Miracle-dz/UniFAR-FTUs huggingface dataset:
Light Nuclei Production in Au+Au Collisions at $\sqrt{s_{NN}}=$ 3 GeV within Thermodynamical Approach: Bulk Properties and Collective Flow

We present results of simulations of light-nuclei production in Au+Au collisions at collision energy of $\sqrt{s_{NN}}=$ 3 GeV within updated Three-fluid Hydrodynamics-based Event Simulator Extended by UrQMD (Ultra-relativistic Quantum Molecular Dynamics) final State interactions (THESEUS). The results are compared with recent STAR data. The light-nuclei production is treated within the thermodynamical approach on equal basis with hadrons. The only additional parameter related to the light nuclei is the energy density of late freeze-out that imitates afterburner stage of the collision because the light nuclei do not participate in the UrQMD evolution. It is found that the late freeze-out is preferable for deuterons, tritons, and $^3$He. Remarkably, the $^4$He observables are better reproduced with the standard freeze-out. This suggests that the $^4$He nuclei better survive in the afterburner stage because they are more spatially compact and tightly bound objects. This is an argument in favor of dynamical treatment of light nuclei. The simulations indicate that the collision dynamics is determined by the hadronic phase. The calculated results reveal not perfect but a good reproduction of the data on bulk observables and directed flow. The elliptic flow turns out to be more intricate.
Toxicity Detection is NOT all you Need: Measuring the Gaps to Supporting Volunteer Content Moderators

Extensive efforts in automated approaches for content moderation have been focused on developing models to identify toxic, offensive, and hateful content with the aim of lightening the load for moderators. Yet, it remains uncertain whether improvements on those tasks have truly addressed moderators' needs in accomplishing their work. In this paper, we surface gaps between past research efforts that have aimed to provide automation for aspects of content moderation and the needs of volunteer content moderators, regarding identifying violations of various moderation rules. To do so, we conduct a model review on Hugging Face to reveal the availability of models to cover various moderation rules and guidelines from three exemplar forums. We further put state-of-the-art LLMs to the test, evaluating how well these models perform in flagging violations of platform rules from one particular forum. Finally, we conduct a user survey study with volunteer moderators to gain insight into their perspectives on useful moderation models. Overall, we observe a non-trivial gap, as missing developed models and LLMs exhibit moderate to low performance on a significant portion of the rules. Moderators' reports provide guides for future work on developing moderation assistant models.
Artificial Text Boundary Detection with Topological Data Analysis and Sliding Window Techniques

Due to the rapid development of text generation models, people increasingly often encounter texts that may start out as written by a human but then continue as machine-generated results of large language models. Detecting the boundary between human-written and machine-generated parts of such texts is a very challenging problem that has not received much attention in literature. In this work, we consider and compare a number of different approaches for this artificial text boundary detection problem, comparing several predictors over features of different nature. We show that supervised fine-tuning of the RoBERTa model works well for this task in general but fails to generalize in important cross-domain and cross-generator settings, demonstrating a tendency to overfit to spurious properties of the data. Then, we propose novel approaches based on features extracted from a frozen language model's embeddings that are able to outperform both the human accuracy level and previously considered baselines on the Real or Fake Text benchmark. Moreover, we adapt perplexity-based approaches for the boundary detection task and analyze their behaviour. We analyze the robustness of all proposed classifiers in cross-domain and cross-model settings, discovering important properties of the data that can negatively influence the performance of artificial text boundary detection algorithms.
On the IRS Deployment in Smart Factories Considering Blockage Effects: Collocated or Distributed?

In this article, we study the collocated and distributed deployment of intelligent reflecting surfaces (IRS) for a fixed total number of IRS elements to support enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services inside a factory. We build a channel model that incorporates the line-of-sight (LOS) probability and power loss of each transmission path, and propose three metrics, namely, the expected received signal-to-noise ratio (SNR), expected finite-blocklength (FB) capacity, and expected outage probability, where the expectation is taken over the probability distributions of interior blockages and channel fading. The expected received SNR and expected FB capacity for extremely high blockage densities are derived in closed-form as functions of the amount and height of IRSs and the density, size, and penetration loss of blockages, which are verified by Monte Carlo simulations. Results show that deploying IRSs vertically higher leads to higher expected received SNR and expected FB capacity. By analysing the average/minimum/maximum of the three metrics versus the number of IRSs, we find that for high blockage densities, both eMBB and URLLC services benefit from distributed deployment; and for low blockage densities, URLLC services benefit from distributed deployment while eMBB services see limited difference between collocated and distributed deployment.
Computational and Statistical Thresholds in Multi-layer Stochastic Block Models

We study the problem of community recovery and detection in multi-layer stochastic block models, focusing on the critical network density threshold for consistent community structure inference. Using a prototypical two-block model, we reveal a computational barrier for such multi-layer stochastic block models that does not exist for its single-layer counterpart: When there are no computational constraints, the density threshold depends linearly on the number of layers. However, when restricted to polynomial-time algorithms, the density threshold scales with the square root of the number of layers, assuming correctness of a low-degree polynomial hardness conjecture. Our results provide a nearly complete picture of the optimal inference in multiple-layer stochastic block models and partially settle the open question in Lei and Lin (2022) regarding the optimality of the bias-adjusted spectral method.
Optimal RIP Matrices with Slightly Less Randomness

A matrix $\Phi \in \mathbb{R}^{Q \times N}$ satisfies the restricted isometry property if $\|\Phi x\|_2^2$ is approximately equal to $\|x\|_2^2$ for all $k$-sparse vectors $x$. We give a construction of RIP matrices with the optimal $Q = O(k \log(N/k))$ rows using $O(k\log(N/k)\log(k))$ bits of randomness. The main technical ingredient is an extension of the Hanson-Wright inequality to $\epsilon$-biased distributions.
Modular Representations of Truncated current Lie algebras

In this paper we consider the structure and representation theory of truncated current algebras $\mathfrak{g}_m = \mathfrak{g}[t]/(t^{m+1})$ associated to the Lie algebra $\mathfrak{g}$ of a standard reductive group over a field of positive characteristic. We classify semisimple and nilpotent elements and describe their associated support varieties. Next, we prove various Morita equivalences for reduced enveloping algebras, including a reduction to nilpotent $p$-characters, analogous to a famous theorem of Friedlander--Parshall. We go on to give precise upper bounds for the dimensions of simple modules for all $p$-characters, and give lower bounds on these dimensions for homogeneous $p$-characters. We then develop the theory of baby Verma modules for homogeneous $p$-characters and, whenever the $p$-character has standard Levi type, we give a full classification of the simple modules. In particular we classify all simple modules with homogeneous $p$-characters for $\mathfrak{g}_m$ when $\mathfrak{g} = \mathfrak{gl}_n$. Finally, we compute the Cartan invariants for the restricted enveloping algebra $U_0(\mathfrak{g}_m)$ and show that they can be described by precise formulae depending on decomposition numbers for $U_0(\mathfrak{g})$.
On the microscopic propagation speed of long-range quantum many-body systems

We consider the time-dependent Schr\"odinger equation that is generated on the bosonic Fock space by a long-range quantum many-body Hamiltonian. We derive the first bound on the maximal speed of particle transport in these systems that is thermodynamically stable and holds all the way down to microscopic length scales. For this, we develop a novel multiscale rendition of the ASTLO (adiabatic spacetime localization observables) method. Our result opens the door to deriving the first thermodynamically stable Lieb-Robinson bounds on general local operators for these long-range interacting bosonic systems.
Improving Domain Robustness in Neural Machine Translation with Fused Topic Knowledge Embeddings

Domain robustness is a key challenge for Neural Machine Translation (NMT). Translating text from a different distribution than the training set requires the NMT models to generalize well to unseen domains. In this work we propose a novel way to address domain robustness, by fusing external topic knowledge into the NMT architecture. We employ a pretrained denoising autoencoder and fuse topic information into the system during continued pretraining, and finetuning of the model on the downstream NMT task. Our results show that incorporating external topic knowledge, as well as additional pretraining can improve the out-of-domain performance of NMT models. The proposed methodology meets state-of-the-art on out-of-domain performance. Our analysis shows that a low overlap between the pretraining and finetuning corpora, as well as the quality of topic representations help the NMT systems become more robust under domain shift.
Fine-tuning MBART-50 with French and Farsi data to improve the translation of Farsi dislocations into English and French

In this paper, we discuss the improvements brought by the fine-tuning of mBART50 for the translation of a specific Farsi dataset of dislocations. Given our BLEU scores, our evaluation is mostly qualitative: we assess the improvements of our fine-tuning in the translations into French of our test dataset of Farsi. We describe the fine-tuning procedure and discuss the quality of the results in the translations from Farsi. We assess the sentences in the French translations that contain English tokens and for the English translations, we examine the ability of the fine- tuned system to translate Farsi dislocations into English without replicating the dislocated item as a double subject. We scrutinized the Farsi training data used to train for mBART50 (Tang et al., 2021). We fine-tuned mBART50 with samples from an in-house French-Farsi aligned translation of a short story. In spite of the scarcity of available resources, we found that fine- tuning with aligned French-Farsi data dramatically improved the grammatical well-formedness of the predictions for French, even if serious semantic issues remained. We replicated the experiment with the English translation of the same Farsi short story for a Farsi-English fine-tuning and found out that similar semantic inadequacies cropped up, and that some translations were worse than our mBART50 baseline. We showcased the fine-tuning of mBART50 with supplementary data and discussed the asymmetry of the situation, adding little data in the fine-tuning is sufficient to improve morpho-syntax for one language pair but seems to degrade translation to English.
Overview of the 10th Workshop on Asian Translation

This paper presents the results of the shared tasks from the 10th workshop on Asian translation (WAT2023). For the WAT2023, 2 teams submitted their translation results for the human evaluation. We also accepted 1 research paper. About 40 translation results were submitted to the automatic evaluation server, and selected submissions were manually evaluated.
Technology Preparedness and Translator Training: Implications for Pedagogy

With increasing acknowledgement of enhanced quality now achievable by Machine Translation, new possibilities have emerged in translation, both vis-à-vis division of labour between human and machine in the translation process and acceptability of lower quality of language in exchange for efficiency. This paper presents surveys of four cohorts of post-graduate students of translation from the University of Macau to see if perceived trainee awareness and preparedness has kept pace with these possibilities. It is found that trainees across the years generally lack confidence in their perceived awareness, are hesitant in employing MT, and show definite reservations when reconsidering issues such as quality and division of labour. While the size of respondents is small, it is interesting to note that the awareness and preparedness mentioned above are found to be similar across the four years. The implication for training is that technology be fully integrated into the translation process in order to provide trainees with a template/framework to handle diverse situations, particularly those that require offering translations of a lower quality with a short turnaround time. The focus here is on Chinese-English translation, but the discussion may find resonance with other language pairs. Keywords Translator training, Computer-Assisted Translation, Machine Translation, translation pedagogy, Chinese-English translation
Abstract representations emerge in human hippocampal neurons during inference behavior

Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization1. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning, and how they relate to behavior2,3. Here we characterized the representational geometry of populations of neurons (single-units) recorded in the hippocampus, amygdala, medial frontal cortex, and ventral temporal cortex of neurosurgical patients who are performing an inferential reasoning task. We find that only the neural representations formed in the hippocampus simultaneously encode multiple task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consisted of disentangled directly observable and discovered latent task variables. Interestingly, learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behavior suggests that abstract/disentangled representational geometries are important for complex cognition.
Sculpting the tumour microenvironment by combining radiotherapy and ATR inhibition for curative-intent anti-PD-L1- and anti-NKG2A-based adjuvant immunotherapy

Despite some success in other cancer types, the results of combining radiotherapy/chemoradiotherapy and immune checkpoint blockade have been disappointing in patients with locally advanced head and neck squamous cell carcinoma (HNSCC). For such a potentially radiocurable disease, there remains an imperative to explore novel combination approaches. Here, we show that combining ATR inhibition with radiotherapy (ATRi/RT) increases the frequency of highly activated NKG2A/PD-1 double-positive T cells in patients and in animal models of HNSCC. Addition of dual anti-NKG2A and anti-PD-1/-PD-L1 blockade to ATRi/RT in the adjuvant, post-radiotherapy setting induces a robust antitumour immune response in HNSCC preclinical models. Efficacy of the combination regimen relies on CD40/CD40L costimulatory-mediated infiltration of activated/proliferative/memory CD8 and CD4 conventional T cells with persistent or new T cell receptor (TCR) signalling, respectively, as defined by tracking of T cell dynamics. In this favourable therapeutic context, TCR sequencing shows increased richness of the TCR repertoire and the emergence of numerous and large TCR clusters that share antigen specificity in response to full combination therapy. Collectively, our data point towards promising combination approaches for future clinical testing in HNSCC.
Bidirectional chemogenetic modulation of claustral activity causes altered cortical dynamics

In recent years, there has been a growing interest in understanding the function of the claustrum. The claustrum is a thin, long, subcortical structure with dense connections to the cortex. Despite these extensive connections, the manner in which the claustrum influences broader cortical activity remains unclear. We used mesoscale calcium fluorescence imaging to examine resting state cortical activity (1-5Hz) and sensory-evoked responses in lightly anesthetized mice, before and after bi-directional chemogenetic modulation of claustrum-prefrontal neurons. Claustrum inhibition resulted in increased activity in anterior-medial cortical areas, whereas claustrum excitation resulted in decreased activity in anterior-medial cortical regions. Claustrum inhibition also led to an increased local coupling between areas of frontal cortex, a decreased correlation between anterior medial regions-of-interest (ROIs) and lateral and posterior ROIs, and an increased sensory evoked response in the visual cortex. These results are consistent with the finding that the claustrum has a large feed-forward inhibitory effect on the PFC, and the concept that specific claustrocortical pathways may modulate recruit and synchronize activity in topographically distinct cortical modules. Together these results show that neural activity in the claustrum modulates the excitability of prefrontal cortical networks, suggesting a potential target for prefrontal-dependent behaviors such as learning, attention, and stress regulation.
3D single-molecule super-resolution imaging of microfabricated fractal substrates for cell culture and self-referenced imaging

Substrate-mediated cell growth engineering has shown increasing interest in promoting in vitro alternatives to animal experiments, as it opens new possibilities for cell culture, proliferation, and differentiation. Still, the mechanisms by which physical cues drive the cell phenotype are not fully understood, hence the necessity to interrogate cell behaviour at the highest resolution. However, cell 3D high-resolution optical imaging on such substrates remains challenging due to their complexity, as well as axial calibration issues. This work uses multiscale 3D SiO2 substrates consisting of spatially arrayed octahedra features of a few micrometers to hundreds of nanometers. Through optimizations of the structures and optical imaging conditions, we demonstrate the potential of these 3D multiscale structures as calibration tools for 3D super-resolution microscopy. We use their intrinsic multiscale and self-referenced nature to simultaneously perform lateral and axial calibrations in 3D single-molecule localization microscopy (SMLM) and assess imaging resolutions. With this proof of feasibility, we then use these substrates as a platform for high-resolution bioimaging. As proof of concept, we cultivate human mesenchymal stem cells cells on them, revealing very different growth patterns compared to flat glass. Specifically, the spatial distribution of cytoskeleton proteins is vastly modified, as we demonstrate with 3D SMLM assessment.
Expression and purification of human neutrophil proteinase 3 from insect cells and characterization of ligand binding

Neutrophil proteinase 3 (PR3) is an important drug target for inflammatory lung diseases such as chronic obstructive pulmonary disease and cystic fibrosis. Drug discovery efforts targeting PR3 require active enzyme for in vitro characterization, such as inhibitor screening, enzymatic assays, and structural studies. Recombinant expression of active PR3 overcomes the need for enzyme supplies from human blood and in addition allows studies on the influence of mutations on enzyme activity and ligand binding. Here, we report the expression of recombinant PR3 (rPR3) using a baculovirus expression system. The purification and activation process described resulted in highly pure and active PR3. The activity of rPR3 in the presence of commercially available inhibitors was compared with human PR3 by using a fluorescence-based enzymatic assay. Purified rPR3 had comparable activity to the native human enzyme, thus being a suitable alternative for enzymatic studies in vitro. Further, we established a surface plasmon resonance-based assay to determine binding affinities and kinetics of PR3 ligands. These methods provide valuable tools for early drug discovery aiming towards treatment of lung inflammation.
Glial ferritin maintains neural stem cells via transporting iron required for self-renewal in Drosophila

Stem cell niche is critical for regulating the behavior of stem cells. Drosophila neural stem cells (Neuroblasts, NBs) are encased by glial niche cells closely, but it still remains unclear whether glial niche cells can regulate the self-renewal and differentiation of NBs. Here we show that ferritin produced by glia, cooperates with Zip13 to transport iron into NBs for the energy production, which is essential to the self-renewal and proliferation of NBs. The knockdown of glial ferritin encoding genes causes energy shortage in NBs, which leads to the low proliferation and premature differentiation of NBs. Moreover, the level of glial ferritin production is affected by the status of NBs, establishing a bicellular iron homeostasis. In this study, we demonstrate that glial cells are indispensable to maintain the self-renewal of NBs, unveiling a novel role of the NB glial niche during brain development.
Structure-property machine learning models with predictive capabilities for glycans in food

Structure-property predictive models for food aim to decipher the complex relation between the physical shape of a molecule and its physical properties and/or the functional role of the molecule in a product formulation. Our focus in this paper is the modeling of glycans (i.e., carbohydrates), which are not only abundant in food, but essential to both food production and, more importantly, human health. In our study, we use regression methods to generalize the relationships between the structure of starch (e.g., chain length and composition of protein and amylose) and a range of its properties (e.g., gelatinization temperature, time series viscosity data, gel consistency, and sensory texture) for 301 samples of rice. Our results indicate that the structure-composition data is a significantly better predictor of sensory mouthfeel than the physical properties, even though the latter is typically used in experimental research. This demonstrates the potential to harness several structure-property relationships and we provide strategies to accelerate further experiment-based research.