 pre-treatment method for co-catalytic applications. Herein it is shown that femtosecond laser irradiation drives exfoliation beyond 100 nm particle sizes, and sheet-like morphologies under extended irradiation, which must be taken into account when using this method to improve material performance.
Association of dietary total antioxidant capacity with all-cause and cardiovascular mortality in patients with chronic kidney disease: based on two retrospective cohort studies of NHANES

Abstract Background The relationship between dietary total antioxidant capacity (DTAC) and death risk among CKD populations remains unclear. Methods Based on vitamin C equivalent antioxidant capacity (VCEAC) and the component dietary antioxidant index (CDAI) indices, we analyzed two cohorts to investigate the association of DTAC with all-cause and CVD mortality in CKD patients using data from National Health and Nutrition Examination Survey (2007–2018). VCEAC (n = 6330) and CDAI (n = 6300) cohorts with mortality follow-up data available through 2018 were included. Cox models with restricted cubic splines was used to model the nonlinear association between VCEAC/CDAI and outcomes in CKD patients. Results Our results showed L-shaped associations of DTAC with all-cause mortality among individuals with CKD stages 1–2 in both cohorts. Compared to the lowest quartile, higher dietary total antioxidant intake was associated with lower all-cause mortality risks among CKD stages 1–2 after adjustment for covariates, with HRs (95%CI) of 1.00, 0.91 (0.71,1.17), 0.69 (0.53,0.90), and 0.70 (0.54,0.91) in VCEAC, and similar respective estimate trends in CDAI. After sensitivity and subgroup analyses, there were no benefits for patients with stage 3–5 CKD or albuminuria. Mediation analysis revealed that the proportions mediated in both cohorts were less consistent. Conclusions Moderate dietary total antioxidants intake has potential benefits for early-stage CKD patients. However, further evidence is needed to confirm whether patients with worsening CKD can benefit in the long term.
Technical Notes and Clinical Outcomes of Full‐Endoscopic Interbody Fusion Via Transforaminal Approach for Hard Disc Herniations in Thoracolumbar Junction

Obtaining sufficient decompression and solid fusion and avoiding approach‐related injuries simultaneously are still challenging for the treatment of hard disc herniation in thoracolumbar junction. A combined full‐endoscopic decompression and interbody fusion via a transforaminal approach was used to achieve this goal. The purpose of this study was to introduce the technical notes and clinical outcomes of this novel technique.
Theoretical study to gain fundamental insight into reaction mechanism of N–S acyl transfer of N‐sulfanylethylanilide‐based protein labeling reagent on protein surface

Elucidation of protein function is one of the central issues in the field of life sciences. To study the function of proteins not in isolation, but in a cell or its lysate, thus, it is necessary to selectively label the target protein in a mixture. Affinity labeling is one of several widely used methods for selective labeling; however, this method has the disadvantage that the labeling reagent is always activated, albeit weakly. Therefore, fine‐tuning of the reactivity and/or reaction conditions is generally required for successful target‐selective labeling. We previously developed a new affinity labeling reagent with N‐sulfanylethylanilide (SEAlide) as a key reactive unit. It was designed based on the following hypotheses. SEAlide is less reactive and does not label in the absence of a target protein. Upon target binding, amino acid side‐chain functional groups on the target surface convert SEAlide into a thioester form via N–S acyl transfer, allowing the target to be labeled. However, no evidence has been obtained so far to directly prove the hypothesis. In this study, we examine whether amino acid side‐chain functional groups can activate SEAlide from the viewpoint of theoretical chemistry. The theoretical studies show that the activation free energy and enthalpy of the acyl transfer of SEAlide are reduced in the presence of methylammonium, which is a model for the protonated side chain of Lys, and acetate, which is a model for the deprotonated side chain of Asp/Glu. It suggests that Lys and Asp/Glu side chains could potentially stabilize the activation transition states to accelerate the thioester formation. Furthermore, the significant decrease in the activation enthalpy indicates that the contribution of entropy to the transition state is large. This result supports the original hypothesis that the SEAlide‐based labeling reagent is efficiently activated by binding to the target protein.
On the performance of state-dependent single server queues

New asymptotic methods for the analysis of queueing systems are introduced and applied to state-dependent $M/G/1$ queues. The methods are used to compute approximations to the stationary density of the queue length, the mean length of a busy period, the mean number of customers served during a busy period as well as other quantities of interest. We obtain results that are superior to those obtained from diffusion approximations in that they are uniformly valid for all values of the traffic intensity while diffusion approximations are adequate only when this quantity is close to one. When specialized to state-independent queues, our approximations are shown to agree with the asymptotic expansions of known exact results. Finally, we show that the behavior of the state-dependent systems is markedly different from that of the corresponding state-independent systems.