Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesizing Political Zero-Shot Relation Classification via Codebook Knowledge, NLI, and ChatGPT

Can we accurately classify political relations within evolving event ontologies without extensive annotations? Our study investigates zero-shot learning methods that utilize only expert knowledge from existing annotation codebook. We assess the performance of advanced ChatGPT (GPT-3.5/4) and a natural language inference (NLI)-based model called ZSP. ChatGPT uses codebooks' label summaries as prompts, whereas ZSP breaks down the classification task into context, event mode, and class disambiguation to refine task-specific hypotheses. This decomposition enhances interpretability, efficiency, and adaptability to schema changes. The experiments reveal ChatGPT's strengths and limitations, and crucially, ZSP's outperformance of dictionary-based methods and its competitive edge over some supervised models. These findings affirm the value of ZSP for validating event records and advancing ontology development. Our study underscores the efficacy of leveraging transfer learning and existing expertise to enhance research efficiency and scalability in this area.
Real Robot Challenge 2022: Learning Dexterous Manipulation from Offline Data in the Real World

Experimentation on real robots is demanding in terms of time and costs. For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and benchmark algorithms. However, insights gained in simulation do not necessarily translate to real robots, in particular for tasks involving complex interactions with the environment. The Real Robot Challenge 2022 therefore served as a bridge between the RL and robotics communities by allowing participants to experiment remotely with a real robot - as easily as in simulation. In the last years, offline reinforcement learning has matured into a promising paradigm for learning from pre-collected datasets, alleviating the reliance on expensive online interactions. We therefore asked the participants to learn two dexterous manipulation tasks involving pushing, grasping, and in-hand orientation from provided real-robot datasets. An extensive software documentation and an initial stage based on a simulation of the real set-up made the competition particularly accessible. By giving each team plenty of access budget to evaluate their offline-learned policies on a cluster of seven identical real TriFinger platforms, we organized an exciting competition for machine learners and roboticists alike. In this work we state the rules of the competition, present the methods used by the winning teams and compare their results with a benchmark of state-of-the-art offline RL algorithms on the challenge datasets.
Sign Gradient Descent Algorithms for Kinetostatic Protein Folding

This paper proposes a sign gradient descent (SGD) algorithm for predicting the three-dimensional folded protein molecule structures under the kinetostatic compliance method (KCM). In the KCM framework, which can be used to simulate the range of motion of peptide-based nanorobots/nanomachines, protein molecules are modeled as a large number of rigid nano-linkages that form a kinematic mechanism under motion constraints imposed by chemical bonds while folding under the kinetostatic effect of nonlinear interatomic force fields. In a departure from the conventional successive kinetostatic fold compliance framework, the proposed SGD-based iterative algorithm in this paper results in convergence to the local minima of the free energy of protein molecules corresponding to their final folded conformations in a faster and more robust manner. KCM-based folding dynamics simulations of the backbone chains of protein molecules demonstrate the effectiveness of the proposed algorithm.
The DKU-MSXF Diarization System for the VoxCeleb Speaker Recognition Challenge 2023

This paper describes the DKU-MSXF submission to track 4 of the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23). Our system pipeline contains voice activity detection, clustering-based diarization, overlapped speech detection, and target-speaker voice activity detection, where each procedure has a fused output from 3 sub-models. Finally, we fuse different clustering-based and TSVAD-based diarization systems using DOVER-Lap and achieve the 4.30% diarization error rate (DER), which ranks first place on track 4 of the challenge leaderboard.
A review on coisotropic reduction in symplectic, cosymplectic, contact and co-contact Hamiltonian systems

In this paper we study coisotropic reduction in different types of dynamics according to the geometry of the corresponding phase space. The relevance of coisotropic reduction is motivated by the fact that these dynamics can always be interpreted as Lagrangian or Legendrian submanifolds. Furthermore, Lagrangian or Legendrian submanifolds can be reduced by a coisotropic one.
SUNRISE: The rich molecular inventory of high-redshift dusty galaxies revealed by broadband spectral line surveys

Understanding the nature of high-$z$ dusty galaxies requires a comprehensive view of their ISM and molecular complexity. However, the molecular ISM at high-$z$ is commonly studied using only a few species beyond CO, limiting our understanding. In this paper, we present the results of deep 3 mm spectral line surveys using the NOEMA targeting two lensed dusty galaxies: APM 08279+5255 (APM), a quasar at redshift $z=3.911$, and NCv1.143 (NC), a $z=3.565$ starburst galaxy. The spectral line surveys cover rest-frame frequencies from about 330-550 GHz. We report the detection of 38 and 25 emission lines in APM and NC, respectively. The spectra reveal the chemical richness and the complexity of the physical properties of the ISM. By comparing the spectra of the two sources and combining the gas excitation analysis, we find that the physical properties and the chemical imprints of the ISM are different between them: the molecular gas is more excited in APM, exhibiting higher molecular-gas temperatures and densities compared to NC; the chemical abundances in APM are akin to the values of local AGN, showing boosted relative abundances of the dense gas tracers that might be related to high-temperature chemistry and/or XDRs, while NC more closely resembles local starburst galaxies. The most significant differences are found in H2O, where the 448GHz H2O line is significantly brighter in APM, likely linked to the intense far-infrared radiation from the dust powered by AGN. Our astrochemical model suggests that at such high column densities, FUV radiation is less important in regulating the ISM, while CRs (X-rays/shocks) are the key players in shaping the abundance of the molecules and the initial conditions of star formation. Such deep spectral line surveys open a new window to study the physical and chemical properties of the ISM and the radiation field of galaxies in the early Universe. (abridged)
Exceptional Set Estimate Through Brascamp–Lieb Inequality

Fix integers $1\le k<n$, and numbers $a,s$ satisfying $0<s<\min \{k,a\}$. The problem of exceptional set estimate is to determine $$\begin{align*} & T(a,s):=\sup_{A\subset \mathbb{R}^{n}\ \textrm{dim}A=a}\textrm{dim}(\{ V\in G(k,n): \textrm{dim}(\pi_{V}(A))<s \}). \end{align*}$$In this paper, we prove a new upper bound for $T(a,s)$ by using Brascamp–Lieb inequality. As one of the corollaries, we obtain the estimate $$\begin{align*} & T(a,\frac{k}{n}a)\le k(n-k)-\min\{k,n-k\}, \end{align*}$$which improves a previous result $T(a,\frac{k}{n}a)\le k(n-k)-1$ of He. By constructing examples, we can determine the explicit value of $T(a,s)$ for certain $(a,s)$: When $k\le \frac{n}{2}$, $\beta \in (0,1]$ and $\gamma \in (\beta ,\frac{k}{n}(1+\beta )]$, we have $$\begin{align*} &T(1+\beta,\gamma)=k(n-k)-k. \end{align*}$$ When $k\ge \frac{n}{2}$, $\beta \in (0,1]$ and $\gamma \in (\beta , (1-\frac{k}{n})+\frac{k}{n}\beta ]$, we have $$\begin{align*} &T(n-1+\beta,k-1+\gamma)=k(n-k)-(n-k). \end{align*}$$
Mixed local-nonlocal quasilinear problems with critical nonlinearities

We study existence and multiplicity of nontrivial solutions of the following problem $$ \left\{ \begin{array}{rcll} -\Delta_p u+(-\Delta_p)^{s} u&=&\lambda|u|^{q-2}u+|u|^{p^{\ast}-2}u&\mbox{ in }\Omega,\\ u&=&0&\mbox{ on } \mathbb{R}^{N} \setminus \Omega, \end{array} \right. $$ where $\Omega\subset \mathbb{R}^N$ is a bounded open set with smooth boundary, dimension $N\geq 2$, parameter $\lambda>0$, exponents $0<s<1<p<N$, while $q\in(1,p^{\ast})$ with $p^{\ast}=\frac{Np}{N-p}$. The problem is driven by an operator of mixed order obtained by the sum of the classical $p$-Laplacian and of the fractional $p$-Laplacian. We analyze three different scenarios depending on exponent $q$. For this, we combine variational methods with some topological techniques, such as the Krasnoselskii genus and the Lusternik-Schnirelman category theories.
Computational General Relativity in the Wolfram Language using Gravitas I: Symbolic and Analytic Computation

We introduce a new, open-source computational general relativity framework for the Wolfram Language called Gravitas, which boasts a number of novel and distinctive features as compared to the many pre-existing computational and numerical relativity frameworks currently available within the open-source community. These include, but are not limited to: seamless integration of its powerful symbolic and numerical subsystems, and, by extension, seamless transition between analytic/continuous representations and numerical/discrete representations of arbitrary spacetime geometries; highly modular, general and extensible representations of spacetime geometries, spacetime topologies, gauge conditions, coordinate systems, matter fields, evolution equations and initial data; ability to set up and run complex numerical relativity simulations, and to perform 2D and 3D visualizations, symbolic computations and numerical analysis (including the extraction of gravitational wave signals) on the resulting data, all from within a single notebook environment; and a totally-unstructured adaptive refinement scheme based on hypergraph rewriting, allowing for exceedingly efficient discretization and numerical evolution of Cauchy initial data for a wide range of challenging computational problems involving strong relativistic field dynamics. In this first in a series of two articles covering the framework, we focus on the design and capabilities of Gravitas's symbolic subsystem, including its general and flexible handling of arbitrary geometries parametrized by arbitrary curvilinear coordinate systems (along with an in-built library of standard metrics and coordinate conditions), as well as its various high-level tensor calculus and differential geometry features. We proceed to show how this subsystem can be used to solve the Einstein field equations both analytically and numerically.
Semantify: Simplifying the Control of 3D Morphable Models using CLIP

We present Semantify: a self-supervised method that utilizes the semantic power of CLIP language-vision foundation model [32] to simplify the control of 3D morphable models. Given a parametric model, training data is created by randomly sampling the model’s parameters, creating various shapes and rendering them. The similarity between the output images and a set of word descriptors is calculated in CLIP’s latent space. Our key idea is first to choose a small set of semantically meaningful and disentangled descriptors that characterize the 3DMM, and then learn a non-linear mapping from scores across this set to the parametric coefficients of the given 3DMM. The nonlinear mapping is defined by training a neural network without a human-in-the-loop. We present results on numerous 3DMMs: body shape models, face shape and expression models, as well as animal shapes. We demonstrate how our method defines a simple slider interface for intuitive modeling, and show how the mapping can be used to instantly fit a 3D parametric body shape to in-the-wild images. See our project page at https://omergral.github. io/Semantify/
Medizinische und therapeutische Aspekte der Sterbebegleitung

Sterbebegleitung wird als Grundaufgabe der ärztlichen Tätigkeit angesehen 1 – oft besteht jedoch Verunsicherung, wie sie konkret auszusehen hat. Dieser Beitrag soll vermitteln, wie eine ideale Sterbebegleitung gestaltet werden sollte, sowie konkrete Hilfestellungen und Anregungen geben, wie dies auch außerhalb einer Palliativstation gelingen kann.
The evolutionary ecology of fungal killer phenotypes

Ecological interactions influence evolutionary dynamics by selecting upon fitness variation within species. Antagonistic interactions often promote genetic and species diversity, despite the inherently suppressive effect they can have on the species experiencing them. A central aim of evolutionary ecology is to understand how diversity is maintained in systems experiencing antagonism. In this review, we address how certain single-celled and dimorphic fungi have evolved allelopathic killer phenotypes that engage in antagonistic interactions. We discuss the evolutionary pathways to the production of lethal toxins, the functions of killer phenotypes and the consequences of competition for toxin producers, their competitors and toxin-encoding endosymbionts. Killer phenotypes are powerful models because many appear to have evolved independently, enabling across-phylogeny comparisons of the origins, functions and consequences of allelopathic antagonism. Killer phenotypes can eliminate host competitors and influence evolutionary dynamics, yet the evolutionary ecology of killer phenotypes remains largely unknown. We discuss what is known and what remains to be ascertained about killer phenotype ecology and evolution, while bringing their model system properties to the reader's attention.
UWB Hybrid Filtering-Based Mobile IoT Device Tracking

The positioning accuracy of UWB-based mobile Internet of Things (IoT) devices is frequently impacted by the complicated indoor environment, which is a common application for automated following mobile IoT devices. To address the issue of abnormal value errors such as high noise and UWB jitter value when tracking and locating mobile IoT devices in complicated indoor environments, this paper proposes to use a hybrid filtering weighted following algorithm based on UWB, which combines the benefits and drawbacks of Gaussian, median, and average filtering techniques, introduces the residual value of ranging, and combines geometric positioning to determine the ideal following value. The experimental results show that the proposed algorithm can effectively filter out the UWB error under multi-factor interference and finally estimate the UWB value closest to the actual value, thereby improving the stability and sensitivity of the following process and obtaining a better follow effect.
MetaCerberus: distributed highly parallelized scalable HMM-based implementation for robust functional annotation across the tree of life

Summary MetaCerberus is an exclusive HMM/HMMER-based tool that is massively parallel, on low memory, and provides rapid scalable annotation for functional gene inference across genomes to metacommunities. It provides robust enumeration of functional genes and pathways across many current public databases including KEGG (KO), COGs, CAZy, FOAM, and viral specific databases (i.e., VOGs and PHROGs). In a direct comparison, MetaCerberus was twice as fast as EggNOG-Mapper, and produced better annotation of viruses, phages, and archaeal viruses than DRAM, PROKKA, or InterProScan. MetaCerberus annotates more KOs across domains when compared to DRAM, with a 186x smaller database and a third less memory. MetaCerberus is fully integrated with differential statistical tools (i.e., DESeq2 and edgeR), pathway enrichment (GAGE R), and Pathview R for quantitative elucidation of metabolic pathways. MetaCerberus implements the key to unlocking the biosphere across the tree of life at scale. Availability and implementation MetaCerberus is written in Python and distributed under a BSD-3 license. The source code of MetaCerberus is freely available at https://github.com/raw-lab/metacerberus. Written in python 3 for both Linux and Mac OS X. MetaCerberus can also be easily installed using mamba create –n metacerberus –c bioconda –c conda-forge metacerberus
Local interneurons in the murine visual thalamus have diverse receptive fields and can provide feature selective inhibition to relay cells

By influencing the type and quality of information that relay cells transmit, local interneurons in thalamus have a powerful impact on cortex. To define the sensory features that these inhibitory neurons encode, we mapped receptive fields of optogenetically identified cells in the murine dorsolateral geniculate nucleus. Although few in number, local interneurons had diverse types of receptive fields, like their counterpart relay cells. This result differs markedly from visual cortex, where inhibitory cells are typically less selective than excitatory cells. To explore how thalamic interneurons might converge on relay cells, we took a computational approach. Using an evolutionary algorithm to search through a library of interneuron models generated from our results, we show that aggregated output from different groups of local interneurons can simulate the inhibitory component of the relay cell’s receptive field. Thus, our work provides proof-of-concept that groups of diverse interneurons can supply feature-specific inhibition to relay cells.
A taxon-restricted duplicate of Iroquois3 is required for patterning the spider waist

The chelicerate body plan is distinguished from other arthropod groups by its division of segments into two tagmata: the anterior prosoma (“cephalothorax”) and the posterior opisthosoma (“abdomen”). Little is understood about the genetic mechanisms that establish the prosomal-opisthosomal (PO) boundary. To discover these mechanisms, we created high-quality genomic resources for the large-bodied spider Aphonopelma hentzi. We sequenced specific territories along the antero-posterior axis of developing embryos and applied differential gene expression analyses to identify putative regulators of regional identity. After bioinformatic screening for candidate genes that were consistently highly expressed in the posterior segments, we validated the function of highly ranked candidates in the tractable spider model Parasteatoda tepidariorum. Here, we show that an arthropod homolog of the Iroquois complex of homeobox genes is required for proper formation of the boundary between arachnid tagmata. The function of this homolog had not been previously characterized, because it was lost in the common ancestor of Pancrustacea, precluding its investigation in well-studied insect model organisms. Knockdown of the spider copy of this gene, which we designate as waist-less, in P. tepidariorum resulted in embryos with defects in the PO boundary, incurring discontinuous spider germ bands. We show that waist-less is required for proper specification of dorso-ventral identity in the segments that span the prosoma-opisthosoma boundary, which in adult spiders corresponds to the narrowed pedicel. Our results suggest the requirement of an ancient, taxon-restricted paralog for the establishment of the tagmatic boundary that defines Chelicerata.
Investigating the Neural Correlates of Processing Basic Emotions: A Functional Near-Infrared Spectroscopy (fNIRS) Study

Emotion regulation, a fundamental aspect of human functioning, involves the ability to monitor, evaluate, and modify emotional responses. Understanding the neural mechanisms underlying emotion regulation holds significant implications across various disciplines, including psychology, neuroscience, and clinical psychiatry. This study aims to explore the neural correlates of emotion regulation using functional near-infrared spectroscopy (fNIRS) with a specific focus on the prefrontal cortex (PFC). fNIRS, a non-invasive and portable brain imaging technology, offers an excellent opportunity to investigate real-life emotion processing with high temporal resolution. Twenty participants underwent an experimental protocol where they viewed emotional pictures from the International Affective Picture System (IAPS) database, varying in valence (positive and negative) and arousal (high and low). fNIRS data were collected during the picture presentation, and the hemodynamic responses in the PFC were analyzed. The findings demonstrated distinct spatiotemporal patterns of activation associated with different emotional states. Positive valence stimuli elicited higher hemodynamic activation in bilateral dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC) regions when compared to negative valence stimuli. On the other hand, negative valence stimuli induced higher activation in the medial prefrontal cortex (mPFC) when compared to positive valence stimuli. Moreover, high arousal positive valence stimuli evoked higher activation in the left DLPFC region when compared to high arousal negative valence stimuli. These results shed light on the differential neural processing of positive and negative emotions within the PFC, supporting the notion of lateralized emotional processing. The study validates the feasibility of fNIRS for objectively capturing emotion-related neural activity, providing valuable insights for future applications in emotion recognition and affective brain-computer interfaces (BCIs). Understanding the neural basis of emotion regulation has significant implications for designing targeted interventions for individuals experiencing emotion dysregulation disorders. Additionally, the integration of fNIRS technology into affective BCIs may offer new possibilities for real-time emotion detection and communication in populations with communication challenges.
Visualizing The Effect Of Frequency And Nature Of Social Media Platforms Usage On Mental Health And Family Functioning From An Ethnographic Point Of View

Social media has become an integral part of our daily lives, with billions of users accessing these platforms mainly from their smartphones. Existing literature suggests that social media has an impact on mental health, but it is not clear whether this impact is positive or negative. This raises concerns about the relationship between social networks and public health, which require further investigation. In response, this doctoral consortium paper proposes a research study that investigates the impact of visualizing the frequency and nature of social media use on mental health and family functioning. The research study consists of two main user studies and four supporting surveys, aimed at reflecting on and adding to the existing literature. Furthermore, the study sampled participants from different ethnographic backgrounds, including the United Kingdom, Saudi Arabia, and Malaysia. This approach is beneficial because it allows cross-cultural comparisons, which can help identify similarities and differences in the impact of social media on mental health and family functioning across different cultures. It is important to examine the impact of social media on mental health and family functioning across cultures because social norms, values, and attitudes may differ between cultures, leading to different results. The analysis includes in-group comparisons between different cultures and gender, with an emphasis on identifying negative impacts. This research addresses a literature gap, providing evidence of the negative impact of social media on mental health and family functioning and the effectiveness of visualizing this effect.