Miracle-dz/UniFAR-FTUs huggingface dataset:
Partial-transpose-guided entanglement classes and minimum noise filtering in many-body Gaussian quantum systems

The reduction and distortion of quantum correlations in the presence of classical noise leads to varied levels of inefficiency in the availability of entanglement as a resource for quantum information processing protocols. While generically minimizing required entanglement for mixed quantum states remains challenging, a class of many-body Gaussian quantum states ($\mathcal{N}$IC) is here identified that exhibits two-mode bipartite entanglement structure, resembling that of pure states, for which the logarithmic negativity entanglement measure remains invariant upon inclusion of the classical correlations and optimal entanglement resources can be clearly quantified. This subclass is found to be embedded within a broader class of many-body Gaussian states ($\mathcal{N}$-SOL) that retain two-mode entanglement structure for detection processes. These two entanglement classes are relevant in theoretical and experimental applications from the scalar field vacuum to the local axial motional modes of trapped ion chains. Utilizing the subspace that heralds inseparability in response to partial transposition, a minimum noise filtering process is designed to be necessary, sufficient, and computable for determining membership in these classes of entanglement structure. Application of this process to spacelike regions of the free scalar field vacuum is found to improve resource upper bounds, providing new understanding of the entanglement required for the quantum simulation of quantum fields as observed by arrays of local detectors.
Higher order corrections to KPV: The nonabelian brane stack perspective

In this work, we study the decay of $\overline{D3}$-branes in the setup of Kachru, Pearson, and Verlinde (KPV) at higher order in $\alpha'$ from the perspective of a nonabelian $\overline{D3}$-brane stack. We extend the leading order analysis of KPV by including higher order commutators as well as higher derivative corrections. Recently, the KPV setup has been studied at higher order in $\alpha'$ from the NS5-brane perspective. It was found that in order to control $\alpha'$ corrections the quantity $g_sM^2$ determining the amount of warping in the Klebanov-Strassler throat has to be much larger than expected. This leads to serious issues when using the $\overline{D3}$-branes as an uplift to dS. The benefit of the analysis in this work is that the $\overline{D3}$-brane perspective is controlled when the distance between the branes inside the brane stack is substringy which is a regime not controlled on the NS5-brane side. As a main result, we find that the strong bound $g_sM^2\sim \mathcal{O}(100)$ obtained on the NS5-brane also holds in the regime accessible from the $\overline{D3}$-brane perspective. We also show that the novel way of uplifting proposed in the recent work on $\alpha'$ corrections to the KPV setup can only work for small warped throats.
Xling: A Learned Filter Framework for Accelerating High-Dimensional Approximate Similarity Join

Similarity join finds all pairs of close points within a given distance threshold. Many similarity join methods have been proposed, but they are usually not efficient on high-dimensional space due to the curse of dimensionality and data-unawareness. We investigate the possibility of using metric space Bloom filter (MSBF), a family of data structures checking if a query point has neighbors in a multi-dimensional space, to speed up similarity join. However, there are several challenges when applying MSBF to similarity join, including excessive information loss, data-unawareness and hard constraint on the distance metric. In this paper, we propose Xling, a generic framework to build a learning-based metric space filter with any existing regression model, aiming at accurately predicting whether a query point has enough number of neighbors. The framework provides a suite of optimization strategies to further improve the prediction quality based on the learning model, which has demonstrated significantly higher prediction quality than existing MSBF. We also propose XJoin, one of the first filter-based similarity join methods, based on Xling. By predicting and skipping those queries without enough neighbors, XJoin can effectively reduce unnecessary neighbor searching and therefore it achieves a remarkable acceleration. Benefiting from the generalization capability of deep learning models, XJoin can be easily transferred onto new dataset (in similar distribution) without re-training. Furthermore, Xling is not limited to being applied in XJoin, instead, it acts as a flexible plugin that can be inserted to any loop-based similarity join methods for a speedup.
Performance Improvement Bounds for Lipschitz Configurable Markov Decision Processes

Configurable Markov Decision Processes (Conf-MDPs) have recently been introduced as an extension of the traditional Markov Decision Processes (MDPs) to model the real-world scenarios in which there is the possibility to intervene in the environment in order to configure some of its parameters. In this paper, we focus on a particular subclass of Conf-MDP that satisfies regularity conditions, namely Lipschitz continuity. We start by providing a bound on the Wasserstein distance between $\gamma$-discounted stationary distributions induced by changing policy and configuration. This result generalizes the already existing bounds both for Conf-MDPs and traditional MDPs. Then, we derive a novel performance improvement lower bound.
Limits of open ASEP stationary measures near a boundary

Consider the stationary measure of open asymmetric simple exclusion process (ASEP) on the lattice $\{1,\dots,n\}$. Taking $n$ to infinity while fixing the jump rates, this measure converges to a measure on the semi-infinite lattice. In the high and low density phases, we characterize the limiting measure and also show that this convergence occurs in total variation distance on a sublattice of scale $n/\log n$. Our approach involves bounding the total variation distance using generating functions, which are further estimated through a subtle analysis of the atom masses of Askey-Wilson signed measures.
Ouroboros: Speculative Decoding with Large Model Enhanced Drafting

Drafting-then-verifying decoding methods such as speculative decoding are widely adopted training-free methods to accelerate the inference of large language models (LLMs). Instead of employing an autoregressive process to decode tokens sequentially, speculative decoding initially creates drafts with an efficient small model. Then LLMs are required to conduct verification and correction in a non-autoregressive fashion to minimize time overhead. Generating longer drafts can lead to even more significant speedups once verified, but also incurs substantial trial and error costs if it fails. Suffering from the high verification failure probability, existing decoding methods cannot draft too much content for verification at one time, achieving sub-optimal inference acceleration. In this paper, we introduce Ouroboros, which constructs a phrase candidate pool from the verification process of LLMs to provide candidates for draft generation of the small model. Thereby, Ouroboros can further improve the efficiency and effectiveness of the initial drafts. The experimental results on typical text generation tasks show that Ouroboros achieves speedups of up to 1.9x and 2.8x compared to lookahead decoding and speculative decoding, respectively. The source code of Ouroboros is available at https://github.com/thunlp/Ouroboros.
How Sparse Can We Prune A Deep Network: A Fundamental Limit Viewpoint

Network pruning is an effective measure to alleviate the storage and computational burden of deep neural networks arising from its high overparameterization. Thus raises a fundamental question: How sparse can we prune a deep network without sacrifice on the performance? To address this problem, in this work we'll take a first principles approach, i.e. we directly impose the sparsity constraint on the original loss function and then characterize the necessary and sufficient condition of the sparsity (\textit{which turns out to nearly coincide}) by leveraging the notion of \textit{statistical dimension} in convex geometry. Through this fundamental limit, we're able to identify two key factors that determine the pruning ratio limit, i.e., weight magnitude and network flatness. Generally speaking, the flatter the loss landscape or the smaller the weight magnitude, the smaller pruning ratio. In addition, we provide efficient countermeasures to address the challenges in computing the pruning limit, which involves accurate spectrum estimation of a large-scale and non-positive Hessian matrix. Moreover, through the lens of the pruning ratio threshold, we can provide rigorous interpretations on several heuristics in existing pruning algorithms. Extensive experiments are performed that demonstrate that the our theoretical pruning ratio threshold coincides very well with the experiments. All codes are available at: https://github.com/QiaozheZhang/Global-One-shot-Pruning
Magnetic soft microfiberbots for robotic embolization

Cerebral aneurysms and brain tumors are leading life-threatening diseases worldwide. By deliberately occluding the target lesion to reduce the blood supply, embolization has been widely used clinically to treat cerebral aneurysms and brain tumors. Conventional embolization is usually performed by threading a catheter through blood vessels to the target lesion, which is often limited by the poor steerability of the catheter in complex neurovascular networks, especially in submillimeter regions. Here, we propose magnetic soft microfiberbots with high steerability, reliable maneuverability, and multimodal shape reconfigurability to perform robotic embolization in submillimeter regions via a remote, untethered, and magnetically controllable manner. Magnetic soft microfiberbots were fabricated by thermal drawing magnetic soft composite into microfibers, followed by magnetizing and molding procedures to endow a helical magnetic polarity. By controlling magnetic fields, magnetic soft microfiberbots exhibit reversible elongated/aggregated shape morphing and helical propulsion in flow conditions, allowing for controllable navigation through complex vasculature and robotic embolization in submillimeter regions. We performed in vitro embolization of aneurysm and tumor in neurovascular phantoms and in vivo embolization of a rabbit femoral artery model under real-time fluoroscopy. These studies demonstrate the potential clinical value of our work, paving the way for a robotic embolization scheme in robotic settings. Remote, untethered, and magnetically controlled soft microfiberbots were developed for in vivo robotic embolization in blood vessels. Editor’s summary As a minimally invasive treatment for aneurysms and brain tumors, surgeons typically insert a slender catheter through the femoral artery and guide it through blood vessels to deliver embolic agents. However, this method of embolization can be difficult to steer through complex vascular networks. A magnetic, soft microfiberbot was developed by Liuet al. to perform remotely controlled robotic embolization. The microfiberbot, composed of a magnetized fiber coiled into a helix shape, can conform to different vessel sizes and performs a corkscrew propulsion when subjected to an external magnetic field. The microfiberbots were used to successfully perform in vitro embolization of aneurysm and tumor in phantom blood vessels and in vivo embolization in a rabbit femoral artery. These proposed robots provide a controllable alternative to conventional catheter-based embolization. —Melisa Yashinski
Steps towards Addressing Text Classification in Low-Resource Languages

Text classification is an area of NLP in which major improvements have been observed in recent years, primarily via pre-training and fine-tuning of large language models (LLMs). However, low-resource languages still face major challenges. We explore how to address this problem us-ing different text classification tasks across two low-resource languages. Our focus is on adopting multilingual LLMs using data expansion techniques (with and without machine translation). Results indicate that pre-trained, fine-tuned models of the resource-poor language appear more promising than multilingual models, we also find that translating into a resource-poor language is not beneficial in our experimental settings.
Automatic Dictionary Generation: Could Brothers Grimm Create a Dictionary with BERT?

The creation of the most famous German dictionary, also referred to as “Deutsches Wörter-buch” or in English “The German Dictionary”, by the two brothers Jacob and Wilhelm Grimm, took more than a lifetime to be finished (1838– 1961). In our work we pose the question, if it would be possible for them to create a dictionary using present technology, i.e., language models such as BERT. Starting with the definition of the task of Automatic Dictionary Generation, we propose a method based on contextualized word embeddings and hierarchical clustering to create a dictionary given unan-notated text corpora. We justify our design choices by running variants of our method on English texts, where ground truth dictionaries are available. Finally, we apply our approach to Shakespeare’s work and automatically generate a dictionary tailored to Shakespearean vocabulary and contexts without human intervention.
Presenting an Annotation Pipeline for Fine-grained Linguistic Analyses of Multimodal Corpora

With the increasing availability of multimodal documents, it is becoming more difficult for researchers to not only find relevant information within documents in various modalities and media formats, but to also explore potential semantic relationships between data objects of two different modalities embedded in a single document. This paper proposes a method rooted in an annotation pipeline that takes as input text data objects that are either native text objects, or textual descriptions of a multimodal object, such as an image or video, and generates as output an attribute-rich document that unites four levels of annotation in a single framework. The annotated files generated by this pipeline lend themselves to exploration either in a non-programmatic way, by using the Corpus Query Language (CQL) in the web-based graphical user interface (GUI) of the IMS Open Corpus Workbench (CWB), or programmatically, using Python and a Jupyter Notebook. We present some preliminary results of analyses performed on the corpus.
The UNSC-Graph: An Extensible Knowledge Graph for the UNSC Corpus

We introduce the UNSC-Graph, a knowledge graph for a corpus of debates of the United Nations Security Council (UNSC) during the period 1995-2020. The graph combines previously disconnected data sources including from the UNSC Repertoire, the UN Library, Wiki-data, and from metadata extracted from the speeches themselves. Beyond existing meta-data detailing debates’ topics and participants, we also extended the graph to include all country mentions in a speech, geographical neighbours of countries mentioned, as well as sentiment scores. By linking the graph to Wikidata, we are able to include additional geopolitical information and extract various country name aliases to extend the coverage of country mentions beyond existing NER-based approaches. Studying mentions of Ukraine after 2014, we present a use case for the graph as a source for continuous analysis of international politics and geopolitical events discussed in the UNSC.
Toward a Multilingual Connective Database: Aligning German/French Concessive Connectives

We report on experiments to align discourse connectives from two language-specific connective lexicons (German and French) by their relation sense. In this case study, we focus on concessive connectives, and align them us-ing a parallel corpus. The ultimate goal is to arrive at bi-(or multi-)lingual connective lexi-cons, that at the same time provide insights on the “semantic space” that connectives cover in different languages.
Including a contemporary NLP application within an introductory course: an example with student feedback from a University of Applied Sciences

The recent extensive media coverage of Large Language Models and their applications like ChatGPT have created an unprecedented awareness and curiosity related to Natural Language Processing (NLP) amongst university students from different fields. However, students must understand and master a number of theoretical topics before they can understand how such models work and how they are applied to real-life examples. Within an introductory NLP course at a University of Applied Sciences, we asked ourselves how to best include teaching material related to contemporary applications in order to encourage students to experiment on their own, not only within the course but also later in their studies or in an industrial setting. What could be the major components and how could it be made accessible for students from different programs? What would be the added value compared to a plethora of existing online videos and tutorials? We present our experience with a step-by-step session on a contemporary applied topic, namely Semantic Textual Similarity. We share the examples, the visualization, the slides and the code samples used in the session. We discuss the students’ feedback as well as further possibilities for similar future sessions.
High-precision photoacoustic neural modulation uses a non-thermal mechanism

Neuromodulation is a powerful tool for fundamental studies in neuroscience and potential treatments of neurological disorders. Both photoacoustic (PA) and photothermal (PT) effects have been harnessed for non-genetic high-precision neural stimulation. Using a fiber-based device excitable by a nanosecond pulsed laser and a continuous wave laser for PA and PT stimulation, respectively, we systematically investigated PA and PT neuromodulation at single neuron level. Our results show that the laser energy needed for PA neurostimulaion is 1/40 of that needed for PT stimulation to achieve the same level of cell response recorded by Ca2+ imaging. The threshold energy for PA stimulation is found to be further reduced in neurons overexpressing mechano-sensitive channels, indicating the direct involvement of mechano-sensitive channels in PA stimulation. Electrophysiology study of single neurons upon PA and PT stimulation was performed by patch clamp recordings. Electrophysiological features stimulated by PA are distinct from those induced by PT, confirming that PA and PT stimulations operate through distinct mechanisms. These insights offer a foundation for rational design of more efficient and safer non-genetic neural modulation approaches.
Knockdown of PHOX2B in the retrotrapezoid nucleus reduces the central CO2 chemoreflex in rats

PHOX2B is a transcription factor essential for the development of different classes of neurons in the central and peripheral nervous system. Heterozygous mutations in the PHOX2B coding region are responsible for the occurrence of Congenital Central Hypoventilation Syndrome (CCHS), a rare neurological disorder characterised by inadequate chemosensitivity and life-threatening sleep- related hypoventilation. Animal studies suggest that chemoreflex defects are caused in part by the improper development or function of PHOX2B expressing neurons in the retrotrapezoid nucleus (RTN), a central hub for CO2 chemosensitivity. Although the function of PHOX2B in rodents during development is well established, its role in the adult respiratory network remains unknown. In this study, we investigated whether reduction in PHOX2B expression in chemosensitive neuromedin-B (NMB) expressing neurons in the RTN altered respiratory function. Four weeks following local RTN injection of a lentiviral vector expressing the short hairpin RNA (shRNA) targeting Phox2b mRNA, a reduction of PHOX2B expression was observed in Nmb neurons compared to both naïve rats and rats injected with the non-target shRNA. PHOX2B knockdown did not affect breathing in room air or under hypoxia, but ventilation was significantly impaired during hypercapnia. PHOX2B knockdown did not alter Nmb expression but it was associated with reduced the expression of both Task2 and Gpr4, two CO2 sensors in the RTN. We conclude that PHOX2B in the adult brain has an important role in CO2 chemoreception and reduced PHOX2B expression in CCHS beyond the developmental period may contribute to the impaired central chemoreflex function.
Intrinsic structural covariation links cerebellum subregions to the cerebral cortex

The human cerebellum is increasingly recognized to be involved in non-motor and higher-order cognitive functions. Yet, its ties with the entire cerebral cortex have not been holistically studied in a whole-brain exploration with a unified analytical framework. Here, we characterized disso-ciable cortical-cerebellar structural covariation patterns across the brain in n=38,527 UK Bio-bank participants. Our results invigorate previous observations in that important shares of corti-cal-cerebellar structural covariation are described as i) a dissociation between the higher-level cognitive system and lower-level sensorimotor system, as well as ii) an anticorrelation between the visual-attention system and advanced associative networks within the cerebellum. We also discovered a novel pattern of ipsilateral, rather than contralateral, cerebral-cerebellar associations. Furthermore, phenome-wide association assays revealed key phenotypes, including cognitive phenotypes, lifestyle, physical properties, and blood assays, associated with each decomposed covariation pattern, helping to understand their real-world implications. This systems neurosci-ence view paves the way for future studies to explore the implications of these structural covaria-tions, potentially illuminating new pathways in our understanding of neurological and cognitive disorders.