Miracle-dz/UniFAR-FTUs huggingface dataset:
An Exploration of the Role of Creativity in Psychotherapy

This paper explores the role of creativity in psychotherapy with focus on the intrinsic and interpersonal application of creativity in this context. The objective is to understand if there is a need for creative training in psychotherapy training. It starts with an exploration of creativity in human behavior and how new ideas originate. Then it covers the interpersonal nature of creativity and the processes that underlie this with focus on the individuality as well as co-creation in the therapeutic space. Finally, it discussed the application of creativity in psychotherapy including the therapeutic alliance, leveraging intuition as well as its limitations.
Associative Concept Dictionary Construction and its Comparison with E l e c t r o n i c C o n c e p t D i c t i o n a r i e s

An associative concept dictionary is constructed using large-scale online association experiments. In the experiments, subjects are presented stimulus words chosen from elementary school textbook of Japanese. They are requested to make association from the stimulus words about 7 semantic relations, such as, higher-level concepts, lower-level concepts, actions, situations. The dictionary is constructed by using all of the associated concepts, which are connected to the stimulus words with calculated distances. The distances are obtained using a linear programming method, which combines two parameters linearly, the frequency of the associated word and the associated order of the word. The associative concept dictionary is compared with the conventional concept dictionaries such as EDR concept dictionary, Nihongo Goi-Taikei and WordNet. The comparison shows that the associative concept dictionary is more similar to WordNet than EDR dictionary or Goi-Taikei.
Simultaneous X-ray and optical observations of rapid variability in Scorpius X-1

Copernicus and SAS-3 observations of Sco X-1 during very active states show strong and rapid X-ray flickering with time scales as short as 20 sec. Simultaneous optical photometry reveals similar and remarkably well correlated flickering of lower amplitude. The level of correlation decreases when the X-ray source becomes fainter, but optical and X-ray fluxes, averaged over 1 min, are usually well correlated for B magnitudes less than 12.9. Cross-correlation analysis of the data during periods of high activity shows no really significant time delay between X-ray and optical features, although the cross-correlation function peaks systematically for positive lag values (optical following X-rays). The amplitude of the X-ray flickering exhibits a strong energy-dependence consistent with the well-known intensity versus spectral-hardness relation derived from previous observations. The highly correlated optical activity is interpreted as due to reprocessing of X-ray photons in the binary system.
From Quality to Information Quality in Official Statistics

Abstract The term quality of statistical data, developed and used in official statistics and international organizations such as the International Monetary Fund (IMF) and the Organisation for Economic Co-operation and Development (OECD), refers to the usefulness of summary statistics generated by producers of official statistics. Similarly, in the context of survey quality, official agencies such as Eurostat, National Center for Science and Engineering Statistics (NCSES), and Statistics Canada have created dimensions for evaluating the quality of a survey and its ability to report ‘accurate survey data’. The concept of Information Quality, or InfoQ provides a general framework applicable to data analysis in a broader sense than summary statistics: InfoQ is defined as “the potential of a data set to achieve a specific (scientific or practical) goal by using a given empirical analysis method. ” It relies on identifying and examining the relationships between four components: the analysis goal, the data, the data analysis, and the utility. The InfoQ framework relies on deconstructing the InfoQ concept into eight dimensions used for InfoQ assessment. In this article, we compare and contrast the InfoQ framework and dimensions with those typically used by statistical agencies. We discuss how the InfoQ approach can support the use of official statistics not only by governments for policy decision making, but also by other stakeholders, such as industry, by integrating official and organizational data.
Ground state of a class of noncritical one-dimensional quantum spin systems can be approximated efficiently

We study families ${H}_{n}$ of one-dimensional (1D) quantum spin systems, where $n$ is the number of spins, which have a spectral gap $\ensuremath{\Delta}E$ between the ground-state and first-excited state energy that scales, asymptotically, as a constant in $n$. We show that if the ground state $\ensuremath{\mid}{\ensuremath{\Omega}}_{m}⟩$ of the Hamiltonian ${H}_{m}$ on $m$ spins, where $m$ is an $O(1)$ constant, is locally the same as the ground state $\ensuremath{\mid}{\ensuremath{\Omega}}_{n}⟩$, for arbitrarily large $n$, then an arbitrarily good approximation to the ground state of ${H}_{n}$ can be stored efficiently for all $n$. We formulate a conjecture that, if true, would imply our result applies to all noncritical 1D spin systems. We also include an appendix on quasiadiabatic evolutions.
Quivers and the modular representation theory of finite groups

﻿ The purpose of this thesis is to discuss the role of certain types of quiver which appear in the modular representation theory of finite groups. It is our concern to study two different types of quiver. First of all we construct the ordinary quiver of certain blocks of defect 2 of the symmetric group, and then apply our results to the alternating group and to the theory of partitions. Secondly, we consider connected components of the stable Auslander-Reiten quiver of certain groups G with normal subgroup N. The main interest lies in comparing the tree class of components of N-modules, with the tree class of components of these modules induced up to G.
Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery

Active search is a setting in adaptive experimental design where we aim to uncover members of rare, valuable class(es) subject to a budget constraint. An important consideration in this problem is diversity among the discovered targets -- in many applications, diverse discoveries offer more insight and may be preferable in downstream tasks. However, most existing active search policies either assume that all targets belong to a common positive class or encourage diversity via simple heuristics. We present a novel formulation of active search with multiple target classes, characterized by a utility function chosen from a flexible family whose members encourage diversity via a diminishing returns mechanism. We then study this problem under the Bayesian lens and prove a hardness result for approximating the optimal policy for arbitrary positive, increasing, and concave utility functions. Finally, we design an efficient, nonmyopic approximation to the optimal policy for this class of utilities and demonstrate its superior empirical performance in a variety of settings, including drug discovery.
Models and Mechanisms for Spatial Data Fairness

Fairness in data-driven decision-making studies scenarios where individuals from certain population segments may be unfairly treated when being considered for loan or job applications, access to public resources, or other types of services. In location-based applications, decisions are based on individual whereabouts, which often correlate with sensitive attributes such as race, income, and education. While fairness has received significant attention recently, e.g., in machine learning, there is little focus on achieving fairness when dealing with location data. Due to their characteristics and specific type of processing algorithms, location data pose important fairness challenges. We introduce the concept of spatial data fairness to address the specific challenges of location data and spatial queries. We devise a novel building block to achieve fairness in the form of fair polynomials. Next, we propose two mechanisms based on fair polynomials that achieve individual spatial fairness, corresponding to two common location-based decision-making types: distance-based and zone-based. Extensive experimental results on real data show that the proposed mechanisms achieve spatial fairness without sacrificing utility.
Making Split Learning Resilient to Label Leakage by Potential Energy Loss

As a practical privacy-preserving learning method, split learning has drawn much attention in academia and industry. However, its security is constantly being questioned since the intermediate results are shared during training and inference. In this paper, we focus on the privacy leakage problem caused by the trained split model, i.e., the attacker can use a few labeled samples to fine-tune the bottom model, and gets quite good performance. To prevent such kind of privacy leakage, we propose the potential energy loss to make the output of the bottom model become a more `complicated' distribution, by pushing outputs of the same class towards the decision boundary. Therefore, the adversary suffers a large generalization error when fine-tuning the bottom model with only a few leaked labeled samples. Experiment results show that our method significantly lowers the attacker's fine-tuning accuracy, making the split model more resilient to label leakage.
Robot Learning Theory of Mind through Self-Observation: Exploiting the Intentions-Beliefs Synergy

—In complex environments, where the human sen- sory system reaches its limits, our behaviour is strongly driven by our beliefs about the state of the world around us. Accessing others’ beliefs, intentions, or mental states in general, could thus allow for more effective social interactions in natural contexts. Yet these variables are not directly observable. Theory of Mind (TOM), the ability to attribute to other agents’ beliefs, intentions, or mental states in general, is a crucial feature of human social interaction and has become of interest to the robotics community. Recently, new models that are able to learn TOM have been introduced. In this paper, we show the synergy between learning to predict low-level mental states, such as intentions and goals, and attributing high-level ones, such as beliefs. Assuming that learning of beliefs can take place by observing own decision and beliefs estimation processes in partially observable environments and using a simple feed-forward deep learning model, we show that when learning to predict others’ intentions and actions, faster and more accurate predictions can be acquired if beliefs attribution is learnt simultaneously with action and intentions prediction. We show that the learning performance improves even when observing agents with a different decision process and is higher when observing beliefs-driven chunks of behaviour. We propose that our architectural approach can be relevant for the design of future adaptive social robots that should be able to autonomously understand and assist human partners in novel natural environments and tasks.
Single-layer organic memory devices based on N,N′-di(naphthalene-l-yl)-N,N′-diphenyl-benzidine

We report a single-layer organic electrically bistable device made of N,N′-di(naphthalene-l-yl)-N,N′-diphenyl-benzidine sandwiched between two electrodes. The measured current-voltage characteristics show two states of different conductivities at the same applied voltage—high-conductance state (ON state) and low-conductance state (OFF state)—and the two states are reproducible by applying a negative writing voltage. It was found that the ratio of the ON∕OFF current depends strongly on the writing voltage, and the bistable characteristics were yet retained for up to hours and days before reading the device after applying a writing voltage. Furthermore, more than 106 write-read-erase-reread cycles have been performed in ambient conditions without degradation. These properties show the devices promising for high-density, low-cost memory application.
Does the Lockstep Growth between Black Holes and Bulges Create Their Mass Relation?

Recent studies have revealed a strong relation between the sample-averaged black hole (BH) accretion rate (BHAR) and star formation rate (SFR) among bulge-dominated galaxies—i.e., “lockstep” BH–bulge growth—in the distant universe. This relation might be closely connected to the BH–bulge mass correlation observed in the local universe. To further understand BH–bulge coevolution, we present Atacama Large Millimeter/submillimeter Array (ALMA) CO(2–1) or CO(3–2) observations of seven star-forming bulge-dominated galaxies at z = 0.5–2.5. Using the ALMA data, we detect significant (>3σ) CO emission from four objects. For our sample of seven galaxies, we measure (or constrain with upper limits) their CO line fluxes and estimate their molecular gas masses (M gas). We also estimate their stellar masses (M star) and SFRs, by modeling their spectral energy distributions. Using these physical properties, we derive the gas depletion timescales (τ dep ≡ M gas/SFR) and compare them with the bulge/BH growth timescales (τ grow ≡ M star/SFR ∼ M BH/BHAR). Our sample generally has τ dep shorter than τ grow by a median factor of ≳4, indicating that the cold gas will be depleted before significant bulge/BH growth takes place. This result suggests that BH–bulge lockstep growth is mainly responsible for maintaining the mass relation, not creating it. We note that our sample is small and limited to z < 2.5; JWST and ALMA will be able to probe to higher redshifts in the near future.
Information-Theoretic Characterizations of Generalization Error for the Gibbs Algorithm

Various approaches have been developed to upper bound the generalization error of a supervised learning algorithm. However, existing bounds are often loose and even vacuous when evaluated in practice. As a result, they may fail to characterize the exact generalization ability of a learning algorithm. Our main contributions are exact characterizations of the expected generalization error of the well-known Gibbs algorithm (a.k.a. Gibbs posterior) using different information measures, in particular, the symmetrized KL information between the input training samples and the output hypothesis. Our result can be applied to tighten existing expected generalization errors and PAC-Bayesian bounds. Our information-theoretic approach is versatile, as it also characterizes the generalization error of the Gibbs algorithm with a data-dependent regularizer and that of the Gibbs algorithm in the asymptotic regime, where it converges to the standard empirical risk minimization algorithm. Of particular relevance, our results highlight the role the symmetrized KL information plays in controlling the generalization error of the Gibbs algorithm.
The Host Galaxies of Hybrid Morphology Radio Sources

Based on their differing radio morphologies, powerful radio galaxies can be separated into the Fanaroff–Riley I (FR I) and II (FR II) classes. Hybrid morphology radio sources (HyMoRS) contain morphologies consistent with each type of jet on either side: a powerful, highly relativistic FR I–like jet terminating in a hotspot on one side and an FR I–like plume on the other. HyMoRS present a unique opportunity to study the conditions that give rise to the dichotomy. Using host galaxy properties, we conduct the first multiwavelength investigation into whether orientation can explain HyMoRS morphology. Through optical spectroscopy and mid-infrared photometry, we analyze the emission characteristics, and evaluate the broad characteristics of five HyMoRS host galaxies at intermediate redshifts (0.4 < z < 1.5). The HyMoRS host galaxies in our sample have properties consistent with typical host galaxies of FR II sources, suggesting that the observed hybrid morphologies may be caused by a dense, cluster-like environment bending FR II jets combined with a favorable orientation that can make one side appear similar to an FR I jet. Our results thus support the hypothesis that HyMoRS are mainly caused by environment and orientation.
Hierarchical Normalization for Robust Monocular Depth Estimation

In this paper, we address monocular depth estimation with deep neural networks. To enable training of deep monocular estimation models with various sources of datasets, state-of-the-art methods adopt image-level normalization strategies to generate affine-invariant depth representations. However, learning with image-level normalization mainly emphasizes the relations of pixel representations with the global statistic in the images, such as the structure of the scene, while the fine-grained depth difference may be overlooked. In this paper, we propose a novel multi-scale depth normalization method that hierarchically normalizes the depth representations based on spatial information and depth distributions. Compared with previous normalization strategies applied only at the holistic image level, the proposed hierarchical normalization can effectively preserve the fine-grained details and improve accuracy. We present two strategies that define the hierarchical normalization contexts in the depth domain and the spatial domain, respectively. Our extensive experiments show that the proposed normalization strategy remarkably outperforms previous normalization methods, and we set new state-of-the-art on five zero-shot transfer benchmark datasets.
Viable hairy black hole solution in Bopp–Podolsky electrodynamics

Following a recent approach in which the gravitational ﬁeld equations in curved spacetimes were presented in the Bopp–Podolsky electrodynamics, we obtained a hairy and approximate spherically symmetric black hole solution in this context. The calculations were carried out up to the linear approximation in both the spacetime geometry and the radial electric ﬁeld. The solution is a hairy black hole because it is not in agreement with the no-hair conjecture, that is to say, the spacetime metric depends on an extra parameter that comes from the Bopp–Podolsky Lagrangian. Moreover, the black hole presented here is viable when its shadow is compared to the Sagittarius A* shadow, recently revealed by the Event Horizon Telescope Collaboration.
Simple Emergent Action Representations from Multi-Task Policy Training

The low-level sensory and motor signals in deep reinforcement learning, which exist in high-dimensional spaces such as image observations or motor torques, are inherently challenging to understand or utilize directly for downstream tasks. While sensory representations have been extensively studied, the representations of motor actions are still an area of active exploration. Our work reveals that a space containing meaningful action representations emerges when a multi-task policy network takes as inputs both states and task embeddings. Moderate constraints are added to improve its representation ability. Therefore, interpolated or composed embeddings can function as a high-level interface within this space, providing instructions to the agent for executing meaningful action sequences. Empirical results demonstrate that the proposed action representations are effective for intra-action interpolation and inter-action composition with limited or no additional learning. Furthermore, our approach exhibits superior task adaptation ability compared to strong baselines in Mujoco locomotion tasks. Our work sheds light on the promising direction of learning action representations for efficient, adaptable, and composable RL, forming the basis of abstract action planning and the understanding of motor signal space. Project page: https://sites.google. com/view/emergent-action-representation/
Role of microgel stiffness in particle self-assembly and suspension rheology across the lower consolute solution temperature

We synthesize thermoresponsive poly(N-isopropylacrylamide) (PNIPAM) colloidal microgel particles of different stiffnesses by controlling the concentration of crosslinker in a one-pot synthesis method. We employ oscillatory rheology and cryogenic scanning electron microscopy to study the temperature and stiffness-induced mechanical properties and microscopic structures of dense aqueous suspensions of the synthesized PNIPAM microgels. Using Fourier transform infrared (FTIR) spectroscopy, we show that particle hydrophobicity increases with increasing suspension temperature and decreasing particle stiffness. Our zeta potential measurements of soft PNIPAM particles and those of intermediate stiffnesses demonstrate that these particles are electrostatically unstable and prone to aggregation even at temperatures below the lower consolute solution temperature (LCST). In contrast, stiff PNIPAM particles in dilute aqueous suspensions are electrostatically stabilized at all temperatures explored in this study. Interestingly, our frequency and strain amplitude sweep rheology experiments reveal that the linear viscoelastic moduli and yield stresses of all the PNIPAM suspensions increase when the temperature is raised above the LCST. Combining cryogenic scanning electron microscopy (cryo-SEM) and rheology, we demonstrate that dense suspensions of soft PNIPAM microgels show a gel-liquid-gel transition with increase in temperature across the LCST. Suspensions of stiff particles, in contrast, exhibit a glass-glass transition under the same temperature sweep conditions and do not pass through an intermediate liquid state.
Observation of Uniaxial Strain Tuned Spin Cycloid in a Freestanding BiFeO3 Film

Bismuth ferrite (BiFeO3) possesses a non‐collinear spin order while the ferroelectric order breaks space inversion symmetry. This allows efficient electric‐field control of magnetism and makes it a promising candidate for applications in low‐power spintronic devices. Epitaxial strain effects have been intensively studied and exhibit significant modulation of the magnetic order in bismuthBiFeO3, but tuning its spin structure with continuously varied uniaxial strain is still lacking at this moment. Herein, in situ uniaxial tensile strain is applied to a freestanding BiFeO3 film by mechanically stretching an organic substrate. A scanning nitrogen‐vacancy (NV) microscopy is applied to image the nanoscale magnetic order in real space. The strain is continuously increased from 0% to 1.5% and four images under different strains are acquired during this period. The images show that the spin cycloid tilts by ≈12.6° when strain approaches 1.5%. A first principle calculation is processed to show that the tilting is energetically favorable under such strain. The in situ strain applying method in combination with scanning NV microscope real‐space imaging ability paves a new way in studying the coupling between magnetic order and strain in BiFeO3 films.