Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones

Hierarchical relations are prevalent and indispensable for organizing human knowledge captured by a knowledge graph (KG). The key property of hierarchical relations is that they induce a partial ordering over the entities, which needs to be modeled in order to allow for hierarchical reasoning. However, current KG embeddings can model only a single global hierarchy (single global partial ordering) and fail to model multiple heterogeneous hierarchies that exist in a single KG. Here we present ConE (Cone Embedding), a KG embedding model that is able to simultaneously model multiple hierarchical as well as non-hierarchical relations in a knowledge graph. ConE embeds entities into hyperbolic cones and models relations as transformations between the cones. In particular, ConE uses cone containment constraints in different subspaces of the hyperbolic embedding space to capture multiple heterogeneous hierarchies. Experiments on standard knowledge graph benchmarks show that ConE obtains state-of-the-art performance on hierarchical reasoning tasks as well as knowledge graph completion task on hierarchical graphs. In particular, our approach yields new state-of-the-art Hits@1 of 45.3% on WN18RR and 16.1% on DDB14 (0.231 MRR). As for hierarchical reasoning task, our approach outperforms previous best results by an average of 20% across the three datasets.
Measurement-based time evolution for quantum simulation of fermionic systems

Quantum simulation using time evolution in phase estimation-based quantum algorithms can yield unbiased solutions of classically intractable models. However, long runtimes open such algorithms to decoherence. We show how measurement-based quantum simulation uses effective time evolution via measurement to allow runtime advantages over conventional circuit-based algorithms that use real-time evolution with quantum gates. We construct a hybrid algorithm to ﬁnd energy eigenvalues in fermionic models using only measurements on graph states. We apply the algorithm to the Kitaev and Hubbard chains. Resource estimates show a runtime advantage if measurements can be performed faster than gates, and graph states compactiﬁcation is fully used. In this letter, we set the stage to allow advances in measurement precision to improve quantum simulation.
A Slot Antenna Array with Reconfigurable RCS Using Liquid Absorber

A method is presented to achieve a slot antenna array with a reconfigurable radar cross section (RCS) by injecting and extracting ethanol. In a complex communication system, this can be used to switch between the detection and stealth modes. The antenna system is formed by combining a liquid absorber with a 2×2 slot antenna array. The liquid absorber consists of a polymethyl methacrylate (PMMA) container, 45% ethanol layer, and metal ground, which is attached to the surface of the slot antenna array. The incident wave can be absorbed by the absorber rather than reflected in other directions when the PMMA container is filled with ethanol, which reduces the monostatic and bistatic RCS. Thus, the RCS of the antenna can be changed by injecting and extracting ethanol while sustaining the antenna’s radiation performance in terms of bandwidth, radiation patterns, and gain. The mechanism of the absorber is investigated. The simulated results show that the antenna with this absorber has monostatic and bistatic RCS reduction bands from 2.0 GHz to 18.0 GHz and a maximum RCS reduction of 35 dB with an average RCS reduction of 13.28 dB. Without ethanol, the antenna realizes a gain of 12.1 dBi, which drops by 2 dB when the lossy ethanol is injected. The measured and simulated results agree well.
O ct 2 02 1 OPE ’ s and recoil

Some issues of recoil effects in AdS/CFT are studied from the point of view of OPE expansions for generalized free fields. We show that the conformal group structure encodes the relative energies and momenta at a collision center. This is done by being careful with the analysis of Clebsch-Gordan coefficients for an SL(2) subalgebra of the conformal group. The collision fraction of kinetic energy carried by the particles is derived from a probability distribution that arises from these coefficients. We specifically identify a precise statement of when recoil of a heavy particle in AdS can be ignored: the maximum probability is for the heavy particle to be in its ground state. We also argue how a notion of reduced mass appears in these collisions, in the limit where the particles are moving slowly with respect to each other. This controls the notion of the impact parameter of the collision.
Self-Supervised Learning Disentangled Group Representation as Feature

A good visual representation is an inference map from observations (images) to features (vectors) that faithfully reflects the hidden modularized generative factors (semantics). In this paper, we formulate the notion of"good"representation from a group-theoretic view using Higgins' definition of disentangled representation, and show that existing Self-Supervised Learning (SSL) only disentangles simple augmentation features such as rotation and colorization, thus unable to modularize the remaining semantics. To break the limitation, we propose an iterative SSL algorithm: Iterative Partition-based Invariant Risk Minimization (IP-IRM), which successfully grounds the abstract semantics and the group acting on them into concrete contrastive learning. At each iteration, IP-IRM first partitions the training samples into two subsets that correspond to an entangled group element. Then, it minimizes a subset-invariant contrastive loss, where the invariance guarantees to disentangle the group element. We prove that IP-IRM converges to a fully disentangled representation and show its effectiveness on various benchmarks. Codes are available at https://github.com/Wangt-CN/IP-IRM.
Photonic Kernel Machine Learning for Ultrafast Spectral Analysis

We introduce photonic kernel machines, a scheme for ultrafast spectral analysis of noisy radio-frequency signals from single-shot optical intensity measurements. The approach combines the versatility of machine learning and the speed of photonic hardware to reach unprecedented throughput rates. We theoretically describe some of the key underlying principles, and then numerically illustrate the reached performances on a photonic lattice-based implementation. We apply the technique both to picosecond pulsed radio-frequency signals, on energy-spectral-density estimation and a shape classification task, and to continuous signals, on a frequency tracking task. The presented optical computing scheme is resilient to noise while requiring minimal control on the photonic-lattice parameters, making it readily implementable in realistic state-of-the-art photonic platforms.
Generating 3D molecules conditional on receptor binding sites with deep generative models

The goal of structure-based drug discovery is to find small molecules that bind to a given target protein. Deep learning has been used to generate drug-like molecules with certain cheminformatic properties, but has not yet been applied to generating 3D molecules predicted to bind to proteins by sampling the conditional distribution of protein–ligand binding interactions. In this work, we describe for the first time a deep learning system for generating 3D molecular structures conditioned on a receptor binding site. We approach the problem using a conditional variational autoencoder trained on an atomic density grid representation of cross-docked protein–ligand structures. We apply atom fitting and bond inference procedures to construct valid molecular conformations from generated atomic densities. We evaluate the properties of the generated molecules and demonstrate that they change significantly when conditioned on mutated receptors. We also explore the latent space learned by our generative model using sampling and interpolation techniques. This work opens the door for end-to-end prediction of stable bioactive molecules from protein structures with deep learning.
Physical black holes in fourth-order gravity

The existence of black holes is a central prediction of general relativity and thus serves as a basic consistency test for modified theories of gravity. In spherical symmetry, only two classes of dynamic solutions are compatible with the formation of an apparent horizon in finite time of a distant observer. Moreover, the formation of black holes follows a unique scenario involving both types of solutions. To be compatible with their existence, any self-consistent theory of modified gravity must satisfy several constraints. We derive properties of the modified gravity terms of f(R) and generic fourth-order gravity theories and find that they naturally accommodate both classes of solutions. Consequently, the observation of an apparent horizon by itself may not suffice to distinguish between general relativity and modifications including up to fourth-order derivatives in the metric.
NeuroBack: Improving CDCL SAT Solving using Graph Neural Networks

Propositional satisfiability (SAT) is an NP-complete problem that impacts many research fields, such as planning, verification, and security. Mainstream modern SAT solvers are based on the Conflict-Driven Clause Learning (CDCL) algorithm. Recent work aimed to enhance CDCL SAT solvers using Graph Neural Networks (GNNs). However, so far this approach either has not made solving more effective, or required substantial GPU resources for frequent online model inferences. Aiming to make GNN improvements practical, this paper proposes an approach called NeuroBack, which builds on two insights: (1) predicting phases (i.e., values) of variables appearing in the majority (or even all) of the satisfying assignments are essential for CDCL SAT solving, and (2) it is sufficient to query the neural model only once for the predictions before the SAT solving starts. Once trained, the offline model inference allows NeuroBack to execute exclusively on the CPU, removing its reliance on GPU resources. To train NeuroBack, a new dataset called DataBack containing 120,286 data samples is created. Finally, NeuroBack is implemented as an enhancement to a state-of-the-art SAT solver called Kissat. As a result, it allowed Kissat to solve 5.2% more problems on the recent SAT competition problem set, SATCOMP-2022. NeuroBack therefore shows how machine learning can be harnessed to improve SAT solving in an effective and practical manner.
RBUE: A ReLU-Based Uncertainty Estimation Method of Deep Neural Networks

Deep neural networks (DNNs) have successfully learned useful data representations in various tasks. However, assessing the reliability of these representations remains a challenge. Deep Ensemble is widely considered the state-of-the-art method which can estimate the uncertainty with higher quality, but it is very expensive to train and test. MC-Dropout is another popular method, which is less expensive but lacks the diversity of predictions. To estimate the uncertainty with higher quality in less time, we introduce a ReLU-Based Uncertainty Estimation (RBUE) method. Instead of randomly dropping some neurons of the network as in MC-Dropout or using the randomness of the initial weights of networks as in Deep Ensemble, RBUE adds randomness to the activation function module, making the outputs diverse. Under the method, we propose two strategies, MC-DropReLU and MC-RReLU, to estimate uncertainty. We analyze and compare the output diversity of MC-Dropout and our method from the variance perspective and obtain the relationship between the hyperparameters and predictive diversity in the two methods. Moreover, our method is simple to implement and does not need to modify the existing model. We experimentally validate the RBUE on three widely used datasets, CIFAR10, CIFAR100, and TinyImageNet. The experiments demonstrate that our method has competitive performance but is more favorable in training time and memory requirements.
The secondary messenger ppGpp interferes with cAMP-CRP regulon by promoting CRP acetylation in Escherichia coli

The cAMP-CRP regulon coordinates transcription regulation of several energy-related genes, the lac operon among them. Lactose, or IPTG, induces the lac operon expression by binding to the LacI repressor, and releasing it from the promoter sequence. At the same time, the expression of the lac operon requires the presence of the CRP-cAMP complex, which promotes the binding of the RNA polymerase to the promoter region. The modified nucleotide cAMP accumulates in the absence of glucose and binds to the CRP protein, but its ability to bind to DNA can be impaired by lysine-acetylation of CRP. Here we add another layer of control, as acetylation of CRP seems to be modified by ppGpp. In cells grown in glycerol minimal media, ppGpp seems to repress the expression of lacZ, where ΔrelA mutants show higher expression of lacZ than in WT. These differences between the WT and ΔrelA strains seem to depend on the levels of acetylated CRP. During the growth in minimal media supplemented with glycerol, ppGpp promotes the acetylation of CRP by the Nε-lysine acetyltransferases YfiQ. Moreover, the expression of the different genes involved in the production and degradation of Acetyl-phosphate (ackA-pta) and the enzymatic acetylation of proteins (yfiQ) are stimulated by the presence of ppGpp, depending on the growth conditions.
The 3-T Model of Informed Consent for Nonstandard Risk Donors: A Proposal for Transplant Clinical Practice

Background. The risk of disease transmission from nonstandard risk donors (NSRDs) is low, and outcomes are similar or better relative to transplants performed with standard criteria donors. However, NSRDs have posed new ethical challenges to the informed consent (IC) process. Based on the shared decision-making model, coinciding with the 3 main timings of the IC process ([1] pretransplant assessments and waiting list registration, [2] time on the waiting list, and [3] time of the organ offer), we put forward a model (3-T Model) to summarize the knowledge on IC for NSRDs and to deliver conceptual and practical support to transplant providers on this emergent issue. Methods. We searched PubMed and analyzed data from our area to provide evidence and ethical arguments to promote standardization of the timing of patient information, degree of patient participation, and disclosure of donor risk factors throughout the 3 stages of the time continuum leading to the potential acceptance of NSRDs. Results. Each of the 3 timings carries special ethical significance and entails well-defined duties for transplant providers relative to patient involvement and information of the benefits and risks associated with NSRDs. Based on our framework, experience, and interpretation of the literature, we put forward a list of recommendations to combine standardization (ie, timing, content, and degree of patient participation) and individualization of IC. Conclusions. The 3-T Model may enable the prevention of physicians’ arbitrariness and the promotion of patient-centered care. Future studies will assess the effectiveness of the 3-T Model in transplant clinical practice.
Affective Episodic Memory System for Virtual Creatures: The First Step of Emotion-Oriented Memory

Episodic memory and emotions are considered essential functions in human cognition. Both allow us to acquire new knowledge from the environment, ranging from the objects around us to how we feel towards them. These qualities make them crucial functions for systems trying to create human-like behaviour. In the field of cognitive architectures (CAs), there are multiple studies covering memory and emotions. However, most of them treat these subjects in an isolated manner, considering emotions only as a reward signal unrelated to a retrieved experience. To address this lack of direct interaction, we propose a computational model that covers the common processes that are related to memory and emotions. Specifically, this proposal focuses on affective evaluations of episodic memories. Neurosciences and psychology are the bases of this model. That is, the model's components and the processes that they carry out on the information they receive are designed based on evidence from these cognitive sciences. The proposed model is a part of Cuáyóllótl, a cognitive architecture for cybernetic entities such as virtual creatures and robots. Case studies validate our proposal. They show the relevance of the integration of emotions and memory in a virtual creature. The virtual creature endowed with our emotional episodic model improves its learning and modifies its behaviour according to planning and decision-making processes.
Advances in Experimental and Clinical Research of the Gouty Arthritis Treatment with Traditional Chinese Medicine

Gouty arthritis (GA) is a multifactorial disease whose pathogenesis is utterly complex, and the current clinical treatment methods cannot wholly prevent GA development. Western medicine is the primary treatment strategy for gouty arthritis, but it owns an unfavorable prognosis. Therefore, the prevention and treatment of GA are essential. In China, traditional Chinese medicine (TCM) has been adopted for GA prevention and treatment for thousands of years. Gout patients are usually treated with TCM according to their different conditions, and long-term results can be achieved by improving their physical condition. And TCM has been proved to be an effective method to treat gout in modern China. Nevertheless, the pharmacological mechanism of TCM for gout is still unclear, which limits its spread. The theory of prevention and treatment of gout with TCM is more well acknowledged in China than in abroad. In this article, Chinese herbs and ancient formula for gout were summarized first. A total of more than 570 studies published from 2004 to June 2021 in PubMed, Medline, CNKI, VIP, Web of Science databases and Chinese Pharmacopoeia and traditional Chinese books were searched; the current status of TCM in the treatment of GA was summarized from the following aspects: articular chondrocyte apoptosis inhibition, antioxidative stress response, inflammatory cytokine levels regulation, uric acid excretion promotion, immune function regulation, uric acid reduction, and intestinal flora improvement in subjects with gout. The literature review concluded that TCM has a specific curative effect on the prevention and treatment of GA, particularly when combined with modern medical approaches. However, lacking a uniform definition of GA syndrome differentiation and the support of evidence-based medicine in clinical practice have provoked considerable concern in previous studies, which needs to be addressed in future research.
Delphinidin induces cell cycle arrest and apoptosis in HER-2 positive breast cancer cell lines by regulating the NF-κB and MAPK signaling pathways

Delphinidin is an anthocyanidin monomer, commonly found in vegetables and fruits, and has demonstrated antitumor effects in the HER-2-positive MDA-MB-453 breast cancer cell line, with low cytotoxicity on normal breast cells. However, the direct functional mechanisms underlying the effect of delphinidin on HER-2-positive breast cancer cells has not been fully characterized. In the present study, it was found that delphinidin could induce G2/M phase cell cycle arrest by inhibiting the protein expression level of cyclin B1 and Cdk1 in HER-2-positive breast cancer cell lines. In addition, delphinidin promoted the mitochondrial apoptosis pathway by inhibiting the ERK and NF-κB signaling pathway and activating the JNK signaling pathway. Therefore, delphinidin markedly suppressed the viability of the HER-2-positive breast cancer cell lines by modulating the cell cycle and inducing apoptosis. Overall, the findings from the present study demonstrated that delphinidin treatment could induce the mitochondrial apoptosis pathway in human HER-2-positive breast cancer cell lines, providing an experimental basis for the prevention and treatment of HER-2-positive breast cancer by flavonoids.
Diagnosis of hearing deficiency using EEG based AEP signals: CWT and improved-VGG16 pipeline

Hearing deficiency is the world’s most common sensation of impairment and impedes human communication and learning. Early and precise hearing diagnosis using electroencephalogram (EEG) is referred to as the optimum strategy to deal with this issue. Among a wide range of EEG control signals, the most relevant modality for hearing loss diagnosis is auditory evoked potential (AEP) which is produced in the brain’s cortex area through an auditory stimulus. This study aims to develop a robust intelligent auditory sensation system utilizing a pre-train deep learning framework by analyzing and evaluating the functional reliability of the hearing based on the AEP response. First, the raw AEP data is transformed into time-frequency images through the wavelet transformation. Then, lower-level functionality is eliminated using a pre-trained network. Here, an improved-VGG16 architecture has been designed based on removing some convolutional layers and adding new layers in the fully connected block. Subsequently, the higher levels of the neural network architecture are fine-tuned using the labelled time-frequency images. Finally, the proposed method’s performance has been validated by a reputed publicly available AEP dataset, recorded from sixteen subjects when they have heard specific auditory stimuli in the left or right ear. The proposed method outperforms the state-of-art studies by improving the classification accuracy to 96.87% (from 57.375%), which indicates that the proposed improved-VGG16 architecture can significantly deal with AEP response in early hearing loss diagnosis.
Fossil birds from the Roof of the World: The first avian fauna from High Asia and its implications for late Quaternary environments in Eastern Pamir

The Eastern Pamir (eastern Tajikistan) is a high-mountain plateau with elevations up to 7000 m, currently characterized by extremely severe environmental conditions and harboring a specialized montane fauna, which in part is shared with that of the Tibetan Plateau. The modern bird fauna of High Asia comprises a diversity of both ancient and recently diverged endemics, and thus is of general importance for historical biogeography and understanding the origin of modern high mountain ecosystems. However, the past history of the Central Asian highland avian communities remains practically unknown, as no fossil bird assemblages from high elevation areas were previously reported. In particular, it remains completely unexplored how birds responded to late Quaternary climatic fluctuations. Here we report the first fossil bird fauna from the High Asia and the first fossil birds from Tajikistan. An assemblage from the late Pleistocene through middle Holocene of Istykskaya cave (4060 m) in Eastern Pamir surprisingly comprises a remarkable diversity of waterbirds, including a few species that are completely absent from High Asia today and that were not reported globally from such high altitudes. The diversity of waterbirds incudes taxa of various ecological preferences (shorebirds, underwater and surface feeders, both zoophagous and phytophagous), strongly indicating the presence of a productive waterbody at the vicinity of the site in the past. These observations correspond to recent palaeoclimatic data, implying increased water availability in this region, currently occupied by high mountain semi-deserts. Our findings for the first time show that milder environmental conditions of late Quaternary attracted lowland species to the Central Asian highland wetlands. The reported assemblage yet contains several characteristic highland taxa, indicating a long-time persistence of some Central Asian montane faunistic elements. In particular, it includes the Tibetan Sandgrouse (Syrrhaptes tibetanus), a highly-specialized montane dweller, which is for the first time found in the fossil record.