Miracle-dz/UniFAR-FTUs huggingface dataset:
Short Communication: Utility Indifference Pricing with High Risk Aversion and Small Linear Price Impact

We consider the Bachelier model with linear price impact. Exponential utility indifference prices are studied for vanilla European options and we compute their non-trivial scaling limit for a vanishing price impact which is inversely proportional to the risk aversion. Moreover, we find explicitly a family of portfolios which are asymptotically optimal.
Edgeworth expansions for volatility models

Motivated from option and derivative pricing, this note develops Edgeworth expansions both in the Kolmogorov and Wasserstein metric for many different types of discrete time volatility models and their possible transformations. This includes, among others, H\"{o}lder-type functions of (augmented) Garch processes of any order, iterated random functions or Volterra-processes.
Optimizing Secure Decision Tree Inference Outsourcing

Outsourcing decision tree inference services to the cloud is highly beneficial, yet raises critical privacy concerns on the proprietary decision tree of the model provider and the private input data of the client. In this paper, we design, implement, and evaluate a new system that allows highly efficient outsourcing of decision tree inference. Our system significantly improves upon prior art in the overall online end-to-end secure inference service latency at the cloud as well as the local-side performance of the model provider. We first present a new scheme which securely shifts most of the processing of the model provider to the cloud, resulting in a substantial reduction on the model provider's performance complexities. We further devise a scheme which substantially optimizes the performance for secure decision tree inference at the cloud, particularly the communication round complexities. The synergy of these techniques allows our new system to achieve up to <inline-formula><tex-math notation="LaTeX">$8 \times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>8</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq1-3194048.gif"/></alternatives></inline-formula> better overall online end-to-end secure inference latency at the cloud side over realistic WAN environment, as well as bring the model provider up to <inline-formula><tex-math notation="LaTeX">$19 \times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>19</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq2-3194048.gif"/></alternatives></inline-formula> savings in communication and <inline-formula><tex-math notation="LaTeX">$18 \times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>18</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wang-ieq3-3194048.gif"/></alternatives></inline-formula> savings in computation.
Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances

Mobile and wearable devices have enabled numerous applications, including activity tracking, wellness monitoring, and human–computer interaction, that measure and improve our daily lives. Many of these applications are made possible by leveraging the rich collection of low-power sensors found in many mobile and wearable devices to perform human activity recognition (HAR). Recently, deep learning has greatly pushed the boundaries of HAR on mobile and wearable devices. This paper systematically categorizes and summarizes existing work that introduces deep learning methods for wearables-based HAR and provides a comprehensive analysis of the current advancements, developing trends, and major challenges. We also present cutting-edge frontiers and future directions for deep learning-based HAR.
Dynamic Pricing and Demand Learning on a Large Network of Products: A PAC-Bayesian Approach

We consider a seller offering a large network of $N$ products over a time horizon of $T$ periods. The seller does not know the parameters of the products' linear demand model, and can dynamically adjust product prices to learn the demand model based on sales observations. The seller aims to minimize its pseudo-regret, i.e., the expected revenue loss relative to a clairvoyant who knows the underlying demand model. We consider a sparse set of demand relationships between products to characterize various connectivity properties of the product network. In particular, we study three different sparsity frameworks: (1) $L_0$ sparsity, which constrains the number of connections in the network, and (2) off-diagonal sparsity, which constrains the magnitude of cross-product price sensitivities, and (3) a new notion of spectral sparsity, which constrains the asymptotic decay of a similarity metric on network nodes. We propose a dynamic pricing-and-learning policy that combines the optimism-in-the-face-of-uncertainty and PAC-Bayesian approaches, and show that this policy achieves asymptotically optimal performance in terms of $N$ and $T$. We also show that in the case of spectral and off-diagonal sparsity, the seller can have a pseudo-regret linear in $N$, even when the network is dense.
Posterior Inference for Quantile Regression: Adaptation to Sparsity

Quantile regression is a powerful data analysis tool that accommodates heterogeneous covariate-response relationships. We find that by coupling the asymmetric Laplace working likelihood with appropriate shrinkage priors, we can deliver posterior inference that automatically adapts to possible sparsity in quantile regression analysis. After a suitable adjustment on the posterior variance, the posterior inference provides asymptotically valid inference under heterogeneity. Furthermore, the proposed approach leads to oracle asymptotic efficiency for the active (nonzero) quantile regression coefficients and super-efficiency for the non-active ones. By avoiding the need to pursue dichotomous variable selection, the Bayesian computational framework demonstrates desirable inference stability with respect to tuning parameter selection. Our work helps to uncloak the value of Bayesian computational methods in frequentist inference for quantile regression.
Smart Fashion: A Review of AI Applications in the Fashion & Apparel Industry

The fashion industry is on the verge of an unprecedented change. The implementation of machine learning, computer vision, and artificial intelligence (AI) in fashion applications is opening lots of new opportunities for this industry. This paper provides a comprehensive survey on this matter, categorizing more than 580 related articles into 22 well-defined fashion-related tasks. Such structured task-based multi-label classification of fashion research articles provides researchers with explicit research directions and facilitates their access to the related studies, improving the visibility of studies simultaneously. For each task, a time chart is provided to analyze the progress through the years. Furthermore, we provide a list of 86 public fashion datasets accompanied by a list of suggested applications and additional information for each.
Sr$_3$LiIrO$_6$: a potential quantum spin liquid candidate in the one dimensional $d^4$ iridate family

Abhisek Bandyopadhyay, A. Chakraborty, S. Bhowal, 5 Vinod Kumar, M. M. Sala, 8 A. Efimenko, C. Meneghini, I. Dasgupta, T. Saha Dasgupta, A. V. Mahajan, and Sugata Ray ∗ School of Materials Sciences, Indian Association for the Cultivation of Science, 2A & 2B Raja S. C. Mullick Road, Jadavpur, Kolkata 700 032, India Department of Physics, Indian Institute of Science Education and Research, Pune, Maharashtra-411008, India School of Physical Sciences, Indian Association for the Cultivation of Science, 2A & 2B Raja S. C. Mullick Road, Jadavpur, Kolkata 700 032, India Department of Physics, Indian Institute of Technology, Kanpur 208016, India Materials Theory, ETH Zurich, Wolfgang-Pauli-Strasse 27, 8093 Zurich, Switzerland Department of Physics, Indian Institute Of Technology Bombay, Powai, Mumbai 400076, India ESRF–The European Synchrotron, 71 Avenue des Martyrs, 38000 Grenoble, France Dipartimento di Fisica, Politecnico di Milano, P.zza Leonardo da Vinci 32, I-20133 Milano, Italy Dipartimento di Scienze, Universitá Roma Tre, Via della Vasca Navale, 84 I-00146 Roma, Italy Department of Condensed Matter Physics and Material Sciences, S. N. Bose National Centre for Basic Sciences, Block JD, Sector 3, Saltlake, Kolkata -700106, India (Dated: November 2, 2021)
TransAug: Translate as Augmentation for Sentence Embeddings

While contrastive learning greatly advances the representation of sentence embeddings, it is still limited by the size of the existing sentence datasets. In this paper, we present TransAug (Translate as Augmentation), which provide the first exploration of utilizing translated sentence pairs as data augmentation for text, and introduce a two-stage paradigm to advances the state-of-the-art sentence embeddings. Instead of adopting an encoder trained in other languages setting, we first distill a Chinese encoder from a SimCSE encoder (pretrained in English), so that their embeddings are close in semantic space, which can be regraded as implicit data augmentation. Then, we only update the English encoder via cross-lingual contrastive learning and frozen the distilled Chinese encoder. Our approach achieves a new state-of-art on standard semantic textual similarity (STS), outperforming both SimCSE and Sentence-T5, and the best performance in corresponding tracks on transfer tasks evaluated by SentEval.
Mirror-prox sliding methods for solving a class of monotone variational inequalities

In this paper we propose new algorithms for solving a class of structured monotone variational inequality (VI) problems over compact feasible sets. By identifying the gradient components existing in the operator of VI, we show that it is possible to skip computations of the gradients from time to time, while still maintaining the optimal iteration complexity for solving these VI problems. Specifically, for deterministic VI problems involving the sum of the gradient of a smooth convex function $\nabla G$ and a monotone operator $H$, we propose a new algorithm, called the mirror-prox sliding method, which is able to compute an $\varepsilon$-approximate weak solution with at most $O((L/\varepsilon)^{1/2})$ evaluations of $\nabla G$ and $O((L/\varepsilon)^{1/2}+M/\varepsilon)$ evaluations of $H$, where $L$ and $M$ are Lipschitz constants of $\nabla G$ and $H$, respectively. Moreover, for the case when the operator $H$ can only be accessed through its stochastic estimators, we propose a stochastic mirror-prox sliding method that can compute a stochastic $\varepsilon$-approximate weak solution with at most $O((L/\varepsilon)^{1/2})$ evaluations of $\nabla G$ and $O((L/\varepsilon)^{1/2}+M/\varepsilon + \sigma^2/\varepsilon^2)$ samples of $H$, where $\sigma$ is the variance of the stochastic samples of $H$.
A robust single-pixel particle image velocimetry based on fully convolutional networks with cross-correlation embedded

Particle image velocimetry (PIV) is essential in experimental fluid dynamics. In the current work, we propose a new velocity field estimation paradigm, which achieves a synergetic combination of the deep learning method and the traditional cross-correlation method. Specifically, the deep learning method is used to optimize and correct a coarse velocity guess to achieve a super-resolution calculation. And the cross-correlation method provides the initial velocity field based on a coarse correlation with a large interrogation window. As a reference, the coarse velocity guess helps with improving the robustness of the proposed algorithm. This fully convolutional network with embedded cross-correlation is named as CC-FCN. CC-FCN has two types of input layers, one is for the particle images, and the other is for the initial velocity field calculated using cross-correlation with a coarse resolution. Firstly, two pyramidal modules extract features of particle images and initial velocity field respectively. Then the fusion module appropriately fuses these features. Finally, CC-FCN achieves the super-resolution calculation through a series of deconvolution layers to obtain the single-pixel velocity field. As the supervised learning strategy is considered, synthetic data sets including ground-truth fluid motions are generated to train the network parameters. Synthetic and real experimental PIV data sets are used to test the trained neural network in terms of accuracy, precision, spatial resolution and robustness. The test results show that these attributes of CC-FCN are further improved compared with those of other tested PIV algorithms. The proposed model could therefore provide competitive and robust estimations for PIV experiments.
Nested Multiple Instance Learning with Attention Mechanisms

Strongly supervised learning requires detailed knowledge of truth labels at instance levels, and in many machine learning applications, this is a major drawback. Multiple instance learning (MIL) is a popular weakly supervised learning method where truth labels are not available at instance level, but only at bag-of-instances level. However, sometimes the nature of the problem requires a more composite description, where a nested architecture of bag-of-bags at different levels can capture underlying relationships, like similar instances grouped together. Predicting the latent labels of instances or inner-bags might be as important as predicting the final bag-of-bags label, but is lost in a straightforward nested setting. We propose a Nested Multiple Instance with Attention (NMIA) model architecture combining the concept of nesting with attention mechanisms. We show that NMIA performs as conventional MIL in simple scenarios and can grasp a complex scenario providing insights to the latent labels at different levels.
LSTA-Net: Long short-term Spatio-Temporal Aggregation Network for Skeleton-based Action Recognition

Modelling various spatio-temporal dependencies is the key to recognising human actions in skeleton sequences. Most existing methods excessively relied on the design of traversal rules or graph topologies to draw the dependencies of the dynamic joints, which is inadequate to reflect the relationships of the distant yet important joints. Furthermore, due to the locally adopted operations, the important long-range temporal information is therefore not well explored in existing works. To address this issue, in this work we propose LSTA-Net: a novel Long short-term Spatio-Temporal Aggregation Network, which can effectively capture the long/short-range dependencies in a spatio-temporal manner. We devise our model into a pure factorised architecture which can alternately perform spatial feature aggregation and temporal feature aggregation. To improve the feature aggregation effect, a channel-wise attention mechanism is also designed and employed. Extensive experiments were conducted on three public benchmark datasets, and the results suggest that our approach can capture both long-and-short range dependencies in the space and time domain, yielding higher results than other state-of-the-art methods. Code available at https://github.com/tailin1009/LSTA-Net.
A novel decompostion-based multiobjective evolutionary algorithm with an application to engineering optimal design problems

Many real-world optimization problems such as engineering design can be eventually modeled as the corresponding multiobjective optimization problems (MOPs) which must be solved to obtain approximate Pareto optimal fronts. Multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been regarded as a very promising approach for solving MOPs. Recent studies have shown that MOEA/D with uniform weight vectors is well-suited to MOPs with regular Pareto optimal fronts, but its performance in terms of diversity deteriorates on MOPs with irregular Pareto optimal fronts such as highly nonlinear and convex. In this way, the solution set obtained by the algorithm can not provide more reasonable choices for decision makers. In order to efficiently overcome this drawback, in this paper, we propose an improved MOEA/D algorithm by virtue of the well-known PascolettiSerafini scalarization method and a new strategy of multi-reference points. Specifically, this strategy consists of the setting and adaptation of reference points generated by the techniques of equidistant partition and projection. For performance assessment, the proposed algorithm is compared with existing four state-of-the-art multiobjective evolutionary algorithms on both benchmark test problems with various types of Pareto optimal fronts and two realworld MOPs including the hatch cover design and the rocket injector design in engineering optimization. According to the experimental results, the proposed algorithm exhibits better diversity performance than that of the other compared algorithms.
Iodine Intake for Pregnant and Breastfeeding Women and Their Infants Remains a Global Concern.

Sufficient dietary iodine intake is required for optimal synthesis of thyroid hormones, which regulate growth and development, particularly of the brain and central nervous system. Iodine deficiency has been described as the single greatest cause of preventable mental impairment,yet equally,excess iodine intake has adverse consequences through inhibiting the synthesis of thyroid hormones (1). Inadequate iodine intake is a global issue affecting low-, middle-, and high-income countries. The Iodine Global Network (IGN) is an international, nongovernmental organization that monitors iodine status worldwide. The most recent IGN scorecard (2021) shows that the number of countries with iodine adequacy has nearly doubled in the last 2 decades, from 67 in 2003 to 118 in 2021.
Facultative Parthenogenesis in California Condors

Abstract Parthenogenesis is a relatively rare event in birds, documented in unfertilized eggs from columbid, galliform, and passerine females with no access to males. In the critically endangered California condor, parentage analysis conducted utilizing polymorphic microsatellite loci has identified two instances of parthenogenetic development from the eggs of two females in the captive breeding program, each continuously housed with a reproductively capable male with whom they had produced offspring. Paternal genetic contribution to the two chicks was excluded. Both parthenotes possessed the expected male ZZ sex chromosomes and were homozygous for all evaluated markers inherited from their dams. These findings represent the first molecular marker-based identification of facultative parthenogenesis in an avian species, notably of females in regular contact with fertile males, and add to the phylogenetic breadth of vertebrate taxa documented to have reproduced via asexual reproduction.
Phospholipase D and retromer promote recycling of TRPL ion channel via the endoplasmic reticulum

Plasma membrane protein trafficking is of fundamental importance for cell function and cell integrity of neurons and includes regulated protein recycling. In this work, we report a novel role of the endoplasmic reticulum (ER) for protein recycling as discovered in trafficking studies of the ion channel TRPL in photoreceptor cells of Drosophila. TRPL is located within the rhabdomeric membrane from where it is endocytosed upon light stimulation and stored in the cell body. Conventional immunohistochemistry as well as stimulated emission depletion super‐resolution microscopy revealed TRPL storage at the ER after illumination, suggesting an unusual recycling route of TRPL. Our results also imply that both phospholipase D (PLD) and retromer complex are required for correct recycling of TRPL to the rhabdomeric membrane. Loss of PLD activity in PLD3.1 mutants results in enhanced degradation of TRPL. In the retromer mutant vps35MH20, TRPL is trapped in a Rab5‐positive compartment. Evidenced by epistatic analysis in the double mutant PLD3.1 vps35MH20, PLD activity precedes retromer function. We propose a model in which PLD and retromer function play key roles in the transport of TRPL to an ER enriched compartment.
“Walking a tightrope”: A grounded theory approach to informal caregiving for amyotrophic lateral sclerosis

Abstract Informal caregivers, mainly family members and friends, provide supportive and palliative care for people with amyotrophic lateral sclerosis (ALS) during their terminal disease course. Informal caregiving for people with ALS continues towards palliative care and end‐of‐life care with the progression of the disease. In this study, we provide a theoretical understanding of informal caregiving in ALS utilising 23 semi‐structured interviews conducted with informal caregivers of people with ALS (pwALS) in Switzerland. Due to the expected death of the care recipient, our grounded theory approach outlines informal caregivers’ caregiving work as an effort to secure a balance amongst different caregiving activities, which feed into the final stage of providing palliative care. Overall, our theoretical understanding of ALS informal caregiving work encompasses the core category ‘holding the balance’ and four secondary categories: ‘Organising support’, ‘being present’, ‘managing everyday life’ and ‘keeping up with ALS’. The core category of holding the balance underlines the significance of ensuring care and normalcy even as disease progresses and until the end of life. For the informal caregivers, this balancing act is the key element of care provision to pwALS and therefore guides decisions surrounding caregiving. On this understanding, those caregivers that succeed in holding the balance can provide care at home until death. The balance is heavily influenced by contextual factors of caregiving, for example relating to personal characteristics of the caregiver, or activities of caregiving where the goal is to ensure the quality of life of the pwALS. As there is a heterogeneity of speed and subtype of progression of ALS, our work accounts for multiple caregiving trajectories.
Intron distribution and emerging role of alternative splicing in fungi.

Spliceosomal introns are non-coding sequences that are spliced from pre-mRNA. They are ubiquitous in eukaryotic genomes, although the average number of introns per gene varies considerably between different eukaryotic species. Fungi are diverse in terms of intron numbers ranging from 4% to 99% genes with introns. Alternative splicing is one of the most common modes of post transcriptional regulation in eukaryotes, giving rise to multiple transcripts from a single pre-mRNA and is widespread in metazoans and drives extensive proteome diversity. Earlier, alternative splicing was considered to be rare in fungi, but recently, increasing numbers of studies have revealed that alternative splicing is also widespread in fungi and has been implicated in the regulation of fungal growth and development, protein localization, and the improvement of survivability, likely underlying their unique capacity to adapt to changing environmental conditions. However, the role of alternative splicing in pathogenicity and development of drug resistance is only recently gaining attention. In this review, we describe the intronic landscape in fungi. We also present in detail the newly discovered functions of alternative splicing in various cellular processes and outline areas particularly in pathogenesis and clinical drug resistance for future studies that could lead to the development of much needed new therapeutics.