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In real-world applications, as data availability increases, obtaining labeled data for machine learning (ML) projects remains challenging due to the high costs and intensive efforts required for data annotation. Many ML projects, particularly those focused on multi-label classification, also grapple with data imbalance issues, where certain classes may lack sufficient data to train effective classifiers. This study introduces and examines a novel oversampling method for multi-label text classification, designed to address performance challenges associated with data imbalance. The proposed method identifies potential new samples from unlabeled data by leveraging similarity measures between instances. By iteratively searching the unlabeled dataset, the method locates instances similar to those in underrepresented classes and evaluates their contribution to classifier performance enhancement. Instances that demonstrate performance improvement are then added to the labeled dataset. Experimental results indicate that the proposed approach effectively enhances classifier performance post-oversampling.

The aim of this study is to investigate the effectiveness of ChatGPT 3.5 in developing algorithms for data generation within the framework of Item Response Theory (IRT) using the R programming language. In this context, validity examinations were conducted on data sets generated according to the Two-Parameter Logistic Model (2PLM) with algorithms written by ChatGPT 3.5 and researchers. These examinations considered whether the data sets met the IRT assumptions and the simulation conditions of the item parameters. As a result, it was determined that while ChatGPT 3.5 was quite successful in generating data that met the IRT assumptions, it was less effective in meeting the simulation conditions of the item parameters compared to the algorithm developed by the researchers. In this regard, ChatGPT 3.5 is recommended as a useful tool that researchers can use in developing data generation algorithms for IRT.

In the modern transportation industry, accurate prediction of travelers' next destinations brings multiple benefits to companies, such as customer satisfaction and targeted marketing. This study focuses on developing a precise model that captures the sequential patterns and dependencies in travel data, enabling accurate predictions of individual travelers' future destinations. To achieve this, a novel model architecture with a sliding window approach based on Long Short-Term Memory (LSTM) is proposed for destination prediction in the transportation industry. The experimental results highlight satisfactory performance and high scores achieved by the proposed model across different data sizes and performance metrics. This research contributes to advancing destination prediction methods, empowering companies to deliver personalized recommendations and optimize customer experiences in the dynamic travel landscape.

With the continuous advancement of nanotechnology, nanodevices have become crucial components in computing, sensing and energy conversion applications. However, the structures of nanodevices typically possess sub-wavelength dimensions and separations, which pose significant challenges for understanding energy transport phenomena in nanodevices. Here, based on a judiciously designed thermal nanodevice, we report the first measurement of near-field energy transport between two coplanar sub-wavelength structures over temperature bias up to ~190 K. Our experimental results demonstrate a remarkable 20-fold enhancement in heat transfer beyond blackbody radiation. In contrast with the well-established near-field interactions between two semi-infinite bodies, the sub-wavelength confinements in nanodevices lead to the increased polariton scattering and the reduction of supporting modes and therefore a lower heat flow at a given separation. Our work unveils exciting opportunities for the rational design of nanodevices, particularly for on-chip near-field energy transport, with important implications for the development of efficient nanodevices for energy harvesting and thermal management., Comment: 35 pages, 19 figures, 2 tables

Accurate direct measurements of far-field thermal infrared emission become increasingly important because conventional methods, relying on indirect assessments, such as reflectance/transmittance, are inaccurate or even unfeasible to characterize state-of-art devices with novel spectra, directionalities, and polarizations. The direct collection of the far-field emission from these tiny devices is also challenging because of their shrinking footprints and uncontrollable radiation noises from their surroundings. Here, we demonstrate a microscopic lock-in FTIR system that realizes significant improvement in signal-to-noise ratio (SNR) by combining a microscope and a lock-in amplifier with an FTIR. The lock-in FTIR is ultrasensitive, with a specific detectivity 10^6 times higher than commercial ones, to overcome the optical loss and background noise during the emission light collection. Based on an analytical model of the signal detection process, we also explore the combination of modulated Joule heating and global heating to fulfill the potential of our system for noise reduction. Our findings show that, compared to previous studies, more than 3 times lower temperatures are sufficient to generate a measurable signal. Under a heating temperature of around 125 {\deg}C, we can achieve an SNR of about 23.7, which is far above the true-signal-threshold (SNR of about 3.0). Furthermore, the system can respond fast enough (up to 175kHz) to record spectral-resolved dynamics of microdevices in the frequency domain. The measurable frequency range can be extended up to MHz or even GHz level by a high-speed circuit model. We believe the system together with the analytical signal processing can be beneficial for next-generation thermal infrared material and device exploration, boosting the applications in lighting, sensing, imaging, and energy harvesting on a small scale., Comment: 19 pages, 4 figures

Differential computation (DC) is a highly general incremental computation/view maintenance technique that can maintain the output of an arbitrary and possibly recursive dataflow computation upon changes to its base inputs. As such, it is a promising technique for graph database management systems (GDBMS) that support continuous recursive queries over dynamic graphs. Although differential computation can be highly efficient for maintaining these queries, it can require a prohibitively large amount of memory. This paper studies how to reduce the memory overhead of DC with the goal of increasing the scalability of systems that adopt it. We propose a suite of optimizations that are based on dropping the differences of operators, both completely or partially, and recomputing these differences when necessary. We propose deterministic and probabilistic data structures to keep track of the dropped differences. Extensive experiments demonstrate that the optimizations can improve the scalability of a DC-based continuous query processor.

Joins in native graph database management systems (GDBMSs) are predefined to the system as edges, which are indexed in adjacency list indices and serve as pointers. This contrasts with and can be more performant than value-based joins in RDBMSs and has lead researchers to investigate ways to integrate predefined joins directly into RDBMSs. Existing approaches adopt a strict separation of graph and relational data and processors, where a graph-specific processor uses left-deep and index nested loop joins for a subset of joins. This may be suboptimal, and may lead to non-sequential scans of data in some queries. We propose a purely relational approach to integrate predefined joins in columnar RDBMSs that uses row IDs (RIDs) of tuples as pointers. Users can predefine equality joins between any two tables, which leads to materializing RIDs in extended tables and optionally in RID indices. Instead of using the RID index to perform the join directly, we use it primarily in hash joins to generate semi-join filters that can be passed to scans using sideways information passing, ensuring sequential scans. In some settings, we also use RID indices to reduce the number of joins in query plans. Our approach does not introduce any graph-specific system components, can execute predefined joins on any join plan, and can improve performance on any workload that contains equality joins that can be predefined. We integrated our approach to DuckDB and call the resulting system {\em GRainDB}. We demonstrate that GRainDB far improves the performance of DuckDB on relational and graph workloads with large many-to-many joins, making it competitive with a state-of-the-art GDBMS, and incurs no major overheads otherwise.

We study two classes of summary-based cardinality estimators that use statistics about input relations and small-size joins in the context of graph database management systems: (i) optimistic estimators that make uniformity and conditional independence assumptions; and (ii) the recent pessimistic estimators that use information theoretic linear programs. We begin by addressing the problem of how to make accurate estimates for optimistic estimators. We model these estimators as picking bottom-to-top paths in a cardinality estimation graph (CEG), which contains sub-queries as nodes and weighted edges between sub-queries that represent average degrees. We outline a space of heuristics to make an optimistic estimate in this framework and show that effective heuristics depend on the structure of the input queries. We observe that on acyclic queries and queries with small-size cycles, using the maximum-weight path is an effective technique to address the well known underestimation problem for optimistic estimators. We show that on a large suite of datasets and workloads, the accuracy of such estimates is up to three orders of magnitude more accurate in mean q-error than some prior heuristics that have been proposed in prior work. In contrast, we show that on queries with larger cycles these estimators tend to overestimate, which can partially be addressed by using minimum weight paths and more effectively by using an alternative CEG. We then show that CEGs can also model the recent pessimistic estimators. This surprising result allows us to connect two disparate lines of work on optimistic and pessimistic estimators, adopt an optimization from pessimistic estimators to optimistic ones, and provide insights into the pessimistic estimators, such as showing that there are alternative combinatorial solutions to the linear programs that define them.

We revisit column-oriented storage and query processing techniques in the context of contemporary graph database management systems (GDBMSs). Similar to column-oriented RDBMSs, GDBMSs support read-heavy analytical workloads that however have fundamentally different data access patterns than traditional analytical workloads. We first derive a set of desiderata for optimizing storage and query processors of GDBMS based on their access patterns. We then present the design of columnar storage, compression, and query processing techniques based on these desiderata. In addition to showing direct integration of existing techniques from columnar RDBMSs, we also propose novel ones that are optimized for GDBMSs. These include a novel list-based query processor, which avoids expensive data copies of traditional block-based processors under many-to-many joins, a new data structure we call single-indexed edge property pages and an accompanying edge ID scheme, and a new application of Jacobson's bit vector index for compressing NULL values and empty lists. We integrated our techniques into the GraphflowDB in-memory GDBMS. Through extensive experiments, we demonstrate the scalability and query performance benefits of our techniques., Comment: VLDB Conference 2021 (https://www.vldb.org/pvldb/vol14/p2491-gupta.pdf)

Graphs are by nature unifying abstractions that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the next decade for big graph processing to continue to succeed?, Comment: 12 pages, 3 figures, collaboration between the large-scale systems and data management communities, work started at the Dagstuhl Seminar 19491 on Big Graph Processing Systems, to be published in the Communications of the ACM

The two most intense wildfires of the last decade that took place in Canada in 2017 and Australia in 2019-2020 were followed by large injections of smoke in the stratosphere due to pyroconvection. It was discovered by Khaykin et al. (2020, doi:10.1038/s43247-020-00022-5) and Kablick et al. (2020, doi:10.1029/2020GL088101) that, after the Australian event, part of this smoke self-organized as anticyclonic confined vortices that rose in the mid-latitude stratosphere up to 35 km. Based on CALIOP observations and the ERA5 reanalysis, this new study analyzes the Canadian case and find, similarly, that a large plume penetrated the stratosphere by 12 August 2017 and got trapped within a meso-scale anticyclonic structure which travelled across the Atlantic. It then broke into three offsprings that could be followed until mid-October performing three round the world journeys and rising up to 23 km. We analyze the dynamical structure of the vortices produced by these two wildfires and demonstrate how they are maintained by the assimilation of data from instruments measuring the signature of the vortices in the temperature and ozone field. We propose that these vortices can be seen as bubbles of low absolute potential vorticity and smoke carried vertically across the stratification from the troposphere inside the middle stratosphere by their internal heating, against the descending flux of the Brewer-Dobson circulation., Comment: Submitted to Atmospheric Chemistry and Physics

Optical materials with colour-changing abilities have been explored for display devices, smart windows, or modulation of visual appearance. The efficiency of these materials, however, has strong wavelength dependence, which limits their functionality to a narrow spectral range. Here, we report graphene-based electro-optical devices with unprecedented optical tunability covering the entire electromagnetic spectrum from the visible to microwave. We achieve this non-volatile and reversible tunability by electro-intercalation of lithium into graphene layers in an optically accessible device structure. This unique colour-changing capability, together with area-selective intercalation, inspires fabrication of new multispectral devices, including display devices and electro-optical camouflage coating. We anticipate that these results provide realistic approaches for programmable smart optical surfaces with a potential utility in many scientific and engineering fields.

This paper presents the design and implementation of a new open-source view-based graph analytics system called Graphsurge. Graphsurge is designed to support applications that analyze multiple snapshots or views of a large-scale graph. Users program Graphsurge through a declarative graph view definition language (GVDL) to create views over input graphs and a Differential Dataflow-based programming API to write analytics computations. A key feature of GVDL is the ability to organize views into view collections, which allows Graphsurge to automatically share computation across views, without users writing any incrementalization code, by performing computations differentially. We then introduce two optimization problems that naturally arise in our setting. First is the collection ordering problem to determine the order of views that leads to minimum differences across consecutive views. We prove this problem is NP-hard and show a constant-factor approximation algorithm drawn from literature. Second is the collection splitting problem to decide on which views to run computations differentially vs from scratch, for which we present an adaptive solution that makes decisions at runtime. We present extensive experiments to demonstrate the benefits of running computations differentially for view collections and our collection ordering and splitting optimizations.

Graph database management systems (GDBMSs) are highly optimized to perform fast traversals, i.e., joins of vertices with their neighbours, by indexing the neighbourhoods of vertices in adjacency lists. However, existing GDBMSs have system-specific and fixed adjacency list structures, which makes each system efficient on only a fixed set of workloads. We describe a new tunable indexing subsystem for GDBMSs, we call A+ indexes, with materialized view support. The subsystem consists of two types of indexes: (i) vertex-partitioned indexes that partition 1-hop materialized views into adjacency lists on either the source or destination vertex IDs; and (ii) edge-partitioned indexes that partition 2-hop views into adjacency lists on one of the edge IDs. As in existing GDBMSs, a system by default requires one forward and one backward vertex-partitioned index, which we call the primary A+ index. Users can tune the primary index or secondary indexes by adding nested partitioning and sorting criteria. Our secondary indexes are space-efficient and use a technique we call offset lists. Our indexing subsystem allows a wider range of applications to benefit from GDBMSs' fast join capabilities. We demonstrate the tunability and space efficiency of A+ indexes through extensive experiments on three workloads.

Recent beyond worst-case optimal join algorithms Minesweeper and its generalization Tetris have brought the theory of indexing and join processing together by developing a geometric framework for joins. These algorithms take as input an index $\mathcal{B}$, referred to as a box cover, that stores output gaps that can be inferred from traditional indexes, such as B+ trees or tries, on the input relations. The performances of these algorithms highly depend on the certificate of $\mathcal{B}$, which is the smallest subset of gaps in $\mathcal{B}$ whose union covers all of the gaps in the output space of a query $Q$. We study how to generate box covers that contain small size certificates to guarantee efficient runtimes for these algorithms. First, given a query $Q$ over a set of relations of size $N$ and a fixed set of domain orderings for the attributes, we give a $\tilde{O}(N)$-time algorithm called GAMB which generates a box cover for $Q$ that is guaranteed to contain the smallest size certificate across any box cover for $Q$. Second, we show that finding a domain ordering to minimize the box cover size and certificate is NP-hard through a reduction from the 2 consecutive block minimization problem on boolean matrices. Our third contribution is a $\tilde{O}(N)$-time approximation algorithm called ADORA to compute domain orderings, under which one can compute a box cover of size $\tilde{O}(K^r)$, where $K$ is the minimum box cover for $Q$ under any domain ordering and $r$ is the maximum arity of any relation. This guarantees certificates of size $\tilde{O}(K^r)$. We combine ADORA and GAMB with Tetris to form a new algorithm we call TetrisReordered, which provides several new beyond worst-case bounds. On infinite families of queries, TetrisReordered's runtimes are unboundedly better than the bounds stated in prior work.

We study the problem of optimizing subgraph queries using the new worst-case optimal join plans. Worst-case optimal plans evaluate queries by matching one query vertex at a time using multiway intersections. The core problem in optimizing worst-case optimal plans is to pick an ordering of the query vertices to match. We design a cost-based optimizer that (i) picks efficient query vertex orderings for worst-case optimal plans; and (ii) generates hybrid plans that mix traditional binary joins with worst-case optimal style multiway intersections. Our cost metric combines the cost of binary joins with a new cost metric called intersection-cost. The plan space of our optimizer contains plans that are not in the plan spaces based on tree decompositions from prior work. In addition to our optimizer, we describe an adaptive technique that changes the orderings of the worst-case optimal sub-plans during query execution. We demonstrate the effectiveness of the plans our optimizer picks and adaptive technique through extensive experiments. Our optimizer is integrated into the Graphflow DBMS.

In nature adaptive coloration has been effectively utilized for concealment and signaling. Various biological mechanisms have evolved that can tune the reflectivity for visible and ultraviolet light. These examples inspire many artificial systems for mimicking adaptive coloration to match the visual appearance to their surroundings. Thermal camouflage, however, has been an outstanding challenge which requires an ability to control of the emitted thermal radiation from the surface. Here we report a new class of active thermal surfaces capable of efficient real-time electrical-control of thermal emission over the full infrared (IR) spectrum without changing the temperature of the surface. Our approach relies on electro-modulation of IR absorptivity and emissivity of multilayer graphene via reversible intercalation of nonvolatile ionic liquids. The demonstrated devices are light (30 g/m2), thin (<50 um) and ultra-flexible which can conformably coat their environment. In addition, combining active thermal surfaces with a feedback mechanism, we demonstrate realization of an adaptive thermal camouflage system which can reconfigure its thermal appearance and blend itself with the thermal background in a few seconds. Furthermore, we show that these devices can disguise hot objects as cold and cold ones as hot in a thermal imaging system. We anticipate that, the electrical control of thermal radiation will impact on a variety of new technologies ranging from adaptive IR optics to heat management for outer space applications., Comment: 37 pages

We study the problem of finding and monitoring fixed-size subgraphs in a continually changing large-scale graph. We present the first approach that (i) performs worst-case optimal computation and communication, (ii) maintains a total memory footprint linear in the number of input edges, and (iii) scales down per-worker computation, communication, and memory requirements linearly as the number of workers increases, even on adversarially skewed inputs. Our approach is based on worst-case optimal join algorithms, recast as a data-parallel dataflow computation. We describe the general algorithm and modifications that make it robust to skewed data, prove theoretical bounds on its resource requirements in the massively parallel computing model, and implement and evaluate it on graphs containing as many as 64 billion edges. The underlying algorithm and ideas generalize from finding and monitoring subgraphs to the more general problem of computing and maintaining relational equi-joins over dynamic relations.

In this Letter, using quartz crystal microbalance (QCM), we experimentally determined the mass density of graphene grown by chemical vapor deposition method. We developed a transfer printing technique to integrate large area single-layer graphene on QCM. By monitoring the resonant frequency of an oscillating quartz crystal loaded with graphene, we were able to measure the mass density of graphene as ~118 ng/cm2, which is significantly larger than the ideal graphene (~76 ng/cm2) mainly due to the presence of wrinkles and organic/inorganic residues on graphene sheets. High sensitivity of quartz crystal resonator allowed us to determine the number of graphene layers in samples. (The technique is very sensitive and able to determine the number of graphene layers in a particular sample.) Besides, we extended our technique to probe interfacial mass variation during adsorption of biomolecules on graphene surface, and plasma-assisted oxidation of graphene. (Besides, we extended our technique to probe interfacial mass variation during adsorption of biomolecules on graphene surface, and plasma-assisted oxidation of graphene.)