Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of positive psychological interventions in anxiety treatment

Psychiatry traditionally emphasizes psychopharmacology and psychotherapy as the primary modalities of treatment. Recently, however, positive psychological interventions are increasingly recognized as a treatment modality that aims to improve a patient’s general wellness. Our goal is to review published data on the efficacy of positive psychological interventions (PPI) in the treatment of anxiety. A systematic literature search was conducted on PubMed, Web of Science, and PsycINFO. The keywords used were “positive psychology” and “treatment of anxiety. ” The search involved all articles published until 2019. To meet inclusion criteria, articles needed to report using positive psychological interventions with patients diagnosed with anxiety or suffering from anxiety symptoms. The main outcome measure was a change in anxiety levels post-intervention. Our initial literature search of 1010 articles was narrowed down to 6 papers that met the inclusion criteria. Interventions used were body-mind-spirit therapy, online positive psychology modules, self-help books, positive activity intervention, future-directed therapy, and mindfulness-based therapies. Pre- and post-treatment anxiety was measured using scales such as State Anxiety Inventory, Depression Anxiety Stress Scale-Short form, Overall Anxiety Severity, Impairment Scale, Hospital Anxiety and Depression Scale, Liebowitz Social Anxiety Scale–Self-Report, Beck Anxiety Inventory, Social Interaction Anxiety Scale, Profile of Mood States, Brief Symptom Inventory, and Revised Children’s Manifest Anxiety Scale. In all six included studies, PPI decreased anxiety symptoms with variable effect sizes. The review suggests that PPI offers therapeutic benefits to patients diagnosed with anxiety symptoms. Thus, more research is needed to examine the feasibility of incorporating PPI into the traditional mental health care.
The Core of the Party System ∗

A core party is undefeated in pair-wise majority preference comparisons against all other political parties. We use survey self-reported voter preference data and moment inequalities testing methods to identify potential core and Condorcet winner parties. We carry out tests for 1,176 parties over 196 election surveys. We cannot reject the hypothesis of a core party in nearly half the tests, while we cannot reject the hypothesis that a party is a Condorcet winner in only about 7.06% of tests. We construct both discrete (test outcome) and continuous ( p -values) measures of core and Condorcet parties, and relate the incidence of such parties to party-speciﬁc observables. We also ﬁnd that these parties are between 20% and 30% more likely to participate in a post-election government, and between 10% (for core) and 40% (for Condorcet parties) more likely to control the premiership, even when controlling for common predictors of holding such oﬃce.
Automatic Robot System In Industries Using Rf Module

Most of the problem relating to the labourer (numbers, work capacity efficiency etc) in the industries can be overcome by using Robots. We have studied the automatic Robot system to transport goods within industries. Our model use a pair of Robots (A & B) which are given commands by RF module following them these commands they travel along the shortest possible path on 4*4 grids system. While crossing the junction Robot A will get precedence over Robot B and thus collision avoided. Hence the automatic system is quiet useful in industries as it save time, man power and is more efficienct.
Applied Biosafety: Legal Framework in Switzerland and Implications for Clinical Laboratories

In Switzerland the use of genetically modified and pathogenic microorganisms is regulated by the Federal Law relating to the Protection of the Environment. Principles and practices of biosafety on the level of enforcement, such as risk assessment, general safety measures for all installations, and specific safety measures for individual types of research and development laboratories, growth rooms and greenhouses, animal units, and production plants, are given by the “Swiss Ordinance on the Contained Use of Organisms. ” This paper presents the ordinance against the background of the public health situation in Switzerland and the implications for construction and operation of clinical laboratories (BSL-2 and BSL-3).
Use of Blumensaat's line for assessment of cranial tibial subluxation in dogs with cranial cruciate ligament deficiency.

BACKGROUND This study aimed to determine whether Blumensaat's line, a consistently present radiographic feature delineating the peak of the femoral intercondylar fossa, could be used to assess for cranial tibial subluxation in canine stifles with cranial cruciate ligament disease. METHODS Thirty sequential, neutrally positioned, standing-angle stifle radiographs were taken from dogs presenting to a specialist referral centre for treatment of cruciate ligament disease. Thirty similarly positioned radiographs of healthy canine stifles were used as a control group. The radiographs were anonymised and submitted to blinded observers for measurement of the tibial plateau angle, patella tendon angle, Blumensaat's line length and the length of Blumensaat's line cranial to the tibial mechanical axis. RESULTS Finding that the tibial mechanical axis intersects Blumensaat's line cranial to its midpoint, as a marker of cranial tibial subluxation, had a positive predictive value of 76% for subsequent surgical identification of cruciate ligament disease. CONCLUSIONS Tibial cranial subluxation is detectable and quantifiable radiographically using the intersection of the tibial mechanical axis and Blumensaat's line. Once quantified, this measurement could be used both as a radiographic marker of cruciate ligament disease and to adjust tibial osteotomy procedures to minimise the risk of under advancement of the tibial tuberosity resulting in a persistently unstable stifle.
Functions of potassium channels blocked by low micromolar 4‐aminopyridine in the crayfish nervous system

4‐aminopyridine (4‐AP) is a potassium channel blocker that has been used to treat patients with multiple sclerosis and Lambert–Eaton disease. The concentration of this drug in the blood of patients was estimated to be in low or submicromolar range. Animal studies have shown that 4‐AP at such low concentration selectively blocks a subset of channels in Kv1 or Kv3 families. The crayfish opener neuromuscular junction and ventral superficial flexor (VSF) preparations were used to examine functions of K+ channels blocked by low concentrations of 4‐AP. At opener motor axons, intracellular recordings show that 4‐AP could increase action potential (AP) amplitude, duration, and after‐depolarization (ADP) at 10 μM. As 4‐AP concentration was increased, in twofold steps, AP amplitude did not increase further up to 5 mM. AP duration and ADP increased significantly mainly in two concentration ranges, 10–50 μM and 1–5 mM. The effects of 50 μM 4‐AP on the VSF were less consistent than that observed at the opener motor axons. 4‐AP did not change AP amplitude of motor axons recorded with an extracellular electrode and change in AP repolarizing potential was observed in ∼25% of the axons. EPSP recorded simultaneously with AP showed an increase in amplitude with 4‐AP treatment only in 30% of the axon‐EPSP pairs. 4‐AP also increased firing frequencies of ∼50% of axons. In four animals, 4‐AP “awakened” the firing of APs from an axon that was silent before the drug. The mixture of positive and negative 4‐AP effects summarized above was observed in the same VSF preparations in all cases (n = 8). We propose that there is a significant diversity in the density 4‐AP‐sensitive potassium channels among motor axons of the VSF. Functional significance in the differences of 4‐AP sensitivity of the two motor systems is discussed.
Spinors in $\mathbb{K}$-Hilbert Spaces

We consider a structure of the K -Hilbert space, where K ≃ R is a ﬁeld of real numbers, K ≃ C is a ﬁeld of complex numbers, K ≃ H is a quaternion algebra, within the framework of division rings of Cliﬀord algebras. The K -Hilbert space is generated by the Gelfand-Naimark-Segal construction, while the generating C ∗ -algebra consists of the energy operator H and the generators of the group SU(2 , 2) attached to H . The cyclic vectors of the K -Hilbert space corresponding to the tensor products of quaternionic algebras deﬁne the pure separable states of the operator algebra. Depending on the division ring K , all states of the operator algebra are divided into three classes: 1) charged states with K ≃ C ; 2) neutral states with K ≃ H ; 3) truly neutral states with K ≃ R . For pure separable states that deﬁne the fermionic and bosonic states of the energy spectrum, the fusion, doubling (complexiﬁcation) and annihilation operations are determined.
Probabilistic Methods in the Study of Topological Indices on Random Spider Trees

: In this paper, we characterize the structure and topological indices of a class of random spider trees (RSTs) such as degree-based Gini index, degree-based Hoover index, generalized Zagreb index and other indices associated with these. We obtain the exact and asymptotic distributions of the number of leaves via probabilistic methods. Moreover, we relate this model to the class of RSTs that evolves in a preferential attachment manner.
EmbedTrack—Simultaneous Cell Segmentation and Tracking Through Learning Offsets and Clustering Bandwidths

To shed light on the processes driving cell migration, a systematic analysis of the cell behavior is required. Since the manual analysis of hundreds or even thousands of cells is infeasible, automated approaches for cell segmentation and tracking are needed. While for the task of cell segmentation deep learning has become the standard, there are few approaches for simultaneous cell segmentation and tracking using deep learning. Here, we present EmbedTrack, a single convolutional neural network for simultaneous cell segmentation and tracking which predicts human comprehensible embeddings. As embeddings, offsets of cell pixels to their cell center and bandwidths are learned which are processed in a subsequent clustering step to generate an instance segmentation and link the segmented instances over time. We benchmark our approach on nine 2D data sets from the Cell Tracking Challenge, where our approach performs on seven out of nine data sets within the top 3 contestants including three top 1 performances. The source code is publicly available at https://git.scc. kit.edu/kit-loe-ge/embedtrack.
MIPR: Automatic Annotation of Medical Images with Pixel Rearrangement

Most of the state-of-the-art semantic segmentation reported in recent years is based on fully supervised deep learning in the medical domain. How? ever, the high-quality annotated datasets require intense labor and domain knowledge, consuming enormous time and cost. Previous works that adopt semi? supervised and unsupervised learning are proposed to address the lack of anno? tated data through assisted training with unlabeled data and achieve good perfor? mance. Still, these methods can not directly get the image annotation as doctors do. In this paper, inspired by self-training of semi-supervised learning, we pro? pose a novel approach to solve the lack of annotated data from another angle, called medical image pixel rearrangement (short in MIPR). The MIPR combines image-editing and pseudo-label technology to obtain labeled data. As the number of iterations increases, the edited image is similar to the original image, and the labeled result is similar to the doctor annotation. Therefore, the MIPR is to get labeled pairs of data directly from amounts of unlabled data with pixel rearrange? ment, which is implemented with a designed conditional Generative Adversarial Networks and a segmentation network. Experiments on the ISIC18 show that the effect of the data annotated by our method for segmentation task is is equal to or even better than that of doctors annotations
The Linear Span of Uniform Matrix Product States

The variety of uniform matrix product states arises both in algebraic geometry as a natural generalization of the Veronese variety, and in quantum many-body physics as a model for a translation-invariant system of sites placed on a ring. Using methods from linear algebra, representation theory, and invariant theory of matrices, we study the linear span of this variety.
Design of Adaptive Compliance Controllers for Safe Robotic Assembly

Insertion operations are a critical element of most robotic assembly operation, and peg-in-hole (PiH) insertion is one of the most widely studied tasks in the industrial and academic manipulation communities. PiH insertion is in fact an entire class of problems, where the complexity of the problem can depend on the type of misalignment and contact formation during an insertion attempt. In this paper, we present the design and analysis of adaptive compliance controllers which can be used in insertion-type assembly tasks, including learning-based compliance controllers which can be used for insertion problems in the presence of uncertainty in the goal location during robotic assembly. We first present the design of compliance controllers which can ensure safe operation of the robot by limiting experienced contact forces during contact formation. Consequently, we present analysis of the force signature obtained during the contact formation to learn the corrective action needed to perform insertion. Finally, we use the proposed compliance controllers and learned models to design a policy that can successfully perform insertion in novel test conditions with almost perfect success rate. We validate the proposed approach on a physical robotic test-bed using a 6-DoF manipulator arm.
Revealing the Galaxy–Halo Connection through Machine Learning

Understanding the connections between galaxy stellar mass, star formation rate, and dark matter halo mass represents a key goal of the theory of galaxy formation. Cosmological simulations that include hydrodynamics, physical treatments of star formation, feedback from supernovae, and the radiative transfer of ionizing photons can capture the processes relevant for establishing these connections. The complexity of these physics can prove difficult to disentangle and obfuscate how mass-dependent trends in the galaxy population originate. Here, we train a machine-learning method called Explainable Boosting Machines (EBMs) to infer how the stellar mass and star formation rate of nearly 6 million galaxies simulated by the Cosmic Reionization on Computers project depend on the physical properties of halo mass, the peak circular velocity of the galaxy during its formation history v peak, cosmic environment, and redshift. The resulting EBM models reveal the relative importance of these properties in setting galaxy stellar mass and star formation rate, with v peak providing the most dominant contribution. Environmental properties provide substantial improvements for modeling the stellar mass and star formation rate in only ≲10% of the simulated galaxies. We also provide alternative formulations of EBM models that enable low-resolution simulations, which cannot track the interior structure of dark matter halos, to predict the stellar mass and star formation rate of galaxies computed by high-resolution simulations with detailed baryonic physics.
Advances in actinide thin films: synthesis, properties, and future directions

Actinide-based compounds exhibit unique physics due to the presence of 5f electrons, and serve in many cases as important technological materials. Targeted thin film synthesis of actinide materials has been successful in generating high-purity specimens in which to study individual physical phenomena. These films have enabled the study of the unique electron configuration, strong mass renormalization, and nuclear decay in actinide metals and compounds. The growth of these films, as well as their thermophysical, magnetic, and topological properties, have been studied in a range of chemistries, albeit far fewer than most classes of thin film systems. This relative scarcity is the result of limited source material availability and safety constraints associated with the handling of radioactive materials. Here, we review recent work on the synthesis and characterization of actinide-based thin films in detail, describing both synthesis methods and modeling techniques for these materials. We review reports on pyrometallurgical, solution-based, and vapor deposition methods. We highlight the current state-of-the-art in order to construct a path forward to higher quality actinide thin films and heterostructure devices.
Facilitating automated conversion of scientific knowledge into scientific simulation models with the Machine Assisted Generation, Calibration, and Comparison (MAGCC) Framework

The Machine Assisted Generation, Comparison, and Calibration (MAGCC) framework provides machine assistance and automation of recurrent crucial steps and processes in the development, implementation, testing, and use of scientific simulation models. MAGCC bridges systems for knowledge extraction via natural language processing or extracted from existing mathematical models and provides a comprehensive workflow encompassing the composition of scientific models and artificial intelligence (AI)-assisted code generation. MAGCC accomplishes this through: 1) the development of a comprehensively expressive formal knowledge representation knowledgebase, the Structured Scientific Knowledge Representation (SSKR) that encompasses all the types of information needed to make any simulation model, 2) the use of an artificially-intelligent logic-reasoning system, the Computational Modeling Assistant (CMA), that takes information from the SSKR and generates, in a traceable fashion, model specifications across a range of simulation modeling methods, and 3) the use of the CMA to generate compliable/executable code for a simulation model from those model specifications. The current MAGCC framework can be customized any scientific domain’s specific knowledgebase and existing mathematical/computational models, and future work will involve expanding the types of computational model representation that can be generated and integrating newly-developed code generating AI systems.
Machines That Learn: Aesthetics of Adaptive Behaviors in Agent-based Art

Since the post-war era, artists have been exploring the use of embodied, artificial agents. This artistic activity runs parallel to research in computer science, in domains such as Cybernetics, Artificial Intelligence and Artificial Life. This thesis offers an account of a particular facet of this broader work — namely, a study of the artistic practice of agent-based, adaptive computational artistic installations that make use of Machine Learning methods. Machine Learning is a sub-field of the computer science area of Artificial Intelligence that employs mathematical models to classify and make predictions based on data or experience rather than on logical rules. These artworks that integrate Machine Learning into their structures raise a number of important questions: (1) What new forms of aesthetic experience do Machine Learning methods enable or make possible when utilized outside of their intended context, and are instead carried over into artistic works? (2) What characterizes the practice of using adaptive computational methods in agent-based artworks? And finally, (3) what kind of worldview are these works fostering? To address these questions, I examine the history of Machine Learning in both art and science, illustrating how artists and engineers alike have made use of these methods historically. I also analyze the defining scientific characteristics of Machine Learning through a practitioner’s lens, concretely articulating how properties of Machine Learning interplay in media artworks that behave and evolve in real time. I later develop a framework for understanding machine behaviors based on the morphological aspects of the temporal unfolding of agent behaviors as a tool for comprehending both adaptive and non-adaptive behaviors in works of art. Finally, I expose how adaptive technologies suggest a new worldview for art that accounts for the performative engagement of agents adapting to one another, which implies a certain way of losing control in the face of the indeterminacy and the unintelligibility of alien agencies and their behaviors.
Non-Uniformly Terminating Chase: Size and Complexity

The chase procedure, originally introduced for checking implication of database constraints, and later on used for computing data exchange solutions, has recently become a central algorithmic tool in rule-based ontological reasoning. In this context, a key problem is non-uniform chase termination: does the chase of a database w.r. t. a rule-based ontology terminate? And if this is the case, what is the size of the result of the chase? We focus on guarded tuple-generating dependencies (TGDs), which form a robust rule-based ontology language, and study the above central questions for the semi-oblivious version of the chase. One of our main findings is that non-uniform semi-oblivious chase termination for guarded TGDs is feasible in polynomial time w.r. t. the database, and the size of the result of the chase (whenever is finite) is linear w.r. t. the database. Towards our results concerning non-uniform chase termination, we show that basic techniques such as simplification and linearization, originally introduced in the context of ontological query answering, can be safely applied to the chase termination problem.
ParkPredict+: Multimodal Intent and Motion Prediction for Vehicles in Parking Lots with CNN and Transformer

The problem of multimodal intent and trajectory prediction for human-driven vehicles in parking lots is addressed in this paper. Using models designed with CNN and Transformer networks, we extract temporal-spatial and contextual information from trajectory history and local bird's eye view (BEV) semantic images, and generate predictions about intent distribution and future trajectory sequences. Our methods outperform existing models in accuracy, while allowing an arbitrary number of modes, encoding complex multi-agent scenarios, and adapting to different parking maps. To train and evaluate our method, we present the first public 4K video dataset of human driving in parking lots with accurate annotation, high frame rate, and rich traffic scenarios.
Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation

The key challenge for few-shot semantic segmentation (FSS) is how to tailor a desirable interaction among sup-port and query features and/or their prototypes, under the episodic training scenario. Most existing FSS methods im-plement such support/query interactions by solely leveraging plain operations - e.g., cosine similarity and feature concatenation - for segmenting the query objects. How-ever, these interaction approaches usually cannot well capture the intrinsic object details in the query images that are widely encountered in FSS, e.g., if the query object to be segmented has holes and slots, inaccurate segmentation al-most always happens. To this end, we propose a dynamic prototype convolution network (DPCN) to fully capture the aforementioned intrinsic details for accurate FSS. Specifi-cally, in DPCN, a dynamic convolution module (DCM) is firstly proposed to generate dynamic kernels from support foreground, then information interaction is achieved by con-volution operations over query features using these kernels. Moreover, we equip DPCN with a support activation mod-ule (SAM) and a feature filtering module (FFM) to generate pseudo mask and filter out background information for the query images, respectively. SAM and FFM together can mine enriched context information from the query features. Our DPCN is also flexible and efficient under the k-shot FSS setting. Extensive experiments on PASCAL-5i and COCO 20i show that DPCN yields superior performances under both 1-shot and 5-shot settings.