Miracle-dz/UniFAR-FTUs huggingface dataset:
z ∼ 7 GALAXIES IN THE HUDF: FIRST EPOCH WFC3/IR RESULTS

We present a sample of 16 robust z ∼ 7 z850-drop galaxies detected by the newly installed Wide Field Camera 3 (WFC3)/IR on the Hubble Space Telescope. Our analysis is based on the first epoch data of the HUDF09 program covering the Hubble Ultra Deep Field with 60 orbits of Y105, J125, and H160 observations. These remarkable data cover 4.7 arcmin2 and are the deepest near infrared images ever taken, reaching to ∼29 mag AB (5σ). The 16 z ∼ 6.5–7.5 galaxies have been identified based on the Lyman Break technique utilizing (z850 − Y105) versus (Y105 − J125) colors. They have magnitudes J125=26.0–29.0 (AB), an average apparent half-light radius of ∼0.16 arcsec (≲1 kpc), and show very blue colors (some even β ≲ −2.5), in particular at low luminosities. The WFC3/IR data confirm previous Near Infrared Camera and Multi-Object Spectrometer detections indicating that the dropout selection at z ∼ 7 is very reliable. Our data allow a first determination of the faint-end slope of the z ∼ 7 luminosity function, reaching down to MUV ∼ −18, a full magnitude fainter than previous measurements. When fixing ϕ* = 1.4 × 10−3 Mpc−3 mag−1 to the value previously measured at z ∼ 6, we find a best-fit value of α = −1.77 ± 0.20, with a characteristic luminosity of M* = −19.91 ± 0.09. This steep slope is similar to what is seen at z ∼ 2–6 and indicates that low-luminosity galaxies could potentially provide adequate flux to reionize the universe. The remarkable depth and resolution of these new images provide insights into the coming power of the James Webb Space Telescope.
Sensory Glove-Based Surgical Robot User Interface

Robotic surgery has reached a high level of maturity and has become an integral part of standard surgical care. However, existing surgeon consoles are bulky and take up valuable space in the operating room, present challenges for surgical team coordination, and their proprietary nature makes it difficult to take advantage of recent technological advances, especially in virtual and augmented reality. One potential area for further improvement is the integration of modern sensory gloves into robotic platforms, allowing surgeons to control robotic arms directly with their hand movements intuitively. We propose one such system that combines an HTC Vive tracker, a Manus Meta Prime 3 XR sensory glove, and God Vision wireless smart glasses. The system controls one arm of a da Vinci surgical robot. In addition to moving the arm, the surgeon can use fingers to control the end-effector of the surgical instrument. Hand gestures are used to implement clutching and similar functions. In particular, we introduce clutching of the instrument orientation, a functionality not available in the da Vinci system. The vibrotactile elements of the glove are used to provide feedback to the user when gesture commands are invoked. A preliminary evaluation of the system shows that it has excellent tracking accuracy and allows surgeons to efficiently perform common surgical training tasks with minimal practice with the new interface; this suggests that the interface is highly intuitive. The proposed system is inexpensive, allows rapid prototyping, and opens opportunities for further innovations in the design of surgical robot interfaces.
Yield, Area, and Energy Optimization in STT-MRAMs Using Failure-Aware ECC

Spin-Transfer Torque MRAMs are attractive due to their non-volatility, high density, and zero leakage. However, STT-MRAMs suffer from poor reliability due to shared read and write paths. Additionally, conflicting requirements for data retention and writeability (both related to the energy barrier height of the storage device) makes design more challenging. Furthermore, the energy barrier height depends on the geometry of the storage. Any variations in the geometry of the storage device lead to variations in the energy barrier height. In order to address the poor reliability of STT-MRAMs, usage of Error Correcting Codes (ECC) has been proposed. Unlike traditional CMOS memory technologies, ECC is expected to correct both soft and hard errors in STT-MRAMs. To achieve acceptable yield with low write power, stronger ECC is required, resulting in increased number of encoded bits and degraded memory capacity. In this article, we propose Failure-aware ECC (FaECC), which masks permanent faults while maintaining the same correction capability for soft errors without increased number of encoded bits. Furthermore, we investigate the impact of process variations on run-time reliability of STT-MRAMs. In order to analyze the effectiveness of our methodology, we developed a cross-layer simulation framework that consists of device, circuit and array level analysis of STT-MRAM memory arrays. Our results show that using FaECC relaxes the requirements on the energy barrier height, which reduces the write energy and results in smaller access transistor size and memory array area.
A Planner for Composing Services Described in DAML-S

A web service is a web-accessible piece of software or hardware. In recent years, industry has been showing increasing interest in web services as a technology for building distributed web applications. However, web services as a technology lacks in several departments. Representations for describing web services have been widely investigated by industry and academia. Service composition-that is, automated methods for constructing a sequence of web services to achieve a desired result- has been relatively neglected. We present in this paper, a planner that composes atomic/basic services described in DAML-S into a composite service. We discuss issues involved with the design of planners for composition. We also propose a set of guidelines for de- scribing services that facilitates composition.
A finite element method for anisotropic crystal growth on surfaces

Phase transition problems on curved surfaces can lead to a panopticon of fascinating patterns. In this paper we consider finite element approximations of phase field models with a spatially inhomogeneous and anisotropic surface energy density. The problems are either posed in $\mathbb R^3$ or on a two-dimensional hypersurface in $\mathbb R^3$. In the latter case, a fundamental choice regarding the anisotropic energy density has to be made. Our numerical method can be employed both situations, where for the problems on hypersurfaces the algorithm uses parametric finite elements. We prove an unconditional stability result for our schemes and present several numerical experiments, including for the modelling of ice crystal growth on a sphere.
Quarklet Characterizations for bivariate Bessel-Potential Spaces on the Unit Square via Tensor Products

In this paper we deduce new characterizations for bivariate Bessel-Potential spaces defined on the unit square via B-spline quarklets. For that purpose in a first step we use univariate boundary adapted quarklets to describe univariate Bessel-Potential spaces on intervals. To obtain the bivariate characterizations a recent result of Hansen and Sickel is applied. It yields that each bivariate Bessel-Potential space on a square can be written as an intersection of function spaces which have a tensor product structure. Hence our main result is a characterization of bivariate Bessel-Potential spaces on squares in terms of quarklets that are tensor products of univariate quarklets on intervals.
Assessment of hydrophobic interactions and their contributions through the analysis of the methane dimer

Hydrophobic Interactions (HIs) are important in many phenomena of molecular recognition in chemistry and biology. Still, the relevance of HIs is sometimes difficult to evaluate particularly in large systems and intramolecular interactions. We put forward a method to estimate the magnitude and the different contributions of a given HI of the C···C, HC···H, and H···H type through (i) the analysis of the electron density in the intermolecular region for eleven relative orientations of the methane dimer and (ii) the subsequent decomposition of the corresponding interaction energy in physically significant contributions using Symmetry Adapted Perturbation Theory (SAPT). Strong correlations were found between the topological properties of ρ(r) calculated at intermolecular bond critical points and EintSAPT plus its different contributions with the C···C distance of the considered orientations of (CH4)2. These correlations were used to construct Mollier‐like diagrams of EintSAPT and its components as a function of the separation between two carbons and the orientation of the groups bonded to these atoms. The ethane dimer and tert‐butylcyclohexane are used as representative examples of this new approach. Overall, we anticipate that this new method might prove useful in the study of both intramolecular and intermolecular HIs particularly of those within large systems wherein SAPT or electronic structure calculations are computationally expensive or even prohibitive. © 2014 Wiley Periodicals, Inc.
On the Power of Quantum Distributed Proofs

Quantum nondeterministic distributed computing was recently introduced as dQMA (distributed quantum Merlin-Arthur) protocols by Fraigniaud, Le Gall, Nishimura and Paz (ITCS 2021). In dQMA protocols, with the help of quantum proofs and local communication, nodes on a network verify a global property of the network. Fraigniaud et al. showed that, when the network size is small, there exists an exponential separation in proof size between distributed classical and quantum verification protocols, for the equality problem, where the verifiers check if all the data owned by a subset of them are identical. In this paper, we further investigate and characterize the power of the dQMA protocols for various decision problems. First, we give a more efficient dQMA protocol for the equality problem with a simpler analysis. This is done by adding a symmetrization step on each node and exploiting properties of the permutation test, which is a generalization of the SWAP test. We also show a quantum advantage for the equality problem on path networks still persists even when the network size is large, by considering ``relay points'' between extreme nodes. Second, we show that even in a general network, there exist efficient dQMA protocols for the ranking verification problem, the Hamming distance problem, and more problems that derive from efficient quantum one-way communication protocols. Third, in a line network, we construct an efficient dQMA protocol for a problem that has an efficient two-party QMA communication protocol. Finally, we obtain the first lower bounds on the proof and communication cost of dQMA protocols. To prove a lower bound on the equality problem, we show any dQMA protocol with an entangled proof between nodes can be simulated with a dQMA protocol with a separable proof between nodes by using a QMA communication-complete problem introduced by Raz and Shpilka (CCC 2004).
Enhancing Historical Image Retrieval with Compositional Cues

In analyzing vast amounts of digitally stored historical image data, existing content-based retrieval methods often overlook significant non-semantic information, limiting their effectiveness for flexible exploration across varied themes. To broaden the applicability of image retrieval methods for diverse purposes and uncover more general patterns, we innovatively introduce a crucial factor from computational aesthetics, namely image composition, into this topic. By explicitly integrating composition-related information extracted by CNN into the designed retrieval model, our method considers both the image's composition rules and semantic information. Qualitative and quantitative experiments demonstrate that the image retrieval network guided by composition information outperforms those relying solely on content information, facilitating the identification of images in databases closer to the target image in human perception. Please visit https://github.com/linty5/CCBIR to try our codes.
The two-phase problem for harmonic measure in VMO via jump formulas for the Riesz transform

Let $\Omega^+\subset\mathbb R^{n+1}$ be an NTA domain and let $\Omega^-= \mathbb R^{n+1}\setminus \overline{\Omega^+}$ be an NTA domain as well. Denote by $\omega^+$ and $\omega^-$ their respective harmonic measures. Assume that $\Omega^+$ is a $\delta$-Reifenberg flat domain, for some $\delta>0$ small enough. In this paper we show that if $\omega^+$ and $\omega^-$ are mutually absolutely continuous and $\log\frac{d\omega^-}{d\omega^+}\in VMO(\omega^+)$, then the inner unit normal of $\Omega^+$ also satisfies $N\in VMO(\omega^+)$. To obtain this result we prove jump formulas for the non-tangential limits of Riesz transforms and other singular integrals which are valid for arbitrary rectifiable sets and have their own interest.
Investigating the validity of structure learning algorithms in identifying risk factors for intervention in patients with diabetes

Diabetes, a pervasive and enduring health challenge, imposes significant global implications on health, financial healthcare systems, and societal well-being. This study undertakes a comprehensive exploration of various structural learning algorithms to discern causal pathways amongst potential risk factors influencing diabetes progression. The methodology involves the application of these algorithms to relevant diabetes data, followed by the conversion of their output graphs into Causal Bayesian Networks (CBNs), enabling predictive analysis and the evaluation of discrepancies in the effect of hypothetical interventions within our context-specific case study. This study highlights the substantial impact of algorithm selection on intervention outcomes. To consolidate insights from diverse algorithms, we employ a model-averaging technique that helps us obtain a unique causal model for diabetes derived from a varied set of structural learning algorithms. We also investigate how each of those individual graphs, as well as the average graph, compare to the structures elicited by a domain expert who categorised graph edges into high confidence, moderate, and low confidence types, leading into three individual graphs corresponding to the three levels of confidence. The resulting causal model and data are made available online, and serve as a valuable resource and a guide for informed decision-making by healthcare practitioners, offering a comprehensive understanding of the interactions between relevant risk factors and the effect of hypothetical interventions. Therefore, this research not only contributes to the academic discussion on diabetes, but also provides practical guidance for healthcare professionals in developing efficient intervention and risk management strategies.
Gen-T: Table Reclamation in Data Lakes

We introduce the problem of Table Reclamation. Given a Source Table and a large table repository, reclamation finds a set of tables that, when integrated, reproduce the source table as closely as possible. Unlike query discovery problems like Query-by-Example or by-Target, Table Reclamation focuses on reclaiming the data in the Source Table as fully as possible using real tables that may be incomplete or inconsistent. To do this, we define a new measure of table similarity, called error-aware instance similarity, to measure how close a reclaimed table is to a Source Table, a measure grounded in instance similarity used in data exchange. Our search covers not only SELECT-PROJECT- JOIN queries, but integration queries with unions, outerjoins, and the unary operators subsumption and complementation that have been shown to be important in data integration and fusion. Using reclamation, a data scientist can understand if any tables in a repository can be used to exactly reclaim a tuple in the Source. If not, one can understand if this is due to differences in values or to incompleteness in the data. Our solution, Gen-T, performs table discovery to retrieve a set of candidate tables from the table repository, filters these down to a set of originating tables, then integrates these tables to reclaim the Source as closely as possible. We show that our solution, while approximate, is accurate, efficient and scalable in the size of the table repository with experiments on real data lakes containing up to 15K tables, where the average number of tuples varies from small (web tables) to extremely large (open data tables) up to 1M tuples.
EDT: Improving Large Language Models' Generation by Entropy-based Dynamic Temperature Sampling

Recently, Large Language Models (LLMs) have demonstrated outstanding performance across a wide range of downstream language tasks. Temperature sampling is a commonly used decoding strategy for LLMs' generation process. However, a fixed temperature parameter is used in most cases, which may not always be an optimal choice for balancing generation quality and diversity. In this paper, we propose an effective Entropy-based Dynamic Temperature (EDT) Sampling method, to achieve a more balanced performance in terms of both generation quality and diversity by dynamically selecting the temperature parameter. Additionally, we also show model performance and comprehensive analyses for 4 different generation benchmarks. Our experiments show that EDT significantly outperforms the existing strategies across different tasks.
Leveraging Linguistically Enhanced Embeddings for Open Information Extraction

Open Information Extraction (OIE) is a structured prediction (SP) task in Natural Language Processing (NLP) that aims to extract structured $n$-ary tuples - usually subject-relation-object triples - from free text. The word embeddings in the input text can be enhanced with linguistic features, usually Part-of-Speech (PoS) and Syntactic Dependency Parse (SynDP) labels. However, past enhancement techniques cannot leverage the power of pretrained language models (PLMs), which themselves have been hardly used for OIE. To bridge this gap, we are the first to leverage linguistic features with a Seq2Seq PLM for OIE. We do so by introducing two methods - Weighted Addition and Linearized Concatenation. Our work can give any neural OIE architecture the key performance boost from both PLMs and linguistic features in one go. In our settings, this shows wide improvements of up to 24.9%, 27.3% and 14.9% on Precision, Recall and F1 scores respectively over the baseline. Beyond this, we address other important challenges in the field: to reduce compute overheads with the features, we are the first ones to exploit Semantic Dependency Parse (SemDP) tags; to address flaws in current datasets, we create a clean synthetic dataset; finally, we contribute the first known study of OIE behaviour in SP models.
Non-Markovian skin effect

The Liouvillian skin effect and the non-Hermitian skin effect have both been used to explain the localization of eigenmodes near system boundaries, though the former is arguably more accurate in some regimes due to its incorporation of quantum jumps. However, these frameworks predominantly focus on weak Markovian interactions, neglecting the potentially crucial role of memory effects. To address this, we investigate, utilizing the powerful hierarchical equations of motion method, how a non-Markovian environment can modify the Liouvillian skin effect. We demonstrate that a non-Markovian environment can induce not only a ``thick skin effect", where the skin mode broadens and shifts into the bulk, but also skin-mode coherence, leading to the coherence-delocalization and oscillatory relaxation with a characteristic linear scaling with system size. Remarkably, both the skin-mode and steady-state coherence exhibit resistance to decoherence from additional environmental noise. These findings highlight the profound impact of system-bath correlations on relaxation and localization, revealing unique phenomena beyond conventional Markovian approximations.
MIRO: a versatile biomimetic edutainment robot

Here we present MIRO, a companion robot designed to engage users in science and robotics via edutainment. MIRO is a robot that is biomimetic in aesthetics, morphology, behaviour, and control architecture. In this paper, we review how these design choices affect its suitability for a companionship role. In particular, we consider how MIRO's emulation of familiar mammalian body language as one component of a broader biomimetic expressive system provides effective communication of emotional state and intent. We go on to discuss how these features contribute to MIRO's potential in other domains such as healthcare, education, and research.
Factor-based Compositional Embedding Models

Introduction Word embeddings, which are distributed word representations learned by neural language models [1, 2, 3], have been shown to be powerful word representations. They have been successfully applied to a range of NLP tasks, including syntax [2, 4, 5] and semantics [6, 7, 8]. Information about language structure is critical in many NLP tasks, where substructures of a sentence and its annotations inform downstream NLP task. Yet word representations alone do not capture such structure. For example, in relation extraction the sentence may be annotated with part-of-speech tags, a dependency parse, and named entities, with the goal of predicting a relation label for a pair of target entities. Semantic role labeling has a similar form. In tasks such as these, it is important to capture information about both individual words and their interactions. The annotations evidence these interactions, and we often deﬁne features over substructures of these annotated sentences (e.g. relative positions of words, words that appear on a dependency path, words with their entity types) to make successful predictions of the label (e.g. relation type). Our goal is to learn representations for the substructures of an annotated sentence which inherit the generalization strength of word representations but remain sufﬁciently expressive for the task. Typically, for each term in a large ﬁnite vocabulary, we learn a unique word embedding. Yet, since the set of annotated sentences is inﬁnite in size, it would be difﬁcult to learn a unique representation of each one. Therefore, research has turned to compositional embedding models: building a representation (embedding) for an annotated sentence based on its component word embeddings. A traditional approach for composition