Miracle-dz/UniFAR-FTUs huggingface dataset:
QAGCN: Answering Multi-Relation Questions via Single-Step Implicit Reasoning over Knowledge Graphs

Multi-relation question answering (QA) is a challenging task, where given questions usually require long reasoning chains in KGs that consist of multiple relations. Recently, methods with explicit multi-step reasoning over KGs have been prominently used in this task and have demonstrated promising performance. Examples include methods that perform stepwise label propagation through KG triples and methods that navigate over KG triples based on reinforcement learning. A main weakness of these methods is that their reasoning mechanisms are usually complex and difficult to implement or train. In this paper, we argue that multi-relation QA can be achieved via end-to-end single-step implicit reasoning, which is simpler, more efficient, and easier to adopt. We propose QAGCN -- a Question-Aware Graph Convolutional Network (GCN)-based method that includes a novel GCN architecture with controlled question-dependent message propagation for the implicit reasoning. Extensive experiments have been conducted, where QAGCN achieved competitive and even superior performance compared to state-of-the-art explicit-reasoning methods. Our code and pre-trained models are available in the repository: https://github.com/ruijie-wang-uzh/QAGCN
Two Weight Inequalities for Discrete Positive Operators

We characterize two weight inequalities for general positive dyadic operators. We consider both weak and strong type inequalities, and general (p,q) mapping properties. Special cases include Sawyers Fractional Integral operator results from 1988, and the bilinear embedding inequality of Nazarov-Treil-Volberg from 1999. The method of proof is an extension of Sawyer's argument.
A quantitative simulation–based modeling approach for college counseling centers

College counseling centers in various universities have been tasked with the important responsibility of attending to the mental health needs of their students. Owing to the unprecedented recent surge of demand for such services, college counseling centers are facing several crippling resource-level challenges. This is leading to longer wait times which limit access to critical mental health services. To address these challenges, we construct a discrete-event simulation model that captures several intricate details of their operations and provides a data-driven framework to quantify the effect of different policy changes. In contrast to existing work on this matter, which is primarily based on qualitative assessments, the considered quantitative approach has the potential to lead to key observations that can assist counseling directors in constructing a system with desirable performance. To demonstrate the benefit of the considered simulation model, we use data specific to Texas A&M’s Counseling & Psychological Services to run a series of numerical experiments. Our results demonstrate the predictive power of the simulation model, highlight a number of key observations, and identify policy changes that result in desirable system performance.
Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices

Deep learning techniques, particularly convolutional neural networks, have shown great potential in computer vision and medical imaging applications. However, deep learning models are computationally demanding as they require enormous computational power and specialized processing hardware for model training. To make these models portable and compatible for prototyping, their implementation on low-power devices is imperative. In this work, we present the implementation of Modified U-Net on Intel/Movidius Neural Compute Stick 2 (NCS-2) for the segmentation of medical images. We selected U-Net because, in medical image segmentation, U-Net is a prominent model that provides improved performance for medical image segmentation even if the dataset size is small. The modified U-Net model is evaluated for performance in terms of dice score. Experiments are reported for segmentation task on three medical imaging datasets: BraTs dataset of brain MRI, heart MRI dataset, and Ziehl–Neelsen sputum smear microscopy image (ZNSDB) dataset. For the proposed model, we reduced the number of parameters from 30 million in the U-Net model to 0.49 million in the proposed architecture. Experimental results show that the modified U-Net provides comparable performance while requiring significantly lower resources and provides inference on the NCS-2. The maximum dice scores recorded are 0.96 for the BraTs dataset, 0.94 for the heart MRI dataset, and 0.74 for the ZNSDB dataset.
Examining the behaviour of state-of-the-art convolutional neural networks for brain tumor detection with and without transfer learning

Distinguishing normal from malignant and determining the tumor type are critical components of brain tumor diagnosis. Two diﬀerent kinds of dataset are investigated using state-of-the-art CNN models in this research work. One dataset(binary) has images of normal and tumor types, while another(multi-class) provides all images of tumors classiﬁed as glioma, meningioma, or pituitary. The experiments were conducted in these dataset with transfer learning from pre-trained weights from ImageNet as well as initializing the weights randomly. The experimental environment is equivalent for all models in this study in order to make a fair comparison. For both of the dataset, the validation set are same for all the models where train data is 60% while the rest is 40% for validation. With the proposed techniques in this research, the EﬃcientNet-B5 architecture outperforms all the state-of-the-art models in the binary-classiﬁcation dataset with the accuracy of 99.75% and 98.61% accuracy for the multi-class dataset. This research also demonstrates the behaviour of convergence of validation loss in diﬀerent weight initialization techniques.
Exploring Cross-lingual Text Detoxification with Large Multilingual Language Models.

Detoxification is a task of generating text in polite style while preserving meaning and fluency of the original toxic text. Existing detoxification methods are monolingual i.e. designed to work in one exact language. This work investigates multilingual and cross-lingual detoxification and the behavior of large multilingual models in this setting. Unlike previous works we aim to make large language models able to perform detoxification without direct fine-tuning in a given language. Experiments show that multilingual models are capable of performing multilingual style transfer. However, tested state-of-the-art models are not able to perform cross-lingual detoxification and direct fine-tuning on exact language is currently inevitable and motivating the need of further research in this direction.
Hybrid Value Estimation for Off-policy Evaluation and Offline Reinforcement Learning

Value function estimation is an indispensable subroutine in reinforcement learning, which becomes more challenging in the offline setting. In this paper, we propose Hybrid Value Estimation (HVE) to reduce value estimation error, which trades off bias and variance by balancing between the value estimation from offline data and the learned model. Theoretical analysis discloses that HVE enjoys a better error bound than the direct methods. HVE can be leveraged in both off-policy evaluation and offline reinforcement learning settings. We, therefore, provide two concrete algorithms Off-policy HVE (OPHVE) and Model-based Offline HVE (MOHVE), respectively. Empirical evaluations on MuJoCo tasks corroborate the theoretical claim. OPHVE outperforms other off-policy evaluation methods in all three metrics measuring the estimation effectiveness, while MOHVE achieves better or comparable performance with state-of-the-art offline reinforcement learning algorithms. We hope that HVE could shed some light on further research on reinforcement learning from fixed data.
A Survey on Sentence Embedding Models Performance for Patent Analysis

Patent data is an important source of knowledge for innovation research, while the technological similarity between pairs of patents is a key enabling indicator for patent analysis. Recently researchers have been using patent vector space models based on different NLP embeddings models to calculate the technological similarity between pairs of patents to help better understand innovations, patent landscaping, technology mapping, and patent quality evaluation. More often than not, Text Embedding is a vital precursor to patent analysis tasks. A pertinent question then arises: How should we measure and evaluate the accuracy of these embeddings? To the best of our knowledge, there is no comprehensive survey that builds a clear delineation of embedding models' performance for calculating patent similarity indicators. Therefore, in this study, we provide an overview of the accuracy of these algorithms based on patent classification performance and propose a standard library and dataset for assessing the accuracy of embeddings models based on PatentSBERTa approach. In a detailed discussion, we report the performance of the top 3 algorithms at section, class, and subclass levels. The results based on the first claim of patents show that PatentSBERTa, Bert-for-patents, and TF-IDF Weighted Word Embeddings have the best accuracy for computing sentence embeddings at the subclass level. According to the first results, the performance of the models in different classes varies, which shows researchers in patent analysis can utilize the results of this study to choose the best proper model based on the specific section of patent data they used.
A factor model of multilayer network interdependence

Multilayer networks describe the rich ways in which nodes are related through diﬀerent types of connections in separate layers. These multiple relationships are naturally represented by an adjacency tensor that can be interpreted using techniques from multilinear algebra. In this work we use the nonnegative Tucker decomposition (NNTuck) with KL-divergence as an expressive factor model for multilayer networks that naturally generalizes existing methods for stochastic block models of multilayer networks. We show how the NNTuck provides an intuitive factor-based perspective on layer dependence and enables linear-algebraic techniques for analyzing dependence with respect to speciﬁc layers. Algorithmically, we show that using expectation maximization (EM) to maximize this log-likelihood under the NNTuck model is step-by-step equivalent to tensorial multiplicative updates for the nonnegative Tucker decomposition under a KL loss, extending a previously known equivalence from nonnegative matrices to nonnegative tensors. Using both synthetic and real-world data, we evaluate the use and interpretation of the NNTuck as a model of multilayer networks. The ability to quantify dependencies between layers has the potential to inform survey instruments for collecting social network data, identify redundancies in the structure of a network, and indicate relationships between disparate layers. Therefore, we propose a deﬁnition of layer dependence based on using a likelihood ratio test to evaluate three nested models: the layer independent, layer dependent, and layer redundant NNTucks. We show how these deﬁnitions, paired with analysis of the factor matrices in the NNTuck, can be used to understand and interpret layer dependence in diﬀerent contexts.
MPE inference using an Incremental Build-Infer-Approximate Paradigm

Exact inference of the most probable explanation (MPE) in Bayesian networks is known to be NP-complete. In this paper, we propose an algorithm for approximate MPE inference that is based on the incremental build-infer-approximate (IBIA) framework. We use this framework to obtain an ordered set of partitions of the Bayesian network and the corresponding max-calibrated clique trees. We show that the maximum belief in the last partition gives an estimate of the probability of the MPE assignment. We propose an iterative algorithm for decoding, in which the subset of variables for which an assignment is obtained is guaranteed to increase in every iteration. There are no issues of convergence, and we do not perform a search for solutions. Even though it is a single shot algorithm, we obtain valid assignments in 100 out of the 117 benchmarks used for testing. The accuracy of our solution is comparable to a branch and bound search in majority of the benchmarks, with competitive run times.
Emergent segmentation from participation dynamics and multi-learner retraining

The choice to participate in a data-driven service, often made on the basis of quality of that service, influences the ability of the service to learn and improve. We study the participation and retraining dynamics that arise when both the learners and sub-populations of users are \emph{risk-reducing}, which cover a broad class of updates including gradient descent, multiplicative weights, etc. Suppose, for example, that individuals choose to spend their time amongst social media platforms proportionally to how well each platform works for them. Each platform also gathers data about its active users, which it uses to update parameters with a gradient step. For this example and for our general class of dynamics, we show that the only asymptotically stable equilibria are segmented, with sub-populations allocated to a single learner. Under mild assumptions, the utilitarian social optimum is a stable equilibrium. In contrast to previous work, which shows that repeated risk minimization can result in representation disparity and high overall loss for a single learner \citep{hashimoto2018fairness,miller2021outside}, we find that repeated myopic updates with multiple learners lead to better outcomes. We illustrate the phenomena via a simulated example initialized from real data.
Interpretable Mixture of Experts for Structured Data

With the growth of machine learning for structured data, the need for reliable model explanations is essential, especially in high-stakes applications. We introduce a novel framework, Interpretable Mixture of Experts (IME) 2 , that provides interpretability for structured data while preserving accuracy. IME consists of an assignment module and a mixture of interpretable experts such as linear models where each sample is assigned to a single interpretable expert. This results in an inherently-interpretable architecture where the explanations produced by IME are the exact descriptions of how the prediction is computed. In addition to constituting a standalone inherently-interpretable architecture, an additional IME capability is that it can be integrated with existing Deep Neural Networks (DNNs) to offer inter-pretability to a subset of samples while maintaining the accuracy of the DNNs. Experiments on various structured datasets demonstrate that IME is more accurate than a single interpretable model and performs comparably to existing state-of-the-art deep learning models in terms of accuracy while providing faithful explanations.
Scrambling of algebras in open quantum systems

Many quantitative approaches to the dynamical scrambling of information in quantum systems involve the study of out-of-time-ordered correlators (OTOCs). In this paper, we introduce an algebraic OTOC ($\mathcal{A}$-OTOC) that allows us to study information scrambling of generalized quantum subsystems under quantum channels. For closed quantum systems, this algebraic framework was recently employed to unify quantum information-theoretic notions of operator entanglement, coherence-generating power, and Loschmidt echo. The main focus of this work is to provide a natural generalization of these techniques to open quantum systems. We first show that, for unitary dynamics, the $\mathcal{A}$-OTOC quantifies a generalized notion of information scrambling, namely between a subalgebra of observables and its commutant. For open quantum systems, on the other hand, we find a competition between the global environmental decoherence and the local scrambling of information. We illustrate this interplay by analytically studying various illustrative examples of algebras and quantum channels. To complement our analytical results, we perform numerical simulations of two paradigmatic systems: the PXP model and the Heisenberg XXX model, under dephasing. Our numerical results reveal connections with many-body scars and the stability of decoherence-free subspaces.
Diffusion Approximation for Multi-Scale McKean-Vlasov SDEs Through Different Methods

Abstract. In this paper, we aim to study the diffusion approximation for multi-scale McKean-Vlasov stochastic differential equations. More precisely, we prove the weak convergence of slow process Xε in C([0, T ];R) towards the limiting process X that is the solution of a distribution dependent stochastic differential equation in which some new drift and diffusion terms compared to the original equation appear. The main contribution is to use two different methods to explicitly characterize the limiting equations respectively. The obtained diffusion coefficients in the limiting equations have different form through these two methods, however it will be asserted that they are essential the same by a comparison.
Quasi-SMC based on MPC for a constrained continuous-time nonlinear system with external disturbances

In this article, a quasi-sliding mode control (QSMC) based on MPC is proposed for the constrained continuous-time nonlinear system with external disturbances. The MPC problem is formulated relating to the design of QSMC, to generate the control input, which can imitate the control process of QSMC and guarantee the satisfaction of state and input constraints. Meanwhile, the cost function of MPC problem is reconstructed, in which the QSMC based on MPC can show better convergence rate by tuning the weight parameters. Finally, a simulation case is provided to demonstate the effectiveness of the proposed approach.
Gorenstein flat dimension of complexes

We define a notion of Gorenstein flat dimension for unbounded complexes over left GF-closed rings. Over Gorenstein rings we introduce a notion of Gorenstein coho- mology for complexes; we also define a generalized Tate cohomol- ogy for complexes over Gorenstein rings, and we show that there is a close connection between the absolute, the Gorenstein and the generalized Tate cohomology.
Scanning electron microscopy for identification of local strain of Aspergillus Parasiticus and its larvicidal efficacy against Aedes Aegypti and non‐target toxicity testing on fingerlings of Hypophthalmichthys Molitrix

Scanning electron microscopy proved to be helpful in identification of Aspergillus parasiticus. A. parasiticus has a worldwide occurrence with high entomopathogenic efficacy against Aedes aegypti. However, assessment of pathogenic effects of entomopathogenic fungi on non‐target organisms is required to evaluate its use as bio control agent. Aim of the present research is to study the parricidal activity of the local strain of A. parasiticus (MK371710) against A. aegypti and its toxicity assessment against aquatic model organism Hypophthalmichthys molitrix. A. parasiticus was isolated from the soil of Jallo Park, Lahore. The larvicidal effect of A. parasiticus was evaluated against fourth instar larvae of A. aegypti. Hundred percent of mortality of larvae was observed after exposure to 1 × 107 conidia/ml of fungal suspension after 72 h post treatment. The LC50 value of A. parsiticus in 24 h post treatment and 48 h post treatment was recorded as 1.0 × 107conidia/ml 2.99 × 105conidia/ml, respectively. However, an in‐depth understanding safety to non‐target organisms is necessary, if we are to properly control the action of these entomopathogenic fungi under natural conditions. For the toxicity assessment fingerlings of H. molitrix were exposed to conidial suspensions of A. parasiticus. Eye pop (64%), fin hemorrhage (33%), and scale infection (30%) were the major morphological effects observed during the study. Results reveal that although A. parasiticus is highly pathogenic to dengue vector but also have significant effects on organisms other than insects and its application as biological control agent requires safety considerations.
TECHNICAL NOTE: Analysis of volatile fatty acids in rumen fluid by gas chromatography mass spectrometry using a dimethyl carbonate extraction.

Analysis of rumen fluid volatile fatty acids (VFA) is typically conducted by injecting acidified aqueous rumen fluid into a gas chromatograph (GC) with a flame ionization detector (FID). Aqueous samples are highly problematic because of the large vapor volume that can lead to poor peak shape and contamination of inlets, potentially causing sample carryover. Methods using aqueous samples are not well suited for use in a mass spectrometer (MS) detector system. The objective of this project was to validate a dimethyl carbonate (DMC) extraction process and GCMS method for rumen VFA analysis. To perform the extraction, 100 µL of sample, KHSO4 (500 g/L), and 2-ethylbutyrate (internal standard; 8.5 mM) were added to a microcentrifuge tube (in order) followed by 1 mL of DMC. The mixture was thoroughly vortexed, and centrifuged. The organic layer (top) was removed and placed in a GC vial. The DMC extract was injected (0.5 µL) into an Agilent 5977B GCMS (8:1 split injection) with a polar DB-FFAP column. The column was held at 105°C for 5 min, increased at 10°C/min to 150°C, then 65°C/min to 240°C, and held constant for 10 min. The peak area of acetate relative to the internal standard is linear from approximately 2 mM to at least 130 mM and encompasses the expected values of rumen concentrations for the other VFA. Recovery of VFA from spiked rumen fluid was tested at three concentrations in rumen fluid from steers fed a finishing diet or grazing wheat pasture. Recovery was not affected by the diet of the animals (P > 0.10) or the amount of VFA spiked (P > 0.19) for acetate, propionate, isobutyrate, or butyrate. There was an interaction of amount of VFA spiked and the diet of the animal (P = 0.021) for valerate and a tendency for an interaction (P = 0.051) for isovalerate, due to the recovery of the VFA being lower in the medium spike amount in rumen fluid from cattle on wheat pasture. Overall, recovery was greatest for propionate (101.9 ± 1.67%) and lowest for valerate (95.7± 1.95%). Including the 10 min hold at 240°C at the end of each run prevented carryover from sample to sample. This method appears to perform well in a GCMS system and accurately and precisely quantifies rumen fluid VFA.
Complete Genome Sequence of a Listeria monocytogenes Strain with Antimicrobial Resistance Genes Isolated from Lettuce in Canada

Listeria monocytogenes, a Gram-positive, rod-shaped, non-spore-forming bacterium, is an important foodborne bacterial pathogen for humans worldwide. Here, we report the complete genome sequence of a Canadian Listeria monocytogenes strain with an antimicrobial resistance (AMR) gene that was isolated from lettuce. ABSTRACT Listeria monocytogenes, a Gram-positive, rod-shaped, non-spore-forming bacterium, is an important foodborne bacterial pathogen for humans worldwide. Here, we report the complete genome sequence of a Canadian Listeria monocytogenes strain with an antimicrobial resistance (AMR) gene that was isolated from lettuce.
Ground States of the Yukawa models with Cutoffs

Ground states of the so called Yukawa model is considered. The Yukawa model describes a Dirac field interacting with a Klein-Gordon field. By introducing both ultraviolet cutoffs and spatial cutoffs, the total Hamiltonian is defined as a self-adjoint operator on a boson-fermion Fock space. It is shown that the total Hamiltonian has a positive spectral gap for all values of coupling constants. In particular the existence of ground states is proven.