Miracle-dz/UniFAR-FTUs huggingface dataset:
Federated Learning of Causal Effects from Incomplete Observational Data

Decentralized and incomplete data sources are prevalent in real-world applications, posing a formidable challenge for causal inference. These sources cannot be consolidated into a single entity owing to privacy constraints, and the presence of missing values within them can potentially introduce bias to the causal estimands. We introduce a new approach for federated causal inference from incomplete data, enabling the estimation of causal effects from multiple decentralized and incomplete data sources. Our approach disentangles the loss function into multiple components, each corresponding to a specific data source with missing values. Our approach accounts for the missing data under the missing at random assumption, while also estimating higher-order statistics of the causal estimands. Our method recovers the conditional distribution of missing confounders given the observed confounders from the decentralized data sources to identify causal effects. Our framework estimates heterogeneous causal effects without the sharing of raw training data among sources, which helps to mitigate privacy risks. The efficacy of our approach is demonstrated through a collection of simulated and real-world instances, illustrating its potential and practicality.
Learning to Intervene on Concept Bottlenecks

While traditional deep learning models often lack interpretability, concept bottleneck models (CBMs) provide inherent explanations via their concept representations. Specifically, they allow users to perform interventional interactions on these concepts by updating the concept values and thus correcting the predictive output of the model. Traditionally, however, these interventions are applied to the model only once and discarded afterward. To rectify this, we present concept bottleneck memory models (CB2M), an extension to CBMs. Specifically, a CB2M learns to generalize interventions to appropriate novel situations via a two-fold memory with which it can learn to detect mistakes and to reapply previous interventions. In this way, a CB2M learns to automatically improve model performance from a few initially obtained interventions. If no prior human interventions are available, a CB2M can detect potential mistakes of the CBM bottleneck and request targeted interventions. In our experimental evaluations on challenging scenarios like handling distribution shifts and confounded training data, we illustrate that CB2M are able to successfully generalize interventions to unseen data and can indeed identify wrongly inferred concepts. Overall, our results show that CB2M is a great tool for users to provide interactive feedback on CBMs, e.g., by guiding a user's interaction and requiring fewer interventions.
Physics-informed neural networks for unsteady incompressible flows with time-dependent moving boundaries

Physics-informed neural networks (PINNs) employed in fluid mechanics deal primarily with stationary boundaries. This hinders the capability to address a wide range of flow problems involving moving bodies. To this end, we propose a novel extension, which enables PINNs to solve incompressible flows with time-dependent moving boundaries. More specifically, we impose Dirichlet constraints of velocity at the moving interfaces and define new loss functions for the corresponding training points. Moreover, we refine training points for flows around the moving boundaries for accuracy. This effectively enforces the no-slip condition of the moving boundaries. With an initial condition, the extended PINNs solve unsteady flow problems with time-dependent moving boundaries and still have the flexibility to leverage partial data to reconstruct the entire flow field. Therefore, the extended version inherits the amalgamation of both physics and data from the original PINNs. With a series of typical flow problems, we demonstrate the effectiveness and accuracy of the extended PINNs. The proposed concept allows for solving inverse problems as well, which calls for further investigations.
ARTIST: ARTificial Intelligence for Simplified Text

Complex text is a major barrier for many citizens when accessing public information and knowledge. While often done manually, Text Simplification is a key Natural Language Processing task that aims for reducing the linguistic complexity of a text while preserving the original meaning. Recent advances in Generative Artificial Intelligence (AI) have enabled automatic text simplification both on the lexical and syntactical levels. However, as applications often focus on English, little is understood about the effectiveness of Generative AI techniques on low-resource languages such as Dutch. For this reason, we carry out empirical studies to understand the benefits and limitations of applying generative technologies for text simplification and provide the following outcomes: 1) the design and implementation for a configurable text simplification pipeline that orchestrates state-of-the-art generative text simplification models, domain and reader adaptation, and visualisation modules; 2) insights and lessons learned, showing the strengths of automatic text simplification while exposing the challenges in handling cultural and commonsense knowledge. These outcomes represent a first step in the exploration of Dutch text simplification and shed light on future endeavours both for research and practice.
Exploring Gender-Based Toxic Speech on Twitter in Context of the #MeToo movement: A Mixed Methods Approach

The #MeToo movement has catalyzed widespread public discourse surrounding sexual harassment and assault, empowering survivors to share their stories and holding perpetrators accountable. While the movement has had a substantial and largely positive influence, this study aims to examine the potential negative consequences in the form of increased hostility against women and men on the social media platform Twitter. By analyzing tweets shared between October 2017 and January 2020 by more than 47.1k individuals who had either disclosed their own sexual abuse experiences on Twitter or engaged in discussions about the movement, we identify the overall increase in gender-based hostility towards both women and men since the start of the movement. We also monitor 16 pivotal real-life events that shaped the #MeToo movement to identify how these events may have amplified negative discussions targeting the opposite gender on Twitter. Furthermore, we conduct a thematic content analysis of a subset of gender-based hostile tweets, which helps us identify recurring themes and underlying motivations driving the expressions of anger and resentment from both men and women concerning the #MeToo movement. This study highlights the need for a nuanced understanding of the impact of social movements on online discourse and underscores the importance of addressing gender-based hostility in the digital sphere.
Efficient Learned Lossless JPEG Recompression

JPEG is one of the most popular image compression methods. It is beneficial to compress those existing JPEG files without introducing additional distortion. In this paper, we propose a deep learning based method to further compress JPEG images losslessly. Specifically, we propose a Multi-Level Parallel Conditional Modeling (ML-PCM) architecture, which enables parallel decoding in different granularities. First, luma and chroma are processed independently to allow parallel coding. Second, we propose pipeline parallel context model (PPCM) and compressed checkerboard context model (CCCM) for the effective conditional modeling and efficient decoding within luma and chroma components. Our method has much lower latency while achieves better compression ratio compared with previous SOTA. After proper software optimization, we can obtain a good throughput of 57 FPS for 1080P images on NVIDIA T4 GPU. Furthermore, combined with quantization, our approach can also act as a lossy JPEG codec which has obvious advantage over SOTA lossy compression methods in high bit rate (bpp$>0.9$).
Quantum Analog of Shannon's Lower Bound Theorem

Shannon proved that almost all Boolean functions require a circuit of size $\Theta(2^n/n)$. We prove a quantum analog of this classical result. Unlike in the classical case the number of quantum circuits of any fixed size that we allow is uncountably infinite. Our main tool is a classical result in real algebraic geometry bounding the number of realizable sign conditions of any finite set of real polynomials in many variables.
Glass transition temperatures and crystallization kinetics of a synthetic, anhydrous, amorphous calcium-magnesium carbonate

We report the first calorimetric observations of glass transition temperatures and crystallization rates of anhydrous, amorphous calcium-magnesium carbonate using fast scanning differential scanning calorimetry. Hydrous amorphous Ca0.95Mg0. 05CO3 · 0.5H2O (ACMC) solid was precipitated from a MgCl2–NaHCO3 buffered solution, separated from the supernatant, and freeze-dried. An aliquot of the freeze-dried samples was additionally dried at 250°C for up to 6 h in a furnace and in a high-purity N2 atmosphere to produce anhydrous ACMC. The glass transition temperature of the anhydrous Ca0.95Mg0. 05CO3 was determined by applying different heating rates (1000–6000 K s−1) and correcting for thermal lag to be 376°C and the relaxational heat capacity was determined to be Cp = 0.16 J/(g K). Additionally, the heating rate dependence of the temperature that is associated with the corrected crystallization peaks is used to determine the activation energy of crystallization to be 275 kJ mol−1. A high-resolution transmission electron microscopy study on the hydrous and anhydrous samples provided further constraints on their compositional and structural states. This article is part of the theme issue 'Exploring the length scales, timescales and chemistry of challenging materials (Part 1)'.
Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique

Abstract Rice is one of the most widely consumed foods all over the world. Various diseases and deficiency disorders impact the rice crop’s growth, thereby hampering the rice yield. Therefore, proper crop monitoring is very important for the early diagnosis of diseases or deficiency disorders. Diagnosis of diseases and disorders requires specialized manpower, which is not scalable and accessible to all farmers. To address this issue, machine learning and deep learning (DL)-driven automated systems are designed, which may help the farmers in diagnosing disease/deficiency disorders in crops so that proper care can be taken on time. Various studies have used transfer learning (TL) models in the recent past. In recent studies, further improvement in rice disease and deficiency disorder diagnosis system performance is achieved by performing the ensemble of various TL models. However, in all these DL-based studies, the segmentation of the region of interest is not done beforehand and the infected-region extraction is left for the DL model to handle automatically. Therefore, this article proposes a novel framework for the diagnosis of rice-infected leaves based on DL-based segmentation with bitwise logical AND operation and DL-based classification. The rice diseases covered in this study are bacterial leaf blight, brown spot, and leaf smut. The rice nutrient deficiencies like nitrogen (N), phosphorous (P), and potassium (K) were also included. The results of the experiment conducted on these datasets showed that the performance of DeepBatch was significantly improved as compared to the conventional technique.
Prebiotic proanthocyanidins inhibit bile reflux-induced esophageal adenocarcinoma through reshaping the gut microbiome and esophageal metabolome

The gut and local esophageal microbiome progressively shift from healthy commensal bacteria to inflammatory-linked pathogenic bacteria in patients with gastroesophageal reflux disease, Barrett’s esophagus and esophageal adenocarcinoma (EAC). However, mechanisms by which microbial communities and metabolites contribute to reflux-driven EAC remain incompletely understood and challenging to target. Herein, we utilized a rat reflux-induced EAC model to investigate targeting the gut microbiome-esophageal metabolome axis with cranberry proanthocyanidins (C-PAC) to inhibit EAC progression. Sprague Dawley rats, with or without reflux-induction received water or C-PAC ad libitum (700 µg/rat/day) for 25 or 40 weeks. C-PAC exerted prebiotic activity abrogating reflux-induced dysbiosis, and mitigating bile acid metabolism and transport, culminating in significant inhibition of EAC through TLR/NF-κB/P53 signaling cascades. At the species level, C-PAC mitigated reflux-induced pathogenic bacteria (Clostridium perfringens, Escherichia coli, and Proteus mirabilis). C-PAC specifically reversed reflux-induced bacterial, inflammatory and immune-implicated proteins and genes including Ccl4, Cd14, Crp, Cxcl1, Il6, Il1β, Lbp, Lcn2, Myd88, Nfkb1, Tlr2 and Tlr4 aligning with changes in human EAC progression, as confirmed through public databases. C-PAC is a safe promising dietary constituent that may be utilized alone or potentially as an adjuvant to current therapies to prevent EAC progression through ameliorating reflux-induced dysbiosis, inflammation and cellular damage.
Duckweed (Lemnaceae) for potentially nutritious human food: A review

ABSTRACT With continuous global population growth, the challenges of expanding demand for sufficient and nutritious foods need to be addressed by exploring new and supplemental agricultural and food systems. Duckweed demonstrates a great potential for human food with many advantages. This review covers recent research on duckweed regarding plant cultivation, nutritional composition and quality, product development, and related safety issues and regulations for human food uses. Challenges and future research needs are discussed. Species and cultivation conditions have a significant effect on chemical and nutritional compositions of duckweed. Certain duckweed species contain high protein content (up to 45%) that provides all the essential amino acids meeting FAO references and can support human body growth and development. Duckweed also contains versatile carbohydrates including starch, cellulose, trace hemicellulose, and pectin that deliver functional properties and nutrients. In addition, duckweed is rich in minerals, vitamins, and phytochemicals, especially lutein and β-carotene which are positively associated with reduced risk of many chronic diseases. Duckweed could be used either in conventional cereal-based foods by partial substitution of wheat flour or for extraction of proteins and phytochemicals as functional ingredients. Studies are warranted for developing a duckweed supply chain and establishing duckweed as a novel staple food.
Plant immunity activated and suppressed by a conserved effector protein family in mirid bug Riptortus pedestris

Riptortus pedestris (Fabricius) a major soybean pest migrates into soybean fields during pod filling stage resulting in a leaf and stem staygreen while pods without beans syndrome. Given the agricultural importance of this species and the lack of characterized HAMP from piercing-sucking insects we performed a large scale of screening by expression of 87 R. pedestris salivary proteins with signal peptides in Nicotiana benthamiana obtaining a candidate HAMP RPH1. RPH1 activated a series of PTI responses including ROS burst upregulation of defense marker genes such as PR1 WRKY7 WRKY8 Acre31 and CYP71D20 MAPK activation and biosynthesis of phytohormones in plants. RPH1 significantly enhances soybean resistance against R. pedestris feeding. PRR coreceptors BAK1 and SOBIR1 were required for RPH1-induced PTI responses. Remarkably RPH1 homologs were widely distributed in herbivorous insects and majority of homologs from selected species induced cell death or ROS. Thus our results demonstrated that RPH1 is a conserved HAMP within chewing and piercing-sucking insects. We also discovered that R. pedestris evolved four paralogs to overcome the plant immunity triggered by RPH1. This study filled a major gap of HAMP identification from piercing-sucking insect and also deciphered a novel evasion strategy of plant immunity exploited by herbivorous insects. One sentence summary Riptortus pedestris RPH1, a conserved HAMP in herbivores, activates a variety of PTI responses in plants. To couterdefense, R. pedestris evolved four paralogs to suppress RPH1-induced PTI responses. The author(s) responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (https://academic.oup.com/plcell/pages/General-Instructions) is: Ai Xia (xia@njau.edu.edu).
Flexible-Routing Anonymous Networks Using Optimal Length of

We present an efficient Hybrid Mix scheme that provides both routing flexibility and the optimal length of ciphertext. Although it is rather easy to embed routing information in the ciphertext, and a scheme that provides the optimal length of ciphertext is already known, it is not a trivial task to achieve both properties all at the same time. A critical obstacle for providing the optimal length of ciphertext is the session-key encapsulation header in a ciphertext that carries the encrypted session-key to each router, which linearly increases according to the number of intermediate routers. We solve this problem by improving the previously reported Hybrid Mix scheme such that the resulting scheme benefits from routing flexibility with a constant length of such headers. Our basic scheme is only secure against honest, but curious intermediate routers. Therefore, we further address the robustness issue to prevent malicious behavior by incorporating and improving an existing efficient approach based on the Message Authentication Code. key words: Mix-net, Hybrid Mix, Onion Routing, anonymity
Tgfbr1 regulates lateral plate mesoderm and endoderm reorganization during the trunk to tail transition

During the trunk to tail transition the mammalian embryo builds the outlets for the intestinal and urogenital tracts, lays down the primordia for the hindlimb and external genitalia, and switches from the epiblast/primitive streak to the tailbud as the driver of axial extension. Genetic and molecular data indicate that Tgfbr1 is a key regulator of the trunk to tail transition. Tgfbr1 has been shown to control the switch of the neuro mesodermal-competent cells from the epiblast to the chordo-neural hinge to generate the tail bud. We now show that Tgfbr1 signaling also controls the remodeling of the lateral plate mesoderm (LPM) and of the embryonic endoderm associated with the trunk to tail transition. In the absence of Tgfbr1 the two LPM layers do not converge at the end of the trunk, extending instead as separate layers enclosing the celomic cavity until the caudal embryonic extremity, and failing to activate markers of primordia for the hindlimb and external genitalia. However, this extended LPM, does not exhibit the molecular signatures characteristic of this tissue in the trunk. The vascular remodeling involving the dorsal aorta and the umbilical artery leading to the connection between embryonic and extraembryonic circulation was also affected in the Tgfbr1 mutant embryos. Similar alterations in the LPM and vascular system were also observed in Isl1 null mutants, indicating that this factor acts in the regulatory cascade downstream of Tgfbr1 in LPM-derived tissues. In addition, in the absence of Tgfbr1 the embryonic endoderm fails to expand to form the endodermal cloaca and to extend posteriorly to generate the tail gut. We present evidence suggesting that the remodeling activity of Tgfbr1 in the LPM and endoderm results from the control of the posterior primitive streak fate after its regression during the trunk to tail transition. Our data, together with previously reported observations, place Tgfbr1 at the top of the regulatory processes controlling the trunk to tail transition.
A Simple Approximation for Normal Distribution Function

This paper proposes an approximation to the standard normal distribution function. The introduced approximation formula is very simple and it has a very acceptable accurate. By comparing the proposed approximation with other existing approximations, it can be observed that the proposed one has a simple, easily computable formula and it gives a good accurate with maximum absolute error equals 0.000444.
University of Birmingham Development and experimental study of a small- scale compressed air radial inflow turbine for distributed power generation

With ever increasing demand on energy, disturbed power generation utilizing efficient technologies such as compressed air energy storage (CAES) or organic Rankine cycle (ORC) are receiving growing attention. Expander for such systems is a key component and its performance has substantial effects on overall system efficiency. This study addresses such component by proposing an effective and comprehensive methodology for developing a small-scale radial inflow turbine (RIT). The methodology consists of 1-D modelling, 3-D aerodynamic investigation and structural analysis, manufacturing with pioneering technique and experimental testing for validation. The proposed 1-D modelling was very effective in determining the primary geometry and performance of turbine based on parametric studies of turbine input design variables. However with CFD analysis, it was shown that more efficient turbine geometry can be achieved that not only provides more realistic turbine performance by capturing the 3-D fluid flow behaviour but also improves turbine efficiency with the aid of parametric studies of turbine geometry parameters. Turbine efficiency was improved from 81.3% obtained from 1-D modelling to 84.5% obtained by CFD. Accuracy of the CFD model was assessed by conducting experiments on the RIT manufactured with stereolithography technique. The CFD model can predict turbine efficiency and power with accuracy of ±16% and ±13% respectively for a wide range of tested operating conditions. Such results highlights the effectiveness of the proposed methodology and the CFD model can be used as benchmarking model for analyses of smallscale RITs. Besides, it was shown that for such applications, the novel manufacturing technique and employed material are very effective for producing prototypes that assist design decisions and validation of CFD model with reasonable accuracy at reasonable cost and in timely manner.
Quasi-linear Masking to Protect Kyber against both SCA and FIA

The recent technological advances in Post-Quantum Cryptography (PQC) rise the questions of robust implementations of new asymmetric cryptographic primitives in today’s technology. This is the case for the lattice-based CRYSTALS-Kyber algorithm which has been selected as the first NIST standard for Public Key Encryption (PKE) and Key Encapsulation Mechanisms (KEM). We have notably to make sure the Kyber implementation is resilient against physical attacks like Side-Channel Analysis (SCA) and Fault Injection Attacks (FIA). To reach this goal, we propose to use the masking coun-termeasure, more precisely the generic Direct Sum Masking method (DSM). By taking inspiration of a previous paper on AES, we extend the method to finite fields of characteristic prime other than 2 and even-length codes. In particular, we investigated its application to Keccak, which is the hash-based function used in Kyber. We also provided the first masked implementation of Kyber providing both SCA and FIA resilience while not requiring any conversion between different masking methods.