Miracle-dz/UniFAR-FTUs huggingface dataset:
Double-square root traveltime approximation for converted waves

SUMMARY Over the past years, multi-parameter stacking has become a standard tool for seismic reflection data processing. Although several traveltime approximations for converted waves may be found in the literature, all of them are designed for a CMP-based observation geometry. In this paper we propose a double-square root traveltime approximation for converted waves from a curved interface. This approximation is appropriate for large offsets. In the special case of monotypic waves it gives comparable results with other multi-parameter moveout formulas. Furthermore, we demonstrate that a CRS-type traveltime approximation for converted waves may be derived from the new approximation. In this context, we also discuss a pragmatic search strategy. The key step of the strategy is the simulation of a zero-offset section by a stack of γ -CMP gathers, which may be considered as a first approximation of common conversion point gathers. For non-converted waves this approach transforms to the well-known pragmatic approach by M¨ uller et al. (1998). The new operator does not require separation of PP and PS wavefields prior to stacking. In our examples, the effective wavefield attributes of converted and monotypic waves obtained by the corresponding type of the operator have comparable values and may be used in joint interpretation.
Assessment of drought stress responsive genes expression profiles and proline accumulation in a diverse set of grapevine rootstocks

Recommended Citation ÖZMEN, CANAN YÜKSEL; BAYDU, FUNDA YILMAZ; HASANZADEH, MOHAMMAD; ÖKTEM, MERT; BABAOĞLU, GÜNSELİ; KİBAR, UMUT; KHABBAZI, SABER DELPASAND; KAZAN, KEMAL; and ERGÜL, ALİ (2023) "Assessment of drought stress responsive genes expression profiles and proline accumulation in a diverse set of grapevine rootstocks," Turkish Journal of Agriculture and Forestry: Vol. 47: No. 3, Article 5. https://doi.org/10.55730/1300-011X.3089 Available at: https://journals.tubitak. gov.tr/agriculture/vol47/iss3/5
A Dynamic Fluid-Kinetic(DyFK) Model for Ionosphere-Magnetosphere Plasma Transport: Effects of Ionization and Thermal Electron Heating by Soft Electron Precipitation

In order to efficiently and self-consistently describe the transport of high-latitude ionospheric plasma into the magnetosphere, we have developed a Dynamic coupled Fluid-Kinetic (DyFK) model for describing plasma flow along a magnetic flux tube. The collision-dominated ionospheric plasma is treated with a low-speed fluid approach for altitudes between 120-1100 km, while a generalized semikinetic approach is used for the topside and higher altitudes, starting at 800 km. This paper presents a description of the new DyFK model, along with illustrative results obtained from modeling the effects of soft (100 eV) auroral electron precipitation using the new model. The results demonstrate the accuracy and efficiency of the new DyFK model and illustrate how soft electron precipitation provides a mechanism to drive observed upflows of high-latitude ionospheric plasma along geomagnetic field lines.
Determination of cadmium and/or drought stress effects on some plant phytohormone contents and hormone gene expressions in bean (Phaseolus vulgaris L)

Recommended Citation EKİNCİ, MELEK; YILDIRIM, ERTAN; AĞAR, GÜLERAY; YÜKSEL, ESRA ARSLAN; AYDIN, MURAT; ÖRS, SELDA; and KUL, RAZİYE (2023) "Determination of cadmium and/or drought stress effects on some plant phytohormone contents and hormone gene expressions in bean (Phaseolus vulgaris L)," Turkish Journal of Agriculture and Forestry: Vol. 47: No. 3, Article 12. https://doi.org/10.55730/1300-011X.3096 Available at: https://journals.tubitak. gov.tr/agriculture/vol47/iss3/12
BRAIN GYM EXERCISES: AN APPROACH IN IMPROVING THE PSYCHOLOGICAL PERCEPTION IN GRADUATE STUDENTS

Brain Gym is an academic kinesiological program that is promoted and applied with a consistent learning purpose that aims at enhancing performance such as memory, psychological perception and cognitive skills. The technique requires the participant to communicate with a series of activities that help the body to understand the primary behaviour and learn how to coordinate the brain and entire body. Brain Gym activities includes of 26 basic motions, which are believed to improve perception and stimulates brain hemisphere by neural re-modelling to facilitate whole brain learning. By ways of balancing both the side of brain, behavioural difficulties, social and intellectual burdens are expected to be reduced. This study aims to improve the psychological perception and decrease depression in the undergraduate physiotherapy students. Here's an idea we suggest to check the psychological perception with brain gym intervention and the duration for practices comprises of three days a week session, duration of 25 minutes, which is completed in one hour. The study design is of pre- post type with simple randomized sampling. Before the treatment the mean stress showed a mean score of 33.87, anxiety showed a mean score of 32.43, depression showed a mean score of 32.34; an improvement was seen after the treatment where the mean score was 18.42, 17.26, 19. 46. The final mean difference was 43.49±17.33 and t -value 30.42 (p=0.0001,S). The data analysis shows that after the Brain Gym Activity intervention there was marked reduction in the score of DASS. The participants involved in the study showed significant improvement in their psychological perceptions.
Types of Context Clues and Vocabulary Meaning Inference: Abilities and Perceptions of English Major Undergraduate Students

The context clue strategy to derive the meaning of unfamiliar words and its effectiveness have been generally accepted, and the strategy is widely taught to ESL/EFL students. This study aimed to (1) compare the abilities in making use of context clues to derive the meaning of vocabulary among four groups of English major undergraduate students (Years 1-4); (2) compare the abilities to identify the context clues used to derive the meaning of unfamiliar words among the four student groups; (3) investigate the correlation between ability and context clues used to derive the meaning of unfamiliar words; and (4) investigate students’ opinions towards using the context clue strategy to derive the meaning of unfamiliar words. A 40-item vocabulary test and a set of questionnaires were administered to English major undergraduate students in the Faculty of Liberal Arts at a university in Thailand in the second semester of the academic year 2020. Then, 39 responses were randomly selected from each student batch to be the study sample. Three students from each student group were randomly selected to be interviewed to gather more in-depth information. The findings revealed that the ability to use context clues to derive the meaning of unfamiliar words among the four groups of English major undergraduate students was significantly different. Conversely, there was no significant difference in their ability to correctly identify the context clue types they used to infer word definitions. Also, there was a significant correlation between both abilities. Moreover, the students showed positive opinions towards the use of context clues to infer the meaning of vocabulary and provided some interesting ideas regarding the teaching of context clues for ESL/EFL teachers and English major program committees.
Few-shot Text-to-SQL Translation using Structure and Content Prompt Learning

A common problem with adopting Text-to-SQL translation in database systems is poor generalization. Specifically, when there is limited training data on new datasets, existing few-shot Text-to-SQL techniques, even with carefully designed textual prompts on pre-trained language models (PLMs), tend to be ineffective. In this paper, we present a divide-and-conquer framework to better support few-shot Text-to-SQL translation, which divides Text-to-SQL translation into two stages (or sub-tasks), such that each sub-task is simpler to be tackled. The first stage, called the structure stage, steers a PLM to generate an SQL structure (including SQL commands such as SELECT, FROM, WHERE and SQL operators such as <", ? >") with placeholders for missing identifiers. The second stage, called the content stage, guides a PLM to populate the placeholders in the generated SQL structure with concrete values (including SQL identifies such as table names, column names, and constant values). We propose a hybrid prompt strategy that combines learnable vectors and fixed vectors (i.e., word embeddings of textual prompts), such that the hybrid prompt can learn contextual information to better guide PLMs for prediction in both stages. In addition, we design keyword constrained decoding to ensure the validity of generated SQL structures, and structure guided decoding to guarantee the model to fill correct content. Extensive experiments, by comparing with ten state-of-the-art Text-to-SQL solutions at the time of writing, show that SC-Prompt significantly outperforms them in the few-shot scenario. In particular, on the widely-adopted Spider dataset, given less than 500 labeled training examples (5% of the official training set), SC-Prompt outperforms the previous SOTA methods by around 5% on accuracy.
The Usability of Digital Game-based Learning for Low Carbon Awareness: Heuristic Evaluation

—Digital Game-based Learning (DGBL) that attracts many practitioners to engage students in promoting low carbon awareness has been understudied. The evaluation phase plays a crucial part in determining the usability of the learning material. This study aims to identify the usability of DGBL which consists of four components: game usability (GU), mobility (MO), playability (P), and learning contents (LC) from the perspective of targeted end-user using heuristic evaluation. This study will also provide recommendations to help improve the quality of any related DGBL for novice designers or practitioners. A prototype of DGBL was developed which aims to promote low carbon awareness by learning about fuel cell. The study was designed in two phases, which are (1) developing the heuristic evaluation instrument validated by experts and (2) playtesting to identify the issues of usability in DGBL via heuristic evaluation by targeted end-users, which are forty-six selected students aged fourteen years old. Hence, it shows that the DGBL prototype developed for fuel cell learning has succeeded in achieving learning objectives while promoting low carbon awareness.
Nimble: Rollback Protection for Confidential Cloud Services (extended version)

This paper introduces Nimble, a cloud service that helps applications running in trusted execution environments (TEEs) to detect rollback attacks (i.e., detect whether a data item retrieved from persistent storage is the latest version). To achieve this, Nimble realizes an append-only ledger service by employing a simple state machine running in a TEE in con-junction with a crash fault-tolerant storage service. Nimble then replicates this trusted state machine to ensure the system is available even if a minority of state machines crash. A salient aspect of Nimble is a new reconfiguration protocol that allows a cloud provider to replace the set of nodes running the trusted state machine whenever it wishes— without affecting safety. We have formally verified Nimble’s core protocol in Dafny, and have implemented Nimble such that its trusted state machine runs in multiple TEE platforms (Intel SGX and AMD SNP-SEV). Our results show that a deployment of Nimble on machines running in different availability zones can achieve from tens of thousands of requests/sec with an end-to-end latency of under 3.2 ms (based on an in-memory key-value store) to several thousands of requests/sec with a latency of 30ms (based on Azure Table).
C HANGE S ENSITIVE R EPRESENTATION FOR R EINFORCEMENT L EARING

Image-based deep reinforcement learning has made a great improvement recently by combining state-of-the-art reinforcement learning algorithms with selfsupervised representation learning algorithms. However, these self-supervised representation learning algorithms are designed to preserve global visual information, which may miss changes in visual information that are important for performing the task, like in Figure 1. To resolve this problem, self-supervised representation learning specifically designed for better preserving task relevant information is necessary. Following this idea, we introduce Temporal Change Sensitive Representation (TCSR), which is designed for reinforcement learning algorithms that have a latent dynamic model. TCSR enforces the latent state representation of the reinforcement agent to put more emphasis on the part of observation that could potentially change in the future. Our method achieves SoTA performance in Atari100K benchmark. Figure 1: The ground truth observation compared with image reconstructed from latent state representation predicted by TCSR and EfficientZero. TCSR can not only predict the movement of enemies in the short term (Marked in the yellow box) but also predict exactly when and where the UFO will release a new enemy till the end of the planning horizon (Marked in the red box). However, EfficientZero fails to predict both of these changes. This shows that TCSR is more sensitive to the changes in the latent state representation. These change includes but not limited to position, appearance and disappearance of task related objects as shown in this figure.
Error of estimation and sample size in the linear mixed model

Daniel Serrano: Error of Estimation and Sample Size in the Linear Mixed Model (Under the direction of Patrick Curran) The linear mixed model is increasingly used in psychological applications. Whereas the model was once only applied rarely, because designs that would require the model were either avoided, or analyzed improperly, the model has found such favor in psychology that study designs are being contrived so as to allow use of the model. A potential complication of this trend is the intersection of the small sample sizes routine in psychological applications and the potential sensitivity of the model to small sample sizes. In truth, little is known about the small sample properties of the linear mixed model (Demidenko, 2004). Over the past three decades a handful of articles have attempted to understand the finite sample properties of linear mixed model estimators. Each of these studies has contributed to our understanding of the behavior of point estimation in the linear mixed model. A limitation of these preceding studies has been: 1. The simulation of balanced data, which rarely occurs in psychological applications 2. The use of simple models, which are also rarely used in psychological applications. In this study both of these limitations are addressed. Outcomes of interest include model convergence rates, point estimate bias and point estimate root mean squared error (RMSE). Findings indicate that high rates of non-convergence are observed under unbalanced data when both few independent sampling units (ISUs) are sampled and few observations are sampled per ISU. Fixed effect point estimates are nearly unbiased in all sample sizes. Consistent with theory (Demidenko, 2004), for some (co)variance
Employment Outcomes for Students with Intellectual Disabilities in Postsecondary Education Programs: A Scoping Review

The purpose of this scoping review was to examine research evidence supporting the use of postsecondary education programs as a facilitator of competitive integrated employment for individuals with intellectual disabilities. Despite previous calls for change, most individuals with intellectual disability remain unem-ployed or segregated in vocational workshops, and do not participate in postsecondary education after exiting high school. This scoping review builds upon the work of previous reviews on postsecondary education programs for students with intellectual disabilities by focusing specifically on employment. This review included a systematic search of peer-reviewed articles and grey literature sources published between 2000 and 2020. A total of eleven studies in the empirical literature and one study in the grey literature met inclusion criteria. The final sample of research articles generally suggests positive results for the effect of postsecondary education programs on employment outcomes that need to be verified through future research and with the help of better reporting. Wide variation in implementation and reporting of employment coursework, applied vocational experiences, and transition processes to employment following program exit were observed across studies. Limitations, implications and recommendations for practice, policy, and future research are discussed.
Factors associated with the usage of health insurance among cancer patients in public hospitals in a middle-income country

Introduction: Private health insurance (PHI) plays an important supplementary role on top of the existing subsidised health financing system to prevent heavy reliance on out-of-pocket (OOP) expenses, especially in diseases with high costly treatment. This study was done to examine the factors associated with PHI usage among cancer patients and its associated influencing factors in Malaysia. Materials and Methods: This cross-sectional study was conducted in three Malaysian public hospitals using a multi-level sampling technique to recruit 630 respondents. A validated self-developed four-domain questionnaire which includes one domain for health insurance was used to collect the relevant data. Results: Approximately 31.7% of the respondents owned PHI. The PHI usage was significantly higher among male respondents ( p =0.035), those aged 18–40 years old ( p <0.001), Indian and Chinese ethnicities ( p =0.002), with tertiary education level ( p <0.001), employed ( p <0.001), working in the private sector ( p <0.001), high household income (T20) ( p <0.001), home near to the hospital ( p =0.001) and medium household size ( p <0.001). The significant predictive factors were age 18–40 years aOR 3.01 (95% CI: 1.67–5.41), age 41–60 years aOR 2.22 (95% CI 1.41–3.49), medium (M40) income aOR 2.90 (95% CI: 1.92–4.39) and high (T20) income aOR 3.86 (95% CI: 1.68–18.91), home near to the hospital aOR 1.68 (95% CI: 1.10–2.55), medium household size aOR 2.20 (95% CI: 1.30–3.72) and female head of household aOR 1.79 (95% CI: 1.01–3.16). The type of cancer treatment, the location of treatment, prior treatment in private healthcare facilities and existence of financial coping mechanisms also were significant factors in determining PHI usage among cancer patients in this study. Conclusion: Several factors are significantly associated with PHI usage in cancer patients. The outcome of this study can guide policymakers to identify high-risk groups which need supplementary health insurance to bear the cost for their cancer treatment so that a better pre-payment health financing system such as a national health insurance can be formulated to cater for these groups.
The Research on the Construction of Obesity Stigmatization Issues in the British Paper Media in the Context of Frame Theory

Obesity in the UK has been on the rise in recent years, and the stigma attached to the term has been so strong that in July 2020, Prime Minister Boris issued a new health promotion policy that set out to improve obesity in the UK at a regulatory level. Focusing on the UK, this study looks at the construction of obesity discourses in the mainstream UK media through two paper news reports, which is The Guardian and The Daily Mail, and explores how this frame is subtly changing the perceptions of obese people. This paper defines three news frames for the identification of obesity coverage: the obesity stigma frame, the obesity transformation frame and the obesity stigma frame. In each of the three frames, the media have used different strategies to achieve different communication effects. Through the medias coverage of the issue, the social context in the UK is analyzed. And the stigma of obesity in the media begins to diminish as a result of government promotion and ideological awakening. The arguments in media coverage of the stigma of obesity are also less convincing than those in coverage of helping the obese, which are fairer and stronger.
Enabling Two-Party Secure Computation on Set Intersection

In this paper, we propose the first linear two-party secure-computation private set intersection (PSI) protocol, in the semi-honest adversary model, computing the following functionality. One of the parties ( P X ) inputs a set of items X = { x j | 1 ≤ j ≤ n X } , whereas the other party ( P Y ) inputs a set of items Y = { y i | 1 ≤ i ≤ n Y } and a set of corresponding data pairs D Y = { ( d 0 i , d 1 i ) | 1 ≤ i ≤ n Y } having the same cardinality with Y . While P Y outputs nothing, P X outputs a set of data D X = { d b i i | b i = 1 if y i ∈ X, b i = 0 otherwise } . This functionality is generally required when the PSI protocol is used as a part of a larger secure two-party computation such as threshold PSI or any function of the intersection in general. In literature, there are linear circuit and secure-computation PSI proposals, such as Pinkas et al. PSI protocol (Eurocrypt 2019), our PSI protocol (CANS 2020) and Chandran et al. PSI protocol (PETS 2022), for similar functionalities but having a cuckoo table mapping in the functionality, which complicates the application of different secure computation techniques on top of the output of the PSI protocol. We also show that the idea in the construction of our secure-computation PSI protocol having the functionality mentioned above can be utilized to convert the existing circuit PSI and secure-computation PSI protocols into the protocols realizing the functionality not having the cuckoo table mapping. We provide this conversion method as a separate protocol, which is one of the main contributions of this work. While creating the protocol, as a side contribution, we provide a one-time batch oblivious programmable pseudo-random function based on garbled Bloom filters.
(Concurrently Secure) Blind Schnorr Signatures

Many applications of blind signatures (notably in blockchains) require the resulting signatures to be compatible with the existing system. This makes schemes that produce Schnorr signatures (now being standardized and supported by major cryptocurrencies like Bitcoin) desirable. Unfortunately, the existing blind-signing protocol has been shown insecure when users can open signing sessions concurrently (Eurocrypt’21). On the other hand, only allowing sequential sessions opens the door to denial-of-service attacks. We present the ﬁrst practical, concurrently secure blind-signing protocol for Schnorr signatures, using the standard primitives NIZK and PKE and assuming that Schnorr signatures themselves are unforgeable. We cast our scheme as a generalization of blind and partially blind signatures: we introduce the notion of predicate blind signatures, in which the signer can deﬁne a predicate that the blindly signed message must satisfy. We provide proof-of-concept implementations and benchmarks for various choices of primitives and scenarios, including blindly signing Bitcoin transactions conditioned on certain properties.