Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to be Safe: Deep RL with a Safety Critic

Safety is an essential component for deploying reinforcement learning (RL) algorithms in real-world scenarios, and is critical during the learning process itself. A natural first approach toward safe RL is to manually specify constraints on the policy's behavior. However, just as learning has enabled progress in large-scale development of AI systems, learning safety specifications may also be necessary to ensure safety in messy open-world environments where manual safety specifications cannot scale. Akin to how humans learn incrementally starting in child-safe environments, we propose to learn how to be safe in one set of tasks and environments, and then use that learned intuition to constrain future behaviors when learning new, modified tasks. We empirically study this form of safety-constrained transfer learning in three challenging domains: simulated navigation, quadruped locomotion, and dexterous in-hand manipulation. In comparison to standard deep RL techniques and prior approaches to safe RL, we find that our method enables the learning of new tasks and in new environments with both substantially fewer safety incidents, such as falling or dropping an object, and faster, more stable learning. This suggests a path forward not only for safer RL systems, but also for more effective RL systems.
A note on decreasing rearrangement and mean oscillation on measure spaces

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Discrete-Time Signatures and Randomness in Reservoir Computing

A new explanation of the geometric nature of the reservoir computing (RC) phenomenon is presented. RC is understood in the literature as the possibility of approximating input–output systems with randomly chosen recurrent neural systems and a trained linear readout layer. Light is shed on this phenomenon by constructing what is called strongly universal reservoir systems as random projections of a family of state-space systems that generate Volterra series expansions. This procedure yields a state-affine reservoir system with randomly generated coefficients in a dimension that is logarithmically reduced with respect to the original system. This reservoir system is able to approximate any element in the fading memory filters class just by training a different linear readout for each different filter. Explicit expressions for the probability distributions needed in the generation of the projected reservoir system are stated, and bounds for the committed approximation error are provided.
Contour Integration using Graph-Cut and Non-Classical Receptive Field

Many edge and contour detection algorithms give a soft-value as an output and the final binary map is commonly obtained by applying an optimal threshold. In this paper, we propose a novel method to detect image contours from the extracted edge segments of other algorithms. Our method is based on an undirected graphical model with the edge segments set as the vertices. The proposed energy functions are inspired by the surround modulation in the primary visual cortex that help suppressing texture noise. Our algorithm can improve extracting the binary map, because it considers other important factors such as connectivity, smoothness, and length of the contour beside the soft-values. Our quantitative and qualitative experimental results show the efficacy of the proposed method.
Lineage Evolution Reinforcement Learning

We propose a general agent population learning system, and on this basis, we propose lineage evolution reinforcement learning algorithm. Lineage evolution reinforcement learning is a kind of derivative algorithm which accords with the general agent population learning system. We take the agents in DQN and its related variants as the basic agents in the population, and add the selection, mutation and crossover modules in the genetic algorithm to the reinforcement learning algorithm. In the process of agent evolution, we refer to the characteristics of natural genetic behavior, add lineage factor to ensure the retention of potential performance of agent, and comprehensively consider the current performance and lineage value when evaluating the performance of agent. Without changing the parameters of the original reinforcement learning algorithm, lineage evolution reinforcement learning can optimize different reinforcement learning algorithms. Our experiments show that the idea of evolution with lineage improves the performance of original reinforcement learning algorithm in some games in Atari 2600.
Character Entropy in Modern and Historical Texts: Comparison Metrics for an Undeciphered Manuscript

This paper outlines the creation of three corpora for multilingual comparison and analysis of the Voynich manuscript: a corpus of Voynich texts partitioned by Currier language, scribal hand, and transcription system, a corpus of 294 language samples compiled from Wikipedia, and a corpus of eighteen transcribed historical texts in eight languages. These corpora will be utilized in subsequent work by the Voynich Working Group at Yale University. We demonstrate the utility of these corpora for studying characteristics of the Voynich script and language, with an analysis of conditional character entropy in Voynichese. We discuss the interaction between character entropy and language, script size and type, glyph compositionality, scribal conventions and abbreviations, positional character variants, and bigram frequency. This analysis characterizes the interaction between script compositionality, character size, and predictability. We show that substantial manipulations of glyph composition are not sufficient to align conditional entropy levels with natural languages. The unusually predictable nature of the Voynichese script is not attributable to a particular script or transcription system, underlying language, or substitution cipher. Voynichese is distinct from every comparison text in our corpora because character placement is highly constrained within the word, and this may indicate the loss of phonemic distinctions from the underlying language.
On computation of the inverse of a polynomial map over finite fields using the reduced Koopman dual linear map

This paper proposes a symbolic representation of non-linear maps $F$ in $\ff^n$ in terms of linear combination of basis functions of a subspace of $(\ff^n)^0$, the dual space of $\ff^n$. Using this representation, it is shown that the inverse of $F$ whenever it exists can also be represented in a similar symbolic form using the same basis functions (using different coefficients). This form of representation should be of importance to solving many problems of iterations or compositions of non-linear maps using linear algebraic methods which would otherwise require solving hard computational problems due to non-linear nature of $F$.
The Amazing Race TM: Robot Edition

State-of-the-art natural-language-driven autonomous-navigation systems generally lack the ability to operate in real unknown environments without crutches, such as having a map of the environment in advance or requiring a strict syntactic structure for natural-language commands. Practical artificial-intelligent systems should not have to depend on such prior knowledge. To encourage effort towards this goal, we propose The Amazing Race TM: Robot Edition, a new task of finding a room in an unknown and unmodified office environment by following instructions obtained in spoken dialog from an untrained person. We present a solution that treats this challenge as a series of sub-tasks: natural-language interpretation, autonomous navigation, and semantic mapping. The solution consists of a finite-state-machine system design whose states solve these sub-tasks to complete The Amazing Race TM. Our design is deployed on a real robot and its performance is demonstrated in 52 trials on 4 floors of each of 3 different previously unseen buildings with 13 untrained volunteers.
Toxoplasma gondii microneme proteins 1 and 4 bind to Toll-like receptors 2 and 4 N-glycans triggering innate immune response

The infection of the host cell with Toxoplasma gondii involves the regulated secretion of microneme proteins (TgMICs). The complex formed by TgMIC1/4/6 on the T. gondii surface participates in the adhesion and invasion processes. Here, we show that TgMIC1-and TgMIC4-stimulated dendritic cells and macrophages produce proinflammatory cytokines through TLR2 and TLR4 signalling. This process depends on sugar recognition, since it was shown to be inhibited by point mutations introduced in the TgMIC1 and TgMIC4 carbohydrate-recognition domains. HEK cells transfected with TLR2 glycomutants were selectively unresponsive to TgMICs. Following parasite infection, phagocytes lacking TLR2 and TLR4 failed to generate an early IL-12 response in contrast to wild type cells. Moreover, TgMIC1-KO and TgMIC1/TgMIC4-DKO parasites stimulated a lower IL-12 response than wild type parasites. Together, our data reveal that TgMIC1 and TgMIC4 interact physically with TLR2 and TLR4 N-glycans to trigger an early IL-12 response to T. gondii, which may contribute to acute control of infection. AUTHOR SUMMARY Toxoplasmosis is caused by the protozoan Toxoplasma gondii, of the Apicomplexa phylum. It comprises important parasites able to infect a broad diversity of animals, including humans. A particularity of T. gondii is the ability to invade all nucleated cells of its hosts through an active process, which depends on the expression of surface adhesion proteins. These proteins are secreted by specialized organelles localized in the parasite apical region, such as micronemes. We show, in this study, that two of the microneme proteins from T. gondii play a function that goes beyond the adhesion activity. These microneme proteins denoted TgMIC1 and TgMIC4 have the ability to recognize sugars. Through this property, they bind to sugars contained in receptors on mammalian cells surface. This binding starts the process of activation of innate immune cells and secretion of cytokines, which may act by making more efficient the host defence mechanisms against the parasite, especially in the beginning of the infection. It results in an appropriate response because it helps to control the parasite replication during the acute infection and favours host healthy.
Lsyn – Esyn, p – δ relation in active galactic nucleus jets and implication for the physical origin of the Lp − Ep,z − Γ0 relation of gamma-ray bursts

High energy photon radiations of gamma-ray bursts (GRBs) and active galactic nuclei (AGNs) are dominated by their jet radiations. We examine whether the synchrotron radiations of jets in BL Lacs, flat spectrum radio quasars (FSRQs), and Narrow Line Seyfert 1 galaxies (NLS1s) follow the relation between the prompt gamma-ray emission and the initial Lorentz factor (Γ0) of GRBs. It is shown that the AGN sample does not agree with the Lp − Ep,z − Γ0 relation of GRBs. In addition, we obtain a tight relation of Lsyn∝Esyn,p0.45±0. 15δ3. 50±0.25 for FSRQs and NLS1 galaxies, where Lsyn is the luminosity at peak photon energy Esyn, p of the synchrotron radiations. This relation is different from the Lp − Ep,z − Γ0 relation of GRBs. The dependence of Lsyn to δ is consistent with the expectation of the Doppler boosting effect for the FSRQs and NLS1 galaxies, but it is not for GRBs. We argue that Γ0 may be a representative of the kinetic power of the radiating region and the tight Lp − Ep, z − Γ0 relation is shaped by the radiation physics and the jet power together.
Comparison Analysis of Tree Based and Ensembled Regression Algorithms for Traffic Accident Severity Prediction

Rapid increase of traffic volume on urban roads over time has changed the traffic scenario globally. It has also increased the ratio of road accidents that can be severe and fatal in the worst case. To improve traffic safety and its management on urban roads, there is a need for prediction of severity level of accidents. Various machine learning models are being used for accident prediction. In this study, tree based ensemble models (Random Forest, AdaBoost, Extra Tree, and Gradient Boosting) and ensemble of two statistical models (Logistic Regression Stochastic Gradient Descent) as voting classifiers are compared for prediction of road accident severity. Significant features that are strongly correlated with the accident severity are identified by Random Forest. Analysis proved Random Forest as the best performing model with highest classification results with 0.974 accuracy, 0.954 precision, 0.930 recall and 0.942 F-score using 20 most significant features as compared to other techniques classification of road accidents severity.
Resolving the outer ring of HD 38206 using ALMA and constraining limits on planets in the system

HD 38206 is an A0V star in the Columba association, hosting a debris disc first discovered by IRAS. Further observations by Spitzer and Herschel showed that the disc has two components, likely analogous to the asteroid and Kuiper belts of the Solar system. The young age of this star makes it a prime target for direct imaging planet searches. Possible planets in the system can be constrained using the debris disc. Here, we present the first ALMA observations of the system’s Kuiper belt and fit them using a forward modelling MCMC approach. We detect an extended disc of dust peaking at around 180 au with a width of 140 au. The disc is close to edge on and shows tentative signs of an asymmetry best fit by an eccentricity of $0.25^{+0.10}_{-0.09}$. We use the fitted parameters to determine limits on the masses of planets interior to the cold belt. We determine that a minimum of four planets are required, each with a minimum mass of 0.64 MJ, in order to clear the gap between the asteroid and Kuiper belts of the system. If we make the assumption that the outermost planet is responsible for the stirring of the disc, the location of its inner edge and the eccentricity of the disc, then we can more tightly predict its eccentricity, mass, and semimajor axis to be $e_{\rm {p}}=0.34^{+0.20}_{-0.13}$, $m_{\rm {p}}=0.7^{+0.5}_{-0.3}\, \rm {\it M}_{\rm {J}}$, and $a_{\rm {p}}=76^{+12}_{-13}\, \rm {au}$.
A methodology of weed-crop classification based on autonomous models choosing and ensemble

Neural networks play an important role in crop-weed classification have high accuracy more than 95%. Manually choosing models and fine-tuning are laborious, yet it is indispensable in most traditional practices and researches. Moreover, classic training metric are not thoroughly compatible with farming tasks, that a model still have a noticeable chance of miss classifying crop to weed while it reach higher accuracy even more than 99%. In this paper we demonstrate a methodology of weed-crop classification based on autonomous models choosing and ensemble that could make models choosing and tunning automatically, and improve the prediction with high accuracy(>99% for both data set) in specific class with low risk in incorrect predicting.
A Comparative Study on the Effects of Postoperative 125I Brachytherapy and Irradiation After Surgical Decompression and Stabilization for Metastatic Spinal Cancers

Purpose The study aims to investigate and compare the efficacy and safety of intraoperative 125I implantation and postoperative irradiation after surgical decompression and stabilization in the treatment of patients with metastatic epidural spinal cord compression (MESCC). Methods The study retrospectively enrolled 122 MESCC patients treated with surgical decompression and pedicle stabilization combined with 125I brachytherapy (the brachytherapy group) or postoperative radiotherapy (the irradiation group). Operation time, intraoperative blood loss, pain relief, postoperative ambulatory status, postoperative survival outcome, complications, and length of hospitalization were collected and compared between the two groups. Ten potential risk factors were analyzed for postoperative survival outcome. Results No significant difference was found in baseline characteristics between the two groups (P>0.05). Postoperative VAS score was significantly decreased, as compared with preoperative scores in both groups (P˂0.001). The VAS in the brachytherapy group was significantly lower than that in the irradiation group at postoperative 1 month, 3 months, and 6 months (P˂0.05). The postoperative ambulatory rates were 90.0% (54/60) in the brachytherapy group and 83.9% (52/62) in the irradiation group (P=0.32). The median overall survival time was similar between the two groups (7.43 months vs 7.27 months, P=0.37). Of all patients in the brachytherapy group, 25.0% (15/60) of patients suffered from complications, while 46.8% (29/62) of patients had complications in the irradiation group (P=0.0086). According to the multiple Cox regression, primary sites (P=0.038), ECOG performance status (P=0.014), and visceral metastases (P=0.0016) showed significance for postoperative survival outcome. Conclusion Surgical decompression and spine stabilization combined with 125I brachytherapy is a relatively safe and useful method in MESCC patients.
Oral Trypanosoma cruzi Transmission Resulting in Advanced Chagasic Cardiomyopathy in an 11-Month-Old Male

Our case report describes the youngest clinical acute Chagas disease case and their unusual disease presentation of cardiac failure. In parts of the Brazilian Amazon, cultural practices include weaning infants from breastmilk to solid foods with açaí consumption serving as an intermediary. This practice could place infants at an increased risk of oral Trypanosoma cruzi infection and severe Chagasic cardiac disease.
Retroperitoneal Extragastrointestinal Stromal Tumors Have a Poor Survival Outcome: A Multicenter Observational Study

Purpose Gastrointestinal stromal tumors (GISTs) are commonly known to be derived from the gastrointestinal (GI) tract, but recently there have been more and more literature describing lesions with similar pathological and immunohistochemical resembling GISTs but located outside the GI tract, and they have been termed as extra-GISTs (eGISTs). However, due to the rare incidence of eGISTs, its association with survival outcomes is poorly understood, especially in the Chinese population. Here, we aimed to identify the risk factors of eGISTs and to assess their association with overall survival (OS) and disease-free survival (DFS). Patients and Methods Data of pathologically confirmed eGISTs cases, without radiological and perioperative evidence of other primary lesions, and with no microscopically identified adhesion between the tumor and the gastrointestinal serosa, which were surgically treated between January 2006 and September 2017 were retrieved from the database of four high-volume hospitals. Immunohistochemical and genetic testing were performed on the postoperative lesions and were staged using the National Institutes of Health (NIH) criteria. Results A total of 55 cases were retrieved. eGISTs were identified from the retroperitoneum (36.4%), mesocolon (25.5%), small bowel mesentery (12.7%), abdominopelvic cavity (12.7%), lesser omental sac (5.5%), ovary (3.6%), pancreatic capsule (1.8%), or urinary bladder (1.8%). Based on the NIH risk classification, majority of the lesion were classified as high risk (85.5%). KIT 11 was the most common mutation site (76.5%) and 25.0% of the cases were wild-type eGISTs. Multivariate analyses showed that tumor location and size were independent factors affecting prognoses. Patients with tumors in the retroperitoneum had significantly poorer OS and DFS as compared to those in the non-retroperitoneum (HR [95% CI] for OS and DFS: 2.546 [1.023–6.337] [P = 0.037] and 2.475 [0.975–6.273] [P = 0.049], respectively). Similar findings were found for tumors of size >15 cm, compared to ≤15 cm (HR [95% CI] for OS and DFS: 5.350 [2.022–14.156] [P < 0.001] and 3.861 [1.493–9.988] [P = 0.003], respectively). Conclusion eGISTs were predominantly found from the retroperitoneum and mostly classified as high risk. Those located in the retroperitoneum and of size >15 cm had the poorer OS and DFS as compared to those in the non-retroperitoneum and of size <15 cm.
ATP6AP2 is Overexpressed in Breast Cancer and Promotes Breast Cancer Progression

Background Adenosine triphosphatase H+ transporting accessory protein 2 (ATP6AP2), also known as (pro)renin receptor, is implicated in tumorigenesis and the progression of several types of cancer. This study investigated the role of ATP6AP2 in breast cancer. Methods UALCAN and ONCOMINE datasets were utilized to compare transcript levels of ATP6AP2 in breast cancer and normal tissues. GOBO datasets were applied to examine ATP6AP2 expression in different breast cancer cell lines. We used the cBioPortal website to explore the gene alterations and copy number alterations of ATP6AP2 in breast cancer. Cell Counting Kit-8 and transwell assays were conducted to evaluate ATP6AP2 function in MCF-7 breast cancer cells. Finally, we used the cBioPortal website to establish the interaction network of ATP6AP2 in breast cancer and performed functional enrichment analysis based on Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. Results ATP6AP2 was overexpressed in breast cancer tissues and breast cancer cell lines in the UALCAN, ONCOMINE, and GOBO datasets. The major type of ATP6AP2 alteration was mRNA upregulation. Moreover, ATP6AP2 was most highly expressed in luminal type breast cancer. Finally, ATP6AP2 knockdown reduced MCF-7 cell proliferation, invasion and migration. Functional enrichment analysis suggested that ATP6AP2 regulates several cancer-related pathways, especially the Wnt/β-catenin signaling pathway. Conclusion Applying multi-dimensional analytical methods, we demonstrate that ATP6AP2 is upregulated in breast cancer and may promote its development and progression.