Miracle-dz/UniFAR-FTUs huggingface dataset:
Did You Get That? Predicting Learners’ Comprehension of a Video Lecture from Visualizations of Their Gaze Data

Abstract In online lectures, unlike in face‐to‐face lectures, teachers lack access to (nonverbal) cues to check if their students are still “with them” and comprehend the lecture. The increasing availability of low‐cost eye‐trackers provides a promising solution. These devices measure unobtrusively where students look and can visualize these data to teachers. These visualizations might inform teachers about students’ level of “with‐me‐ness” (i.e., do students look at the information that the teacher is currently talking about) and comprehension of the lecture, provided that (1) gaze measures of “with‐me‐ness” are related to comprehension, (2) people not trained in eye‐tracking can predict students’ comprehension from gaze visualizations, (3) we understand how different visualization techniques impact this prediction. We addressed these issues in two studies. In Study 1, 36 students watched a video lecture while being eye‐tracked. The extent to which students looked at relevant information and the extent to which they looked at the same location as the teacher both correlated with students’ comprehension (score on an open question) of the lecture. In Study 2, 50 participants watched visualizations of students’ gaze (from Study 1), using six visualization techniques (dynamic and static versions of scanpaths, heatmaps, and focus maps) and were asked to predict students’ posttest performance and to rate their ease of prediction. We found that people can use gaze visualizations to predict learners’ comprehension above chance level, with minor differences between visualization techniques. Further research should investigate if teachers can act on the information provided by gaze visualizations and thereby improve students’ learning.
Effects of diazoxide on streptozotocin induced β cell damage via HSP70/HSP90/TLR4/AMPK signaling pathways

ABSTRACT I investigated the effects of diazoxide, a mitochondrial potassium channel opener, on streptozotocin (STZ) induced pancreatic β cell damage via the HSP70/HSP90/TLR4/AMPK signaling pathways in vitro. I used the pancreatic β cell line, 1.1B4, to create four groups: control, STZ treated, diazoxide treated, STZ + diazoxide treated. The STZ treated cells were exposed to 20 µM STZ for 2 h with or without 100 µM diazoxide for 24 h. Total antioxidant status (TAS), total oxidant status (TOS), cell viability and mitochondrial membrane potential (MMP) were measured. Expression of ATP-sensitive potassium channel (KATP) subunits, heat shock protein-70 (HSP70), heat shock protein-90 (HSP90), toll-like receptor 4 (TLR4), AMP-activated protein kinase (AMPK) and some apoptotic proteins were detected using western blotting. Apoptosis was assessed using TUNEL staining. STZ increased TOS and OSI in the pancreatic β cells; however, diazoxide failed to improve oxidative stress. Also, STZ increased tunnel positive cells in the pancreatic β cells. Diazoxide decreased the tunnel positive cells in the STZ treated β cell. STZ decreased MMP; however, diazoxide did not normalize MMP in the STZ induced β cells. Diazoxide increased the HSP70:HSP90 protein expression ratio. STZ decreased expression of AMPK and subunits of KATP channel and increased the expression of caspase-3 and TLR4 protein; diazoxide normalized the expression of all proteins studied. KATP channel opening by diazoxide protects pancreatic β cells against STZ toxicity via HSP70/HSP90/TLR4/AMPK signaling.
Question‐based development of high‐risk medical devices: A proposal for a structured design and review process

The recent introduction of the European Medical Device Regulation poses stricter legislation for manufacturers developing medical devices in the EU. Many devices have been placed into a higher risk category, thus requiring more data before market approval, and a much larger focus has been placed on safety. For implantable and Class III devices, the highest risk class, clinical evidence is a necessity. However, the requirements of clinical study design and developmental outcomes are only described in general terms due to the diversity of devices.
EAACI guidelines on environmental science in allergic diseases and asthma – Leveraging artificial intelligence and machine learning to develop a causality model in exposomics

Allergic diseases and asthma are intrinsically linked to the environment we live in and to patterns of exposure. The integrated approach to understanding the effects of exposures on the immune system includes the ongoing collection of large‐scale and complex data. This requires sophisticated methods to take full advantage of what this data can offer. Here we discuss the progress and further promise of applying artificial intelligence and machine‐learning approaches to help unlock the power of complex environmental data sets toward providing causality models of exposure and intervention. We discuss a range of relevant machine‐learning paradigms and models including the way such models are trained and validated together with examples of machine learning applied to allergic disease in the context of specific environmental exposures as well as attempts to tie these environmental data streams to the full representative exposome. We also discuss the promise of artificial intelligence in personalized medicine and the methodological approaches to healthcare with the final AI to improve public health.
A comparison of dynamical and molecular gas masses in very luminous infrared galaxies

Observations of the 2.3 micron bands of CO have been used to measure the central stellar velocity dispersions in NGC 1614 and IC 694. K-band images were used to constrain models of the stellar mass distribution. With these data and a spectrum of Arp 220 from the literature, we have determined the total mass within the central regions of all three galaxies. The central molecular gas masses inferred from (12)CO J = 1-0 fluxes are larger than the total masses determined from stellar kinematics. Subtraction of the likely stellar mass from the total mass yields upper limits for the H2 mass that are 4-10 times less than the previously reported masses. This result supports other arguments that the I(sub CO)/M(sub H2) ratio is not the same in ultraluminous galaxies as it is in Milky Way giant molecular clouds.
Nitrogen availability mediates soil carbon cycling response to climate warming: A meta‐analysis

Global climate warming may induce a positive feedback through increasing soil carbon (C) release to the atmosphere. Although warming can affect both C input to and output from soil, direct and convincing evidence illustrating that warming induces a net change in soil C is still lacking. We synthesized the results from field warming experiments at 165 sites across the globe and found that climate warming had no significant effect on soil C stock. On average, warming significantly increased root biomass and soil respiration, but warming effects on root biomass and soil respiration strongly depended on soil nitrogen (N) availability. Under high N availability (soil C:N ratio < 15), warming had no significant effect on root biomass, but promoted the coupling between effect sizes of root biomass and soil C stock. Under relative N limitation (soil C:N ratio > 15), warming significantly enhanced root biomass. However, the enhancement of root biomass did not induce a corresponding C accumulation in soil, possibly because warming promoted microbial CO2 release that offset the increased root C input. Also, reactive N input alleviated warming‐induced C loss from soil, but elevated atmospheric CO2 or precipitation increase/reduction did not. Together, our findings indicate that the relative availability of soil C to N (i.e., soil C:N ratio) critically mediates warming effects on soil C dynamics, suggesting that its incorporation into C‐climate models may improve the prediction of soil C cycling under future global warming scenarios.
NDUFA8 potentially rescues Wolbachia‐induced cytoplasmic incompatibility in Laodelphax striatellus

The endosymbiont Wolbachia manipulates host reproduction by several strategies, one of the most important of which is cytoplasmic incompatibility (CI). CI can be rescued when Wolbachia‐infected males mate with females infected with the same Wolbachia strain. However, the potential rescue mechanism of CI in the small brown planthopper Laodelphax striatellus is unclear. In this study, comparative transcriptome analysis was applied to explore the effect of Wolbachia on L. striatellus eggs. A total of 1387 differentially expressed genes were identified. RNA interference of 7 Wolbachia‐upregulated key planthopper genes reduced egg reproduction, suggesting that Wolbachia might improve fecundity in L. striatellus by affecting these 7 genes. Suppressing the expression of another upregulated gene, NDUFA8 (encoding NADH dehydrogenase [ubiquinone] 1 α subcomplex subunit 8‐like) by RNA interference significantly increased the mortality of early embryos without affecting the number of deposited eggs. Wolbachia infection upregulated the mRNA level of NDUFA8, and dsNDUFA8 treatment of Wolbachia‐infected females recreated CI‐like symptoms, suggesting that NDUFA8 is associated with the rescue phenotype. Because all L. striatellus populations worldwide are infected with Wolbachia, NDUFA8 is a potential pest control target.
The role of serial cardiac biomarkers in prognostication and risk prediction of chronic heart failure: additional scientific insights with hemodynamic feedback

ABSTRACT Introduction Heart failure (HF) is considered as a chronic long-term and lethal disease and will continue to be a major public health problem. Studying (circulating) biomarkers is a promising field of research and could be the first step toward HF tailored prognostic strategies as well as understanding the response to HF drugs in CHF patients. Areas covered In literature, there has been considerable research on elevated biomarker levels that are related to a poor prognosis for HF. Since biomarker levels change over time, it is important to study serial (repeated) biomarker measurements which may help us better understand the dynamic course of HF illness. However, the majority of research focuses predominantly on baseline values of biomarkers. Additionally, remote monitoring devices, like sensors, can be used to link hemodynamic information to freshen biomarker data in order to further ameliorate the management of HF. Expert opinion Novel biomarkers and additional scientific insights with hemodynamic feedback strongly aid in the prognostication and risk prediction of chronic HF. Graphical abstract
CHiLS: Zero-Shot Image Classification with Hierarchical Label Sets

Open vocabulary models (e.g. CLIP) have shown strong performance on zero-shot classification through their ability generate embeddings for each class based on their (natural language) names. Prior work has focused on improving the accuracy of these models through prompt engineering or by incorporating a small amount of labeled downstream data (via finetuning). However, there has been little focus on improving the richness of the class names themselves, which can pose issues when class labels are coarsely-defined and are uninformative. We propose Classification with Hierarchical Label Sets (or CHiLS), an alternative strategy for zero-shot classification specifically designed for datasets with implicit semantic hierarchies. CHiLS proceeds in three steps: (i) for each class, produce a set of subclasses, using either existing label hierarchies or by querying GPT-3; (ii) perform the standard zero-shot CLIP procedure as though these subclasses were the labels of interest; (iii) map the predicted subclass back to its parent to produce the final prediction. Across numerous datasets with underlying hierarchical structure, CHiLS leads to improved accuracy in situations both with and without ground-truth hierarchical information. CHiLS is simple to implement within existing zero-shot pipelines and requires no additional training cost. Code is available at: https://github.com/acmi-lab/CHILS.
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Retrieval

Image-text retrieval (ITR) is a task to retrieve the relevant images/texts, given the query from another modality. The conventional dense retrieval paradigm relies on encoding images and texts into dense representations using dual-stream encoders, however, it faces challenges with low retrieval speed in large-scale retrieval scenarios. In this work, we propose the lexicon-weighting paradigm, where sparse representations in vocabulary space are learned for images and texts to take advantage of the bag-of-words models and efficient inverted indexes, resulting in significantly reduced retrieval latency. A crucial gap arises from the continuous nature of image data, and the requirement for a sparse vocabulary space representation. To bridge this gap, we introduce a novel pre-training framework, Lexicon-Bottlenecked Language-Image Pre-Training (LexLIP), that learns importance-aware lexicon representations. This framework features lexicon-bottlenecked modules between the dual-stream encoders and weakened text decoders, allowing for constructing continuous bag-of-words bottlenecks to learn lexicon-importance distributions. Upon pre-training with same-scale data, our LexLIP achieves state-of-the-art performance on two benchmark ITR datasets, MSCOCO and Flickr30k. Furthermore, in large-scale retrieval scenarios, LexLIP outperforms CLIP with a 5.5 ~ 221.3X faster retrieval speed and 13.2 ~ 48.8X less index storage memory.
Being at the core: firm product specialisation

We propose a novel measure to investigate firms' product specialisation: product coreness, that captures the centrality of exported products within the firm's export basket. We study product coreness using firm-product level data between 2018 and 2020 for Colombia, Ecuador, and Peru. Three main findings emerge from our analysis. First, the composition of firms' export baskets changes relatively little from one year to the other, and products far from the firm's core competencies, with low coreness, are more likely to be dropped. Second, higher coreness is associated with larger export flows at the firm level. Third, such firm-level patterns also have implications at the aggregate level: products that are, on average, exported with higher coreness have higher export flows at the country level, which holds across all levels of product complexity. Therefore, the paper shows that how closely a product fits within a firm's capabilities is important for economic performance at both the firm and country level. We explore these issues within an econometric framework, finding robust evidence both across our three countries and for each country separately.
An Inexact Deflected Subgradient Algorithm in Infinite Dimensional spaces

We propose a duality scheme for solving constrained nonsmooth and nonconvex optimization problems in a reflexive Banach space. We establish strong duality for a very general type of augmented Lagrangian, in which we assume a less restrictive type of coercivity on the augmenting function. We solve the dual problem (in a Hilbert space) using a deflected subgradient method via this general augmented Lagrangian. We provide two choices of step-size for the method. For both choices, we prove that every weak accumulation point of the primal sequence is a primal solution. We also prove strong convergence of the dual sequence.
Irreversibility analysis for a gravity driven non-Newtonian liquid film along an inclined isothermal plate

In this paper, the first and second laws of thermodynamics are employed in order to study the inherent irreversibility of a gravity driven non-Newtonian Ostwald-de-Waele power law liquid film along an inclined isothermal plate. Based on some simplified assumptions, the governing equations are obtained and solved analytically. Expressions for fluid velocity, temperature, volumetric entropy generation numbers, irreversibility distribution ratio and the Bejan number (Be) are also determined.
Tighter Information-Theoretic Generalization Bounds from Supersamples

In this work, we present a variety of novel information-theoretic generalization bounds for learning algorithms, from the supersample setting of Steinke&Zakynthinou (2020)-the setting of the"conditional mutual information"framework. Our development exploits projecting the loss pair (obtained from a training instance and a testing instance) down to a single number and correlating loss values with a Rademacher sequence (and its shifted variants). The presented bounds include square-root bounds, fast-rate bounds, including those based on variance and sharpness, and bounds for interpolating algorithms etc. We show theoretically or empirically that these bounds are tighter than all information-theoretic bounds known to date on the same supersample setting.
Linear programming bounds for hyperbolic surfaces

We adapt linear programming methods from sphere packings to closed hyperbolic surfaces and obtain new upper bounds on their systole, their kissing number, the first positive eigenvalue of their Laplacian, the multiplicity of their first eigenvalue, and their number of small eigenvalues. Apart from a few exceptions, the resulting bounds are the current best known both in low genus and as the genus tends to infinity. Our methods also provide lower bounds on the systole (achieved in genus $2$ to $7$, $14$, and $17$) that are sufficient for surfaces to have a spectral gap larger than $1/4$.
Hybrid Multiparty Session Types - Full Version

Multiparty session types (MPST) are a specification and verification framework for distributed message-passing systems. The communication protocol of the system is specified as a global type, from which a collection of local types (local process implementations) is obtained by endpoint projection. A global type is a single disciplining entity for the whole system, specified by one designer that has full knowledge of the communication protocol. On the other hand, distributed systems are often described in terms of their components: a different designer is in charge of providing a subprotocol for each component. The problem of modular specification of global protocols has been addressed in the literature, but the state of the art focuses only on dual input/output compatibility. Our work overcomes this limitation. We propose the first MPST theory of multiparty compositionality for distributed protocol specification that is semantics-preserving, allows the composition of two or more components, and retains full MPST expressiveness. We introduce hybrid types for describing subprotocols interacting with each other, define a novel compatibility relation, explicitly describe an algorithm for composing multiple subprotocols into a well-formed global type, and prove that compositionality preserves projection, thus retaining semantic guarantees, such as liveness and deadlock freedom. Finally, we test our work against real-world case studies and we smoothly extend our novel compatibility to MPST with delegation and explicit connections.
Variable Stiffness Improves Safety and Performance in Soft Robotics

This paper proposes a new variable stiffness soft gripper that enables high-performance grasping tasks in industrial applications. The design of the proposed monolithic soft gripper includes a middle bellow and two side bellows (i.e., fingers). The positions of the fingers are regulated by adjusting the negative pressure in the middle bellow actuator via an on-off controller. The stiffness of the soft gripper is modulated by controlling the positive pressure in the fingers through the use of a proportional air-pressure regulator. It is experimentally shown that the proposed soft gripper can modulate its stiffness by 125% within 250ms. It is also shown that the variable stiffness soft gripper can help improve the safety and performance of grasping tasks in industrial applications.
Personalized Interpretable Classification

How to interpret a data mining model has received much attention recently, because people may distrust a black-box predictive model if they do not understand how the model works. Hence, it will be trustworthy if a model can provide transparent illustrations on how to make the decision. Although many rule-based interpretable classification algorithms have been proposed, all these existing solutions cannot directly construct an interpretable model to provide personalized prediction for each individual test sample. In this paper, we make a first step towards formally introducing personalized interpretable classification as a new data mining problem to the literature. In addition to the problem formulation on this new issue, we present a greedy algorithm called PIC (Personalized Interpretable Classifier) to identify a personalized rule for each individual test sample. To demonstrate the necessity, feasibility and advantages of such a personalized interpretable classification method, we conduct a series of empirical studies on real data sets. The experimental results show that: (1) The new problem formulation enables us to find interesting rules for test samples that may be missed by existing non-personalized classifiers. (2) Our algorithm can achieve the same-level predictive accuracy as those state-of-the-art (SOTA) interpretable classifiers. (3) On a real data set for predicting breast cancer metastasis, such a personalized interpretable classifier can outperform SOTA methods in terms of both accuracy and interpretability.
Optimal Diffusion Auctions

Diffusion auction design is a new trend in mechanism design for which the main goal is to incentivize existing buyers to invite new buyers, who are their neighbors on a social network, to join an auction even though they are competitors. With more buyers, a diffusion auction will be able to give a more efficient allocation and receive higher revenue. Existing studies have proposed many interesting diffusion auctions to attract more buyers, but the seller's revenue is not optimized. Hence, in this study, we investigate what optimal revenue the seller can achieve by attracting more buyers. In traditional single-item auctions, Myerson has proposed a mechanism to achieve optimal revenue. However, in the network setting, we prove that a globally optimal mechanism for all structures does not exist. Instead, we show that given a structure, we have a mechanism to get the optimal revenue under this structure only. Since a globally optimal mechanism does not exist, our next goal is to design a mechanism that has a bounded approximation of the optimal revenue in all structures. The approximation of all the early diffusion auctions is zero, and we propose the first mechanism with a non-zero approximation.