Miracle-dz/UniFAR-FTUs huggingface dataset:
Interleukin-7 receptor signaling is crucial for enhancer-dependent TCRδ germline transcription mediated through STAT5 recruitment

γδ T cells play important roles in immune responses by rapidly producing large quantities of cytokines. Recently, γδ T cells have been found to be involved in tissue homeostatic regulation, playing roles in thermogenesis, bone regeneration and synaptic plasticity. Nonetheless, the mechanisms involved in γδ T-cell development, especially the regulation of TCRδ gene transcription, have not yet been clarified. Previous studies have established that NOTCH1 signaling plays an important role in the Tcrg and Tcrd germline transcriptional regulation induced by enhancer activation, which is mediated through the recruitment of RUNX1 and MYB. In addition, interleukin-7 signaling has been shown to be required for Tcrg germline transcription, VγJγ rearrangement and γδ T-lymphocyte generation as well as for promoting T-cell survival. In this study, we discovered that interleukin-7 is required for the activation of enhancer-dependent Tcrd germline transcription during thymocyte development. These results indicate that the activation of both Tcrg and Tcrd enhancers during γδ T-cell development in the thymus depends on the same NOTCH1- and interleukin-7-mediated signaling pathways. Understanding the regulation of the Tcrd enhancer during thymocyte development might lead to a better understanding of the enhancer-dependent mechanisms involved in the genomic instability and chromosomal translocations that cause leukemia. Graphical Abstract
Potential therapeutic applications of infusions and hydroalcoholic extracts of Romanian glutinous sage (Salvia glutinosa L.)

Ethnopharmacological relevance: Salvia glutinosa, also known as the glutinous sage, has been used in Romanian folk medicine in the treatment of inflammation, injuries, and mild infections. However, there is no direct scientific evidence to demonstrate these activities. Aim of the Study: The present research was based on evaluating antioxidant, antiproliferative, and α-glucosidase inhibitory activity of S. glutinosa extracts, as well as the in vivo anti-inflammatory activity. Materials and Methods: Infusions and 70% (v:v) ethanol solution extracts of S. glutinosa stems and leaves, collected from two different locations in Romania, were prepared. Ten phenolic compounds were identified and quantified using the LC-DAD-ESI/MSn method, and total phenolic and flavonoid content, as well as in vitro antioxidant (DPPH, ABTS, and FRAP assays), antiproliferative, anti-inflammatory and alpha-glucosidase inhibitory activities were determined. A rat model of induced inflammation with turpentine oil was used for the examination of in vivo effects of the extracts, using diclofenac as an anti-inflammatory control. Results: The highest inhibitory α-glucosidase activity was determined to be IC50 = 0.546 mg/ml for the hydroalcoholic extract made with plant material collected on the road to Sighișoara. The highest cytotoxic activity against HepG2 cell line was determined to be GI50 = 131.68 ± 5.03 μg/ml, for the hydroalcoholic extract made with plant material from Sighișoara. In vivo administration of extract (200 mg lyophilized powder/ml) showed a significant reduction of NO production. Conclusion: Our findings indicate that S. glutinosa extracts exhibit antioxidant, α-glucosidase inhibitory activity, as well as a modest cytotoxic effect on HepG2 cell line. By in vivo administration, the extracts show anti-inflammatory and antioxidant activity, which correlates with the traditional use of the species. The environmental conditions seemed to induce important changes in the chemical composition and the bioactivity of the herbal preparations derived from S. glutinosa.
Gradient Information Matters in Policy Optimization by Back-propagating through Model

Model-based reinforcement learning provides an efficient mechanism to find the optimal policy by interacting with the learned environment. In addition to treating the learned environment like a black-box simulator, a more effective way to use the model is to exploit its differentiability. Such methods require the gradient information of the learned environment model when calculating the policy gradient. However, since the error of gradient is not considered in the model learning phase, there is no guarantee for the model’s accuracy. To address this problem, we first analyze the convergence rate for the policy optimization methods when the policy gradient is calculated using the learned environment model. The theoretical results show that the model gradient error matters in the policy optimization phrase. Then we propose a two-model-based learning method to control the prediction error and the gradient error. We separate the different roles of these two models at the model learning phase and coordinate them at the policy optimization phase. After proposing the method, we introduce the directional derivative projection policy optimization (DDPPO) algorithm as a practical implementation to find the optimal policy. Finally, we empirically demonstrate the proposed algorithm has better sample efficiency when achieving a comparable or better performance on benchmark continuous control tasks. Codes are available at https://github.com/CCreal/ddppo
Solar Hidden Photon Search

The Solar Hidden Photon Search (SHIPS) is a joint astroparticle project of the Hamburger Sternwarte and DESY. The main target is to detect the solar emission of a new species of particles, so called Hidden Photons (HPs). Due to kinetic mixing, photons and HPs can convert into each other as they propagate. A small number of solar HPs - originating from photon to HP oscillations in the interior of the Sun - can be converted into photons in a long vacuum pipe pointing to the Sun - the SHIPS helioscope.
Ecological Risk Assessment of Antibiotics Distribution Characteristics on Urban Lakes of Xi'an-China

The solid phase extraction-ultra high performance liquid chromatography-tandem mass spectrometry was used to detect and analyze antibiotics in the water of four urban lakes in Xi'an—Hancheng Lake, Xingqing Lake, Nanhu Lake and Tao Huatan. A total of 15 types of antibiotics were detected in the water samples at a concentration between ND-280.16ng/L, of which Sulfamethoxazole (SMX), Sulfamonomethoxine (SMM), Ofloxacin (OFX), dehydrated red Erythromycin (ETM)and Roxithromycin (RTM) are the main types of antibiotics in four lakes in Xi'an; The ecological risk of antibiotics in four lakes was evaluated by Risk Quotients method (RQ). The results showed that the five antibiotics in Tao Huatan were at high risk level. Three antibiotics were at high risk level in Hancheng Lake and Xingqing Lake, and two antibiotics were at high risk level in Nanhu Lake.
Programmatic Reinforcement Learning without Oracles

Deep reinforcement learning (RL) has led to encouraging successes in many challenging control tasks. However, a deep RL model lacks interpretability due to the difficulty of identifying how the model’s control logic relates to its network structure. Programmatic policies structured in more interpretable representations emerge as a promising solution. Yet two shortcomings remain: First, synthesizing programmatic policies requires optimizing over the discrete and non-differentiable search space of program architectures. Previous works are suboptimal because they only enumerate program architectures greedily guided by a pretrained RL oracle. Second, these works do not exploit compositionality, an important programming concept, to reuse and compose primitive functions to form a complex function for new tasks. Our first contribution is a programmatically interpretable RL framework that conducts program architecture search on top of a continuous relaxation of the architecture space defined by programming language grammar rules. Our algorithm allows policy architectures to be learned with policy parameters via bilevel optimization using efficient policy-gradient methods, and thus does not require a pretrained oracle. Our second contribution is improving programmatic policies to support compositionality by integrating primitive functions learned to grasp task-agnostic skills as a composite program to solve novel RL problems. Experiment results demonstrate that our algorithm excels in discovering optimal programmatic policies that are highly interpretable.
Learning meta-features for AutoML

This paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter conﬁguration most appropriate to the dataset at hand. The proposed approach, MetaBu, learns new meta-features via an Optimal Transport procedure, aligning the manually designed meta-features with the space of distributions on the hyper-parameter conﬁgurations. MetaBu meta-features, learned once and for all, induce a topology on the set of datasets that is exploited to deﬁne a distribution of promising hyper-parameter conﬁgurations amenable to AutoML. Experiments on the OpenML CC-18 benchmark demonstrate that using MetaBu meta-features boosts the performance of state of the art AutoML systems, AutoSkLearn (Feurer et al. 2015) and Probabilistic Matrix Factorization (Fusi et al. 2018). Furthermore, the inspection of MetaBu meta-features gives some hints into when an ML algorithm does well. Finally, the topology based on MetaBu meta-features enables to estimate the intrinsic dimensionality of the OpenML benchmark w.r. t. a given ML algorithm or pipeline.
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms

Graph neural networks (GNNs) are widely used in regression and classiﬁcation problems applied to text, in areas such as sentiment analysis and medical decision-making processes. We propose a novel form for node attributes within a GNN based model that captures node-speciﬁc embeddings for every word in the vocabulary. This provides a global representation at each node, coupled with node-level updates according to associations among words in a transcript. We demonstrate the efﬁcacy of the approach by augmenting the accuracy of measuring major depressive disorder (MDD). Prior research has sought to make a diagnostic prediction of depression levels from patient data using several modalities, including audio, video, and text. On the DAIC-WOZ benchmark, our method outperforms state-of-art methods by a substantial margin, including those using multiple modalities. Moreover, we also evaluate the performance of our novel model on a Twitter sentiment dataset. We show that our model outperforms a general GNN model by leveraging our novel 2-D node attributes. These results demonstrate the generality of the proposed method.
Immersive ERP Experiment Based on 3D-Reconstruction Model of Panoramic Images

This study extends the application field of VR technology, and studies on the physical image problem of inventory management in ERP immersive experiment under VR technology, using the SSD block matching algorithm and the panoramic image in-depth information extraction algorithm to extract the two-dimensional in-depth information in the panoramic images. Then we reconstructed the object in 3D. The results show that the image reconstructed in 3D is clearer, more stereoscopic, and interactive, which solve the problems of low resolution, distortion, and low interaction in ERP virtual teaching environment, while using multimedia and camera roaming to enhance students' immersive experience, achieve high-precision, real scene, better virtual teaching environment interaction. It can be seen that the immersive ERP experiment based on VR technology is conducive to improving students 'professional ability and docking ability, as well as improving students' overall cognitive ability and operation ability of the ERP system.
A simple strategy for addition of degron tags to endogenous genes harboring prior insertions of fluorescent protein.

There exist insufficient validated “entry portal” sites in the C. elegans genome for CRISPR/Cas9-dependent insertion into endogenous genes to confer diverse spatiotemporal patterns and levels of expression on exogenous sequences. Consequently, we recognized the most common potential “entry portal” sequences: genes previously tagged with fluorescent proteins using CRISPR/Cas9. As proof of concept, we used existing mKate2-encoding sequences inserted in the 5’ end of genes as an insertion point for the auxin inducible degron, AID*. This sequence permits reasonably efficient insertion that can be employed using a variety of approaches for different end goals. Our strategy is thus generalizable to many needs.
The Spatiotemporal Neural Dynamics of Object Recognition for Natural Images and Line Drawings

Drawings offer a simple and efficient way to communicate meaning. While line drawings capture only coarsely how objects look in reality, we still perceive them as resembling real-world objects. Previous work has shown that this perceived similarity is mirrored by shared neural representations for drawings and natural images, which suggests that similar mechanisms underlie the recognition of both. However, other work has proposed that representations of drawings and natural images become similar only after substantial processing has taken place, suggesting distinct mechanisms. To arbitrate between those alternatives, we measured brain responses resolved in space and time using fMRI and MEG, respectively, while human participants (female and male) viewed images of objects depicted as photographs, line drawings, or sketch-like drawings. Using multivariate decoding, we demonstrate that object category information emerged similarly fast and across overlapping regions in occipital, ventral-temporal, and posterior parietal cortex for all types of depiction, yet with smaller effects at higher levels of visual abstraction. In addition, cross-decoding between depiction types revealed strong generalization of object category information from early processing stages on. Finally, by combining fMRI and MEG data using representational similarity analysis, we found that visual information traversed similar processing stages for all types of depiction, yet with an overall stronger representation for photographs. Together, our results demonstrate broad commonalities in the neural dynamics of object recognition across types of depiction, thus providing clear evidence for shared neural mechanisms underlying recognition of natural object images and abstract drawings. SIGNIFICANCE STATEMENT When we see a line drawing, we effortlessly recognize it as an object in the world despite its simple and abstract style. Here we asked to what extent this correspondence in perception is reflected in the brain. To answer this question, we measured how neural processing of objects depicted as photographs and line drawings with varying levels of detail (from natural images to abstract line drawings) evolves over space and time. We find broad commonalities in the spatiotemporal dynamics and the neural representations underlying the perception of photographs and even abstract drawings. These results indicate a shared basic mechanism supporting recognition of drawings and natural images.
Emergent Communication at Scale

Emergent communication aims for a better understanding of human language evolution and building more efficient representations. We posit that reaching these goals will require scaling up, in contrast to a significant amount of literature that focuses on setting up small-scale problems to tease out desired properties of the emergent languages. We focus on three independent aspects to scale up, namely the dataset, task complexity, and population size. We provide a first set of results for large populations solving complex tasks on realistic large-scale datasets, as well as an easy-to-use codebase to enable further experimentation1. In more complex tasks and datasets, we find that RL training can become unstable, but responds well to established stabilization techniques. We also identify the need for a different metric than topographic similarity, which does not correlate with the generalization performances when working with natural images. In this context, we probe ease-of-learnability and transfer methods to assess emergent languages. Finally, we observe that larger populations do not induce robust emergent protocols with high generalization performance, leading us to explore different ways to leverage population, through voting and imitation learning.
Lossy Compression with Distribution Shift as Entropy Constrained Optimal Transport

We study an extension of lossy compression where the reconstruction distribution is different from the source distribution in order to account for distributional shift due to processing. We formulate this as a generalization of optimal transport with an entropy bottleneck to account for the rate constraint due to compression. We provide expressions for the tradeoff between compression rate and the achievable distortion with and without shared common randomness between the encoder and decoder. We study the examples of binary, uniform and Gaussian sources (in an asymptotic setting) in detail and demonstrate that shared randomness can strictly improve the tradeoff. For the case without common randomness and squared-Euclidean distortion, we show that the optimal solution partially decouples into the problem of optimal compression and transport and also characterize the penalty associated with fully decoupling them. We provide experimental results by training deep learning end-to-end compression systems for performing denoising on SVHN and super-resolution on MNIST suggesting consistency with our theoretical results.
Usando Funções de Similaridade para Redução de Conjuntos de Casos de Teste em Estratégias de Teste Baseado em Modelos

O uso de funções de similaridade para a redução de conjuntos de casos de teste é investigado neste trabalho. É dado enfoque a conjuntos de casos de teste funcionais gerados automaticamente a partir de modelos. No geral, estratégias de geração automática de casos de teste produzem conjuntos muito grandes e com muitos casos de teste redundantes, contribuindo para um aumento significativo e desnecessário nos custos de geração e execução de casos de teste. Redução com base em funções de similaridade visa produzir um conjunto com um número menor de casos de teste que contenha os casos de teste menos similares entre si. Com isto, espera-se obter uma cobertura mais efetiva de funcionalidades. As estratégias de similaridade consideradas são comparadas, através de estudos de caso, com seleção aleatória, com base em dois critérios: cobertura de transições e cobertura de defeitos.
Frequency Dependent Convolutions for Music Tagging

We present a deep convolutional neural network approach for emotions and themes recognition in music using the MTG-Jamendo dataset. The model takes mel spectrograms as input and tries to leverage translation invariance in the time dimension while allowing convolution filters to depend on the frequency. It has led the lileonardo team to achieve the highest score of the 2021 MediaEval Multimedia Evaluation benchmark for this task 1 .
W-CTC: a Connectionist Temporal Classification Loss with Wild Cards

Connectionist Temporal Classification (CTC) loss is commonly used in sequence learning applications. For example, in Automatic Speech Recognition (ASR) task, the training data consists of pairs of audio (input sequence) and text (output label), without temporal alignment information. Standard CTC computes a loss by aggregating over all possible alignment paths, that map the entire sequence to the entire label (full alignment). However, in practice, there are often cases where the label is incomplete. Specifically, we solve the partial alignment problem where the label only matches a middle part of the sequence. This paper proposes the wild-card CTC (W-CTC) to address this issue, by padding wild-cards at both ends of the labels. Consequently, the proposed W-CTC improves the standard CTC via aggregating over even more alignment paths. Evaluations on a number of tasks in speech and vision domains, show that the proposed W-CTC consistently outperforms the standard CTC by a large margin when label is incomplete. The effectiveness of the proposed method is further confirmed in an ablation study.
Understanding and Leveraging Overparameterization in Recursive Value Estimation

The theory of function approximation in reinforcement learning (RL) typically considers low capacity representations that incur a tradeoff between approximation error, stability and generalization. Current deep architectures, however, operate in an overparameterized regime where approximation error is not necessarily a bottle-neck. To better understand the utility of deep models in RL we present an analysis of recursive value estimation using overparameterized linear representations that provides useful, transferable ﬁndings. First, we show that classical updates such as temporal difference (TD) learning or ﬁtted-value-iteration (FVI) converge to different ﬁxed points than residual minimization (RM) in the overparameterized linear case. We then develop a uniﬁed interpretation of overparameterized linear value estimation as minimizing the Euclidean norm of the weights subject to alternative constraints. A practical consequence is that RM can be modiﬁed by a simple alteration of the backup targets to obtain the same ﬁxed points as FVI and TD (when they converge), while universally ensuring stability. Further, we provide an analysis of the generalization error of these methods, demonstrating per iterate bounds on the value prediction error of FVI, and ﬁxed point bounds for TD and RM. Given this understanding, we then develop new algorithmic tools for improving recursive value estimation with deep models. In particular, we extract two regularizers that penalize out-of-span top-layer weights and co-linearity in top-layer features respectively. Empirically we ﬁnd that these regularizers dramatically improve the stability of TD and FVI, while allowing RM to match and even sometimes surpass their generalization performance with assured stability.
switch-GLAT: Multilingual Parallel Machine Translation Via Code-Switch Decoder

Multilingual machine translation aims to develop a single model for multiple language directions. However, existing multilingual models based on Transformer are limited in terms of both translation performance and inference speed. In this paper, we propose switch-GLAT, a non-autoregressive multilingual machine translation model with a code-switch decoder. It can generate contextual codeswitched translations for a given source sentence, and perform code-switch backtranslation, greatly boosting multilingual translation performance. In addition, its inference is highly efficient thanks to its parallel decoder. Experiments show that our proposed switch-GLAT outperform the multilingual Transformer with as much as 0.74 BLEU improvement and 6.2x faster decoding speed in inference.
Neural Relational Inference with Node-Specific Information

Inferring interactions among entities is an important problem in studying dynamical systems, which greatly impacts the performance of downstream tasks, such as prediction. In this paper, we tackle the relational inference problem in a setting where each entity can potentially have a set of individualized information that other entities cannot have access to. Specifically, we represent the system using a graph in which the individualized information become node-specific information (NSI). We build our model in the framework of Neural Relation Inference (NRI), where the interaction among entities are uncovered using variational inference. We adopt NRI model to incorporate the individualized information by introducing private nodes in the graph that represent NSI. Such representation enables us to uncover more accurate relations among the agents and therefore leads to better performance on the downstream tasks. Our experiment results over real-world datasets validate the merit of our proposed algorithm.