Miracle-dz/UniFAR-FTUs huggingface dataset:
Hierarchical and Contrastive Representation Learning for Knowledge-Aware Recommendation

Incorporating knowledge graph into recommendation is an effective way to alleviate data sparsity. Most existing knowledge-aware methods usually perform recursive embedding propagation by enumerating graph neighbors. However, the number of nodes’ neighbors grows exponentially as the hop number increases, forcing the nodes to be aware of vast neighbors under this recursive propagation for distilling the high-order semantic relatedness. This may induce more harmful noise than useful information into recommendation, leading the learned node representations to be indistinguishable from each other, that is, the well-known over-smoothing issue. To relieve this issue, we propose a Hierarchical and CONtrastive representation learning framework for knowledge-aware recommendation named HiCON. Specifically, for avoiding the exponential expansion of neighbors, we propose a hierarchical message aggregation mechanism to interact separately with low-order neighbors and meta-path-constrained high-order neighbors. Moreover, we also perform cross-order contrastive learning to enforce the representations to be more discriminative. Extensive experiments on three datasets show the remarkable superiority of HiCON over state-of-the-art approaches. The code is available now 1.
Weighted extremal metrics on blowups

We show that if a compact K\"ahler manifold admits a weighted extremal metric for the action of a torus, so too does its blowup at a relatively stable point that is fixed by both the torus action and the extremal field. This generalises previous results on extremal metrics by Arezzo--Pacard--Singer and Sz\'ekelyhidi to many other canonical metrics, including extremal Sasaki metrics, deformations of K\"ahler--Ricci solitons and $\mu$-cscK metrics. In a sequel to this paper, we use this result to study the weighted K-stability of weighted extremal manifolds.
Microwave Shielding of Bosonic NaRb Molecules

Recent years have witnessed tremendous progresses in creating and manipulating ground-state ultracold polar molecules. However, the two-body loss regardless of the chemical reactivities is still a hurdle for many future explorations. Here, we investigate the loss suppression of non-reactive bosonic $^{23}$Na$^{87}$Rb molecules with a circular polarized microwave blue-detuned to the rotational transition. We achieve suppression of the loss by two orders of magnitude with the lowest two-body loss rate coefficient reduced to $3\times10^{-12}~\rm{cm^3/s}$. Meanwhile, the elastic collision rate coefficient is increased to the $10^{-8}~\rm{cm^3/s}$ level. The large good-to-bad collision ratio has allowed us to carry out evaporative cooling of $^{23}$Na$^{87}$Rb with an efficiency of 1.7(2), increasing the phase-space density by a factor of 10. With further improvements, this technique holds great promises for creating a Bose-Einstein condensate of ultracold polar molecules.
Neural Machine Translation For Low Resource Languages

Neural Machine translation is a challenging task due to the inherent complex nature and the fluidity that natural languages bring. Nonetheless, in recent years, it has achieved state-of-the-art performance in several language pairs. Although, a lot of traction can be seen in the areas of multilingual neural machine translation (MNMT) in the recent years, there are no comprehensive survey done to identify what approaches work well. The goal of this paper is to investigate the realm of low resource languages and build a Neural Machine Translation model to achieve state-of-the-art results. The paper looks to build upon the mBART language model and explore strategies to augment it with various NLP and Deep Learning techniques like back translation and transfer learning. This implementation tries to unpack the architecture of the NMT application and determine the different components which offers us opportunities to amend the said application within the purview of the low resource languages problem space.
ESTIMATING NONLINEAR TIME-SERIES MODELS USING

SUMMARY This paper develops two new methods for conducting formal statistical inference in nonlinear dynamic economic models. The two methods require very little analytical tractability, relying instead on numerical simulation of the model's dynamic behaviour. Although one of the estimators is asymptotically more efficient than the other, a Monte Carlo study shows that, for a specific application, the less efficient estimator has smaller mean squared error in samples of the size typically encountered in macroeconomics. The estimator with superior small sample performance is used to estimate the parameters of a real business cycle model using observed US time-series data.
ABSORBING BOUNDARY CONDITION WITH CAP- TURED MATCHED LAYER

Inaeroacousticscomputations, animportanttreatmentforabsorbingboundarycondition(ABC) is to create a matched layer at the boundary, as in the well-known PML (perfectly matched layer) method. In the present paper, it is shown that with cell-centered finite volume (FV) schemes, it is possible to capture the matched layer directly as a discontinuity across the boundary, rendering an extremely simple yet robust ABC. The new ABC turns out to have no direct relation to any existing nonreflecting boundary condition (NRBC), but should be attributed to the shock-capturing capability of the FV scheme, as well as a nonreflecting (NR) observation that for any scheme, no spurious reflection is generated at any interior point of the domain. The NR observation is justified and the ABC is consequently established via a Fourier analysis with plane waves to the local Euler solution. The ABC performs perfectly with zero reflection in 1-D space. In multi-dimensional spaces, the phase error and reflection due to discretization are discussed. With appropriate boundary grid resolution, one can always suppress spurious reflection to a designated level. Several nontrivial numerical examples in one and multi-dimensional spaces are tested to demonstrate its robustness.
Understanding Cosmological Measurements with a large number of mock galaxy catalogues.

Abstract Mock galaxy catalogues are essential to the error analysis of cosmological measurements from big galaxy surveys covering thousands of square degrees in the sky, like BOSS, WiggleZ, DES, or Euclid. The PTHalos mock galaxy catalogues were used in the BOSS survey to analyse the BAO measurement from the CMASS (z∼ 0.57) and LOWZ (z∼ 0.32) galaxy samples, which provided the best estimate to date of the cosmic distance scale from galaxy surveys at these redshifts. We review the PTHalos mocks galaxy catalogues and their key contributions to these analyses.
Metric-agnostic Ranking Optimization

Ranking is at the core of Information Retrieval. Classic ranking optimization studies often treat ranking as a sorting problem with the assumption that the best performance of ranking would be achieved if we rank items according to their individual utility. Accordingly, considerable ranking metrics have been developed and learning-to-rank algorithms that have been designed to optimize these simple performance metrics have been widely used in modern IR systems. As applications evolve, however, people's need for information retrieval have shifted from simply retrieving relevant documents to more advanced information services that satisfy their complex working and entertainment needs. Thus, more complicated and user-centric objectives such as user satisfaction and engagement have been adopted to evaluate modern IR systems today. Those objectives, unfortunately, are difficult to be optimized under existing learning-to-rank frameworks as they are subject to great variance and complicated structures that cannot be explicitly explained or formulated with math equations like those simple performance metrics. This leads to the following research question -- how to optimize result ranking for complex ranking metrics without knowing their internal structures? To address this question, we conduct formal analysis on the limitation of existing ranking optimization techniques and describe three research tasks in Metric-agnostic Ranking Optimization: (1) develop surrogate metric models to simulate complex online ranking metrics on offline data; (2) develop differentiable ranking optimization frameworks for list or session level performance metrics without fine-grained supervision signals; and (3) develop efficient parameter exploration and exploitation techniques for ranking optimization in metric-agnostic scenarios. Through the discussion of potential solutions to these tasks, we hope to encourage more people to look into the problem of ranking optimization in complex search and recommendation scenarios.
Code-centric Learning-based Just-In-Time Vulnerability Detection

Attacks against computer systems exploiting software vulnerabilities can cause substantial damage to the cyber-infrastructure of our modern society and economy. To minimize the consequences, it is vital to detect and fix vulnerabilities as soon as possible. Just-in-time vulnerability detection (JIT-VD) discovers vulnerability-prone ("dangerous") commits to prevent them from being merged into source code and causing vulnerabilities. By JIT-VD, the commits' authors, who understand the commits properly, can review these dangerous commits and fix them if necessary while the relevant modifications are still fresh in their minds. In this paper, we propose CodeJIT, a novel code-centric learning-based approach for just-in-time vulnerability detection. The key idea of CodeJIT is that the meaning of the code changes of a commit is the direct and deciding factor for determining if the commit is dangerous for the code. Based on that idea, we design a novel graph-based representation to represent the semantics of code changes in terms of both code structures and program dependencies. A graph neural network model is developed to capture the meaning of the code changes represented by our graph-based representation and learn to discriminate between dangerous and safe commits. We conducted experiments to evaluate the JIT-VD performance of CodeJIT on a dataset of 20K+ dangerous and safe commits in 506 real-world projects from 1998 to 2022. Our results show that CodeJIT significantly improves the state-of-the-art JIT-VD methods by up to 66% in Recall, 136% in Precision, and 68% in F1. Moreover, CodeJIT correctly classifies nearly 9/10 of dangerous/safe (benign) commits and even detects 69 commits that fix a vulnerability yet produce other issues in source code
Photon Field Networks for Dynamic Real-Time Volumetric Global Illumination

Volume data is commonly found in many scientific disciplines, like medicine, physics, and biology. Experts rely on robust scientific visualization techniques to extract valuable insights from the data. Recent years have shown path tracing to be the preferred approach for volumetric rendering, given its high levels of realism. However, real-time volumetric path tracing often suffers from stochastic noise and long convergence times, limiting interactive exploration. In this paper, we present a novel method to enable real-time global illumination for volume data visualization. We develop Photon Field Networks—a phase-function-aware, multi-light neural representation of indirect volumetric global illumination. The fields are trained on multi-phase photon caches that we compute a priori. Training can be done within seconds, after which the fields can be used in various rendering tasks. To showcase their potential, we develop a custom neural path tracer, with which our photon fields achieve interactive framerates even on large datasets. We conduct in-depth evaluations of the method's performance, including visual quality, stochastic noise, inference and rendering speeds, and accuracy regarding illumination and phase function awareness. Results are compared to ray marching, path tracing and photon mapping. Our findings show that Photon Field Networks can faithfully represent indirect global illumination within the boundaries of the trained phase spectrum while exhibiting less stochastic noise and rendering at a significantly faster rate than traditional methods.
Statistical Analysis of Qualitative Variation

Many variables of interest in the social sciences are measurable only at the nominal level. That is, they represent types of phenomena such as race, ethnicity, religious affiliation, or political party preference. It is sometimes of interest to measure the amount of variation, or heterogeneity, within a population with respect to one or more of these variables. By a measure of variation for a qualitative variable, we mean a description of the dispersion of the population over a number of nominal categories. We shall refer to such measures as indices of qualitative variation or diversity. The ecological sciences have made considerable use of
Programming techniques for improving rule readability for rule-based information extraction natural language processing pipelines of unstructured and semi-structured medical texts

Background Extraction of medical terms and their corresponding values from semi-structured and unstructured texts of medical reports can be a time-consuming and error-prone process. Methods of natural language processing (NLP) can help define an extraction pipeline for accomplishing a structured format transformation strategy. Objectives In this paper, we build an NLP pipeline to extract values of the classification of malignant tumors (TNM) from unstructured and semi-structured pathology reports and import them further to a structured data source for a clinical study. Our research interest is not focused on standard performance metrics like precision, recall, and F-measure on the test and validation data. We discuss how with the help of software programming techniques the readability of rule-based (RB) information extraction (IE) pipelines can be improved, and therefore minimize the time to correct or update the rules, and efficiently import them to another programming language. Methods The extract rules were manually programmed with training data of TNM classification and tested in two separate pipelines based on design specifications from domain experts and data curators. Firstly we implemented each rule directly in one line for each extraction item. Secondly, we reprogrammed them in a readable fashion through decomposition and intention-revealing names for the variable declaration. To measure the impact of both methods we measure the time for the fine-tuning and programming of the extractions through test data of semi-structured and unstructured texts. Results We analyze the benefits of improving through readability of the writing of rules, through parallel programming with regular expressions (REGEX), and the Apache Uima Ruta language (AURL). The time for correcting the readable rules in AURL and REGEX was significantly reduced. Complicated rules in REGEX are decomposed and intention-revealing declarations were reprogrammed in AURL in 5 min. Conclusion We discuss the importance of factor readability and how can it be improved when programming RB text IE pipelines. Independent of the features of the programming language and the tools applied, a readable coding strategy can be proven beneficial for future maintenance and offer an interpretable solution for understanding the extraction and for transferring the rules to other domains and NLP pipelines.
Continuous and intermittent theta burst stimulation of primary visual cortex do not modulate resting state functional connectivity: A sham‐controlled multi‐echo fMRI study

Introduction: Theta burst stimulation (TBS) is a type of rTMS protocol which has the advantage of a shorter delivery time over traditional rTMS. When applied to motor cortex, intermittent TBS (iTBS) has been shown to yield excitatory aftereffects, whereas continuous TBS (cTBS) may lead to inhibitory aftereffects, both lasting from minutes to hours. The majority of TBS research has targeted motor, frontal, and parietal regions, and to date very few studies have examined its efficacy at visual areas. We designed a sham‐controlled study to investigate the immediate poststimulation and short‐term (1 h post‐stimulation) effects of iTBS and cTBS to V1.
Exogenous ketosis elevates circulating erythropoietin and stimulates muscular angiogenesis during endurance training overload

De novo capillarization is a primary muscular adaptation to endurance exercise training and is crucial to improving performance. Excess training load, however, impedes such beneficial adaptations, yet we recently demonstrated that such downregulation may be counteracted by ketone ester ingestion (KE) post‐exercise. Therefore, we investigated whether KE could increase pro‐angiogenic factors and thereby stimulate muscular angiogenesis during a 3‐week endurance training‐overload period involving 10 training sessions/week in healthy, male volunteers. Subjects received either 25 g of a ketone ester (KE, n = 9) or a control drink (CON, n = 9) immediately after each training session and before sleep. In KE, but not in CON, the training intervention increased the number of capillary contacts and the capillary‐to‐fibre perimeter exchange index by 44% and 42%, respectively. Furthermore, KE also substantially increased vascular endothelial growth factor (VEGF) and endothelial nitric oxide synthase (eNOS) expression both at the protein and at the mRNA level. Serum erythropoietin concentration was concomitantly increased by 26%. Conversely, in CON the training intervention increased only the protein content of eNOS. These data indicate that intermittent exogenous ketosis during endurance overload training stimulates muscular angiogenesis. This likely resulted from a direct stimulation of muscle angiogenesis, which may be at least partly due to stimulation of erythropoietin secretion and elevated VEGF activity, and/or an inhibition of the suppressive effect of overload training on the normal angiogenic response to training. This study provides novel evidence to support the potential of exogenous ketosis to benefit endurance training‐induced muscular adaptation.
BPS Operators and Brane Geometries

In this thesis we explore the finite N spectrum of BPS operators in four-dimensional supersymmetric conformal field theories (CFT), which have dual AdS gravitational descriptions. In the first part we analyze the spectrum of chiral operators in the free limit of quiver gauge theories. We find explicit counting formulas at finite N for arbitrary quivers, construct an orthogonal basis in the free inner product, and derive the chiral ring structure constants. In order to deal with arbitrarily complicated quivers, we develop convenient diagrammatic techniques: the results are expressed by associating Young diagrams and Littlewood-Richardson coefficients to modifications of the original quiver. We develop the notion of a “quiver character”, which is a generalization of the symmetric group character, obeying analogous orthogonality properties. In the second part we analyze how the BPS spectrum changes at weak coupling, focusing on the N = 4 supersymmetric Yang-Mills. We find a formal expression for the complete set of eighth-BPS operators at finite N , and use it to derive corrections to a near-BPS operator. In the third part of this thesis we move on to the strong coupling regime, where the dual gravitational description applies. The BPS spectrum on the gravity side includes D3-branes wrapping arbitrary holomorphic surfaces, a generalization of the spherical giant gravitons. Quantizing this moduli space gives a Hilbert space, which, via duality and nonrenormalization theorems, should map to the space of BPS operators derived in the weak coupling regime. We apply techniques from fuzzy geometry to study this correspondence between D3-brane geometries, quantum states, and BPS operators in field theory.
Tunable-chirp pulse compression in quasi-phase-matched second harmonic generation

Summary from only given. We performed quasi phase matched (QPM) SHG tunable pulse compression at a FH wavelength of 1.554 /spl mu/m with a periodically poled LiNbO/sub 3/ grating measuring 4.7 cm long and 1 cm wide, and a nominal QPM period of 18.6 /spl mu/m.
Multi-view clustering by CPS-merge analysis with application to multimodal single-cell data

Multi-view data can be generated from diverse sources, by different technologies, and in multiple modalities. In various fields, integrating information from multi-view data has pushed the frontier of discovery. In this paper, we develop a new approach for multi-view clustering, which overcomes the limitations of existing methods such as the need of pooling data across views, restrictions on the clustering algorithms allowed within each view, and the disregard for complementary information between views. Our new method, called CPS-merge analysis, merges clusters formed by the Cartesian product of single-view cluster labels, guided by the principle of maximizing clustering stability as evaluated by CPS analysis. In addition, we introduce measures to quantify the contribution of each view to the formation of any cluster. CPS-merge analysis can be easily incorporated into an existing clustering pipeline because it only requires single-view cluster labels instead of the original data. We can thus readily apply advanced single-view clustering algorithms. Importantly, our approach accounts for both consensus and complementary effects between different views, whereas existing ensemble methods focus on finding a consensus for multiple clustering results, implying that results from different views are variations of one clustering structure. Through experiments on single-cell datasets, we demonstrate that our approach frequently outperforms other state-of-the-art methods.