Miracle-dz/UniFAR-FTUs huggingface dataset:
Research Statement | Xiaokang Qiu

My research mission is to make computer programming easier, more reliable, and more productive. Computer software has revolutionized our daily lives, but software developers’ lives have not advanced commensurately. Programming remains “one of our most demanding intellectual activities” as E.W. Dijkstra said three decades ago [2]. For example, as of 2018, it would cost approximately $14.7 billion USD to re-develop the Linux kernel [28], giving a cost per line equal to $733. The main thrust of my research is to develop algorithmic techniques to advance program synthesis — the process of automatically generating programs that meet the user’s intent. As one of the most central research problems in computer science and AI, program synthesis is notoriously challenging. Nonetheless, in the last two decades, largely driven by the flowering of constraint solving and machine learning techniques, this traditional research area has experienced a renaissance and witnessed emerging programming paradigms (e.g., program sketching [20]), automated tools (e.g., the plethora of syntax-guided synthesis solvers [1]), and prominent applications (e.g., the FlashFill feature of Microsoft Excel [4]). Despite the promising progress, program synthesis still has a long way to go. On one hand, to replace human written code with machine generated code in any production software, the synthesizer has to formally guarantee that the produced program is functionally correct, performant, preferable etc. These topics intersect with program verification, a well known challenging research area which has been studied for more than five decades [5]. On the other hand, Today’s program synthesizers still can only be mastered by experts who can articulate their intent using formal specification or informative examples, determine preferable programs by writing ranking functions, and guide the synthesizer with domain knowledge. Moreover, while each of the two problems alone is already challenging enough, they are unfortunately tangled and have to be reconciled in a synthesizer. To this end, my general research program is to develop accessible synthesis tools that help regular programmers produce high-quality, trustworthy programs. In the rest of this statement, I will highlight some of my main contributions to program synthesis, while mentioning along the way how the accessibility and trustworthiness are reconciled in these results. I will then conclude with my very recent or ongoing work and future directions.
This paper is included in the Proceedings of the 2022 USENIX Annual Technical Conference.

First-party datacenter workloads present new challenges to kernel memory management (MM), which allocates and maps memory and must balance competing performance concerns in an increasingly complex environment. In a datacenter, performance must be both good and consistent to satisfy service-level objectives. Unfortunately, current MM designs often exhibit inconsistent, opaque behavior that is difﬁcult to reproduce, decipher, or ﬁx, stemming from (1) a lack of high-quality information for policymaking, (2) the cost-unawareness of many current MM policies, and (3) opaque and distributed policy implementations that are hard to debug. For example, the Linux huge page implementation is distributed across 8 ﬁles and can lead to page fault latencies in the 100s of ms. In search of a MM design that has consistent behavior, we designed Cost-Beneﬁt MM (CBMM), which uses empirically based cost-beneﬁt models and pre-aggregated proﬁling information to make MM policy decisions. In CBMM, policy decisions follow the guiding principle that userspace bene-ﬁts must outweigh userspace costs . This approach balances the performance beneﬁts obtained by a kernel policy against the cost of applying it. CBMM has competitive performance with Linux and HawkEye, a recent research system, for all the workloads we ran, and in the presence of fragmentation, CBMM is 35% faster than Linux on average. Meanwhile, CBMM nearly always has better tail latency than Linux or HawkEye, particularly on fragmented systems. It reduces the cost of the most expensive soft page faults by 2-3 orders of magnitude for most of our workloads, and reduces the frequency of 10-1000 µs -long faults by around 2 orders of magnitude for multiple workloads.
The role of personality traits and engagement factors towards the use of paid stickers in personal communication messages [version 1; peer review: awaiting peer review]

Background: The research investigated the personality traits and engagement factors that influence the use of paid stickers in personal communication. The study was driven by the increasing significance of stickers such as emoticons and emojis in online dialogues. The study was hinged on the Theory of the big five personality traits; neuroticism, extraversion, openness to experience, agreeableness and conscientiousness. The study tested seven hypotheses to ascertain the effect of personality traits and engagement factors towards the utilization of paid stickers in personal communication. Methods: The research applied the quantitative survey research design, where primary data was collected from respondents that had experience in using emojis and emoticons in their communications. The data was collected from respondents in Bangkok, Thailand using a structured questionnaire between May 14, 2022, and May 31, 2022. Results: The results from 391 respondents indicated that conscientiousness, extraversion, openness, and neuroticism influence perceived enjoyment, while perceived enjoyment and perceived ease of use have a significant and positive influence on the intention to use paid stickers. Two elements of engagement, interactive engagement, and personal engagement were used to assess the influence of engagement parameters on the behavioral intention to use paid stickers. The intention to employ paid stickers in personal messages was found to be strongly and favorably influenced by these two engagement characteristics. Conclusions: The study recommends that the creators of stickers, emoticons, and emojis should consider user personality features, sticker engagement, sticker simplicity of use, and the personal delight of users an in the range of each of the five qualities. Each attribute assesses a distinct facet of human personality:
Topological aspects of band theory

OF THE DISSERTATION Topological aspects of band theory by Alexey A. Soluyanov Dissertation Director: David Vanderbilt Band theory has proven to be one of the most successful developments in condensed matter theory. It is the basis of our current understanding of crystalline solids, describing complex electronic behavior in terms of a single quasi-particle that moves in some effective field of the crystal lattice environment and other particles. In recent years topological and geometrical considerations opened a fundamentally new branch of research in band theory. One of the major advances in this field came with the realization that insulating band structures can be classified according to the values of some topological invariants associated with the occupied single-particle states. Insulators that correspond to non-trivial values of these topological invariants realize new states of matter with properties drastically different from those attributed to an ordinary insulator. In this work we address questions that arise in the context of band theory in the presence of topologically non-trivial bands. Part of the thesis is aimed at the actual determination of the presence of non-trivial band topology. We develop a method to distinguish an ordinary insulator from a topological one in the presence of time-reversal symmetry. The method is implemented within the
Broadband Ocean Bottom Seismometer Noise Properties

1. Department of Earth Sciences, University of Hawaiʻi at Mānoa, Honolulu, HI 2. Dept. of Earth Science, University of California, Santa Barbara, CA 3. Dept. of Earth, Environmental and Planetary Sciences, Brown University, Providence, RI 4. School of Earth & Sustainability, Northern Arizona University, Flagstaff, AZ 5. Department of Earth and Environmental Sciences, University of Ottawa, Ottawa, Canada 6. Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY
Antidiabetic 1 Kanamycin sulphate 1 Teneligliptin 2 Amoxicillin 2 Acarbose 3 Aztreonam

FTIR spectroscopy is powerful optical spectroscopy based on vibration measurement of an excited molecule by IR radiation at a specific wavelength range, which identifies the vibration characteristics of chemical functional groups in a sample. It is a fast and non-destructive analytical method.However it is suitable for analysis of solid, liquid and biotechnological pharmaceutical forms. Fourier-transform infrared (FTIR) spectroscopy has become a valuable analytical technique for structural or functional studies related to foods as a rapid, cost-efficient, and sensitive physicochemical fingerprinting method. FTIR is used to determine among structural and geometric isomers, identify all types of organic and many types of inorganic materials, quantify species in difficult mixtures, determine the molecular composition of surface species, and identify molecular orientation in polymers and solutions. This review focuses on pharmaceutical FTIR applications used for qualitative and quantitative analysis. The FTIR is helpful to identify and quantify the active constituents. It is also advanced in analysis of biomedical, food, drug and counterfeit drugs.
Phytoplankton reaction to an intense storm in the north-western Mediterranean Sea

Abstract. The study of extreme weather events and their impact on ocean physics and biogeochemistry is challenging due to the difficulty involved with collecting in situ data. However, recent research has pointed out the major influence of such physical forcing events on microbiological organisms. Moreover, the occurrence of such intense events may increase in the future in the context of global change. In May 2019, an intense storm occurred in the Ligurian Sea (north-western Mediterranean Sea) and was captured during the FUMSECK (Facilities for Updating the Mediterranean Submesoscale – Ecosystem Coupling Knowledge) cruise. In situ multi-platform (vessel-mounted acoustic Doppler current profiler, thermosalinometer, fluorometer, flow cytometer, a moving vessel profiler equipped with a multi-sensor towed vehicle, and a glider) measurements along with satellite data and a 3D atmospheric model were used to characterise the fine-scale dynamics occurring in the impacted oceanic zone. The most affected area was marked by a lower water temperature (1 ∘C colder), a factor of 2 increase in surface chlorophyll a, and a factor of 7 increase in the nitrate concentration, exhibiting strong gradients with respect to the surrounding waters. Our results show that this storm led to a deepening of the mixed-layer depth from 15 to 50 m and a dilution of the deep chlorophyll maximum. As a result, the surface biomass of most phytoplankton groups identified by automated flow cytometry increased by up to a factor of 2. Conversely, the carbon / chlorophyll ratio of most phytoplankton groups decreased by a factor of 2, evidencing significant changes in the phytoplankton cell composition. These results suggest that the role of storms on the biogeochemistry and ecology of the Mediterranean Sea may be underestimated and highlight the need for high-resolution measurements during these events coupling physics and biology.
Discontinuity in Equilibrium Wave‐Current Ripple Size and Shape and Deep Cleaning Associated With Cohesive Sand‐Clay Beds

Mixtures of cohesive clay and noncohesive sand are widespread in many aquatic environments. Ripple dynamics in sand‐clay mixtures have been studied under current‐alone and wave‐alone conditions but not combined wave‐current conditions, despite their prevalence in estuaries and the coastal zone. The present flume experiments examine the effect of initial clay content, C0, on ripples by considering a single wave‐current condition and, for the first time, quantify how changing clay content of substrate impacts ripple dimensions during development. The results show inverse relationships between C0 and ripple growth rates and clay winnowing transport rates out of the bed, which reduce as the ripples develop toward equilibrium. For C0 ≤ 10.6%, higher winnowing rates lead to clay loss, and thus the presence of clean sand, far below the base of equilibrium ripples. This hitherto unquantified “deep‐cleaning” of clay does not occur for C0 > 10.6%, where clay‐loss rates are much lower. The clay‐loss behavior is associated with two distinct types of equilibrium combined flow ripples: (a) Large asymmetric ripples with dimensions and plan geometries comparable to their clean‐sand counterparts for C0 ≤ 10.6% and (b) small, flat ripples for C0 > 10.6%. The 10.6% threshold, which may be specific to the experimental conditions, corresponds to a more general 8% threshold found beneath the ripple base, suggesting that clay content here must be <8% for clean‐sand‐like ripples to develop in sand‐clay beds. This ripple‐type discontinuity comprises a threefold reduction in ripple height, with notable implications for bed roughness.
Approach for Named Entity Recognition and Case Identification Implemented by ZuKyo-JA Sub-team at the NTCIR-16 Real-MedNLP Task

In this NTCIR-16 Real-MedNLP shared task paper, we present the methods of the ZuKyo-JA subteam for solving the Japanese part of Subtask1 and Subtask3 (Subtask1-CR-JA, Subtask1-RR-JA, Subtask3-RR-JA). Our solution is based on a sliding-window approach using a Japanese BERT pre-trained masked-language model. , which was used as a common architecture for addressing the specific subtasks. We additionally present a method that makes extensive use of medical knowledge for the same case identification subtask3-RR-JA.
مطالعهی مقایسه¬ای روشهای تحلیلی برای کاهش آرتیفکت فلزی در تصاویر CT

Over the past few decades, computed tomography (CT) imaging has been one of the leading cross-sectional imaging techniques in a wide range of clinical applications in diagnostic radiology, oncology, and multimodal molecular imaging. Despite the recognized value of this imaging modality, the quality and accuracy of CT images can be compromised by a number of implants. The presence of metal objects such as dental fillings, hip or knee prostheses, heart pacemakers, war fragments, and spinal cages can cause severe image artifacts. These types of artifacts appear as black and white streaks in the CT images, obscuring the structures and tissues around the metal implant, decreasing the images' diagnostic values. Metal artifacts also affect the accuracy of radiation therapy treatment planning, which relies on X-ray images to determine electron density and estimate the relative stopping power of particles. Different algorithms for the Metal Artifact Reduction (MAR) have been proposed over the decades to address this issue. This study evaluated five commonly used MAR algorithms in clinical practice using simulated and clinical datasets. These algorithms include linear interpolation (LI_MAR) of the degraded data in the sinogram space, reduction of metal artifacts by normalization method (NMAR), metal deletion technique (MDT), and Orthopedic metal artifact reduction (OMAR), and a method based on iteration algorithms (MAP). Clinical CT images in different anatomical regions of the body, with different dimensions and types of metal implants, have been studied to evaluate the performance of the MAR algorithms. In order to quantitatively evaluate the quality of CT images corrected by the different MAR algorithms, the Normalized Root Mean Square Error (NRMSE) metric was employed. The quantitative analysis demonstrated the overall superior performance of the NMAR algorithm in effective metal artifact reduction compared to the other algorithms. The NMAR method exhibited relatively less signal distortion and reasonable processing time, making it a dependable solution in clinical practice.
The Shape of Emotion Regulation: Trait Emotion Regulation as Density Distributions of States

Other findings this study are reported in We report how we determined our sample size, all data exclusions, and all measures as relevant for the research questions. There were no manipulations. Siebert the student of the Emmy Noether Independent Junior Research Group to Major Life Events” their help with the data collection. also Abstract The Density Distribution approach to personality characterizes traits using both mean levels and within-person variability of behaviors. Recent theory highlights that emotion regulation (ER) is inherently variable, and this Density Distribution approach seems particularly suitable to understand both average tendencies and dynamics of ER as person-specific characteristics. However, there is not yet empirical evidence for this suggestion. To fill this gap, we investigated the reliability of density distribution information gathered from repeated assessments of state ER (within-person mean levels and standard deviations). Specifically, we studied the reliability of ER strategy use in terms of internal consistency and short- and long-term stability within and across two waves of experience sampling ( N = 153, M = 70 measurement occasions). Across both average tendencies and within-person variation, we found that individuals used different ER strategies relatively consistently. Overall, within-person ER mean levels and standard deviations were stable within and across the waves. Taken together, this suggests that the person-specific overall pattern of ER use in daily life is captured reliably using ESM.
NYUCIN at the NTCIR-16 Dataset Search 2 Task

In this paper, we describe the approach and results of the NYUCIN team in the NTCIR-16 conference. We participated in the Data Search 2 Task, which is a shared task on ad-hoc retrieval for governmental statistical data composed of multiple subtasks. We report our work on the two subtasks we participated in: the English IR Subtask and the UI Subtask. For the IR Subtask, we explored learning-to-rank approaches based on deep learning models. Given the limited training data available for this task, we employed a transfer learning method to train a deep neural network that learns how to match web tables and news articles using data available on the Web. The official evaluation shows that our approach attained the highest score among all submitted runs across all evaluation metrics. In particular, for the nDCG@5 measure, our score of 0.246 represents a 30% improvement compared to the second-best result in NTCIR-16 Data Search 2 Task. For the experimental UI Subtask, we performed two user studies to evaluate the design decisions and the usability of Auctus, a dataset search engine developed by our team.
Satellite observations of seasonality and long-term trends in cirrus cloud properties over Europe: investigation of possible aviation impacts

Abstract. Linear contrails and contrail cirrus induced by global aviation have long been known to contribute to climate change by warming the atmosphere. Besides increasing global cirrus cloudiness, aviation may also alter the properties of natural cirrus clouds by soot emissions which lead to more heterogeneous freezing. During the first COVID-19 lockdown in Europe, changes in the properties and occurrence of cirrus clouds were determined with the lidar measurements of CALIPSO, which are presumed to be caused by the corresponding reduction in civil aviation. In the 10 years before the COVID-19 outbreak, however, aviation grew strongly in terms of CO2 emissions and flight densities in Europe. In this study, 10-year lidar measurements with CALIPSO are analysed to determine the seasonality and long-term trends in cirrus clouds as well as their correlations with the ambient temperatures and air traffic. The results show that there is a distinct seasonal cycle in the occurrence rates (ORs) and particle linear depolarization ratio (PLDR) of cirrus clouds. In addition, cirrus clouds appear within a broader altitude range in winter than in summer and they are characterized by larger OR and PLDR values in winter than in summer. The monthly medians of PLDR as well as their deseasonalized time series in the 10-year period before COVID-19 both show positive trends, which are statistically significant according to the Mann–Kendall (MK) significance test. However, the ORs of cirrus clouds show a negative trend, which might be connected with the background meteorological conditions. Since the cirrus PLDR strongly depends on the ambient temperatures, the contributions induced by temperature are further removed from the cirrus PLDR with a simple linear regression model. The derived residuals show significant positive trends according to the MK test. To compare the cirrus PLDR and air traffic (with the CO2 emissions from aviation as a proxy), the deseasonalization of both datasets were previously conducted since the seasonal cycles in both are not consistent. The deseasonalized time series determined for the cirrus PLDR and CO2 emissions from aviation both show increasing trends and their correlation coefficient is r=0.54 at the confidence level above 99.5 %. Finally, comparisons between the cirrus PLDR and aviation in every season were made and revealed a strong correlation in other seasons than in summer.
Real-Time Object Tracking For Assembly

In recent years, the importance of object tracking in many technological areas, such as augmented reality, autonomous driving, robotic interaction, or assembly applications, is increasing rapidly as autonomous systems interact more and more in and with the environment. Since most real-world tasks take place in partially observable environments, noise, inaccuracies, and occlusions have a major impact on the tracking of objects. Therefore, many learningand model-based approaches exist that try to determine reliable poses even under the previously mentioned influences. In this work, we present a model-based object tracking algorithm based on the Point Set Registration (PSR) method proposed by the FilterReg [1] paper. Hereby, we develop a framework that enables object tracking with PSR algorithms and propose several extensions e.g. additional features to improve the tracking result. Moreover, we test our proposed tracking algorithm on accuracy and real-time capability on a dataset that was recorded in a real robotic assembly setup. From our results, we conclude that our proposed object tracking algorithm achieves a Euclidean accuracy of 2cm compared to the ground truth, and has a maximum error of 20° in the rotation, given in Euler angles, while frame rates of 30Hz− 76Hz can be achieved. Furthermore, we state that our efficient object tracking algorithm can handle real-time applications, and can be further explored to improve the tracking result.
The Effectiveness of Biodiversity on Mount Ungaran Central Java Electronic Encyclopedia as Learning Resources in The Pandemic Era

This study aims to determine the effectiveness of the Biodiversity on Mount Ungaran electronic encyclopedia in learning Biology in the Covid-19 Pandemic Era. The research design used was one group pretest-posttest design. The instrument used was about thinking skills. The sample in this study was 34 public school students in Central Java. The instrument used was a test of learning outcomes with 35 questions. Data were analyzed using the N-Gain test and Paired Sample t-test. The percentage analysis was used to see students’ responses to the e-encyclopedia used during the learning process. Google classroom and google meet were used in the learning process. The data analysis showed that the N-Gain result was 0.625 with a classical completeness value of 94.11%. The t-test results showed a significant difference in the pretest and post-test data with a significant value of <0.05. In addition, the response of students tested on a small scale obtained 76.90% in the high category. Therefore, the effectiveness of the electronic encyclopedia of biodiversity on Mount Ungaran was moderate.