Miracle-dz/UniFAR-FTUs huggingface dataset:
Transformation of Version Space with Constructive Induction : The VS · Algorithm

In inductive 1eaming, learned descriptions can be either overgeneralized or undergeneralized, and finding the proper balance is difficult, panicularly when only insufficient data is available. For this reason, the classification of instances which are not covered by any rule, or covered by many rules, is often postponed to the testing phase. During this phase, different measures are applied to decide how to classify particular instances. To improve this sitUation, a method of inductive learning is proposed which enables the change of representation space in the learning process through the extension of attribute domains. Constructive induction and generalized descriptions of remaining classes are used in the method to improve classification rules of a given class. Two types of generalization are used; generalization of training examples, and generalization of the representation space. The ,generalization of the representation space is in the form of hypothesis­ driven constructive induction (HCI). HCI takes inductive rules generated from primary training examples, and constructs examples for additional learning. Empirical evidence shows that the method cuts overgeneralized areas and fills up, undergeneralized gaps of classification rules obtained by selective learning. The paper explains the method using diagrammatic visualization.
Faster WCET flow analysis by program slicing

Static Worst-Case Execution Time (WCET) analysis is a technique to derive upper bounds for the execution times of programs. Such bounds are crucial when designing and verifying real-time systems. WCET analysis needs a program flow analysis to derive constraints on the possible execution paths of the analysed program, like iteration bounds for loops and dependences between conditionals.Current WCET analysis tools typically obtain flow information through manual annotations. Better support for automatic flow analysis would eliminate much of the need for this laborious work. However, to automatically derive high-quality flow information is hard, and solution techniques with large time and space complexity are often required.In this paper we describe how to use program slicing to reduce the computational need of flow analysis methods. The slicing identifes statements and variables which are guaranteed not to influence the program flow. When these are removed, the calculation time of our different flow analyses decreases, in some cases considerably.We also show how program slicing can be used to identify the input variables and globals that control the outcome of a particular loop or conditional. This should be valuable aid when performing WCET analysis and systematic testing of large and complex real-time programs.
New Finnish growth references for children and adolescents aged 0 to 20 years: Length/height-for-age, weight-for-length/height, and body mass index-for-age

Abstract Background and objectives. Growth curves require regular updates due to secular trends in linear growth. We constructed contemporary growth curves, assessed secular trends in height, and defined body mass index (BMI) cut-off points for thinness, overweight, and obesity in Finnish children. Material and methods: Mixed cross-sectional/longitudinal data of 73,659 healthy subjects aged 0–20 years (born 1983–2008) were collected from providers in the primary health care setting. Growth references for length/height-for-age, weight-for-length/height, and BMI-for-age were fitted using generalized additive models for location, scale, and shape (GAMLSS). BMI percentile curves passing through BMIs 30, 25, 18.5, 17, and 16 kg/m2 at the age of 18 years were calculated to define limits for obesity, overweight, and various grades of thinness. Results. Increased length/height-for-age was seen in virtually all age-groups when compared to previous Finnish growth data from 1959 to 1971. Adult height was increased by 1.9 cm in girls and 1.8 cm in boys. The largest increases were seen during the peripubertal years: up to 2.8 cm in girls and 5.6 cm in boys. Median weight-for-length/height had not increased. Conclusions. New Finnish references for length/height-for-age, weight-for-length/height, and BMI-for-age were constructed and should be implemented to monitor growth of children in Finland.
The role of explicit cognition in addiction: Development of the mental representations scale

We argue that explicit cognitions in the form of mental representations (MR), in addition to habits, constitute an important aspect of smoking. Two studies were performed with the aim of developing a measure of MR in the context of smoking behaviour. MRs refer to explicit and conscious cognitive processes which involve the strength of thoughts, planning, anticipation and self-awareness regarding future actions. In Study 1 we generated a pool of items assumed to assess smokers’ MRs, and explored the factor structure and internal consistency of the MR scale. In Study 2 we further expanded the theoretical aspects of the scale, and we explored the psychometric properties of the MR scale in terms of temporal stability and convergent and discriminant validity. The results indicated that these properties were satisfactory, thus encouraging the further use of the MR scale in the domain of addictive behaviours. Implications of the results are discussed.
General criterion for non-Hermitian skin effects and Application: Fock space skin effects in many body systems

Non-Hermiticity enables macroscopic accumulation of bulk states, named non-Hermitian skin effects. The non-Hermitian skin effects are well-established for single-particle systems, but their proper characterization for general systems is elusive. Here, we propose a general criterion of non-Hermitian skin effects, which works for any finite-dimensional system evolved by a linear operator. The applicable systems include many-body systems and network systems. A system meeting the criterion exhibits enhanced non-normality of the evolution operator, accompanied by exceptional characteristics intrinsic to non-Hermitian systems. Applying the criterion, we discover a new type of non-Hermitian skin effect in many-body systems, which we dub the Fock space skin effect. We also discuss the Fock space skin effect-induced slow dynamics, which gives an experimental signal for the Fock space skin effect.
Surface critical phenomena and scaling in the eight-vertex model.

We give a physical interpretation of the entries of the reﬂection K -matrices of Baxter’s eight-vertex model in terms of an Ising interaction at an open boundary. Although the model still deﬁes an exact solution we nevertheless obtain the exact surface free energy from a crossing-unitarity relation. The singular part of the surface energy is described by the critical exponents α s = 2 − π 2 µ and α 1 = 1 − πµ , where µ controls the strength of the four-spin interaction. These values reduce to the known Ising exponents at the decoupling point µ = π/ 2 and conﬁrm the scaling relations α s = α b + ν and α 1 = α b − 1.
Helping or Hindering? Technology’s Impact on Secondary Students’ Self-Regulated Learning

This paper reports on a technology-related theme emerging from a doctoral study examining a whole-school approach to self-regulated learning. An in-depth case study of an Australian secondary school was used to explore approaches taken to developing selfregulated learners and the perceptions of the school community with respect to these approaches. As part of this study, students’ and parents’ perceptions of the impact of technology on self-regulated learning were examined. Findings indicated that while students were generally positive about the role of technology as a support to self-regulation, particularly for use as a research tool, many students reported that technology was often a distraction from their studies. These perceptions were also reflected in responses from parents. Educators need to address the concerns of students and parents providing support and strategies for maximizing technology as a learning tool as opposed to a negative influence on students’ selfregulated learning.
Shapes of dark matter haloes with discrete globular cluster dynamics: The example of NGC 5128 (Centaurus A)

Within the $\Lambda$CDM cosmology, dark matter haloes are expected to deviate from spherical symmetry. Constraining the halo shapes at large galactocentric distances is challenging due to the low density of luminous tracers. The well-studied early-type galaxy NGC 5128 (Centaurus A - CenA), has a large number of radial velocities for globular clusters (GCs) and planetary nebulae (PNe) of its extended stellar halo. In this work, we aim to determine the deviation from spherical symmetry of the dark matter halo of CenA at 5 $R_{\rm e}$ using its GCs as kinematic tracers. We used the largest photometric catalogue of GC candidates to accurately characterise the spatial distribution of the relaxed population and investigated the presence of non-relaxed structures in the kinematic catalogue of GCs using the relaxed point-symmetric velocity field as determined by the host's PNe population. We used anisotropic Jeans modelling under axisymmetric assumptions together with the Gaussian likelihood and GCs as discrete tracers. The gravitational potential is generated by flattened stellar and dark matter distributions. We leveraged different orbital properties of the blue and red GCs to model them separately. We find that discrete kinematics of the GCs are consistent with being drawn from an underlying relaxed velocity field determined from PNe. The best-fit parameters of the gravitational potential recovered from the blue and red GCs separately agree well and the joint results are: $M_{200} = 1.86^{1.61}_{-0.69}\times 10^{12}$ M$_\odot$, $M_\star/L_{\rm B} = 2.98^{+0.96}_{-0.78}$ and the flattening $q_{\rm DM} = 1.45^{+0.78}_{-0.53}$. Both GC populations show mild rotation, with red having a slightly stronger rotational signature and radially biased orbits, and blue GCs preferring negative velocity anisotropy. An oblate or a spherical dark matter halo of CenA is strongly disfavoured by our modelling.
The Avian XPR1 Gammaretrovirus Receptor Is under Positive Selection and Is Disabled in Bird Species in Contact with Virus-Infected Wild Mice

ABSTRACT Xenotropic mouse leukemia viruses (X-MLVs) are broadly infectious for mammals except most of the classical strains of laboratory mice. These gammaretroviruses rely on the XPR1 receptor for entry, and the unique resistance of laboratory mice is due to two mutations in different putative XPR1 extracellular loops. Cells from avian species differ in susceptibility to X-MLVs, and 2 replacement mutations in the virus-resistant chicken XPR1 (K496Q and Q579E) distinguish it from the more permissive duck and quail receptors. These substitutions align with the two mutations that disable the laboratory mouse XPR1. Mutagenesis of the chicken and duck genes confirms that residues at both sites are critical for virus entry. Among 32 avian species, the 2 disabling XPR1 mutations are found together only in the chicken, an omnivorous, ground-dwelling fowl that was domesticated in India and/or Southeast Asia, which is also where X-MLV-infected house mice evolved. The receptor-disabling mutations are also present separately in 5 additional fowl and raptor species, all of which are native to areas of Asia populated by the virus-infected subspecies Mus musculus castaneus. Phylogenetic analysis showed that the avian XPR1 gene is under positive selection at sites implicated in receptor function, suggesting a defensive role for XPR1 in the avian lineage. Contact between bird species and virus-infected mice may thus have favored selection of mouse virus-resistant receptor orthologs in the birds, and our data suggest that similar receptor-disabling mutations were fixed in mammalian and avian species exposed to similar virus challenges.
Joint prosodic and segmental unit selection speech synthesis

We describe a unit selection technique for text-to-speech synthesis which jointly searches the space of possible diphone sequences and the space of possible prosodic unit sequences in order to produce synthetic speech with more natural prosody. We demonstrates that this search, although currently computationally expensive, can achieve improved intonation compared to a baseline in which only the space of possible diphone sequences is searched. We discuss ways in which the search could be made sufficiently efficient for use in a real-time system.
SPTNet: An Efficient Alternative Framework for Generalized Category Discovery with Spatial Prompt Tuning

Generalized Category Discovery (GCD) aims to classify unlabelled images from both `seen' and `unseen' classes by transferring knowledge from a set of labelled `seen' class images. A key theme in existing GCD approaches is adapting large-scale pre-trained models for the GCD task. An alternate perspective, however, is to adapt the data representation itself for better alignment with the pre-trained model. As such, in this paper, we introduce a two-stage adaptation approach termed SPTNet, which iteratively optimizes model parameters (i.e., model-finetuning) and data parameters (i.e., prompt learning). Furthermore, we propose a novel spatial prompt tuning method (SPT) which considers the spatial property of image data, enabling the method to better focus on object parts, which can transfer between seen and unseen classes. We thoroughly evaluate our SPTNet on standard benchmarks and demonstrate that our method outperforms existing GCD methods. Notably, we find our method achieves an average accuracy of 61.4% on the SSB, surpassing prior state-of-the-art methods by approximately 10%. The improvement is particularly remarkable as our method yields extra parameters amounting to only 0.117% of those in the backbone architecture. Project page: https://visual-ai.github. io/sptnet.
Algorithmic solution to superactivation of zero-capacity optical quantum channels

The superactivation of zero-capacity quantum channels makes it possible to use two zero-capacity quantum channels with a positive joint capacity at the output. Currently, we have no theoretical results for describing all possible combinations of superactive zero-capacity channels, hence there should be many possible — still undiscovered — combinations. Our method gives an algorithmic solution to the superactivation problem, and provides an efficient algorithmic solution for discovering “superactive” zero-capacity optical quantum channels. We analyze the superactivation property of the amplitude damping quantum channel model. This model has great importance in practical communications, since this channel model describes energy dissipation due to the noise of the optical channel. To discover these superactive zero-capacity channels, an extremely large set of possible quantum states has to be analyzed, however an efficient algorithmic framework is still missing for this purpose. We show a fundamentally new method of finding the conditions for “superactive” zero-capacity optical quantum channels.
Modeling the Label Distributions for Weakly-Supervised Semantic Segmentation

Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation models by weak labels, which is receiving significant attention due to its low annotation cost. Existing approaches focus on generating pseudo labels for supervision while largely ignoring to leverage the inherent semantic correlation among different pseudo labels. We observe that pseudo-labeled pixels that are close to each other in the feature space are more likely to share the same class, and those closer to the distribution centers tend to have higher confidence. Motivated by this, we propose to model the underlying label distributions and employ cross-label constraints to generate more accurate pseudo labels. In this paper, we develop a unified WSSS framework named Adaptive Gaussian Mixtures Model, which leverages a GMM to model the label distributions. Specifically, we calculate the feature distribution centers of pseudo-labeled pixels and build the GMM by measuring the distance between the centers and each pseudo-labeled pixel. Then, we introduce an Online Expectation-Maximization (OEM) algorithm and a novel maximization loss to optimize the GMM adaptively, aiming to learn more discriminative decision boundaries between different class-wise Gaussian mixtures. Based on the label distributions, we leverage the GMM to generate high-quality pseudo labels for more reliable supervision. Our framework is capable of solving different forms of weak labels: image-level labels, points, scribbles, blocks, and bounding-boxes. Extensive experiments on PASCAL, COCO, Cityscapes, and ADE20K datasets demonstrate that our framework can effectively provide more reliable supervision and outperform the state-of-the-art methods under all settings. Code will be available at https://github.com/Luffy03/AGMM-SASS.
DNA methylation analysis by digital bisulfite genomic sequencing and digital MethyLight

Alterations in cytosine-5 DNA methylation are frequently observed in most types of human cancer. Although assays utilizing PCR amplification of bisulfite-converted DNA are widely employed to analyze these DNA methylation alterations, they are generally limited in throughput capacity, detection sensitivity, and or resolution. Digital PCR, in which a DNA sample is analyzed in distributive fashion over multiple reaction chambers, allows for enumeration of discrete template DNA molecules, as well as sequestration of non-specific primer annealing templates into negative chambers, thereby increasing the signal-to-noise ratio in positive chambers. Here, we have applied digital PCR technology to bisulfite-converted DNA for single-molecule high-resolution DNA methylation analysis and for increased sensitivity DNA methylation detection. We developed digital bisulfite genomic DNA sequencing to efficiently determine single-basepair DNA methylation patterns on single-molecule DNA templates without an interim cloning step. We also developed digital MethyLight, which surpasses traditional MethyLight in detection sensitivity and quantitative accuracy for low quantities of DNA. Using digital MethyLight, we identified single-molecule, cancer-specific DNA hypermethylation events in the CpG islands of RUNX3, CLDN5 and FOXE1 present in plasma samples from breast cancer patients.
The role of organizational knowledge management strategies in the quest for business intelligence

Knowledge management (KM) and Business Intelligence (BI) are topics, which are receiving much currency in the literature of academia and the general media over the past several years. KM, in this paper, is explored from the perspective of the acquisition of business intelligence inside and outside the organization. An attempt is made to discuss how IT (Information Technology), and specifically, synchronous and asynchronous Groupware, may be integrated with KM in a drive towards creating BI. The term 'business intelligence' is being studied with specific relevance to the identification of business opportunities. In seeking to clarify the argument being made here, a model is presented and discussed in terms of the transformation process from KM to BI and the subsequent competitive advantage achieved. The paper also outlines ten principles, which managers may use as a template to help their organizations create a successful KM initiative. These principles focus on those systems which organizations should implement to enable managers/agents to contribute new knowledge during their natural workflow interactions. This is critical to ensure that solutions that are currently in the system can be quickly added once the resolution has been determined. The principles in question range from a definition of KM as a disciple, the process of creating a change management plan, a template for focusing on the organizations' strategic goals, approaches in setting expectations or risk extinction metrics, identification of implementation strategies for integrating KM into existing systems, and initiatives which may be used to educate an organizations' self-service users. We shall finally, discuss how the proposed KM-BI model may be used to assist an organization to become a knowledge-enabled organization.
Feature extraction by incremental parsing for music indexing

In this paper, we employ a linguistic-processing approach to the content-based retrieval of music information. Central to the approach is the use of a lossy version of the Lempel-Ziv incremental parsing (LZIP) algorithm, which constructs a dictionary by incrementally parsing music feature vectors. LZIP is adopted as a source characterization technique owing to it's universal-coding nature, and asymptotic convergence to the entropy of the source. The dictionary is composed of variable-length parsed representations, which are used to construct a highly sparse co-occurrence matrix, which counts the occurrence of the parsed representations in each music. As a feature analysis framework, Latent Semantic Analysis (LSA) is then applied to the co-occurrence matrix to generate a lower-dimensional approximation that exposes the most salient features of the represented audio documents. The aforementioned approach, in addition to adopting reduced sampling rates and quantized feature vectors, yields a system with reduced requirements in terms of processing and storage, and increases the tolerance to noisy queries. We demonstrate the performance of the system in the music genre classification problem, and analyze its robustness to perturbed queries. Moreover, we demonstrate that using the incremental parsing algorithm in forming the audio dictionary has superior retrieval performance compared to techniques yielding a dictionary with fixed-length entries such as vector quantization.
Understanding the performance of the Java operating system JX using visualization techniques

In the JX project we build a complete operating system using the type-safe, object-oriented language Java. One of the major challenges is achieving a performance that is competitive to mainstream operating systems, such as Linux or Solaris. It is in the nature of our microkernel-based system, that most time is spent executing Java code. Achieving a good performance therefore requires an optimizing bytecode-to-nativecode translator and instrumentation/visualization tools to locate performance problems. In this paper we concentrate on the second requirement. We instrumented the microkernel and the bytecode-to-nativecode translator to get information about nearly every aspect of the systems behavior. The gathered data can be visualized to show CPU time consumption of methods, thread scheduling, object allocation behavior, and object ages.
Importance of Variables Semantic in CNF Encoding of Cardinality Constraints

In the satisfiability domain, it is well-known that a SAT algorithm may solve a problem instance easily and another instance hardly, whilst these two instances are equivalent CNF encodings of the original problem. Moreover, different algorithms may disagree on which encoding makes the problem easier to solve. In this paper, we focus on the CNF encoding of cardinality constraints, which states that exactly k propositional variables in a given set are assigned totrue. We demonstrate the importance of the semantics of the SAT variables in the encoding of this constraint. We implement several variants of the CNF encoding in which the close semantic variables are grouped. We then examine these new encodings on problems generated from diagnosis of discrete-event system. Our results demonstrate that both stochastic and systematic SAT algorithms can now solve most of the problem instances, which were unreachable before (Grastien et al. 2007). These results also indicate that, on average cases, there is an encoding that suits well both SLS and DPLL algorithms.