Miracle-dz/UniFAR-FTUs huggingface dataset:
Quinoa extruded snacks with probiotics: Physicochemical and sensory properties

The consumption of probiotic foods has grown rapidly, and these are generally found in dairy matrices where their growth is favored. Therefore, this study aimed to develop a new probiotic snack made from quinoa and added with spore-forming probiotic bacteria in two concentrations of 0.3 and 0.35%. The probiotic was added by spraying, after the extrusion process, together with salt and oil, at 70°C under dry conditions. Bacterial viability, resistance to simulated gastric juice, physical, chemical, and sensory tests were then evaluated during 120 days of storage at room temperature (20°C) and compared to a controlled snack without probiotic. The probiotic Bacillus coagulans was tested for the molecular identification and inhibition of pathogenic bacteria. Viability assessment was remained above 107 CFU/g of snacks. The intestinal tract simulation resistance test showed a viability of 70%. The physicochemical and sensory properties evaluated had no significant changes during storage time compared to control snack. The results of the taxonomic analysis indicate that the analyzed strain has, on average, 98% identity in 98% of its length belonging to Bacillus coagulans and Bacillus badius species. The probiotic showed inhibition against pathogenic bacteria. The new snack with probiotic is stable during storage.
Competition between Vertical and Diagonal Stripes in the Hartree‐Fock Approximation

Using the Hartree‐Fock approximation we analyze the properties and stability of the filled (one doped hole per stripe site) vertical stripes (VS) and diagonal stripes (DS), found in the two‐dimensional Hubbard model at two representative doping levels x = 1/8 and x = 1/6, by varying the ratio of the on‐site Coulomb repulsion U to the hopping element t. In the weak coupling regime of U ∼ 3.5t the stability of VS is best explained by the solitonic mechanism which leads to the kinetic energy gain due to the hopping perpendicular to the stripes, while DS have a lower potential energy which explains their stability in the regime of large U. The results obtained beyond the Hubbard model are shortly reviewed as well.
Efficient implementation of selective recoupling in heteronuclear spin systems using hadamard matrices

We present an efficient scheme which couples any designated pair of spins in heteronuclear spin systems. The scheme is based on the existence of Hadamard matrices. For a system of $n$ spins with pairwise coupling, the scheme concatenates $cn$ intervals of system evolution and uses at most $c n^2$ pulses where $c \approx 1$. Our results demonstrate that, in many systems, selective recoupling is possible with linear overhead, contrary to common speculation that exponential effort is always required.
Challenges and opportunities in nonalcoholic steatohepatitis

Abstract Nonalcoholic steatohepatitis (NASH) has emerged as the leading cause of chronic liver disease worldwide and is rapidly increasing in prevalence due to the obesity epidemic. There are currently no Food and Drug Administration (FDA) approved drugs to treat NASH, and therefore a critical need exists for novel therapies that can halt or reverse the progression to hepatic fibrosis, cirrhosis, and hepatocellular carcinoma. Clinical trials to date using single drugs to treat NASH have shown disappointing efficacy. Combination therapies to attack different targets underlying disease pathogenesis of NASH are being explored as a strategy currently. Novel RNA therapies are also being developed to target previously “undruggable” targets and are close to the maturity necessary to be viable therapeutic approaches for the treatment of NASH and fibrosis. Identifying circulating biomarkers of fibrosis could serve as a valuable, non-invasive diagnostic tool to guide clinical practice. Despite progress in translational and clinical research, one of the major reasons for the absence of effective therapeutics is our incomplete understanding of the pathophysiology that underlies the progression from steatosis to NASH and its most deadly consequence-fibrosis. Multi-omics platforms will help to drive effective precision medicine development in NASH and hepatology.
CONSEQUENCES — Causality, Counterfactuals and Sequential Decision-Making for Recommender Systems

Recommender systems are more and more often modelled as repeated decision making processes – deciding which (ranking of) items to recommend to a given user. Each decision to recommend or rank an item has a significant impact on immediate and future user responses, long-term satisfaction or engagement with the system, and possibly valuable exposure for the item provider. This interactive and interventionist view of the recommender uncovers a plethora of unanswered research questions, as it complicates the typically adopted offline evaluation or learning procedures in the field. We need an understanding of causal inference to reason about (possibly unintended) consequences of the recommender, and a notion of counterfactuals to answer common “what if”-type questions in learning and evaluation. Advances at the intersection of these fields can foster progress in effective, efficient and fair learning and evaluation from logged data. These topics have been emerging in the Recommender Systems community for a while, but we firmly believe in the value of a dedicated forum and place to learn and exchange ideas. We welcome contributions from both academia and industry and bring together a growing community of researchers and practitioners interested in sequential decision making, offline evaluation, batch policy learning, fairness in online platforms, as well as other related tasks, such as A/B testing.
Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS’22)

The constant increase in the amount of data and information available on the Web has made the development of systems that can support users in making relevant decisions increasingly important. Recommender systems (RSs) have emerged as tools to address this task. RSs use the preferences expressed by a user, either explicitly or implicitly, to filter the available information and proactively suggest items that might be of interest to him or her. Although in early works about the topic there was a strong interest in ways to make such systems proactive, user-friendly, and persuasive, over time they became increasingly focused on the algorithmic component solely. However, this trend is gradually being reversed and always more attention is nowadays placed also on Human Decision Making models that focus on supporting the end user in understanding what is being proposed through RSs by using dynamic and persuasive interfaces. A recommender system should be based on valuable strategies for proactively guiding users to items that match their preferences and therefore should put attention on how it is possible to make this process trustable, pleasant, and user-friendly. Such systems, moreover, should take into account psychological, cognitive and emotional aspects to enable personalization that is appropriate not only to the context of use but also to the psychological reactions of the end user. The workshop provides a venue for works that invest in the design of recommender systems which consider users’ experience during the interaction, as well as for works that explore the implications of human-computer interactions with different theories of human decision-making. In this summary, we introduce the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at RecSys’22, review its history, and discuss the most important topics considered at the workshop.
On the nullcone and the variety $\chi_\mathfrak{g}$ of a semisimple lie algebra

Let g be a semisimple Lie algebra of finite dimension. The nullcone N of g is the set of (x, y) in g\timesg such that x and y are nilpotents and are in the same Borel subalgebra. The main result of this paper is that N is a closed and irreducible subvariety of g \times g whose normalization has rational singularities and such that the normalization morphism is bijective.
Who do you think I am? Interactive User Modelling with Item Metadata

Recommender systems are used in many different applications and contexts, however their main goal can always be summarised as “connecting relevant content to interested users”. Explanations have been found to help recommender systems achieve this goal by giving users a look under the hood that helps them understand why they are recommended certain items. Furthermore, explanations can be considered to be the first step towards interacting with the system. Indeed, for a user to give feedback and guide the system towards better understanding her preferences, it helps if the user has a better idea of what the system has already learned. To this end, we propose a linear collaborative filtering recommendation model that builds user profiles within the domain of item metadata. Our method is hence inherently transparent and explainable. Moreover, since recommendations are computed as a linear function of item metadata and the interpretable user profile, our method seamlessly supports interactive recommendation. In other words, users can directly tweak the weights of the learned profile for more fine-grained browsing and discovery of content based on their current interests. We demonstrate the interactive aspect of this model in an online application for discovering cultural events in Belgium.
DAGFiNN: A Conversational Conference Assistant

DAGFiNN is a conversational conference assistant that can be made available for a given conference both as a chatbot on the website and as a Furhat robot physically exhibited at the conference venue. Conference participants can interact with the assistant to get advice on various questions, ranging from where to eat in the city or how to get to the airport to which sessions we recommend them to attend based on the information we have about them. The overall objective is to provide a personalized and engaging experience and allow users to ask a broad range of questions that naturally arise before and during the conference.
K-Periodic Scheduling for Throughput-Buffering Trade-Off Exploration of CSDF

The design of time-critical embedded systems often requires static models of computation such as cyclo-static dataflow. These models enable performance guarantees, execution correctness, and optimized memory usage. Nonetheless, determining optimal buffer sizing of dataflow applications remains difficult: existing methods offer either approximate solutions or fail to provide solutions for complex instances. We propose a throughput-buffering trade-off exploration that uses K-periodic scheduling to direct a design-space exploration—providing optimal solutions while significantly reducing the search space compared to existing methodologies. We compare this strategy against previous approaches and demonstrate search-space reductions over two benchmark suites, resulting in significant improvements in computation times while retaining optimal results.
Self-Supervised Bot Play for Transcript-Free Conversational Recommendation with Rationales

Conversational recommender systems offer a way for users to engage in multi-turn conversations to find items they enjoy. For users to trust an agent and give effective feedback, the recommender system must be able to explain its suggestions and rationales. We develop a two-part framework for training multi-turn conversational recommenders that provide recommendation rationales that users can effectively interact with to receive better recommendations. First, we train a recommender system to jointly suggest items and explain its reasoning via subjective rationales. We then fine-tune this model to incorporate iterative user feedback via self-supervised bot-play. Experiments on three real-world datasets demonstrate that our system can be applied to different recommendation models across diverse domains to achieve state-of-the-art performance in multi-turn recommendation. Human studies show that systems trained with our framework provide more useful, helpful, and knowledgeable suggestions in warm- and cold-start settings. Our framework allows us to use only product reviews during training, avoiding the need for expensive dialog transcript datasets that limit the applicability of previous conversational recommender agents.
Reducing Cross-Topic Political Homogenization in Content-Based News Recommendation

Content-based news recommenders learn words that correlate with user engagement and recommend articles accordingly. This can be problematic for users with diverse political preferences by topic — e.g., users that prefer conservative articles on one topic but liberal articles on another. In such instances, recommenders can have a homogenizing effect by recommending articles with the same political lean on both topics, particularly if both topics share salient, politically polarized terms like “far right” or “radical left. ” In this paper, we propose attention-based neural network models to reduce this homogenization effect by increasing attention on words that are topic specific while decreasing attention on polarized, topic-general terms. We find that the proposed approach results in more accurate recommendations for simulated users with such diverse preferences.
Global and Personalized Graphs for Heterogeneous Sequential Recommendation by Learning Behavior Transitions and User Intentions

Heterogeneous sequential recommendation (HSR) is a very important recommendation problem, which aims to predict a user’s next interacted item under a target behavior type (e.g., purchase in e-commerce sites) based on his/her historical interactions with different behaviors. Though existing sequential methods have achieved advanced performance by considering the varied impacts of interactions with sequential information, a large body of them still have two major shortcomings. Firstly, they usually model different behaviors separately without considering the correlations between them. The transitions from item to item under diverse behaviors indicate some users’ potential behavior manner. Secondly, though the behavior information contains a user’s fine-grained interests, the insufficient consideration of the local context information limits them from well understanding user intentions. Utilizing the adjacent interactions to better understand a user’s behavior could improve the certainty of prediction. To address these two issues, we propose a novel solution utilizing global and personalized graphs for HSR (GPG4HSR) to learn behavior transitions and user intentions. Specifically, our GPG4HSR consists of two graphs, i.e., a global graph to capture the transitions between different behaviors, and a personalized graph to model items with behaviors by further considering the distinct user intentions of the adjacent contextually relevant nodes. Extensive experiments on four public datasets with the state-of-the-art baselines demonstrate the effectiveness and general applicability of our method GPG4HSR.
Identifying New Podcasts with High General Appeal Using a Pure Exploration Infinitely-Armed Bandit Strategy

Podcasting is an increasingly popular medium for entertainment and discourse around the world, with tens of thousands of new podcasts released on a monthly basis. We consider the problem of identifying from these newly-released podcasts those with the largest potential audiences so they can be considered for personalized recommendation to users. We first study and then discard a supervised approach due to the inadequacy of either content or consumption features for this task, and instead propose a novel non-contextual bandit algorithm in the fixed-budget infinitely-armed pure-exploration setting. We demonstrate that our algorithm is well-suited to the best-arm identification task for a broad class of arm reservoir distributions, out-competing a large number of state-of-the-art algorithms. We then apply the algorithm to identifying podcasts with broad appeal in a simulated study, and show that it efficiently sorts podcasts into groups by increasing appeal while avoiding the popularity bias inherent in supervised approaches.
Out‐of‐plane Stability Design by GMNIA – Equivalent Imperfections and Strain Limits

An accurate and consistent approach to the out‐of‐plane stability design of steel beams and structures utilising geometrically and materially nonlinear analysis with imperfections (GMNIA) and strain limits is proposed. The method is implemented using computationally efficient beam elements, with the ultimate structural resistance defined either by (i) the ultimate load factor or (ii) the load factor at which a strain limit, determined on the basis of the continuous strength method (CSM), is attained, whichever occurs first. In the present paper, the scope of this method is extended from the in‐plane design of steel structures and structural components to scenarios in which out‐of‐plane stability effects, with a focus on lateral‐torsional buckling (LTB), govern. First, two shapes and corresponding amplitudes of equivalent imperfections, which consider the combined influence of both geometric imperfections and residual stresses, for the out‐of‐stability design of steel and stainless steel members by GMNIA are established. The existing CSM strain limits for in‐plane design are then extended to allow for the influence of shear, torsion and warping, to enable their general applicability to three‐dimensional buckling problems. It is shown that employing the proposed design method together with the proposed equivalent imperfections generally provides more accurate and consistent ultimate strength predictions than traditional Eurocode design calculations and also streamlines the design process by eliminating the need for cross‐section classification and individual member design checks.
Scale invariant matter distribution in the universe. I: Counts in cells

Les denombrements des galaxies ou le contenu en matiere sont calcules a l'interieur d'une cellule placee aleatoirement, sous la seule hypothese de l'invariance d'echelle des fonctions de correlation N corps. Les nombreuses formes prises par la probabilite de trouver N objets dans un volume donne sont obtenues comme fonction de sa taille. On montre que la probabilite a grande echelle du vide est une extrapolation sans echelle de son comportement a petite echelle. Ce comportement a grande echelle est rattache a la loi en puissance observee a l'extremite faible des fonctions de luminosite. Une loi d'echelle est trouvee, les distributions des galaxies et des amas etant exprimees par la meme fonction universelle
Completing the spectrum of semiframes with block size three

A k ‐semiframe of type g u is a k ‐GDD of type g u ( X , G , ℬ ) , in which the collection of blocks ℬ can be written as a disjoint union ℬ = P ∪ Q , where P is partitioned into parallel classes of X and Q is partitioned into holey parallel classes, each holey parallel class being a partition of X \ G for some G ∈ G . In this paper, we will introduce a new concept of t ‐perfect semiframe and use it to prove the existence of a 3‐semiframe of type g u with even group size. This completes the proof of the existence of 3‐semiframes with uniform group size.
Converting natural language policy article into MBSE model

System Engineers work on designing human interfaces for easy access to governance and technology. As we move to digital engineering, the policy documents are still written in natural language that sometimes is obscure and verbose. One way of digitizing is to convert natural language policies into machine readable system engineering models. In the past, converting natural language written policy documents into machine readable models involves great human effort and expert knowledge in relevant domains, which is a time consuming, tedious, and sometimes impossible task. Artificial Intelligence and its application have shown the potential to accelerate the process. In this study, we proposed a natural language processing (NLP)‐based framework for information extraction under the general condition that can automatically detect the actors and their responsible actions. To validate the performance of the model developed, we compared the NLP generated report with manually created SysML model. The result shows that the precision and recall rate of extracting roles and responsibility is ~0.86 and ~0.66, respectively, representing that this text‐to‐model framework has the potential to accurately convert general policy documents into SysML.
Seismic Stability from Low Ductility to Enhanced Resilience

The fundamental basis for seismic stability is clear – during an earthquake, a building must be capable of carrying gravity loads while developing large inelastic deformations and associated lateral displacements. However, structural design to achieve this performance objective is complex and depends upon multiple system parameters. In current seismic provisions, the global destabilizing effects of gravity are typically considered with simplistic models that may not properly address the actual inelastic stability response of building structures. Building systems with high ductility are used extensively in high‐seismic regions, but in moderate‐seismic regions, systems with low ductility are ubiquitous. The inelastic responses of low‐ductility and high‐ductility systems are starkly different, and life safety must be ensured across this wide variation in behavior. This paper will discuss foundational seismic stability principles and explore the range of inelastic response that is associated with conventional seismic systems and novel next‐generation systems. Results from large‐scale component tests, full‐scale frame tests and numerical earthquake simulations will be used to demonstrate several important concepts: (1) high‐ductility response is an insufficient, and not always necessary, condition for seismic stability; (2) the most critical parameter for seismic stability is persistent secondary stiffness; (3) emerging technologies like recentering, which limit damage, are promising solutions for enhanced seismic resilience.
Modeling of J / ψ modifications in deuteron-nucleus collisions at high energies

Understanding the detailed production and hadronization mechanisms for heavy quarkonia and their modification in a nuclear environment presents one of the major challenges in QCD. Calculations including nuclear-modified parton distribution functions (nPDFs) and fitting of break-up cross sections (sigma_breakup) as parameters have been successful at describing many features of J/psi modification in proton(deuteron)-nucleus collisions. In this paper, we extend these calculations to explore different geometric dependencies of the modification and confront them with new experimental results from the PHENIX experiment. We find that no combination of nPDFs and sigma_breakup, regardless of the nPDF parameter set and the assumed geometric dependence, can simultaneously describe the entire rapidity and centrality dependence of J/psi modifications in d+Au collisions at sqrt(s_NN) = 200 GeV. We also compare the data with coherence calculations and find them unable to describe the full rapidity and centrality dependence as well. We discuss how these calculations might be extended and further tested, in addition to discussing other physics mechanisms including initial-state parton energy loss.