Miracle-dz/UniFAR-FTUs huggingface dataset:
Phase diagram of alpha matter with Skyrme-like scalar interaction

L. M. Satarov, R. V. Poberezhnyuk, 2 I. N. Mishustin, 3 and H. Stoecker 4, 5 1 Frankfurt Institute for Advanced Studies, D-60438 Frankfurt am Main, Germany 2 Bogolyubov Institute for Theoretical Physics, 03680 Kiev, Ukraine 3 National Research Center ”Kurchatov Institute” 123182 Moscow, Russia 4 Institut für Theoretische Physik, Goethe Universität Frankfurt, D-60438 Frankfurt am Main, Germany 5 GSI Helmholtzzentrum für Schwerionenforschung GmbH, D-64291 Darmstadt, Germany Abstract The equation of state and phase diagram of strongly interacting matter composed of α particles are studied in the mean-field approximation. The particle interactions are included via a Skyrmelike mean field, containing both attractive and repulsive terms. The model parameters are found by fitting known values of binding energy and baryon density in the ground state of α matter, obtained from microscopic calculations by Clark and Wang. Thermodynamic quantities of α matter are calculated in the broad domains of temperature and baryon density, which can be reached in heavy-ion collisions at intermediate energies. The model predicts both first-order liquid-gas phase transition and Bose-Einstein condensation of α particles. We present the profiles of scaled variance, sound velocity and isochoric heat capacity along the isentropic trajectories of α matter. Strong density fluctuations are predicted in the vicinity of the critical point at temperature Tc ≈ 14 MeV and density nc ≈ 0.012 fm−3.
SEMI: Self-supervised Exploration via Multisensory Incongruity

Efficient exploration is a long-standing problem in reinforcement learning since extrinsic rewards are usually sparse or missing. A popular solution to this issue is to feed an agent with novelty signals as intrinsic rewards. In this work, we introduce SEMI, a self-supervised exploration policy by incentivizing the agent to maximize a new novelty signal: multisensory incongruity, which can be measured in two aspects, perception incongruity and action incongruity. The former represents the misalignment of the multisensory inputs, while the latter represents the variance of an agent's policies under different sensory inputs. Specifically, an alignment predictor is learned to detect whether multiple sensory inputs are aligned, the error of which is used to measure perception incongruity. A policy model takes different combinations of the multisensory observations as input, and outputs actions for exploration. The variance of actions is further used to measure action incongruity. Using both incongruities as intrinsic rewards, SEMI allows an agent to learn skills by exploring in a self-supervised manner without any external rewards. We further show that SEMI is compatible with extrinsic rewards and it improves sample efficiency of policy learning. The effectiveness of SEMI is demonstrated across a variety of benchmark environments including object manipulation and audio-visual games.
The Huge Variable Space in Empirical Studies for Visualization - A Challenge as well as an opportunity for Visualization Psychology

In each of the last five years, a few dozen empirical studies appeared in visualization journals and conferences. The existing empirical studies have already featured a large number of variables. There are many more variables yet to be studied. While empirical studies enable us to obtain knowledge and insight about visualization processes through observation and analysis of user experience, it seems to be a stupendous challenge for exploring such a huge variable space at the current pace. In this position paper, we discuss the implication of not being able to explore this space effectively and efficiently, and propose means for addressing this challenge.
Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey

Deep} reinforcement learning has recently seen huge success across multiple areas in the robotics domain. Owing to the limitations of gathering real-world data, i.e., sample inefficiency and the cost of collecting it, simulation environments are utilized for training the different agents. This not only aids in providing a potentially infinite data source, but also alleviates safety concerns with real robots. Nonetheless, the gap between the simulated and real worlds degrades the performance of the policies once the models are transferred into real robots. Multiple research efforts are therefore now being directed towards closing this sim-toreal gap and accomplish more efficient policy transfer. Recent years have seen the emergence of multiple methods applicable to different domains, but there is a lack, to the best of our knowledge, of a comprehensive review summarizing and putting into context the different methods. In this survey paper, we cover the fundamental background behind sim-to-real transfer in deep reinforcement learning and overview the main methods being utilized at the moment: domain randomization, domain adaptation, imitation learning, meta-learning and knowledge distillation. We categorize some of the most relevant recent works, and outline the main application scenarios. Finally, we discuss the main opportunities and challenges of the different approaches and point to the most promising directions.
Hydrocortisone with fludrocortisone for septic shock: a systematic review and meta‐analysis

Combined hydrocortisone and fludrocortisone therapy for septic shock has not been evaluated with an independent systematic review. We aimed to elucidate the beneficial effects of a dual corticosteroid treatment regime involving both hydrocortisone and fludrocortisone for adult patients with septic shock on mortality.
Best Policy Identification in discounted MDPs: Problem-specific Sample Complexity

We investigate the problem of best-policy identification in discounted Markov Decision Processes (MDPs) with finite state and action spaces. We assume that the agent has access to a generative model and that the MDP possesses a unique optimal policy. In this setting, we derive a problem-specific lower bound of the sample complexity satisfied by any learning algorithm. This lower bound corresponds to an optimal sample allocation that solves a non-convex program, and hence, is hard to exploit in the design of efficient algorithms. We provide a simple and tight upper bound of the sample complexity lower bound, whose corresponding nearly-optimal sample allocation becomes explicit. The upper bound depends on specific functionals of the MDP such as the sub-optimal gaps and the variance of the next-state value function, and thus really summarizes the hardness of the MDP. We devise KLB-TS (KL Ball Track-and-Stop), an algorithm tracking this nearly-optimal allocation, and provide asymptotic guarantees for its sample complexity (both almost surely and in expectation). The advantages of KLB-TS against state-of-the-art algorithms are finally discussed.
The mass assembly of high-redshift black holes

We use the Delphi semi-analytic model to study the mass assembly and properties of high-redshift ($z>4$) black holes over a wide mass range, $10^3 < M_{bh}/M_\odot < 10^{10}$. Our black hole growth implementation includes a critical halo mass ($M_{h}^{crit}$) below which the black hole is starved and above which it is allowed to grow either at the Eddington limit or proportionally to the gas content of the galaxy. As a consequence, after an initial growth phase dominated by black hole mergers down to $z \sim 7 (9)$, supermassive black holes in $z=4$ halo masses of $M_h|_{z=4} \sim 10^{11.75} ~ (10^{13.4}) M_\odot$ mainly grow by gas accretion from the interstellar medium. In particular, we find that: (i) while most of the accretion occurs in the major branch for $M_h|_{z=4} \sim 10^{11-12} M_\odot$ halos, accretion in secondary branches plays a significant role in assembling the black hole mass in higher-mass halos ($M_h|_{z=4} \gtrsim 10^{12} M_\odot$); (ii) while the Eddington ratio increases with decreasing redshift for low-mass ($M_{bh} < 10^5 M_\odot$) black holes, it shows the opposite trend for larger masses. In addition, since the accretion rate depends on the gas mass present in the host halo, the duty cycle of the Eddington-limited accretion phase -- which can last up to $\approx 650$ Myr -- is crucially linked to the joint assembly history of the black hole and its host halo.
Quantal synaptic dilution enhances sparse encoding and dropout regularisation in deep networks

Dropout is a technique that silences the activity of units stochastically while training deep networks to reduce overfitting. Here we introduce Quantal Synaptic Dilution (QSD), a biologically plausible model of dropout regularisation based on the quantal properties of neuronal synapses, that incorporates heterogeneities in response magnitudes and release probabilities for vesicular quanta. QSD outperforms standard dropout in ReLU multilayer perceptrons, with enhanced sparse encoding at test time when dropout masks are replaced with identity functions, without shifts in trainable weight or bias distributions. For convolutional networks, the method also improves generalisation in computer vision tasks with and without inclusion of additional forms of regularisation. QSD also outperforms standard dropout in recurrent networks for language modelling and sentiment analysis. An advantage of QSD over many variations of dropout is that it can be implemented generally in all conventional deep networks where standard dropout is applicable.
Complementary Meta-Reinforcement Learning for Fault-Adaptive Control

Faults are endemic to all systems. Adaptive fault-tolerant control maintains degraded performance when faults occur as opposed to unsafe conditions or catastrophic events. In systems with abrupt faults and strict time constraints, it is imperative for control to adapt quickly to system changes to maintain system operations. We present a meta-reinforcement learning approach that quickly adapts its control policy to changing conditions. The approach builds upon model-agnostic meta learning (MAML). The controller maintains a complement of prior policies learned under system faults. This "library" is evaluated on a system after a new fault to initialize the new policy. This contrasts with MAML, where the controller derives intermediate policies anew, sampled from a distribution of similar systems, to initialize a new policy. Our approach improves sample efficiency of the reinforcement learning process. We evaluate our approach on an aircraft fuel transfer system under abrupt faults.
Virtual Proximity Citation (VCP): A Supervised Deep Learning Method to Relate Uncited Papers On Grounds of Citation Proximity

Citation based approaches have seen good progress for recommending research papers using citations in the paper. Citation proximity analysis which uses the in-text citation proximity to find relatedness between two research papers is better than co-citation analysis and bibliographic analysis. However, one common problem which exists in each approach is that paper should be well cited. If documents are not cited properly or not cited at all, then using these approaches will not be helpful. To overcome the problem, this paper discusses the approach Virtual Citation Proximity (VCP) which uses Siamese Neural Network along with the notion of citation proximity analysis and content-based filtering. To train this model, the actual distance between the two citations in a document is used as ground truth, this distance is the word count between the two citations. VCP is trained on Wikipedia articles for which the actual word count is available which is used to calculate the similarity between the documents. This can be used to calculate relatedness between two documents in a way they would have been cited in the proximity even if the documents are uncited. This approach has shown a great improvement in predicting proximity with basic neural networks over the approach which uses the Average Citation Proximity index value as the ground truth. This can be improved by using a complex neural network and proper hyper tuning of parameters.
Dictionary Learning With Low-Rank Coding Coefficients for Tensor Completion

In this article, we propose a novel tensor learning and coding model for third-order data completion. The aim of our model is to learn a data-adaptive dictionary from given observations and determine the coding coefficients of third-order tensor tubes. In the completion process, we minimize the low-rankness of each tensor slice containing the coding coefficients. By comparison with the traditional predefined transform basis, the advantages of the proposed model are that: 1) the dictionary can be learned based on the given data observations so that the basis can be more adaptively and accurately constructed and 2) the low-rankness of the coding coefficients can allow the linear combination of dictionary features more effectively. Also we develop a multiblock proximal alternating minimization algorithm for solving such tensor learning and coding model and show that the sequence generated by the algorithm can globally converge to a critical point. Extensive experimental results for real datasets such as videos, hyperspectral images, and traffic data are reported to demonstrate these advantages and show that the performance of the proposed tensor learning and coding method is significantly better than the other tensor completion methods in terms of several evaluation metrics.
Deep Reinforcement Learning for Process Synthesis

This paper demonstrates the application of reinforcement learning (RL) to process synthesis by presenting Distillation Gym, a set of RL environments in which an RL agent is tasked with designing a distillation train, given a user defined multi-component feed stream. Distillation Gym interfaces with a process simulator (COCO and ChemSep) to simulate the environment. A demonstration of two distillation problem examples are discussed in this paper (a Benzene, Toluene, P-xylene separation problem and a hydrocarbon separation problem), in which a deep RL agent is successfully able to learn within Distillation Gym to produce reasonable designs. Finally, this paper proposes the creation of Chemical Engineering Gym, an all-purpose reinforcement learning software toolkit for chemical engineering process synthesis.
Presence of a Fundamental Acceleration Scale in Galaxy Clusters.

An acceleration scale of order $10^{-10}\mathrm{m/s^2}$ is implicit in the baryonic Tully-Fisher and baryonic Faber-Jackson relations, independently of any theoretical preference or bias. We show that the existence of this scale in the baryonic Faber-Jackson relation is most apparent when data from pressure supported systems of vastly different scales including globular clusters, elliptical galaxies, and galaxy clusters are analyzed together. This suggests the relevance of the acceleration scale $10^{-10}\mathrm{m/s^2}$ to structure formation processes at many different length scales and could be pointing to a heretofore unknown property of dark matter.
Ultraviolet-reflective film applied to windows reduces the likelihood of collisions for two species of songbird

Perhaps a billion birds die annually from colliding with residential and commercial windows. Therefore, there is a societal need to develop technologies that reduce window collisions by birds. Many current window films that are applied to the external surface of windows have human-visible patterns that are not esthetically preferable. BirdShades have developed a short wavelength (ultraviolet) reflective film that appears as a slight tint to the human eye but should be highly visible to many bird species that see in this spectral range. We performed flight tunnel tests of whether the BirdShades external window film reduced the likelihood that two species of song bird (zebra finch, Taeniopygia guttata and brown-headed cowbird, Molothrus ater) collide with windows during daylight. We paid particular attention to simulate the lighting conditions that birds will experience while flying during the day. Our results indicate a 75–90% reduction in the likelihood of collision with BirdShades-treated compared with control windows, in forced choice trials. In more ecologically relevant comparison between trials where all windows were either treated or control windows, the estimated reduction in probability of collision was 30–50%. Further, both bird species slow their flight by approximately 25% when approaching windows treated with the BirdShades film, thereby reducing the force of collisions if they were to happen. Therefore, we conclude that the BirdShades external window film will be effective in reducing the risk of and damage caused to populations and property by birds’ collision with windows. As this ultraviolet-reflective film has no human-visible patterning to it, the product might be an esthetically more acceptable low cost solution to reducing bird-window collisions. Further, we call for testing of other mitigation technologies in lighting and ecological conditions that are more similar to what birds experience in real human-built environments and make suggestions for testing standards to assess collision-reducing technologies.
Limited Added Value of Oropharyngeal Swabs for Detecting Pneumococcal Carriage in Adults

Abstract We compared pneumococcal isolation rates and evaluated the benefit of using oropharyngeal (OP) specimens in addition to nasopharyngeal (NP) specimens collected from adults in rural Kenya. Of 846 adults, 52.1% were colonized; pneumococci were detected from both NP and OP specimens in 23.5%, NP only in 22.9%, and OP only in 5.7%. Ten-valent pneumococcal conjugate vaccine strains were detected from both NP and OP in 3.4%, NP only in 4.1%, and OP only in 0.7%. Inclusion of OP swabs increased carriage detection by 5.7%; however, the added cost of collecting and processing OP specimens may justify exclusion from future carriage studies among adults.
Matrine Combined with DDP on Human Hepatocarcinoma Cell Line SMMC-7721: Apoptosis-inducing Effect and Its Mechanism

Objective: To investigate the apoptosis-inducing and its mechafiism of matrine combined with DDP on human hepatocarcinoma SMMC-7721 cells.Methods:The proliferation inhibitory rate was evaluated bv MTT assay.Induction of cell apoptotic rate was determined by flow cytometry.The interaction between Matrine and DDP was analyzed by means of Jin′s formulation.The expression of fas and caspase-3 mRNA was detected by semi-quantitive RT—PCR.Results:Compared with single drug group,the inhibitory rate of combined group was increased obviously(P0.05),the apoptotic rate was increased markedly(P0.05),and their synergistic effect could be observed.The expression of fas and caspase-3 mRNA was increased significantly(P0.05). Conclusions:Matrine combined with DDP could obviously induce apoptosis of human hepatocarcinoma SMMC-7721 cells and display the synergistic effect,which may be involved in increasing the expressions of fas and caspase-3 gene.
Anatomic Characteristics of Tissues Attached to the Fifth Metatarsal Bone

Background: Two types of stress, bending stress and traction stress, have been reported to be involved in the mechanism of Jones fracture. However, little is known about the risk factors for traction stress. Purpose: To classify the attachment position of the peroneus brevis muscle (PB), peroneus tertius (PT), lateral band of the plantar aponeurosis (LB), and the long plantar ligament (LPL), focusing on the zone where a Jones fracture occurs (zone 2), and to compare the footprint area of each tissue type. Study Design: Descriptive laboratory study. Methods: This study examined 102 legs from 55 Japanese cadavers. Type classification was performed by focusing on the positional relationship between each tissue attachment and the zone where Jones fracture occurs (zone 2). The classifications were as follows: type I, attached proximal to the border between zones 1 and 2; type IIa, attached to the border between zones 1 and 2 with one attached part; and type IIb, attached across the border between zones 1 and 2 with two or more attached parts. The footprint areas of the PB, PT, LB, and LPL were compared between tissue types and within each attachment classification. Results: The PB was recorded as type I in 41 feet (40.2%), type IIa in 56 feet (54.9%), and type IIb in 5 feet (4.9%); the PT was recorded as type IIa in 54 feet (60.0%) and type IIb in 36 feet (40.0%); and the LB was recorded as type I in 27 feet (26.5%) and type IIa in 75 feet (73.5%). The LPL did not attach to the fifth metatarsal bone. No significant difference was found in the footprint area between type I PB and type I LB. Conclusion: The results indicate that type I, which attaches proximal to zone 2, occurs with PB and LB, and there was no significant difference in the footprint area between them. These findings suggest that type I is involved in traction stress. In the future, biomechanical research based on the results of this study will be necessary. Clinical Relevance: The results of this study provide basic research for investigating the mechanism of Jones fracture and the cause of delayed healing.
Cysteine and homocysteine as biomarker of various diseases

Abstract Cysteine and homocysteine (Hcy), both sulfur‐containing amino acids (AAs), produced from methionine another sulfur‐containing amino acid, which is converted to Hcy and further converted to cysteine. This article aims to highlight the link between cysteine and Hcy, and their mechanisms, important functions, play in the body and their role as a biomarker for various types of diseases. So that using cysteine and Hcy as a biomarker, we can prevent and diagnose many diseases. This review concluded that hyperhomocysteinemia (elevated levels of homocysteine) is considered as toxic for cells and is associated with different health problems. Hyperhomocysteinemia and low levels of cysteine associated with various diseases like cardiovascular diseases (CVD), ischemic stroke, neurological disorders, diabetes, cancer like lung and colorectal cancer, renal dysfunction‐linked conditions, and vitiligo.
An Investigation on How Inhibition in Cognitive Processing Contributes to Fluid Reasoning

This article reports an investigation of how inhibition contributes to fluid reasoning when it is decomposed into the reasoning ability, item-position, and speed components to control for possible method effects. Working memory was also taken into consideration. A sample of 223 university students completed a fluid reasoning scale, two tasks tapping prepotent response inhibition, and two working memory tasks. Fixed-links modeling was used to separate the effect of reasoning ability from the effects of item-position and speed. The goodness-of-fit results confirmed the necessity to consider the reasoning ability, item-position, and speed components simultaneously. Prepotent response inhibition was only associated with reasoning ability. This association disappeared when working memory served as a mediator. Taken together, these results reflect the inhomogeneity of what is tapped by the fluid reasoning scale on one hand and, on the other, suggest inhibition as an important component of working memory.