Miracle-dz/UniFAR-FTUs huggingface dataset:
Earthquake Directivity, Orientation, and Stress Drop Within the Subducting Plate at the Hikurangi Margin, New Zealand

We develop an approach to calculate earthquake source directivity and rupture velocity for small earthquakes, using the whole source time function rather than just an estimate of the duration. We apply the method to an aftershock sequence within the subducting plate beneath North Island, New Zealand, and investigate its resolution. We use closely located, highly correlated empirical Green's function (EGF) events to obtain source time functions (STFs) for this well‐recorded sequence. We stack the STFs from multiple EGFs at each station, to improve the stability of the STFs. Eleven earthquakes (M 3.3–4.5) have sufficient azimuthal coverage, and both P and S STFs, to investigate directivity. The time axis of each STF in turn is stretched to find the maximum correlation between all pairs of stations. We then invert for the orientation and rupture velocity of both unilateral and bilateral line sources that best match the observations. We determine whether they are distinguishable and investigate the effects of limited frequency bandwidth. Rupture orientations are resolvable for eight earthquakes, seven of which are predominantly unilateral, and all are consistent with rupture on planes similar to the main shock fault plane. Purely unilateral rupture is rarely distinguishable from asymmetric bilateral rupture, despite a good station distribution. Synthetic testing shows that rupture velocity is the least well‐resolved parameter; estimates decrease with loss of high‐frequency energy, and measurements are best considered minimum values. We see no correlation between rupture velocity and stress drop, and spatial stress drop variation cannot be explained as an artifact of varying rupture velocity.
Putting consistency back into eventual consistency

Geo-replicated storage systems are at the core of current Internet services. The designers of the replication protocols used by these systems must choose between either supporting low-latency, eventually-consistent operations, or ensuring strong consistency to ease application correctness. We propose an alternative consistency model, Explicit Consistency, that strengthens eventual consistency with a guarantee to preserve specific invariants defined by the applications. Given these application-specific invariants, a system that supports Explicit Consistency identifies which operations would be unsafe under concurrent execution, and allows programmers to select either violation-avoidance or invariant-repair techniques. We show how to achieve the former, while allowing operations to complete locally in the common case, by relying on a reservation system that moves coordination off the critical path of operation execution. The latter, in turn, allows operations to execute without restriction, and restore invariants by applying a repair operation to the database state. We present the design and evaluation of Indigo, a middleware that provides Explicit Consistency on top of a causally-consistent data store. Indigo guarantees strong application invariants while providing similar latency to an eventually-consistent system in the common case.
In-Place Learning for Positional and Scale Invariance

In-place learning is a biologically inspired concept, meaning that the computational network is responsible for its own learning. With in-place learning, there is no need for a separate learning network. We present in this paper a multiple-layer in-place learning network (MILN) for learning positional and scale invariance. The network enables both unsupervised and supervised learning to occur concurrently. When supervision is available (e.g., from the environment during autonomous development), the network performs supervised learning through its multiple layers. When supervision is not available, the network practices while using its own practice motor signal as self-supervision (i.e., unsupervised per classical definition). We present principles based on which MILN automatically develops positional and scale invariant neurons in different layers. From sequentially sensed video streams, the proposed in-place learning algorithm develops a hierarchy of network representations. The global invariance was achieved through multi-layer quasi-invariances, with increasing invariance from early layers to the later layers. Experimental results are presented to show the effects of the principles.
Denotational Semantics and Refinement

Denotational descriptions are frequently presented in terms of a sequence of descriptions of programming languages of increasing complexity. We discuss a formal approach to the study of such description sequences in terms of a refinement relation, showing how the informal use made of this approach in tutorial introductions to denotational semantics may be formally justified.
Exploring Breed-Specific Milk Coagulation in Spanish Dairy Sheep: A Canonical Correlation Approach

Simple Summary The transformation of milk into cheese is a complex process influenced by various factors, including the breed of the sheep providing the milk. Our study focused on understanding how different Spanish dairy sheep breeds affect the quality and properties of milk intended for cheesemaking. We examined milk samples from four different breeds, analyzing their composition and how they coagulate, a key step in cheese production. We found that while there are some common patterns in how milk from these breeds coagulates, certain breeds have unique characteristics. Our research highlights the importance of considering breed selection in coagulation, offering insights that could lead to better cheese production techniques. Abstract The transformation of milk into cheese largely depends on the technological properties of the raw material, with breed being a crucial factor that influences both the composition and coagulation properties of the milk used for cheesemaking. This study uses canonical correlation analysis to explore the relationships between physicochemical traits and coagulation properties in milk from various Spanish breeds, aiming to identify both common and breed-specific patterns that impact milk technological aptitude. A total of 832 milk samples from Manchega, Assaf, Merino de Grazalema, and Merino de Los Pedroches breeds were analyzed. The milk characteristics investigated included pH, composition (fat, protein, lactose, total solids), and coagulation properties (curd firmness—A60, rennet clotting time—RCT, curd firming time—k20, and individual laboratory curd yield—ILCY). The results reveal a shared correlation structure across breeds and unique covariation patterns in some breeds that deviate from the general trend. While Assaf and Merino de Los Pedroches follow the common correlation pattern, Manchega and Merino de Grazalema exhibit distinct patterns. This research underscores the need for in-depth study and suggests that the dairy industry could benefit from shifting from the traditional focus on maximizing fat and protein for higher curd yields to considering technological traits for selective breeding.
Type Casting Verification: Stopping an Emerging Attack Vector

Many applications such as the Chrome and Firefox browsers are largely implemented in C++ for its performance and modularity. Type casting, which converts one type of an object to another, plays an essential role in enabling polymorphism in C++ because it allows a program to utilize certain general or specific implementations in the class hierarchies. However, if not correctly used, it may return unsafe and incorrectly casted values, leading to so-called bad-casting or type-confusion vulnerabilities. Since a bad-casted pointer violates a programmer's intended pointer semantics and enables an attacker to corrupt memory, bad-casting has critical security implications similar to those of other memory corruption vulnerabilities. Despite the increasing number of bad-casting vulnerabilities, the bad-casting detection problem has not been addressed by the security community. In this paper, we present CAVER, a runtime bad-casting detection tool. It performs program instrumentation at compile time and uses a new runtime type tracing mechanism--the type hierarchy table--to overcome the limitation of existing approaches and efficiently verify type casting dynamically. In particular, CAVER can be easily and automatically adopted to target applications, achieves broader detection coverage, and incurs reasonable runtime overhead. We have applied CAVER to large-scale software including Chrome and Firefox browsers, and discovered 11 previously unknown security vulnerabilities: nine in GNU libstdc++ and two in Firefox, all of which have been confirmed and subsequently fixed by vendors. Our evaluation showed that CAVER imposes up to 7.6% and 64.6% overhead for performance-intensive benchmarks on the Chromium and Firefox browsers, respectively.
Correlations of Personality Traits and Romantic Relationship Satisfaction

This study investigates how personality traits correlate with satisfaction levels in romantic relationships among Psychology majors enrolled at the University of Cebu Main Campus during the Academic Year 2022-2023. Employing a quantitative research approach, a descriptive correlational design was utilized to establish connections between personality traits and levels of relationship satisfaction, enabling prediction and explanation of variable relationships. The research addresses conflicting findings on the influence of personality traits on successful romantic relationships, particularly emphasizing trait similarity versus the "Opposites Attract" notion, alongside considerations of emotionality, maladaptive tendencies, self-esteem, and partner perspectives. Methodological variations in self-report assessments versus multiple-source and observation-based approaches have contributed to inconsistent results in previous investigations.
A Multi-User MIMO Downlink Receiver and Quantizer Design Based on SINR Optimization

In a quantization based multiuser multiple-input/multiple-output (MU-MIMO) broadcast channel, the effective channel gain (i.e., the norm of a channel vector obtained from the receive combiner) needs to be considered in addition to a reduction of quantization errors for maximizing the signal-to-interference plus noise ratio (SINR). In this work, we prove that the effective channel gains in the space limited by an N_R x M_T MIMO channel form a min(M_T,N_R)-dimensional ellipsoid in the channel space. Utilizing the geometric proof, the achievable effective channel gain is derived as a function of its direction for a given MIMO channel. Based on the derivation, we finally propose a quantization vector selection criterion and an optimal receive combiner to maximize the SINR. The proposed maximum SINR based combining (MSC) is proven to be a better solution for maximizing the SINR compared to quantization based combining (QBC) and maximum gain based combining (MGC), each of which attempts to minimize quantization errors and maximize the effective channel gain.
NLM Anatomical Ontology Alignment System. Results of the 2006 Ontology Alignment Contest

An ontology is a formal representation of a domain modeling the entities in the domain and their relations. When a domain is represented by multiple ontologies, there is need for creating mappings among these ontologies in order to facilitate the integration of data annotated with and reasoning across these ontologies. The objective of this paper is to present our experience in aligning two large anatomical ontologies and to reflect on some of the issues and challenges encountered along the way. The anatomical ontologies under investigation are the Foundational Model of Anatomy (FMA) and GALEN. Our approach to aligning concepts is automatic, rule-based, and operates at the schema level, generating mostly point-to-point mappings. It uses a combination of domain-specific lexical techniques and structural and semantic techniques (to validate the mappings suggested lexically). It also takes advantage of domain-specific knowledge (lexical knowledge from external resources such as the Unified Medical Language System, as well as knowledge augmentation and inference techniques). Overall, the lexical alignment followed by structural validation identified 3,029 pairs of equivalent concepts in the FMA and GALEN, accounting for about 4% of all FMA concepts and 32% of all GALEN concepts.
Effect of Cold Starvation, Acid Stress, and Nutrients on Metabolic Activity of Helicobacter pylori

ABSTRACT Helicobacter pylori can transform, in vivo as well as in vitro, from dividing spiral-shaped forms into nonculturable coccoids, with intermediate forms called U forms. The importance of nonculturable coccoid forms of H. pylori in disease transmission and antibiotic treatment failures is unclear. Metabolic activities of actively growing as well as nonculturable H. pylori were investigated by comparing the concentrations of cellular ATP and total RNA, gene expression, presence of cytoplasmic polyphosphate granules and iron inclusions, and cellular morphology during extended broth culture and nutritional cold starvation. In addition, the effect of exposing broth-cultured or cold-starved cells to a nutrient-rich or acidic environment on the metabolic activities was investigated. ATP was detectable up to 14 days and for at least 25 days after transformation from the spiral form to the coccoid form or U form in broth-cultured and cold-starved cells, respectively. mRNAs of VacA, a 26-kDa protein, and urease A were detected by using reverse transcription-PCR in cells cultured for 2 months in broth or cold starved for at least 28 months. The ATP concentration was not affected during exposure to fresh or acidified broth, while 4- to 12-h exposures of nonculturable cells to lysed human erythrocytes increased cellular ATP 12- to 150-fold. Incubation of nonculturable cold-starved cells with an erythrocyte lysate increased total RNA expression and ureA mRNA transcription as measured by quantitative real-time reverse transcription-PCR. Furthermore, the number of structurally intact starved coccoids containing polyphosphate granules increased almost fourfold (P = 0.0022) under the same conditions. In conclusion, a specific environmental stimulus can induce ATP, polyphosphate, and RNA metabolism in nonculturable H. pylori, indicating viability of such morphological forms.
Parsimonious data: How a single Facebook like predicts voting behavior in multiparty systems

This study shows how liking politicians’ public Facebook posts can be used as an accurate measure for predicting present-day voter intention in a multiparty system. We highlight that a few, but selective digital traces produce prediction accuracies that are on par or even greater than most current approaches based upon bigger and broader datasets. Combining the online and offline, we connect a subsample of surveyed respondents to their public Facebook activity and apply machine learning classifiers to explore the link between their political liking behaviour and actual voting intention. Through this work, we show that even a single selective Facebook like can reveal as much about political voter intention as hundreds of heterogeneous likes. Further, by including the entire political like history of the respondents, our model reaches prediction accuracies above previous multiparty studies (60–70%). The main contribution of this paper is to show how public like-activity on Facebook allows political profiling of individual users in a multiparty system with accuracies above previous studies. Beside increased accuracies, the paper shows how such parsimonious measures allows us to generalize our findings to the entire population of a country and even across national borders, to other political multiparty systems. The approach in this study relies on data that are publicly available, and the simple setup we propose can with some limitations, be generalized to millions of users in other multiparty systems.
Investigation of the Association of TLR2 and TLR4 Polymorphisms with Susceptibility to Helicobacter pylori-Related Gastrointestinal Diseases

Background and Aim: Toll-like receptor (TLR) 2 and TLR4 are cell surface signaling receptors that are involved in the recognition of and host response to Helicobacter pylori. Our aim was to investigate the association between TLR gene polymorphisms and susceptibility of Japanese subjects to 4 H. pylori-related gastrointestinal diseases. Methodology: A total of 100 patients with histologically diagnosed gastric cancer, 105 patients with gastric ulcer, 102 with atrophic gastritis, 72 with duodenal ulcer and 428 healthy controls were recruited. A TaqMan assay was used to genotype 7 single nucleotide polymorphisms (SNPs) in TLR2 and 6 SNPs in TLR4. Results: There was a tendency for TLR4 rs10759932 TC/CC genotypes to be associated with a decreased risk of gastric cancer (p = 0.059); however, this did not reach statistical significance. No significant associations were found between polymorphisms in TLR2 or TLR4 and the risks for gastric cancer, gastric ulcer, duodenal ulcer, or atrophic gastritis. Conclusion: The 13 SNPs inTLR2 and TLR4 examined in this study may not be linked with the development of H. pylori-related gastrointestinal diseases. Further studies with larger numbers of subjects are necessary to verify the present findings.
Abstention or intervention for isolated hypotension in the first 3 days of life in extremely preterm infants: association with short-term outcomes in the EPIPAGE 2 cohort study

Objective To compare outcomes at hospital discharge for preterm infants born before 29 weeks of gestation who had at least one episode of isolated hypotension during their first 72 hours of life for which they did or did not receive antihypotensive treatment. Design Etude Epidémiologique sur les Petits Ages Gestationnels 2 (EPIPAGE 2) French national prospective population-based cohort study in 2011. Setting 60 neonatal intensive care units. Patients All infants with a minimum mean arterial blood pressure less than gestational age (in weeks) (minMAP<GA) within 72 hours of birth. Infants whose reason for receiving antihypotensive treatments was isolated hypotension only were compared with untreated hypotensive infants by propensity score matching. Treatments Fluid bolus and/or inotropes and/or corticosteroids. Main outcomes and measures The primary outcome was survival at hospital discharge without major morbidity, defined as any of necrotising enterocolitis, severe cerebral abnormalities, severe bronchopulmonary dysplasia or severe retinopathy of prematurity. Results Among the 1532 infants with available data, 662 had a minMAP<GA; 206 were treated for unknown or other reasons than isolated hypotension, 131 were treated for isolated hypotension only and 325 were untreated; 119 infants from each of these last two groups were matched. Treated infants had a significantly higher survival rate without major morbidity (61.3% vs 48.7%; OR, 1.67, 95% CI 1.00 to 2.78, p=0.049) and a lower rate of severe cerebral abnormalities (10.1% vs 26.5%, p=0.002). Conclusions In this population, antihypotensive treatment was associated with improved short-term outcomes. Therapeutic abstention should be cautiously considered for early isolated hypotension in extremely premature infants.
Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization

Asynchronous parallel implementations of stochastic gradient (SG) have been broadly used in solving deep neural network and received many successes in practice recently. However, existing theories cannot explain their convergence and speedup properties, mainly due to the nonconvexity of most deep learning formulations and the asynchronous parallel mechanism. To fill the gaps in theory and provide theoretical supports, this paper studies two asynchronous parallel implementations of SG: one is on the computer network and the other is on the shared memory system. We establish an ergodic convergence rate $O(1/\sqrt{K})$ for both algorithms and prove that the linear speedup is achievable if the number of workers is bounded by $\sqrt{K}$ ($K$ is the total number of iterations). Our results generalize and improve existing analysis for convex minimization.
Adaptive neuro-fuzzy inference system (ANFIS) in modelling breast cancer survival

Medical prognosis is the prediction of the future course and outcome of a disease and an indication of the likelihood of recovery from that disease. Soft-computing approaches including artificial neural networks and fuzzy inference have been used widely to model expert behaviour. In this paper, we propose the use of an adaptive fuzzy inference system (ANFIS) technique in the estimation of survival prediction. This paper describes the methodology by which ANFIS was used to model survival and presents a comparison of this new method with existing methods in the capability to predict the survival rate in a given medical data set concerning survival of patients following operative surgery for breast cancer.
The primacy of self-referent information in perceptions of social consensus.

People's own responses to a social stimulus (i.e. whether they endorse it or reject it) predict how they expect other people to respond (consensus estimates). This correlation has long been accepted as evidence for social projection. There has been little direct evidence, however, for the assumption that self-referent judgments shape judgments about others. Supporting the projection model, Expt 1 shows that self-referent information is more accessible than consensus estimates. Once they have been made, people's own endorsements and rejections of a stimulus facilitate consensus estimates. In turn, consensus estimates facilitate endorsements (but less so). Judgments about the physical properties of the stimulus facilitate neither type of social judgment. Supporting the view that projection is egocentric, Expt 2 shows that, when making consensus estimates, people rely more on their own endorsements than on the endorsements made by another individual. This self-other difference does not depend on whose endorsements are revealed first or on whether the other person is anonymous or individual.
A Similarity Measure for the Description Logic EL with Unfoldable Terminologies

Description Logics (DLs) are a family of logic-based knowledge representation formalisms, which can be used to develop ontologies in a formally well-founded way. The standard reasoning service of subsumption has proved indispensable in ontology design and maintenance. This checks, relative to the logical definitions in the ontology, whether one concept is more general/specific than another. When no subsumption relationship is identified, however, no information about the two concepts can be given. This work presents a new notion of semantic similarity which stems from the known homomorphism-based structural subsumption algorithm. The proposed similarity measure computes a numerical degree of similarity between two EL concept descriptions despite not being in the subsumption relation.