Miracle-dz/UniFAR-FTUs huggingface dataset:
Positivity, low twist dominance and CSDR for CFTs

We consider a crossing symmetric dispersion relation (CSDR) for CFT four point correlation with identical scalar operators, which is manifestly symmetric under the cross-ratios u,vu,v interchange. This representation has several features in common with the CSDR for quantum field theories. It enables a study of the expansion of the correlation function around u=v=1/4u=v=1/4, which is used in the numerical conformal bootstrap program. We elucidate several remarkable features of the dispersive representation using the four point correlation function of \Phi_{1,2}Φ1,2 operators in 2d minimal models as a test-bed. When the dimension of the external scalar operator (\Delta_\sigmaΔσ) is less than \frac{1}{2}12, the CSDR gets contribution from only a single tower of global primary operators with the second tower being projected out. We find that there is a notion of low twist dominance (LTD) which, as a function of \Delta_\sigmaΔσ, is maximized near the 2d Ising model as well as the non-unitary Yang-Lee model. The CSDR and LTD further explain positivity of the Taylor expansion coefficients of the correlation function around the crossing symmetric point and lead to universal predictions for specific ratios of these coefficients. These results carry over to the epsilon expansion in 4-\epsilon4−ϵ dimensions. We also conduct a preliminary investigation of geometric function theory ideas, namely the Bieberbach-Rogosinski bounds.
JWST's PEARLS: Bright 1.5–2.0 μm Dropouts in the Spitzer/IRAC Dark Field

Using the first epoch of four-band NIRCam observations obtained by the James Webb Space Telescope (JWST) Prime Extragalactic Areas for Reionization and Lensing Science Program in the Spitzer IRAC Dark Field, we search for F150W and F200W dropouts. In 14.2 arcmin2, we have found eight F150W dropouts and eight F200W dropouts, all brighter than 27.5 mag (the brightest being ∼24 mag) in the band to the red side of the break. As they are detected in multiple bands, these must be real objects. Their nature, however, is unclear, and characterizing their properties is important for realizing the full potential of JWST. If the observed color decrements are due to the Lyman break, these objects should be at z ≳ 11.7 and z ≳ 15.4, respectively. The color diagnostics show that at least four F150W dropouts are far away from the usual contaminators encountered in dropout searches (red galaxies at much lower redshifts or brown dwarf stars). While the diagnostics of the F200W dropouts are less certain due to the limited number of passbands, at least one of them is likely not a known type of contaminant, and the rest are consistent with either high-redshift galaxies with evolved stellar populations or old galaxies at z ≈ 3–8. If a significant fraction of our dropouts are indeed at z ≳ 12, we have to face the severe problem of explaining their high luminosities and number densities. Spectroscopic identifications of such objects are urgently needed.
Random sequential covering

In random sequential covering, identical objects are deposited randomly, irreversibly, and sequentially; only attempts increasing the coverage are accepted. A finite system eventually gets congested, and we study the statistics of congested configurations. For the covering of an interval by dimers, we determine the average number of deposited dimers, compute all higher cumulants, and establish the probabilities of reaching minimally and maximally congested configurations. We also investigate random covering by segments with ℓ sites and sticks. Covering an infinite substrate continues indefinitely, and we analyze the dynamics of random sequential covering of Z and Rd .
RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk

Prior work on safe Reinforcement Learning (RL) has studied risk-aversion to randomness in dynamics (aleatory) and to model uncertainty (epistemic) in isolation. We propose and analyze a new framework to jointly model the risk associated with epistemic and aleatory uncertainties in ﬁnite-horizon and discounted inﬁnite-horizon MDPs. We call this framework that combines Risk-Averse and Soft-Robust methods RASR. We show that when the risk-aversion is deﬁned using either EVaR or the entropic risk, the optimal policy in RASR can be computed eﬃciently using a new dynamic program formulation with a time-dependent risk level. As a result, the optimal risk-averse policies are deterministic but time-dependent, even in the inﬁnite-horizon discounted setting. We also show that particular RASR objectives reduce to risk-averse RL with mean posterior transition probabilities. Our empirical results show that our new algorithms consistently mitigate uncertainty as measured by EVaR and other standard risk measures.
Functional dimension of feedforward ReLU neural networks

It is well-known that the parameterized family of functions representable by fully-connected feedforward neural networks with ReLU activation function is precisely the class of piecewise linear functions with finitely many pieces. It is less well-known that for every fixed architecture of ReLU neural network, the parameter space admits positive-dimensional spaces of symmetries, and hence the local functional dimension near any given parameter is lower than the parametric dimension. In this work we carefully define the notion of functional dimension, show that it is inhomogeneous across the parameter space of ReLU neural network functions, and continue an investigation – initiated in [14] and [5] – into when the functional dimension achieves its theoretical maximum. We also study the quotient space and fibers of the realization map from parameter space to function space, supplying examples of fibers that are disconnected, fibers upon which functional dimension is non-constant, and fibers upon which the symmetry group acts non-transitively.
Quantum Computing Approach for Energy Optimization in a Prosumer Community

This paper presents a quantum approach for the formulation and solution of the prosumer problem, i.e., the problem of minimizing the energy cost incurred by a number of users in an energy community, while addressing the constraints given by the balance of energy and the user requirements. As the problem is NP-complete, a hybrid quantum/classical algorithm could help to acquire a significant speedup, which is particularly useful when the problem size is large. This work describes the steps through which the problem can be transformed, reformulated and given as an input to Quantum Approximate Optimization Algorithm (QAOA), and reports some experimental results, in terms of the quality of the solution and time to achieve it, obtained with a quantum simulator, when varying the number of constraints and, correspondingly, the number of qubits.
Quantitative analysis of dynamic CT imaging of methane-hydrate formation with a hybrid machine learning approach

Fast multi-phase processes in methane hydrate-bearing samples are challenging for micro-CT quantitative study because of complex tomographic data analysis involving time-consuming segmentation procedures. This is due to the sample multi-scale structure changing in time, low X-ray attenuation and phase contrast between solid and fluid materials, as well as large amount of data acquired during dynamic processes. We propose a hybrid approach for automatic segmentation of tomographic data from time-resolved imaging of methane gas-hydrate formation in sandy granular media. First, we use an optimized 3D U-net neural network to perform segmentation of mineral grains that are characterized by low contrast to the surrounding pore brine-saturated phases. Then, we perform statistical clustering based on the Gaussian mixture model for separating the pore-space phases that are characterized by gray-level instabilities caused by dynamic processes during hydrate formation. The proposed approach was used for segmenting several hundred gigabytes of data acquired during an in-situ tomographic experiment at a synchrotron. Automatic segmentation allowed for studying properties of the hydrate growth in pores, as well as dynamic processes such as the incremental pore-brine flow and redistribution.
Multi-NeuS: 3D Head Portraits From Single Image With Neural Implicit Functions

We present an approach for the reconstruction of textured 3D meshes of human heads from one or few views. Since such few-shot reconstruction is underconstrained, it requires prior knowledge which is hard to impose on traditional 3D reconstruction algorithms. In this work, we rely on the recently introduced 3D representation– neural implicit functions– which, being based on neural networks, allows to naturally learn priors about human heads from data, and is directly convertible to textured mesh. Namely, we extend NeuS, a state-of-the-art neural implicit function formulation, to represent multiple objects of a class (human heads in our case) simultaneously. The underlying neural net architecture is designed to learn the commonalities among these objects and to generalize to unseen ones. Our model is trained on just a hundred smartphone videos and does not require any scanned 3D data. Afterwards, the model can fit novel heads in the few-shot or one-shot modes with good results.
Bounded Satisfiability Checking of Metric First-order Temporal Logic

Legal properties involve reasoning about data values and time. Metric first-order temporal logic (MFOTL) provides a rich formalism for specifying legal properties. While MFOTL has been successfully used for verifying legal properties over operational systems via runtime monitoring, no solution exists for MFOTL-based verification in early-stage system development captured by requirements. Given a legal property and system requirements both formalized in MFOTL, the compliance of the property can be verified on the requirements via satisfiability checking. In this paper, we propose a practical, sound, and complete (within a given bound) satisfiability checking approach for MFOTL. The approach, based on satisfiability modulo theories (SMT), employs a counterexample-guided strategy to incrementally search for a satisfying solution. We implemented our approach in a tool called LEGOS , and evaluated it on five case studies spanning the healthcare, business administration, banking and aviation domains. Our results indicate that our approach can efficiently determine whether legal properties of interest are met, or generate counterexamples that lead to compliance violations.
A Memory-Related Multi-Task Method Based on Task-Agnostic Exploration

—We pose a new question: Can agents learn how to combine actions from previous tasks to complete new tasks, just as humans ? In contrast to imitation learning, there is no expert data, only the data collected through environmental exploration. Compared with ofﬂine reinforcement learning, the problem of data distribution shift is more serious. Since the action sequence to solve the new task may be the combination of trajectory segments of multiple training tasks, in other words, the test task and the solving strategy do not exist directly in the training data. This makes the problem more difﬁcult. We propose a Memory-related Multi-task Method (M3) to address this problem. The method consists of three stages. First, task-agnostic exploration is carried out to collect data. Different from previous methods, we organize the exploration data into a knowledge graph. We design a model based on the exploration data to extract action effect features and save them in memory, while an action predictive model is trained. Secondly, for a new task, the action effect features stored in memory are used to generate candidate actions by a feature decomposition-based approach. Finally, a multi-scale candidate action pool and the action predictive model are fused to generate a strategy to complete the task. Experimental results show that the performance of our proposed method is signiﬁcantly improved compared with the baseline. Code and visualizations: https://github.com/Xianqi-Zhang/M3
Decisions over Sequences

This paper introduces a class of objects called decision rules that map infinite sequences of alternatives to a decision space. These objects can be used to model situations where a decision maker encounters alternatives in a sequence such as receiving recommendations. Within the class of decision rules, we study natural subclasses: stopping and uniform stopping rules. Our main result establishes the equivalence of these two subclasses of decision rules. Next, we introduce the notion of computability of decision rules using Turing machines and show that computable rules can be implemented using a simpler computational device: a finite automaton. We further show that computability of choice rules -- an important subclass of decision rules -- is implied by their continuity with respect to a natural topology. Finally, we introduce some natural heuristics in this framework and provide their behavioral characterization.
Quadratic Relations of the Deformed W-Algebra for the Twisted Affine Lie Algebra of Type A(2)2N

We revisit the free field construction of the deformed $W$-algebra by Frenkel and Reshetikhin [Comm. Math. Phys. 197 (1998), 1-32], where the basic $W$-current has been identified. Herein, we establish a free field construction of higher $W$-currents of the deformed $W$-algebra associated with the twisted affine Lie algebra $A_{2N}^{(2)}$. We obtain a closed set of quadratic relations and duality, which allows us to define deformed $W$-algebra ${\mathcal W}_{x,r}\big(A_{2N}^{(2)}\big)$ using generators and relations.
Snowmass 2021 topical group report: Neutrinos from Natural Sources

This is the final report from the Snowmass 2021 Neutrino Frontier Topical Group on Neutrinos from Natural Sources. It covers a broad range of neutrino sources, from low-energy neutrinos from the early universe to ultra high-energy sources. We divide this report by source, and discuss the motivations for pursuing searches in each case, the current state of the field, and the prospects for future theoretical and experimental developments. We consider neutrinos produced in the early universe; solar neutrinos; geoneutrinos; supernova neutrinos, including the diffuse supernova neutrino background (DSNB); neutrinos produced in the atmosphere; and high-energy astrophysical neutrinos.
Analyse Structurelle en Angiographie cardiaque

This work is aimed at the extraction of vascular structures and the formation of high level structural entities, i.e vascular b ranches. The proposed approach is based on a string matching technique to associate two types of lines : contours and centerlines. We us e attributed strings and the measure of similarity between these strings is performed using Wagner and Fischer's algorithm. The pairing is f irst performed from pairwise comparisons, then from the simultaneous three lines matching.
Are effort‐based decision‐making tasks worth the effort?—A study on the associations between effort‐based decision‐making tasks and self‐report measures

Amotivation is a common symptom in various mental disorders, including psychotic or depressive disorders. Effort‐based decision‐making (EBDM)‐tasks quantifying amotivation at a behavioral level have been on the rise. Task performance has been shown to differentiate patient groups from healthy controls. However, findings on indicators of construct validity, such as the correlations between different tasks and between tasks and self‐reported/observer‐rated amotivation in clinical and healthy samples have been inconclusive.
Discovery of a cold and gravitationally unstable cloud fragment

1300 μm mapping of the region around the energy source of HH24 in the dark cloud L1652 has revealed a neighbouring strong, previously unknown source of dust emission. Photometry at 450, 800, 1100 and 1300 μm yields a very low dust temperature of around 10 K. Estimates for the mass of the object lead to values of 36 and 52 M ○ . for radii of 4.1 . 10 16 and 8.2 . 10 16 cm which is at least a factor of 50 above the Jeans mass. The 1300 μm contours show an elongation along N-S and a strong central increase in density. These observational facts lead us to suggest that we have detected a cold and gravitationally unstable cloud fragment which might be a protostar
Analysis of Swarm Behavior in Two Dimensions

We investigate the steady state solutions that can exist for a two dimensional swarm of biological organisms, which have pairwise social interaction forces. The three steady states we investigate using a continuum model are a ribbon migrating swarm, a circular migrating swarm, and a milling swarm. We solve these numerically by reformulating the integral equation that arises from the continuum model as an energy minimization problem. For the ribbon migrating solution, we are able to determine an analytic solution from Carleman’s equation which arises after an asymptotic expansion of the social interaction potential. Using this technique we are able to show the existence of a square root singularity that emerges at the boundary of the compactly supported swarm. The analytic solution agrees with the numerical solution for certain parameter values in the social interaction potential. We then demonstrate the existence of solutions for a migrating and milling circular swarm which contain a square root singularity. The milling swarm looks similar to the infinite ribbon, so we are able to use an asymptotic expansion of the potential to obtain an analytic solution in this case as well. The singularities in the density of the swarm suggest that the Morse potential should not be used for modeling biological swarming.
Predation risk regulates prey assortative mating by reducing the expected reproductive value of mates

Abstract Many animals exhibit size assortative mating (SAM), but how predation affects it remains largely unknown. We hypothesized that predation risk may turn prey less choosy, disrupting SAM, or reduce the expected reproductive value of mates, maintaining SAM but with different size ratio between mates. Using a manipulative field experiment, we found that desert isopods under risk of scorpion predation maintained SAM, but that males that choose and fight over females were on average smaller for a given female size. Less pairs were formed in risky sites, but there were no differences in female sizes and progeny number, size and age near and away from scorpion burrows. Our complementary behavioral experiments revealed that bigger males stayed longer near safe burrows, and won more male–male contests than smaller conspecifics. Our findings highlight that prey can anticipate future costs of predation and use this information to assess the expected reproductive value of mates.
A novel circRNA-miRNA association prediction model based on structural deep neural network embedding

A large amount of clinical evidence began to mount, showing that circular ribonucleic acids (RNAs; circRNAs) perform a very important function in complex diseases by participating in transcription and translation regulation of microRNA (miRNA) target genes. However, with strict high-throughput techniques based on traditional biological experiments and the conditions and environment, the association between circRNA and miRNA can be discovered to be labor-intensive, expensive, time-consuming, and inefficient. In this paper, we proposed a novel computational model based on Word2vec, Structural Deep Network Embedding (SDNE), Convolutional Neural Network and Deep Neural Network, which predicts the potential circRNA-miRNA associations, called Word2vec, SDNE, Convolutional Neural Network and Deep Neural Network (WSCD). Specifically, the WSCD model extracts attribute feature and behaviour feature by word embedding and graph embedding algorithm, respectively, and ultimately feed them into a feature fusion model constructed by combining Convolutional Neural Network and Deep Neural Network to deduce potential circRNA-miRNA interactions. The proposed method is proved on dataset and obtained a prediction accuracy and an area under the receiver operating characteristic curve of 81.61% and 0.8898, respectively, which is shown to have much higher accuracy than the state-of-the-art models and classifier models in prediction. In addition, 23 miRNA-related circular RNAs (circRNAs) from the top 30 were confirmed in relevant experiences. In these works, all results represent that WSCD would be a helpful supplementary reliable method for predicting potential miRNA-circRNA associations compared to wet laboratory experiments.
Modelling attenuation of irregular wave fields by artificial ice floes in the laboratory

A summary is given on the utility of laboratory experiments for gaining understanding of wave attenuation in the marginal ice zone, as a complement to field observations, theory and numerical models. It is noted that most results to date are for regular incident waves, which, combined with the highly nonlinear wave–floe interaction phenomena observed and measured during experimental tests, implies that the attenuation of regular waves cannot necessarily be used to infer the attenuation of irregular waves. Two experiments are revisited in which irregular wave tests were conducted but not previously reported, one involving a single floe and the other a large number of floes, and the transmission coefficients for the irregular and regular wave tests are compared. The transmission spectra derived from the irregular wave tests agree with the regular wave data but are overpredicted by linear models due to nonlinear dissipative processes, regardless of floe configuration. This article is part of the theme issue ‘Theory, modelling and observations of marginal ice zone dynamics: multidisciplinary perspectives and outlooks’.
Embedding-Enhanced GIZA++: Improving Low-Resource Word Alignment Using Embeddings

Word alignment has been dominated until re-001 cently by GIZA++, a statistical method based 002 on the 30-year-old IBM models. New meth-003 ods primarily rely on large machine translation 004 models, massively multilingual language mod-005 els, or supervision. We introduce Embedding-006 Enhanced GIZA++, and outperform GIZA++ 007 without any of the aforementioned factors. 008 Taking advantage of monolingual embedding 009 spaces of source and target language only, 010 we exceed GIZA++’s performance in every 011 tested scenario for three languages pairs. In 012 the lowest-resource setting, we outperform 013 GIZA++ by 8.5, 10.9, and 12 AER for Ro-En, 014 De-En, and En-Fr, respectively. We release our 015 code at www.blind-review.code . 016
SG Translate Together - Uplifting Singapore’s translation standards with the community through technology

The Singapore’s Ministry of Communications and Information (MCI) has officially launched the SG Translate Together (SGTT) web portal on 27 June 2022, with the aim of partnering its citizens to improve translation standards in Singapore. This web portal houses the Singapore Government’s first neural machine translation (MT) engine, known as SG Translate, which was jointly developed by MCI and the Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR). Adapted using localised translation data, SG Translate is able to generate translations that are attuned to Singapore’s context and supports Singapore’s four (4) official languages – English (Singapore), Chinese (Singapore), Bahasa Melayu (Singapore) and Tamil (Singapore). Upon completion of development, MCI allowed all Government agencies to use SG Translate for their daily operations. This presentation will briefly cover the methodologies adopted and showcase SG Translate’s capability to translate content involving local culture, everyday life and government policies and schemes. This presentation will also showcase MCI’s sustainable approach for the continual training of the SG Translate MT engine through citizenry participation.