Miracle-dz/UniFAR-FTUs huggingface dataset:
An Interpretable Determinantal Choice Model for Subset Selection

Understanding how subsets of items are chosen from offered sets is critical to assortment planning, wireless network planning, and many other applications. There are two seemingly unrelated subset choice models that capture dependencies between items: intuitive and interpretable random utility models; and tractable determinantal point processes (DPPs). This paper connects the two. First, all DPPs are shown to be random utility models. Next, a determinantal choice model that enjoys the best of both worlds is specified; the model is shown to subsume logistic regression when dependence is minimal, and MNL when dependence is maximally negative. This makes the model interpretable, while retaining the tractability of DPPs. A simulation study verifies that the model can learn a continuum of negative dependencies from data, and an applied study using original experimental data produces novel insights on wireless interference in LoRa networks.
Singular value decomposition based matrix surgery

This paper aims to develop a simple procedure to reduce and control the condition number of random matrices, and investigate the effect on the persistent homology (PH) of point clouds of well- and ill-conditioned matrices. For a square matrix generated randomly using Gaussian/Uniform distribution, the SVD-Surgery procedure works by: (1) computing its singular value decomposition (SVD), (2) replacing the diagonal factor by changing a list of the smaller singular values by a convex linear combination of the entries in the list, and (3) compute the new matrix by reversing the SVD. Applying SVD-Surgery on a matrix often results in having different diagonal factor to those of the input matrix. The spatial distribution of random square matrices are known to be correlated to the distribution of their condition numbers. The persistent homology (PH) investigations, therefore, are focused on comparing the effect of SVD-Surgery on point clouds of large datasets of randomly generated well-conditioned and ill-conditioned matrices, as well as that of the point clouds formed by their inverses. This work is motivated by the desire to stabilise the impact of Deep Learning (DL) training on medical images in terms of the condition numbers of their sets of convolution filters as a mean of reducing overfitting and improving robustness against tolerable amounts of image noise. When applied to convolution filters during training, the SVD-Surgery acts as a spectral regularisation of the DL model without the need for learning extra parameters. We shall demonstrate that for several point clouds of sufficiently large convolution filters our simple strategy preserve filters norm and reduces the norm of its inverse depending on the chosen linear combination parameters. Moreover, our approach showed significant improvements towards the well-conditioning of matrices and stable topological behaviour.
Multi-modal Machine Learning in Engineering Design: A Review and Future Directions

In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of multiple data modalities has the potential to reshape various applications. This paper presents a comprehensive overview of the current state, advancements, and challenges of MMML within the sphere of engineering design. The review begins with a deep dive into five fundamental concepts of MMML:multi-modal information representation, fusion, alignment, translation, and co-learning. Following this, we explore the cutting-edge applications of MMML, placing a particular emphasis on tasks pertinent to engineering design, such as cross-modal synthesis, multi-modal prediction, and cross-modal information retrieval. Through this comprehensive overview, we highlight the inherent challenges in adopting MMML in engineering design, and proffer potential directions for future research. To spur on the continued evolution of MMML in engineering design, we advocate for concentrated efforts to construct extensive multi-modal design datasets, develop effective data-driven MMML techniques tailored to design applications, and enhance the scalability and interpretability of MMML models. MMML models, as the next generation of intelligent design tools, hold a promising future to impact how products are designed.
Sensitivity of principal components to system changes in the presence of non-stationarity

Non-stationarity affects the sensitivity of change detection in correlated systems described by sets of measurable variables. We study this by projecting onto different principal components. Non-stationarity is modeled as multiple normal states that exist in the system even before a change occurs. The studied changes occur in mean values, standard deviations or correlations of the variables. Monte Carlo simulations are performed to test the sensitivity for change detection with and without knowledge about non-stationarity for different system dimensions and numbers of normal states. A comparison clearly shows that knowledge about the non-stationarity of the system greatly improves change detection sensitivity for all principal components. This improvement is largest for those components that already provide the greatest possibility for change detection in the stationary case. We illustrate our results with an example using real traffic flow data, in which we detect a weekend and a bank holiday start as anomalies.
Teal: Learning-Accelerated Optimization of WAN Traffic Engineering

The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for commercial optimization engines to efficiently solve network traffic engineering (TE) problems at scale. Existing acceleration strategies decompose TE optimization into concurrent subproblems but realize limited parallelism due to an inherent tradeoff between run time and allocation performance. We present Teal, a learning-based TE algorithm that leverages the parallel processing power of GPUs to accelerate TE control. First, Teal designs a flow-centric graph neural network (GNN) to capture WAN connectivity and network flows, learning flow features as inputs to downstream allocation. Second, to reduce the problem scale and make learning tractable, Teal employs a multi-agent reinforcement learning (RL) algorithm to independently allocate each traffic demand while optimizing a central TE objective. Finally, Teal fine-tunes allocations with ADMM (Alternating Direction Method of Multipliers), a highly parallelizable optimization algorithm for reducing constraint violations such as overutilized links. We evaluate Teal using traffic matrices from Microsoft's WAN. On a large WAN topology with >1,700 nodes, Teal generates near-optimal flow allocations while running several orders of magnitude faster than the production optimization engine. Compared with other TE acceleration schemes, Teal satisfies 6--32% more traffic demand and yields 197--625× speedups.
Entanglement negativity in 
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We apply a suitable replica technique to develop a perturbative expression for the entanglement negativity of bipartite mixed states in T$\overline{\text{T}}$-deformed CFT$_2$s up to the first order in the deformation parameter. Utilizing our perturbative construction we compute the entanglement negativity for various bipartite mixed states involving two disjoint intervals, two adjacent intervals, and a single interval in a T$\overline{\text{T}}$-deformed CFT$_2$ at a finite temperature, in the large central charge limit. Subsequently, we advance appropriate holographic constructions to compute the entanglement negativity for such bipartite states in T$\overline{\text{T}}$-deformed thermal CFT$_2$s dual to BTZ black holes in a finite cut-off bulk geometry and find agreement with the corresponding field theoretic results in the limit of small deformation parameter.
Beyond Gradient Descent for Regularized Segmentation Losses

The simplicity of gradient descent (GD) made it the default method for training ever-deeper and complex neural networks. Both loss functions and architectures are often explicitly tuned to be amenable to this basic local optimization. In the context of weakly-supervised CNN segmentation, we demonstrate a well-motivated loss function where an alternative optimizer (ADM) achieves the state-of-the-art while GD performs poorly. Interestingly, GD obtains its best result for a "smoother" tuning of the loss function. The results are consistent across different network architectures. Our loss is motivated by well-understood MRF/CRF regularization models in "shallow" segmentation and their known global solvers. Our work suggests that network design/training should pay more attention to optimization methods.
Using porous silicon as semi-insulating substrate for /spl beta/-SiC high temperature optical-sensing devices

This work demonstrates the availability of using porous silicon as semi-insulating substrate for /spl beta/-SiC high temperature optical sensing devices. An MSM structure was fabricated both on a porous silicon substrate and a conventional silicon substrate, respectively. Experimental results show the optical current ratio can be improved up to 400% at room temperature and 3000% at 200/spl deg/C operating temperature, respectively, with the porous silicon substrate.
Soft robotic patient-specific hydrodynamic model of aortic stenosis and ventricular remodeling

Aortic stenosis (AS) affects about 1.5 million people in the United States and is associated with a 5-year survival rate of 20% if untreated. In these patients, aortic valve replacement is performed to restore adequate hemodynamics and alleviate symptoms. The development of next-generation prosthetic aortic valves seeks to provide enhanced hemodynamic performance, durability, and long-term safety, emphasizing the need for high-fidelity testing platforms for these devices. We propose a soft robotic model that recapitulates patient-specific hemodynamics of AS and secondary ventricular remodeling which we validated against clinical data. The model leverages 3D-printed replicas of each patient’s cardiac anatomy and patient-specific soft robotic sleeves to recreate the patients’ hemodynamics. An aortic sleeve allows mimicry of AS lesions due to degenerative or congenital disease, whereas a left ventricular sleeve recapitulates loss of ventricular compliance and diastolic dysfunction (DD) associated with AS. Through a combination of echocardiographic and catheterization techniques, this system is shown to recreate clinical metrics of AS with greater controllability compared with methods based on image-guided aortic root reconstruction and parameters of cardiac function that rigid systems fail to mimic physiologically. Last, we leverage this model to evaluate the hemodynamic benefit of transcatheter aortic valves in a subset of patients with diverse anatomies, etiologies, and disease states. Through the development of a high-fidelity model of AS and DD, this work demonstrates the use of soft robotics to recreate cardiovascular disease, with potential applications in device development, procedural planning, and outcome prediction in industrial and clinical settings. Description A soft robotics-driven model recreates patient-specific biomechanics and hemodynamics of cardiovascular disease.
The dipole anisotropies of the IRAS galaxies and the microwave background radiation

IRAS 60 . mu.m sources are used to map the local (< or approx. =200 h/sup -1/ Mpc) gravitational field, and to determine its dipole component, on the assumption that the infrared radiation traces the matter. The dipole moment is found to point in the direction l = 248 +- 9 and b = 40 +- 8, 26/sup 0/ +- 10/sup 0/ away from the direction of the velocity of the Local Group relative to the microwave background radiation (statistical sampling errors only). Comparison of the two anisotropies, using the luminosity function of the IRAS galaxies and linear perturbation theory, yields an estimate of the cosmological density parameter, . cap omega. /sub 0/ = 0.85 +- 0.16 (statistical IRAS error only), with nonlinear effects increasing . cap omega. /sub 0/ by approx.0.15%. These values are in apparent disagreement with the values . cap omega. /sub 0/ = 0.1--0.2 deduced from the Virgocentric infall and the cosmic virial theorem.
Negative Linear or Unimodal: Why Forest Soil Fungal Latitudinal Diversity Differs across China

Latitudinal gradients of forest soil fungal biodiversity in China have been previously investigated; however, the results of these studies were inconsistent. In the present study, I reanalyzed the data from these studies on all forest types in China and showed that the differences in forest soil fungal latitudinal diversity were caused by the different environments of continents and islands, as well as by the inconsistency between different classification standards. ABSTRACT To identify the reasons for the inconsistency in patterns of latitudinal gradients of forest soil fungal biodiversity in China, a reanalysis of data was performed. Causes are linked to the different environments of continents and islands and the inconsistency between different classification standards. The following three suggestions are made for future studies: sites on the mainland and islands should be distinguished in these types of studies, the Shannon index should be used to represent fungal diversity instead of operational taxonomic unit (OTU) richness, and using the diversity of higher taxa (such as family level) instead of OTU level represents a potential proxy for species-level diversity. IMPORTANCE Latitudinal gradients of forest soil fungal biodiversity in China have been previously investigated; however, the results of these studies were inconsistent. In the present study, I reanalyzed the data from these studies on all forest types in China and showed that the differences in forest soil fungal latitudinal diversity were caused by the different environments of continents and islands, as well as by the inconsistency between different classification standards. Accordingly, three suggestions were outlined for future studies on this and similar topics. This study makes a significant contribution to the literature because these findings can be used to improve our understanding of the forest soil fungal latitudinal diversity and as a basis for future studies.
16S rRNA Gene Amplicon Sequencing Data for Pteris vittata Rhizosphere Soils

Data on the 16S rRNA gene amplicon sequences from Pteris vittata rhizosphere soils are reported. The following phyla were recorded in arsenic-rich soils: Actinobacteria (59%), Proteobacteria (26%), Chloroflexi (17%), and Acidobacteria (9%). Actinobacteria (45%), Proteobacteria (22%), Chloroflexi (10%), and Acidobacteria (11%) were in natural-mineral soils. ABSTRACT Data on the 16S rRNA gene amplicon sequences from Pteris vittata rhizosphere soils are reported. The following phyla were recorded in arsenic-rich soils: Actinobacteria (59%), Proteobacteria (26%), Chloroflexi (17%), and Acidobacteria (9%). Actinobacteria (45%), Proteobacteria (22%), Chloroflexi (10%), and Acidobacteria (11%) were in natural-mineral soils.
Particle-Resolved Direct Numerical Simulation for Gas-Solid Flow Model Development

Gas-solid flows in nature and industrial applications are characterized by multiscale and nonlinear interactions that manifest as rich flow physics and pose unique modeling challenges. In this article, we review particle-resolved direct numerical simulation (PR-DNS) of the microscale governing equations for understanding gas-solid flow physics and obtaining quantitative information for model development. A clear connection between a microscale realization and meso/macroscale representation is necessary for PR-DNS to be used effectively for model development at the meso- and macroscale. Furthermore, the design of PR-DNS must address the computational challenges of parameterizing models in a high-dimensional parameter space and obtaining accurate statistics of flow properties from a finite number of realizations at acceptable grid resolution. This review also summarizes selected recent insights into the physics of momentum, kinetic energy, and heat transfer in gas-solid flows obtained from PR-DNS. Promising future applications of PR-DNS include the study of the effect of number fluctuations on hydrodynamics, instabilities in gas-solid flow, and wall-bounded flows.
Feature Missing-aware Routing-and-Fusion Network for Customer Lifetime Value Prediction in Advertising

Nowadays, customer lifetime value (LTV) plays an important role in mobile game advertising, since it can be beneficial to adjust ad bids and ensure that the games are promoted to the most valuable users. Some neural models are utilized for LTV prediction based on the rich user features. However, in the advertising scenario, due to the privacy settings or limited length of log retention, etc, most of existing approaches suffer from the missing feature problem. Moreover, only a small fraction of purchase behaviours can be observed. The label sparsity inevitably limits model expressiveness. To tackle the aforementioned challenges, we propose a feature missing-aware routing-and-fusion network (MarfNet) to reduce the effect of the missing features while training. Specifically, we calculate the missing states of raw features and feature interactions for each sample. Based on the missing states, two missing-aware layers are designed to route samples into different experts, thus each expert can focus on the real features of samples assigned to it. Finally we get the missing-aware representation by the weighted fusion of the experts. To alleviate the label sparsity, we further propose a batch-in dynamic discrimination enhanced (Bidden) loss weight mechanism, which can automatically assign greater loss weights to difficult samples in the training process. Both offline experiments and online A/B tests have validated the superiority of our proposed Bidden-MarfNet.
Noise-predictive maximum likelihood (NPML) detection

Sequence detectors for the digital magnetic recording channel that are based on noise-predictive partial-response equalization are described. Called Noise-Predictive Maximum Likelihood (NPML) detectors, they arise by imbedding a noise prediction/whitening process into the branch metric computation of a Viterbi detector. NPML detectors can be realized in a form that allows RAM table look-up implementation of the imbedded feedback. Alternatively, the noise prediction/whitening mechanism can be implemented as an infinite impulse response (IIR) filter. For a Lorentzian channel with operating points in the range 0.5<PW50/T<3.5, IIR predictors with at most two zeros and two poles offer the best possible performance. Simulation results obtained for Lorentzian channels show that a judicious tradeoff between performance and state complexity leads to practical schemes offering substantial performance gains over both PRML and extended PRML detectors. An important practical advantage of the family of NPML detectors is that they can be conveniently integrated into existing PRML architectures.
Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning

We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the challenge, we propose a novel game-theoretic offline value-based reinforcement learning method using mixed policies. The proposed method reduces the need to store infinitely many policies in previous methods to only constantly many policies, which achieves nearly optimal policy efficiency, making it practical and favorable for industrial usage. We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation. Our experiments on a large-scale marketing campaign with tens-of-millions users and more than one billion budget verify the theoretical results and show that the proposed method outperforms various baseline methods. The proposed method has been successfully deployed to serve all the traffic of this marketing campaign.
Metropolitan-scale Mobility Digital Twin

Mobility digital twin, which is a a virtual replica of the mobility in the physical world, is the key building block of modern smart city applications at a metropolitan scale, including traffic regulation, emergency management and epidemic control. To duplicate the mobility in the physical world and show the potential outcome based on either the current state or manipulated conditions, we are facing with three main challenges: 1) how to sense real-time human mobility at a large scale and assimilate different data sources to infer a dynamic city mobility state; 2) how to make an accurate prediction for the mobility replica that adapts with dynamic city mobility state; 3) how to simulate the mobility in response to different conditions. In this talk, we will present our recent studies, practices and perspectives on mobility digital twin in addressing these above challenges, with applications in the real-world scenarios with industrial connections.
Relation Preference Oriented High-order Sampling for Recommendation

The introduction of knowledge graphs (KG) into recommendation systems (RS) has been proven to be effective because KG introduces a variety of relations between items. In fact, users have different relation preferences depending on the relationship in KG. Existing GNN-based models largely adopt random neighbor sampling strategies to process propagation; however, these models cannot aggregate biased relation preference local information for a specific user, and thus cannot effectively reveal the internal relationship between users' preferences. This will reduce the accuracy of recommendations, while also limiting the interpretability of the results. Therefore, we propose a Relation Preference oriented High-order Sampling (RPHS) model to dynamically sample subgraphs based on relation preference and hard negative samples for user-item pairs. We design a path sampling strategy based on relation preference, which can encode the critical paths between specific user-item pairs to sample the paths in the high-order message passing subgraphs. Next, we design a mixed sampling strategy and define a new propagation operation to further enhance RPHS's ability to distinguish negative signals. Through the above sampling strategies, our model can better aggregate local relation preference information and reveal the internal relationship between users' preferences. Experiments show that our model outperforms the state-of-the-art models on three datasets by 14.98%, 5.31%, and 8.65%, and also performs well in terms of interpretability. The codes are available at https://github.com/RPHS/RPHS.git