Miracle-dz/UniFAR-FTUs huggingface dataset:
Scenario-based Multi-product Advertising Copywriting Generation for E-Commerce

In this paper, we proposed an automatic Scenario-based Multi-product Advertising Copywriting Generation system (SMPACG) for E-Commerce, which has been deployed on a leading Chinese e-commerce platform. The proposed SMPACG consists of two main components: 1) an automatic multi-product combination selection module, which itself is consisted of a topic prediction model, a pattern and attribute-based selection model and an arbitrator model; and 2) an automatic multi-product advertising copywriting generation module, which combines our proposed domain-specific pretrained language model and knowledge-based data enhancement model. The SMPACG is the first system that realizes automatic scenario-based multi-product advertising contents generation, which achieves significant improvements over other state-of-the-art methods. The SMPACG has been not only developed for directly serving for our e-commerce recommendation system, but also used as a real-time writing assistant tool for merchants.
On the Parametrized Complexity of the s-Club Cluster Edge Deletion Problem

We study the parameterized complexity of the $s$-Club Cluster Edge Deletion problem: Given a graph $G$ and two integers $s \ge 2$ and $k \ge 1$, is it possible to remove at most $k$ edges from $G$ such that each connected component of the resulting graph has diameter at most $s$? This problem is known to be NP-hard already when $s = 2$. We prove that it admits a fixed-parameter tractable algorithm when parameterized by $s$ and the treewidth of the input graph.
Neospora caninum infection in aborting bovines and lost fetuses: A systematic review and meta-analysis

Background Neospora caninum (N. caninum) is known to be a major cause of reproductive failure in cattle herds around the world. Therefore, the current comprehensive study was performed to estimate the global prevalence of N. caninum infection in bovines that had an abortion and aborted fetuses. Methods In this study, PubMed, ScienceDirect, Web of Science, Scopus, and ProQuest databases were systematically searched for relevant studies up until November 4, 2021. Pooled prevalence and corresponding 95% confidence intervals (CI) were estimated using a random effect model. Other analyzes performed on the data of this study include sensitivity analysis, publication bias test, and quality assessment. Results The final analyses included 71 studies conducted on 2965 abortive cattle and 4805 aborted fetuses. The overall prevalence rates of N. caninum infection in bovines that had an abortion were 47% and 1% using serological and molecular methods. Furthermore, overall prevalence rates of N. caninum infection in bovine aborted fetuses globally were 35% (95% CI: 8%–62%) and 43% (95% CI: 35%–52%) using serological and molecular methods. Conclusions The results of this study showed the high prevalence of N. caninum infection in bovines that had an abortion and aborted fetuses. It is hoped that the results of this study will help prevent abortion in bovines around the world and encourage further studies to determine the impact of this parasite on the occurrence of abortion that may help reduce the economic damage caused by abortion worldwide.
Evaluation of User Perception on Biometric Fingerprint System

Biometric systems involve security assurance to make our system highly secured and robust. Nowadays, biometric technology has been fixed into new systems with the aim of enforcing strong privacy and security. Several innovative system have been introduced, and most of them have biometrics installed to protect military bases, banking machines, and other sophisticated systems, such as online tracking systems. Businesses can now focus on their core functions and feel confident about their data security. Despite the benefits and enhancements in security that biometrics offer, there are also some vulnerabilities. This study aimed to investigate the biometric vulnerabilities in a healthcare facility and propose possible countermeasures for biometric system vulnerabilities.
B.S.T.J. briefs: Axis-crossing intervals of Rayleigh processes

Let R(t,a) denote the envelope of a stationary random process consisting of a sinusoidal signal of amplitude $\sqrt{2a}$ and frequen f 0 10 plus Gaussian noise of unit variance having a narrow-band power spectral density which is symmetrical about f 0 . When a = 0 Rice1 presented some theoretical results which are very useful for studying statistical properties of the axis-crossing intervals of R(t,0). The axis-crossing points and the axis-crossing intervals of the Rayleigh process R(t,a) are defined in Fig. 1. Some recent work concerning the axis-crossing in tervals of R(t,a) was reported by Levin and Fomin2, Goryainov,3 and Rainal.4,5 The purpose of this brief is to present some theoretical results when a ≧ 0, These results stcm from a straightforward extension of Rice's analysis. The Rayleigh process R(t,a) occurs at the output of a typical radio or radar receiver duringing the reception of a sinusoidal signal immersed in Gaussian noise.
Local Byte Fusion for Neural Machine Translation

Subword tokenization schemes are the dominant technique used in current NLP models. However, such schemes can be rigid and tokenizers built on one corpus may not adapt well to other parallel corpora. It has also been observed that in multilingual corpora, subword tokenization schemes oversegment low-resource languages, leading to a drop in translation performance. An alternative to subword tokenizers is byte-based tokenization, i.e., tokenization into byte sequences using the UTF-8 encoding scheme. Byte tokens often represent inputs at a sub-character granularity, i.e., one character can be represented by a span of byte tokens. This results in much longer byte sequences that are hard to interpret without aggregating local information from multiple byte tokens. In this paper, we propose a Local Byte Fusion (LOBEF) method for byte-based machine translation—utilizing byte n-gram and word boundaries—to aggregate local semantic information. Extensive experiments on multilingual translation, zero-shot cross-lingual transfer, and domain adaptation reveal a consistent improvement over vanilla byte-based models. Further analysis also indicates that our byte-based models are parameter-efficient and perform competitive to subword models.
Computation Against a Neighbour

Recent works in contexts like the Internet of Things (IoT) and large-scale Cyber-Physical Systems (CPS) propose the idea of programming distributed systems by focussing on their global behaviour across space and time. In this view, a potentially vast and heterogeneous set of devices is considered as an "aggregate" to be programmed as a whole, while abstracting away the details of individual behaviour and exchange of messages, which are expressed declaratively. One such a paradigm, known as aggregate programming, builds on computational models inspired by field-based coordination. Existing models such as the field calculus capture interaction with neighbours by a so-called "neighbouring field" (a map from neighbours to values). This requires ad-hoc mechanisms to smoothly compose with standard values, thus complicating programming and introducing clutter in aggregate programs, libraries and domain-specific languages (DSLs). To address this key issue we introduce the novel notion of "computation against a neighbour", whereby the evaluation of certain subexpressions of the aggregate program are affected by recent corresponding evaluations in neighbours. We capture this notion in the neighbours calculus (NC), a new field calculus variant which is shown to smoothly support declarative specification of interaction with neighbours, and correspondingly facilitate the embedding of field computations as internal DSLs in common general-purpose programming languages -- as exemplified by a Scala implementation, called ScaFi. This paper formalises NC, thoroughly compares it with respect to the classic field calculus, and shows its expressiveness by means of a case study in edge computing, developed in ScaFi.
Some intersection theorems for finite sets

Let n , r , k 1 , . , k r and t be positive integers with r ≥ 2, and F i (1 ≤ i ≤ r ) a family of k i -subsets of an n -set V . The families F 1 , F 2 , . , F r are said to be r -cross t -intersecting if | F 1 ∩ F 2 ∩ · · · ∩ F r | ≥ t for all F i ∈ F i (1 ≤ i ≤ r ) , and said to be nontrivial if | ∩ 1 ≤ i ≤ r ∩ F ∈F i F | < t . If the r -cross t -intersecting families F 1 , . , F r satisfy F 1 = · · · = F r = F , then F is well known as r -wise t -intersecting family. In this paper, we describe the structure of non-trivial r -wise t -intersecting families with maximum size, and give a stability result for these families. We also determine the structure of nontrivial 2-cross t -intersecting families with maximum product of their sizes. AMS
Distortion‐corrected image reconstruction with deep learning on an MRI‐Linac

MRI is increasingly utilized for image‐guided radiotherapy due to its outstanding soft‐tissue contrast and lack of ionizing radiation. However, geometric distortions caused by gradient nonlinearities (GNLs) limit anatomical accuracy, potentially compromising the quality of tumor treatments. In addition, slow MR acquisition and reconstruction limit the potential for effective image guidance. Here, we demonstrate a deep learning‐based method that rapidly reconstructs distortion‐corrected images from raw k‐space data for MR‐guided radiotherapy applications.
Stability of the scattering transform for deformations with minimal regularity

Within the mathematical analysis of deep convolutional neural networks, the wavelet scattering transform introduced by St´ephane Mallat is a unique example of how the ideas of multiscale analysis can be combined with a cascade of modulus nonlinearities to build a nonexpansive, translation invariant signal representation with provable geometric stability properties, namely Lipschitz continuity to the action of small C 2 diﬀeomorphisms – a remarkable result for both theoretical and practical purposes, inherently depending on the choice of the ﬁlters and their arrangement into a hierarchical architecture. In this note, we further investigate the intimate relationship between the scattering structure and the regularity of the deformation in the H¨older regularity scale C α , α > 0. We are able to precisely identify the stability threshold, proving that stability is still achievable for deformations of class C α , α > 1, whereas instability phenomena can occur at lower regularity levels modelled by C α , 0 ≤ α < 1. While the behaviour at the threshold given by Lipschitz (or even C 1 ) regularity remains beyond reach, we are able to prove a stability bound in that case, up to ε losses.
Data augmentation for efficient learning from parametric experts

We present a simple, yet powerful data-augmentation technique to enable data-efficient learning from parametric experts for reinforcement and imitation learning. We focus on what we call the policy cloning setting, in which we use online or offline queries of an expert or expert policy to inform the behavior of a student policy. This setting arises naturally in a number of problems, for instance as variants of behavior cloning, or as a component of other algorithms such as DAGGER, policy distillation or KL-regularized RL. Our approach, augmented policy cloning (APC), uses synthetic states to induce feedback-sensitivity in a region around sampled trajectories, thus dramatically reducing the environment interactions required for successful cloning of the expert. We achieve highly data-efficient transfer of behavior from an expert to a student policy for high-degrees-of-freedom control problems. We demonstrate the benefit of our method in the context of several existing and widely used algorithms that include policy cloning as a constituent part. Moreover, we highlight the benefits of our approach in two practically relevant settings (a) expert compression, i.e. transfer to a student with fewer parameters; and (b) transfer from privileged experts, i.e. where the expert has a different observation space than the student, usually including access to privileged information.
Distinct Speed and Direction Memories of Migrating Cells Diversify Their Possible Search Strategies

Migrating cells exhibit various motility patterns, resulting from diﬀerent migration mechanisms, cell properties, or cell-environment interactions. The complexity of cell dynamics is reﬂected, e.g., in the diversity of the observed forms of velocity autocorrelation function— that has been widely served as a measure of diﬀusivity and spreading—. By analyzing the dynamics of migrating dendritic cells in vitro , we disentangle the contributions of direction θ and speed v ( ≡| v | ) to the velocity autocorrelation. We ﬁnd that the ability of cells to maintain their speed or direction of motion is unequal, reﬂected in diﬀerent temporal decays of speed and direction autocorrelation functions, AC v ( t ) ∼ t − 1 . 2 and AC θ ( t ) ∼ t − 0 . 5 , respectively. The larger power-law exponent of AC v ( t ) indicates that the cells lose their speed memory considerably faster than the direction memory. Using numerical simulations, we investigate the inﬂuence of AC θ and AC v as well as the direction-speed cross-correlation C θ - v on the search time of a persistent random walker to ﬁnd a randomly located target in conﬁnement. Although AC θ and C θ - v play the major roles, we ﬁnd that the speed autocorrelation AC v can be also tuned to minimize the search time. Adopting an optimal AC v can reduce the search time even up to 10% compared to uncorrelated spontaneous speeds. Our results suggest that migrating cells can improve their search eﬃciency, especially in crowded environments, through the directional or speed persistence or the speed-direction correlation. SIGNIFICANCE Various biological processes rely on the ability of cells to migrate. Of partic-ular interest is the immune response, which crucially depends on the eﬃciency of ﬁnding harmful pathogens by migrating immune cells. By studying the in vitro dynamics of dendritic immune cells, we observe that the local directional change and instantaneous speed of the cell are cross-correlated to each other. However, the speed autocorrelation of the cell decays faster than its direction autocorrelation. Our numerical simulations of a correlated persistent search to ﬁnd a randomly located target in conﬁnement reveal that optimal search strategies can be achieved by minimizing the search time with respect to the speed or direction memory or the degree of speed-direction coupling.
Cohomology, Bocksteins, and resonance varieties in characteristic 2

We use the action of the Bockstein homomorphism on the cohomology ring $H^*(X,\mathbb{Z}_2)$ of a finite-type CW-complex $X$ in order to define the resonance varieties of $X$ in characteristic 2. Much of the theory is done in the more general framework of the Maurer-Cartan sets and the resonance varieties attached to a finite-type commutative differential graded algebra. We illustrate these concepts with examples mainly drawn from closed manifolds, where Poincar\'e duality over $\mathbb{Z}_2$ has strong implications on the nature of the resonance varieties.
Knowledge-Rich Self-Supervision for Biomedical Entity Linking

Entity linking faces significant challenges such as prolific variations and prevalent ambiguities, especially in high-value domains with myriad entities. Standard classification approaches suffer from the annotation bottleneck and cannot effectively handle unseen entities. Zero-shot entity linking has emerged as a promising direction for generalizing to new entities, but it still requires example gold entity mentions during training and canonical descriptions for all entities, both of which are rarely available outside of Wikipedia. In this paper, we explore Knowledge-RIch Self-Supervision ($\tt KRISS$) for biomedical entity linking, by leveraging readily available domain knowledge. In training, it generates self-supervised mention examples on unlabeled text using a domain ontology and trains a contextual encoder using contrastive learning. For inference, it samples self-supervised mentions as prototypes for each entity and conducts linking by mapping the test mention to the most similar prototype. Our approach can easily incorporate entity descriptions and gold mention labels if available. We conducted extensive experiments on seven standard datasets spanning biomedical literature and clinical notes. Without using any labeled information, our method produces $\tt KRISSBERT$, a universal entity linker for four million UMLS entities that attains new state of the art, outperforming prior self-supervised methods by as much as 20 absolute points in accuracy.
Atypical Lower Limb Mechanics During Weight Acceptance of Stair Descent at Different Time Frames After Anterior Cruciate Ligament Reconstruction

Background: An anterior cruciate ligament (ACL) rupture may result in poor sensorimotor knee control and, consequentially, adapted movement strategies to help maintain knee stability. Whether patients display atypical lower limb mechanics during weight acceptance of stair descent at different time frames after ACL reconstruction (ACLR) is unknown. Purpose: To compare the presence of atypical lower limb mechanics during the weight acceptance phase of stair descent among athletes at early, middle, and late time frames after unilateral ACLR. Study Design: Controlled laboratory study. Methods: A total of 49 athletes with ACLR were classified into 3 groups according to time after ACLR—early (<6 months; n = 17), middle (6-18 months; n = 16), and late (>18 months; n = 16)—and compared with asymptomatic athletes (control; n = 18). Sagittal plane hip, knee, and ankle angles; angular velocities; moments; and powers were compared between the ACLR groups’ injured and noninjured legs and the control group as well as between legs within groups using functional data analysis methods. Results: All 3 ACLR groups showed greater knee flexion angles and moments than the control group for injured and noninjured legs. For the other outcomes, the early group had, compared with the control group, less hip power absorption, more knee power absorption, lower ankle plantarflexion angle, lower ankle dorsiflexion moment, and less ankle power absorption for the injured leg and more knee power absorption and higher vertical ground reaction force for the noninjured leg. In addition, the late group showed differences from the control group for the injured leg revealing more knee power absorption and lower ankle plantarflexion angle. Only the early group took a longer time than the control group to complete weight acceptance and demonstrated asymmetry for multiple outcomes. Conclusion: Athletes with different time frames after ACLR revealed atypically large knee angles and moments during weight acceptance of stair descent for both the injured and the noninjured legs. These findings may express a chronically adapted strategy to increase knee control. In contrast, atypical hip and ankle mechanics seem restricted to an early time frame after ACLR. Clinical Relevance: Rehabilitation after ACLR should include early training in controlling weight acceptance. Including a control group is essential when evaluating movement patterns after ACLR because both legs may be affected.
A Dynamic Weighted Tabular Method for Convolutional Neural Networks

Traditional Machine Learning (ML) models are generally preferred for classification tasks on tabular datasets, which often produce unsatisfactory results in complex tabular datasets. Recent works, using Convolutional Neural Networks (CNN) with embedding techniques, outperform the traditional classifiers on tabular dataset. However, these embedding techniques fail to use an automated approach after analyzing the importance of the features in the dataset accurately. This study introduces a novel feature embedding technique named Dynamic Weighted Tabular Method (DWTM), which dynamically uses feature weights based on their strength of the correlations to the class labels during applying any CNN architectures on the tabular datasets. DWTM converts each data point into images and then feeds to a CNN architecture. It dynamically embeds the features of the tabular dataset based on their strength and assigns pixel positions to the appropriate features in the image canvas space instead of using any static configuration. In this paper, DWTM embedding method is applied over six benchmark tabular datasets independently by using three different CNN architectures (i.e., ResNet-18, DenseNet and InceptionV1) and an outstanding performance (an average accuracy of 98%) has obtained, which outperforms any traditional and CNN based classifiers as well.
A Survey of Research on Fair Recommender Systems

Recommender systems can strongly inﬂuence which information we see online, e.g, on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we ﬁrst review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterwards, we provide a survey of how research in this area is currently operationalized, for example, in terms of the general research methodology, fairness metrics, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we ﬁnd that in many research works in computer science very abstract problem operationalizations are prevalent, which circumvent the fundamental and important question of what represents a fair recommendation in the context of a given application.