Miracle-dz/UniFAR-FTUs huggingface dataset:
QueryBooster: Improving SQL Performance Using Middleware Services for Human-Centered Query Rewriting

SQL query performance is critical in database applications, and query rewriting is a technique that transforms an original query into an equivalent query with a better performance. In a wide range of database-supported systems, there is a unique problem where both the application and database layer are black boxes, and the developers need to use their knowledge about the data and domain to rewrite queries sent from the application to the database for better performance. Unfortunately, existing solutions do not give the users enough freedom to express their rewriting needs. To address this problem, we propose QueryBooster, a novel middleware-based service architecture for human-centered query rewriting, where users can use its expressive and easy-to-use rule language (called VarSQL) to formulate rewriting rules based on their needs. It also allows users to express rewriting intentions by providing examples of the original query and its rewritten query. QueryBooster automatically generalizes them to rewriting rules and suggests high-quality ones. We conduct a user study to show the benefits of VarSQL to formulate rewriting rules. Our experiments on real and synthetic workloads show the effectiveness of the rule-suggesting framework and the significant advantages of using QueryBooster for human-centered query rewriting to improve the end-to-end query performance.
Axisymmetric monopole and dipole flow singularities in proximity of a stationary no-slip plate immersed in a Brinkman fluid

Green's function plays an important role in many areas of physical sciences and is a prime tool for solving diverse hydrodynamic equations in the linear regime. In the present contribution, the axisymmetric low-Reynolds-number Brinkman flow induced by monopole and dipole singularities in proximity of a stationary plate of circular shape is theoretically investigated. The flow singularities are directed along the central axis of the plate. No-slip boundary conditions are assumed to hold at the surface of the plate. The Green's functions are determined to a large extent analytically, reducing the solution of the linear hydrodynamic equations to well-behaved one-dimensional integrals amenable to numerical computation. In our approach, the Brinkman flow problem is formulated as a mixed boundary value problem that is subsequently mapped in the form of dual integral equations on the domain boundaries. Thereupon, the solution of the equations of fluid motion is eventually reduced to the solution of two independent Fredholm integral equations of the first kind. The overall flow structure and emerging eddy patterns are found to strongly depend on the magnitude of the relevant geometrical and physical parameters of the system. Moreover, the effect of the confining plate on the dynamics of externally driven or force-free particles is assessed through the calculation of the relevant hydrodynamic reaction functions. The effect of the plate on the locomotory behavior of a self-propelling active dipole swimmer is shown to be maximum when the radius of the plate is comparable to the distance separating the swimmer from the plate. Our results may prove useful for characterizing transport processes in microfluidic devices and may pave the way toward understanding and controlling of small-scale flows in porous media.
PolarDB-IMCI: A Cloud-Native HTAP Database System at Alibaba

Cloud-native databases have become the de-facto choice for mission-critical applications on the cloud due to the need for high availability, resource elasticity, and cost efficiency. Meanwhile, driven by the increasing connectivity between data generation and analysis, users prefer a single database to efficiently process both OLTP and OLAP workloads, which enhances data freshness and reduces the complexity of data synchronization and the overall business cost. In this paper, we summarize five crucial design goals for a cloud-native HTAP database based on our experience and customers' feedback, i.e., transparency, competitive OLAP performance, minimal perturbation on OLTP workloads, high data freshness, and excellent resource elasticity. As our solution to realize these goals, we present PolarDB-IMCI, a cloud-native HTAP database system designed and deployed at Alibaba Cloud. Our evaluation results show that PolarDB-IMCI is able to handle HTAP efficiently on both experimental and production workloads; notably, it speeds up analytical queries up to ×149 on TPC-H (100GB). PolarDB-IMCI introduces low visibility delay and little performance perturbation on OLTP workloads (<5%), and resource elasticity can be achieved by scaling out in tens of seconds.
EASpy: fast simulation of fluorescence and Cherenkov light from extended air showers at large zenith angles

The detailed simulation of extended air showers (EAS) and their emission of Cherenkov and fluorescence light requires increasing computation time and storage volume with increasing energy of the primary particle. Given these limitations, it is currently challenging to optimize configurations of imaging air Cherenkov telescopes at photon energies beyond approximately 100 TeV. Additionally, the existing simulation frameworks are not capable of capturing the interplay of Cherenkov and fluorescence light emission at large zenith angle distances (≳ 70°), where the collection area of Cherenkov telescopes considerably increases. Here, we present EASpy, a framework for the simulation of EAS at large zenith angles using parametrizations for electron-positron distributions. Our proposed approach for the emission of fluorescence and Cherenkov light and the subsequent imaging of these components by Imaging Atmospheric Cherenkov Telescopes (IACTs) aims to provide flexibility and accuracy while at the same time it reduces the computation time considerably compared to full Monte Carlo simulations. We find excellent agreement of the resulting Cherenkov images when comparing results obtained from EASpy with the de-facto standard simulation tool CORSIKA and sim_telarray. In the process of verifying our approach, we have found that air shower images appear wider and longer with increasing impact distance at large zenith angles, an effect that has previously not been noted. We also investigate the distribution of light on the ground for fluorescence and Cherenkov emission and highlight their key differences to distributions at moderate zenith angles.
A dual approach to ShEx visualization with complexity management

Shape Expressions (ShEx) are used in various fields of knowledge to define RDF graph structures. ShEx visualizations enable all kinds of users to better comprehend the underlying schemas and perceive its properties. Nevertheless, the only antecedent (RDFShape) suffers from limited scalability which impairs comprehension in large cases. In this work, a visual notation for ShEx is defined which is built upon operationalized principles for cognitively efficient design. Furthermore, two approaches to said notation with complexity management mechanisms are implemented: a 2D diagram (Shumlex) and a 3D Graph (3DShEx). A comparative user evaluation between both approaches and RDFShape was performed. Results show that Shumlex users were significantly faster than 3DShEx users in large schemas. Even though no significant differences were observed for success rates and precision, only Shumlex achieved a perfect score in both. Moreover, while users' ratings were mostly positive for all tools, their feedback was mostly favourable towards Shumlex. By contrast, RDFShape and 3DShEx's scalability is widely criticised. Given those results, it is concluded that Shumlex may have potential as a cognitively efficient visualization of ShEx. In contrast, the more intricate interaction with a 3D environment appears to hinder 3DShEx users.
Task-Oriented Communication Design at Scale

With countless promising applications in various domains such as IoT and industry 4.0, task-oriented communication design (TOCD) is getting accelerated attention from the research community. This paper presents a novel approach for designing scalable task-oriented quantization and communications in cooperative multi-agent systems (MAS). The proposed approach utilizes the TOCD framework and the value of information (VoI) concept to enable efficient communication of quantized observations among agents while maximizing the average return performance of the MAS, a parameter that quantifies the MAS's task effectiveness. The computational complexity of learning the VoI, however, grows exponentially with the number of agents. Thus, we propose a three-step framework: i) learning the VoI (using reinforcement learning (RL)) for a two-agent system, ii) designing the quantization policy for an $N$-agent MAS using the learned VoI for a range of bit-budgets and, (iii) learning the agents' control policies using RL while following the designed quantization policies in the earlier step. We observe that one can reduce the computational cost of obtaining the value of information by exploiting insights gained from studying a similar two-agent system - instead of the original $N$-agent system. We then quantize agents' observations such that their more valuable observations are communicated more precisely. Our analytical results show the applicability of the proposed framework under a wide range of problems. Numerical results show striking improvements in reducing the computational complexity of obtaining VoI needed for the TOCD in a MAS problem without compromising the average return performance of the MAS.
Environmental Dependence of the Mass–Metallicity Relation in Cosmological Hydrodynamical Simulations

We investigate the environmental dependence of the gas-phase metallicity for galaxies at z = 0 to z ≳ 2 and the underlying physical mechanisms driving this dependence using state-of-the-art cosmological hydrodynamical simulations. We find that, at fixed stellar mass, central galaxies in massive halos have lower gas-phase metallicity than those in low-mass halos, while satellite galaxies residing in more massive halos are more metal-rich. The combined effect is that massive galaxies are more metal-poor in massive halos and low-mass galaxies are more metal-rich in massive halos. By inspecting the environmental dependence of other galaxy properties, we identify that the accretion of low-metallicity gas is responsible for the environmental dependence of central galaxies at high z, whereas the AGN feedback processes play a crucial role at low z. For satellite galaxies, we find that both the suppression of gas accretion and the stripping of existing gas are responsible for their environmental dependence, with negligible effect from the AGN feedback. Finally, we show that the difference of gas-phase metallicity as a function of stellar mass between protocluster and field galaxies agrees with recent observational results, for example from the MAMMOTH-Grism survey.
A debate on the neuronal origin of focal seizures

A critical question regarding how focal seizures start is whether we can identify particular cell classes that drive the pathological process. This was the topic for debate at the recent International Conference for Technology and Analysis of Seizures (ICTALS) meeting (July 2022, Bern, CH) that we summarize here. The debate has been fueled in recent times by the introduction of powerful new ways to manipulate subpopulations of cells in relative isolation, mostly using optogenetics. The motivation for resolving the debate is to identify novel targets for therapeutic interventions through a deeper understanding of the etiology of seizures.
Initial report on special operations clinical training at a civilian academic medical center

West Virginia has a proud tradition of military service and WVU Medicine proudly continues that tradition by providing training and research for U.S. Army Special Forces BACKGROUND Military-civilian partnerships for combat casualty care skills training have mostly focused on traditional, combat surgical team training. We sought to better understand US Special Forces (SF) Medics' training at West Virginia University in Morgantown, West Virginia, a Level 1 trauma center, via assessments of medical knowledge, clinical skills confidence, and technical performance. METHODS Special Forces Medics were evaluated using posttraining medical knowledge tests, procedural skills confidence surveys (using a 5-point Likert scale), and technical skills assessments using fresh perfused cadavers in a simulated combat casualty care environment. Data from these tests, surveys, and assessments were analyzed for 18 consecutive SF medic rotations from the calendar years 2019 through 2021. RESULTS A total of 108 SF Medics' tests, surveys, and assessments were reviewed. These SF Medics had an average of 5.3 years of active military service; however, deployed experience was minimal (73% never deployed). Review of knowledge testing demonstrated a slight increase in mean test score between the precourse (80% ± 14%; range, 50–100%) when compared with the postcourse (82% ± 14%; range, 50–100%). Skills confidence scores increased between courses, specifically within the point of injury care (p = 0.09) and prolonged field care (p < 0.001). Technical skills assessments included cricothyroidotomy, chest tube insertion, and tourniquet placement. CONCLUSION This study provides preliminary evidence supporting military-civilian partnerships at an academic Level 1 trauma center to provide specialty training to SF Medics as demonstrated by increase in medical knowledge and confidence in procedural skills. Additional opportunities exist for the development technical skills assessments. LEVEL OF EVIDENCE Therapeutic/Care Management; Level IV.
The Predictive Value of Serum ß-HCG Levels in The Detection of Ectopic Pregnancy Sixteen Days after Embryo Transfer: A Cross-Sectional Study

Background: To detect the predictive value of beta human chorionic gonadotropin (β-hCG) levels 16 days post embryo transfer (ET) regarding detection of an ectopic pregnancy (EP) in assisted reproductive technology (ART) cycles. Materials and Methods: In this cross-sectional study, we reviewed the database of Royan Institute from January 2011 to December 2014 and from January 2017 to December 2019 retrospectively. All cases with positive β-hCG levels sixteen days after ET were screened (n=4149). The pregnancies with oocyte or embryo donation and the multiple pregnancies based on the first ultrasound were excluded. All eligible singleton pregnancies with documented serum β-hCG levels at Royan institute laboratory (n=765) were included and then classified according to the type of pregnancy: EP (n=189) or non-EP (n=576). The data of the treatment cycle was extracted from the patients’ files. A receiver operating characteristic (ROC) curve was used to detect the predictive power of the first measurement of β-hCG level in distinguishing EP from ongoing pregnancy in the ART and intrauterine insemination (IUI) cycles separately. Sensitivity, specificity, area under the ROC curve and 95% confidence intervals (CI) were calculated for each of the estimates. Results: The mean levels of β-hCG 16 days after ET were remarkably higher in the ongoing pregnancy group than the EP group (1592.35 ± 87 IU/L vs. 369.69 ± 50.61 IU/L, P<0.001). The β-hCG thresholds predictive of ongoing pregnancy were 278 IU/L as the most suitable cut-off to predict viable pregnancy with a sensitivity of 72.8%, a specificity of 67.5%, a positive predictive value of 77.8%, standard error of 0.02, and a confidence interval of 73.8- 81.7%. However, this relationship was not found in IUI cycles. Conclusion: Based on these findings, if β-hCG levels 16 days after ET are below 278 IU/l, close follow-up is recommended, until either the diagnosis of EP or miscarriage is established.
Analysis and Detectability of Offline Data Poisoning Attacks on Linear Dynamical Systems

In recent years, there has been a growing interest in the effects of data poisoning attacks on data-driven control methods. Poisoning attacks are well-known to the Machine Learning community, which, however, make use of assumptions, such as cross-sample independence, that in general do not hold for linear dynamical systems. Consequently, these systems require different attack and detection methods than those developed for supervised learning problems in the i.i. d. \ setting. Since most data-driven control algorithms make use of the least-squares estimator, we study how poisoning impacts the least-squares estimate through the lens of statistical testing, and question in what way data poisoning attacks can be detected. We establish under which conditions the set of models compatible with the data includes the true model of the system, and we analyze different poisoning strategies for the attacker. On the basis of the arguments hereby presented, we propose a stealthy data poisoning attack on the least-squares estimator that can escape classical statistical tests, and conclude by showing the efficiency of the proposed attack.
The past does matter: correlation of subsequent states in trajectory predictions of Gaussian Process models

Computing the distribution of trajectories from a Gaussian Process model of a dynamical system is an important challenge in utilizing such models. Motivated by the computational cost of sampling-based approaches, we consider approximations of the model's output and trajectory distribution. We show that previous work on uncertainty propagation, focussed on discrete state-space models, incorrectly included an independence assumption between subsequent states of the predicted trajectories. Expanding these ideas to continuous ordinary differential equation models, we illustrate the implications of this assumption and propose a novel piecewise linear approximation of Gaussian Processes to mitigate them.
Sets of $r$-graphs that color all $r$-graphs

An $r$-regular graph is an $r$-graph, if every odd set of vertices is connected to its complement by at least $r$ edges. Let $G$ and $H$ be $r$-graphs. An $H$-coloring of $G$ is a mapping $f\colon E(G) \to E(H)$ such that each $r$ adjacent edges of $G$ are mapped to $r$ adjacent edges of $H$. For every $r\geq 3$, let $\mathcal{H}_r$ be an inclusion-wise minimal set of connected $r$-graphs, such that for every connected $r$-graph $G$ there is an $H \in \mathcal{H}_r$ which colors $G$. We show that $\mathcal{H}_r$ is unique and characterize $\mathcal{H}_r$ by showing that $G \in \mathcal{H}_r$ if and only if the only connected $r$-graph coloring $G$ is $G$ itself. The Petersen Coloring Conjecture states that the Petersen graph $P$ colors every bridgeless cubic graph. We show that if true, this is a very exclusive situation. Indeed, either $\mathcal{H}_3 = \{P\}$ or $\mathcal{H}_3$ is an infinite set and if $r \geq 4$, then $\mathcal{H}_r$ is an infinite set. Similar results hold for the restriction on simple $r$-graphs. By definition, $r$-graphs of class $1$ (i.e. those having edge-chromatic number equal to $r$) can be colored with any $r$-graph. Hence, our study will focus on those $r$-graphs whose edge-chromatic number is bigger than $r$, also called $r$-graphs of class $2$. We determine the set of smallest $r$-graphs of class 2 and show that it is a subset of $\mathcal{H}_r$.
Achieving Capacity on Non-Binary Channels with Generalized Reed–Muller Codes

Recently, the authors showed that Reed–Muller (RM) codes achieve capacity on binary memoryless symmetric (BMS) channels with respect to bit error rate. This paper extends that work by showing that RM codes defined on non-binary fields, known as generalized RM codes, achieve capacity on sufficiently symmetric non-binary channels with respect to symbol error rate. The new proof also simplifies the previous approach (for BMS channels) in a variety of ways that may be of independent interest.
Unsupervised Sentence Representation Learning with Frequency-induced Adversarial Tuning and Incomplete Sentence Filtering

Pre-trained Language Model (PLM) is nowadays the mainstay of Unsupervised Sentence Representation Learning (USRL). However, PLMs are sensitive to the frequency information of words from their pre-training corpora, resulting in anisotropic embedding space, where the embeddings of high-frequency words are clustered but those of low-frequency words disperse sparsely. This anisotropic phenomenon results in two problems of similarity bias and information bias, lowering the quality of sentence embeddings. To solve the problems, we fine-tune PLMs by leveraging the frequency information of words and propose a novel USRL framework, namely Sentence Representation Learning with Frequency-induced Adversarial tuning and Incomplete sentence filtering (SLT-FAI). We calculate the word frequencies over the pre-training corpora of PLMs and assign words thresholding frequency labels. With them, (1) we incorporate a similarity discriminator used to distinguish the embeddings of high-frequency and low-frequency words, and adversarially tune the PLM with it, enabling to achieve uniformly frequency-invariant embedding space; and (2) we propose a novel incomplete sentence detection task, where we incorporate an information discriminator to distinguish the embeddings of original sentences and incomplete sentences by randomly masking several low-frequency words, enabling to emphasize the more informative low-frequency words. Our SLT-FAI is a flexible and plug-and-play framework, and it can be integrated with existing USRL techniques. We evaluate SLT-FAI with various backbones on benchmark datasets. Empirical results indicate that SLT-FAI can be superior to the existing USRL baselines. Our code is released in \url{https://github.com/wangbing1416/SLT-FAI}.
RC3: Regularized Contrastive Cross-lingual Cross-modal Pre-training

Multilingual vision-language (V&L) pre-training has achieved remarkable progress in learning universal representations across different modalities and languages. In spite of recent success, there still remain challenges limiting further improvements of V&L pre-trained models in multilingual settings. Particularly, current V&L pre-training methods rely heavily on strictly-aligned multilingual image-text pairs generated from English-centric datasets through machine translation. However, the cost of collecting and translating such strictly-aligned datasets is usually unbearable. In this paper, we propose Regularized Contrastive Cross-lingual Cross-modal (RC^3) pre-training, which further exploits more abundant weakly-aligned multilingual image-text pairs. Specifically, we design a regularized cross-lingual visio-textual contrastive learning objective that constrains the representation proximity of weakly-aligned visio-textual inputs according to textual relevance. Besides, existing V&L pre-training approaches mainly deal with visual inputs by either region-of-interest (ROI) features or patch embeddings. We flexibly integrate the two forms of visual features into our model for pre-training and downstream multi-modal tasks. Extensive experiments on 5 downstream multi-modal tasks across 6 languages demonstrate the effectiveness of our proposed method over competitive contrast models with stronger zero-shot capability.