Miracle-dz/UniFAR-FTUs huggingface dataset:
Drought stress-induced the formation of heteromorphic leaves of Populus euphratica Oliv: evidence from gene transcriptome

Populus euphratica Oliv. , a dominant species of arid desert community, grows heteromorphic leaves at different crown positions. Whether heteromorphic leaves are a strategy of plant adaptation to drought stress is rarely reported. This study sequenced the transcriptome of three typical heteromorphic leaves (lanceolate, ovate and broad-ovate leaves) of P. euphratica, and measured their drought stress. We wanted to reveal the molecular mechanisms underlying the formation of heteromorphic leaves. Drought stress was increased significantly from lanceolate to ovate to broad-ovate leaves. Gene ontology (GO) and KEGG enrichment analysis showed that the MADs-box gene regulated the expression of peroxidase (POD) in the phenylpropane biosynthetic pathway. The up-regulated expression of the chalcone synthase (CHS) gene in broad-ovate leaves significantly activated the flavonoid biosynthetic pathway. In the process of leaf shape change, the different expressions of homeodomain leucine zipper (HD-ZIP) among the three heteromorphic leaves had potential interactions on the AUX and ABA pathways. The expression of Sucrose phosphate synthase (SPS) and sucrose synthase (SUS) increased from lanceolate to broad-ovate leaves, resulting in a consistent change in starch and sucrose content. We concluded that these resistance-related pathways are expressed in parallel with leaf formation genes, thereby inducing the formation of heteromorphic leaves. Our work provided a new insights for desert plants to adapt to drought stress.
Development of virus-like particles with inbuilt immunostimulatory properties as vaccine candidates

The development of virus-like particle (VLP) based vaccines for human papillomavirus, hepatitis B and hepatitis E viruses represented a breakthrough in vaccine development. However, for dengue and COVID-19, technical complications, such as an incomplete understanding of the requirements for protective immunity, but also limitations in processes to manufacture VLP vaccines for enveloped viruses to large scale, have hampered VLP vaccine development. Selecting the right adjuvant is also an important consideration to ensure that a VLP vaccine induces protective antibody and T cell responses. For diseases like COVID-19 and dengue fever caused by RNA viruses that exist as families of viral variants with the potential to escape vaccine-induced immunity, the development of more efficacious vaccines is also necessary. Here, we describe the development and characterisation of novel VLP vaccine candidates using SARS-CoV-2 and dengue virus (DENV), containing the major viral structural proteins, as protypes for a novel approach to produce VLP vaccines. The VLPs were characterised by Western immunoblot, enzyme immunoassay, electron and atomic force microscopy, and in vitro and in vivo immunogenicity studies. Microscopy techniques showed proteins self-assemble to form VLPs authentic to native viruses. The inclusion of the glycolipid adjuvant, α-galactosylceramide (α-GalCer) in the vaccine formulation led to high levels of natural killer T (NKT) cell stimulation in vitro, and strong antibody and memory CD8+ T cell responses in vivo, demonstrated with SARS-CoV-2, hepatitis C virus (HCV) and DEN VLPs. This study shows our unique vaccine formulation presents a promising, and much needed, new vaccine platform in the fight against infections caused by enveloped RNA viruses.
Novel EAAT2 activators improve motor and cognitive impairment in a transgenic model of Huntington’s disease

Introduction Glutamate excitotoxicity is causal in striatal neurodegeneration underlying motor dysfunction and cognitive deficits in Huntington’s disease (HD). Excitatory amino acid transporter 2 (EAAT2), the predominant glutamate transporter accounting for >90% of glutamate transport, plays a key role in preventing excitotoxicity by clearing excess glutamate from the intrasynaptic cleft. Accordingly, EAAT2 has emerged as a promising therapeutic target for prevention of neuronal excitotoxicity underlying HD and other neurodegenerative diseases. Methods We have previously designed novel EAAT2 positive allosteric modulator GT951, GTS467, and GTS551, with low nanomolar efficacy in glutamate uptake and favorable pharmacokinetic properties. In this study, we test the neuroprotective abilities of these novel EAAT2 activators in vivo using the robust Drosophila HD transgenic model expressing human huntingtin gene with expanded repeats (Htt128Q). Results All three compounds significantly restored motor function impaired under HD pathology over a wide dose range. Additionally, treatment with all three compounds significantly improved HD-associated olfactory associative learning and short-term memory defects, while GT951 and GTS551 also improved middle-term memory in low-performing group. Similarly, treatment with GT951 and GTS551 partially protected against early mortality observed in our HD model. Further, treatment with all three EAAT2 activators induced epigenetic expression of EAAT2 Drosophila homolog and several cognition-associated genes. Conclusion Together, these results highlight the efficacy of GT951, GTS467 and GTS551 in treating motor and cognitive impairments under HD pathology and support their development for treatment of HD.
FairPrism: Evaluating Fairness-Related Harms in Text Generation

It is critical to measure and mitigate fairness-related harms caused by AI text generation systems, including stereotyping and demeaning harms. To that end, we introduce FairPrism, a dataset of 5,000 examples of AI-generated English text with detailed human annotations covering a diverse set of harms relating to gender and sexuality. FairPrism aims to address several limitations of existing datasets for measuring and mitigating fairness-related harms, including improved transparency, clearer specification of dataset coverage, and accounting for annotator disagreement and harms that are context-dependent. FairPrism’s annotations include the extent of stereotyping and demeaning harms, the demographic groups targeted, and appropriateness for different applications. The annotations also include specific harms that occur in interactive contexts and harms that raise normative concerns when the “speaker” is an AI system. Due to its precision and granularity, FairPrism can be used to diagnose (1) the types of fairness-related harms that AI text generation systems cause, and (2) the potential limitations of mitigation methods, both of which we illustrate through case studies. Finally, the process we followed to develop FairPrism offers a recipe for building improved datasets for measuring and mitigating harms caused by AI systems.
Video Object Extraction using the Curvelet Transform

: In this paper a novel scheme for video object extraction based on the second generation of the curvelet transform which is called Fast Discrete Curvelet Transform (FDCT) via wrapping is introduced. The main advantages of using FDCT are the approximate properties and the high directional sensitivity of this transform. An imaginary component of the curvelet coefﬁcients to extract the moving objects in the video sequence is used. The proposed algorithm is mainly divided into two steps. The ﬁrst step is based on estimating the static background from the initial frames using FDCT, the seconded step is based on subtracting the background from each video frame for obtaining the moving objects. Experimental results show a promising results than traditional wavlelet transform, where the accuracy ratios of each video frame is maximized while the error ratios are minimized compared with the wavelet transform.
8: Linear Systems & optics

Focus on linear, intensity-based models. When we look at a point of light, like a star, what is the image on the retina? It is slightly blurred image called the “Point Spread Function” or PSF. (in other contexts, the “impulse response function”) The PSF of the human eye is determined by optical aberrations, defocus, and in the limit of small pupil size, by diffraction due to the wave nature of light. For a one dimensional image or signal, the principle of superposition says that the intensity of the output, ri, corresponding to pixel location i can be expressed as: ri =  i'=0 wi+i' li'
Improving the Reliability of Health Information Credibility Assessments

The applicability of retrieval algorithms to real data relies heavily on the quality of the training data. Currently, the creation process of training and test collections for retrieval systems is often based on annotations produced by human assessors following a set of guidelines. Some concepts, however, are prone to subjectivity, which could restrict the utility of any algorithm developed with the resulting data in real world applications. One such concept is credibility, which is an important factor in user’s judgements on whether retrieved information helps to answer an information need. In this paper, we evaluate an existing set of assessment guidelines with respect to their ability to generate reliable credibility judgements across multiple raters. We identify reasons for disagreement and adapt the guidelines to create an actionable and traceable annotation scheme that i) leads to higher inter-annotator reliability, and ii) can inform about why a rater made a specific credibility judgement. We provide promising evidence about the robustness of the new guidelines and conclude that they could be a valuable resource for building future test collections for misinformation detection.
On the Episodic Difficulty of Few-shot Learning

Dog vs. hot dog and dog vs. wolf , which one tends to be a harder comparison task? While simple, this question can be meaningful for few-shot classification. Few-shot learning enables trained models to recognize unseen classes through just a few labelled samples. As such, trained few-shot models usually have to possess the ability to assess the similarity degree between the unlabelled and labelled samples. In each few-shot learning episode, a combination of the labelled support set and unlabelled query set are sampled from the training dataset for model-training. In the episodic settings of few-shot learning, most algorithms draw the data samples uniformly at random for training. However, this approach disregards concepts of difficulty of each training episode, which may make a difference. After all, it is usually easier to differentiate between a dog and a hot dog, versus the dog and a wolf. Therefore, in this paper, we delve into the concept of episodic difficulty, or difficulty of each training episode, discovering several insights and proposing strategies to utilize the difficulty. Firstly, defining episodic difficulty as a training loss, we find and study the correlation between episodic difficulty and visual similarity among data samples in each episode. Secondly, we assess the respective usefulness of easy and difficult episodes for the training process. Lastly, based on the assessment, we design a curriculum for few-shot learning to support training with incremental difficulty. We observe that such an approach can achieve faster convergence for few-shot algorithms, reducing the average training time by around 50%. It can also make meta-learning algorithms achieve an increase in final testing accuracy scores. Our major implementation is available at: https://github.com/WendyBaiYunwei/EpisodicDifficulty.
Learning Constrained Structured Spaces with Application to Multi-Graph Matching

Multi-graph matching is a prominent structured prediction task, in which the predicted label is constrained to the space of cycle-consistent matchings. While direct loss minimization is an effective method for learning predictors over structured label spaces, it cannot be applied efficiently to the problem at hand, since executing a specialized solver across sets of matching predictions is computationally prohibitive. Moreover, there’s no supervision on the ground-truth matchings over cycle-consistent prediction sets. Our key insight is to strictly enforce the matching constraints in pairwise matching predictions and softly enforce the cycle-consistency constraints by casting them as weighted loss terms, such that the severity of inconsistency with global predictions is tuned by a penalty parameter. Inspired by the classic penalty method, we prove that our method theoretically recovers the optimal multi-graph matching constrained solution. Our method’s advantages are brought to light in experimental results on the popular keypoint matching task on the Pascal VOC and the Willow ObjectClass datasets.
A Review on Multilingual Food Recommendation Systems for Critical Medical Conditions In Pregnancy Care

AMedical conditions require proper attention as some of them especially those occurring in women that are expecting pregnancy require proper care. Although pregnancy exposes them to several critical health conditions, most of these conditions were actually in existence before pregnancy with their causes traced back to poor nutrition resulting from food intake. However, there have been a good number of food recommendation systems in existence with few of them being context-aware. This work is aimed at performing a critical review of the food recommender systems that considers medical conditions and design the food ontology for critical medical conditions that adopts the pathology test results of patients in caring for the existing medical conditions such as pregnancy. Focus of the framework is on deploying a multilingual ontology-based approach in addition to other required approaches for recommendations. This work also introduces the addition of uncooked food in the food ontology for recommendations. The proposed food ontology for critical medical conditions considers the required nutrients based on the combination of several factors including the pathology test results, location/language, medical conditions and user preferences. The use of this food ontology framework for food recommender is reliable due to the additional factors.
Continuous-Time Decision Transformer for Healthcare Applications

Offline reinforcement learning (RL) is a promising approach for training intelligent medical agents to learn treatment policies and assist decision making in many healthcare applications, such as scheduling clinical visits and assigning dosages for patients with chronic conditions. In this paper, we investigate the potential usefulness of Decision Transformer (Chen et al., 2021)-a new offline RL paradigm-in medical domains where decision making in continuous time is desired. As Decision Transformer only handles discrete-time (or turn-based) sequential decision making scenarios, we generalize it to Continuous-Time Decision Transformer that not only considers the past clinical measurements and treatments but also the timings of previous visits, and learns to suggest the timings of future visits as well as the treatment plan at each visit. Extensive experiments on synthetic datasets and simulators motivated by real-world medical applications demonstrate that Continuous-Time Decision Transformer is able to outperform competitors and has clinical utility in terms of improving patients' health and prolonging their survival by learning high-performance policies from logged data generated using policies of different levels of quality.
Knowledge Acquisition for Human-In-The-Loop Image Captioning

Image captioning offers a computational process to understand the semantics of images and convey them using descriptive language. However, automated captioning models may not always generate satisfactory captions due to the complex nature of the images and the quality/size of the training data. We propose an interactive captioning framework to improve machine-generated cap-tions by keeping humans in the loop and performing an online-offline knowledge acquisition (KA) process. In particular, online KA accepts a list of keywords specified by human users and fuses them with the image features to generate a read-able sentence that captures the semantics of the image. It leverages a multimodal conditioned caption completion mechanism to ensure the appearance of all user-input keywords in the generated caption. Offline KA further learns from the user inputs to update the model and benefits caption generation for unseen images in the future. It is built upon a Bayesian transformer architecture that dynamically allocates neural resources and supports uncertainty-aware model updates to mitigate overfitting. Our theoretical analysis also proves that Offline KA automatically selects the best model capacity to accommodate the newly acquired knowledge. Experiments on real-world data demonstrate the effectiveness of the proposed framework.
Symmetry in frontal but not motor and somatosensory corticocortical and corticostriatal 1 circuitry 2 3 Abbreviated title : Corticocortical and corticostriatal symmetry 4 5

40 Neocortex and striatum are topographically organized by cortical areas representing sensory 41 and motor functions, where primary cortical areas are generally used as models for other 42 cortical regions. But different cortical areas are specialized for distinct purposes, with sensory 43 and motor areas lateralized for touch and motor control, respectively. Frontal areas are involved 44 in decision making, where lateralization of function may be less important. This study contrasted 45 the topographic precision of ipsilateral and contralateral projections from cortex based on the 46 injection site location. While sensory cortical areas had strongly topographic outputs to 47 ipsilateral cortex and striatum, they were weaker and not as topographically strong to 48 contralateral targets. Motor cortex had somewhat stronger projections, but still relatively weak 49 contralateral topography. In contrast, frontal cortical areas had high degrees of topographic 50 similarity for both ipsilateral and contralateral projections to cortex and striatum. This 51 contralateral connectivity reflects on the pathways in which corticostriatal computations might 52 integrate input outside closed basal ganglia loops, enabling the two hemispheres to act as a 53 single unit and converge on one result in motor planning and decision making. 54 (Word count: 179) 55 56 Significance Statement 57 The mammalian brain is divided into two cerebral hemispheres, each responsible for 58 sensation and movement on the opposite side of the body. The two sides communicate via an 59 enormous bundle of midline-crossing fibers, the corpus callosum. The callosal projections 60 mainly target neocortex and striatum. While callosal projections originate from most areas of the 61 neocortex, how the anatomy and function of these projections might vary across motor, sensory, 62 and frontal regions is unknown. In particular, callosal projections are proposed here to play a 63 large role in frontal areas, where maintaining unity across hemispheres in value assessment 64 and decision-making for the whole individual is key, but a smaller role for sensory 65 representations where perceptions from the contralateral body are less informative. 66 (Word count: 119) 67
Boosting Dependency Parsing Performance by Incorporating Additional Features for Agglutinative Languages

In recent studies, the use of language models has increased noticeably and has made quite good contribu-tions. However, using the proper representation and taking into account the complementary components are still among the issues to be considered. In this research, the impact of sub-word level sentence piece based word representation on the performance of dependency parsing has been demonstrated for agglutinative languages. Furthermore, we propose to use the sentence representation that holds all meaning of the sentence as an additional feature to improve dependency parsing. Our proposed enhancements are experimented on nine agglutinative languages; Estonian, Finnish, Hungarian, Indonesian, Japanese, Kazakh, Korean, Turkish, and Uyghur. We found that the sentence piece based token encoding has contributed parsing performance for the majority of the experimented languages. Using the entire meaning of the sentence as a complementary feature has enhanced parsing performance for six languages out of nine.
Fast Variational Estimation of Mutual Information for Implicit and Explicit Likelihood Models

Computing mutual information (MI) of random variables lacks a closed-form in nontrivial models. Variational MI approximations are widely used as flexible estimators for this purpose, but computing them typically requires solving a costly nonconvex optimization. We prove that a widely used class of variational MI estimators can be solved via moment matching operations in place of the numerical optimization methods that are typically required. We show that the same moment matching solution yields variational estimates for so-called "implicit" models that lack a closed form likelihood function. Furthermore, we demonstrate that this moment matching solution has multiple orders of magnitude computational speedup compared to the standard optimization-based solutions. We show that theoretical results are supported by numerical evaluation in fully parameterized Gaussian mixture models and a generalized linear model with implicit likelihood due to nuisance variables. We also demonstrate on the implicit simulation-based likelihood SIR epidemiology model, where we avoid costly likelihood free inference and observe many orders of magnitude speedup.
Analysis of Semantic Similarity between Sentences Using Transformer-based Deep Learning Methods

This paper presents an analysis of modern Transformer-based approaches to the semantic modelling of words and sentences. It covers the research and design of semantic similarity and paraphrase identification methods, as well as experimental evaluation of their performance. Metric learning approach and Transformer-based models are analysed as a basis for possible applications for solving tasks related to semantic similarity estimation. Experimental results for Siamese and triplet networks are presented along with a comparison of various aggregation functions. Experiments demonstrate that the considered deep language models based on the Transformer architecture can be used to obtain efficient latent words' features and to analyse their connections within a sentence and links between sentences. The proposed combined approach, which is based on using the BERT-like models fine-tuning, has shown significant improvements to the various popular strategies.
Pricing against a Budget and ROI Constrained Buyer

Internet advertisers (buyers) repeatedly procure ad impressions from ad platforms (sellers) with the aim to maximize total conversion (i.e. ad value) while respecting both budget and return-on-investment (ROI) constraints for efficient utilization of limited monetary resources. Facing such a constrained buyer who aims to learn her optimal strategy to acquire impressions, we study from a seller’s perspective how to learn and price ad impressions through repeated posted price mechanisms to maximize revenue. For this two-sided learning setup, we propose a learning algorithm for the seller that utilizes an episodic binary-search procedure to identify a revenue-optimal selling price. We show that such a simple learning algorithm enjoys low seller regret when within each episode, the budget and ROI constrained buyer approximately best responds to the posted price. We present simple yet natural buyer’s bidding algorithms under which the buyer approximately best responds while satisfying budget and ROI constraints, leading to a low regret for our proposed seller pricing algorithm. The design of our seller algorithm is motivated by the fact that the seller’s revenue function admits a bell-shaped structure when the buyer best responds to prices under budget and ROI constraints, enabling our seller algorithm to identify revenue-optimal selling prices efficiently.