Extractable Information Research Articles

You Are What You Write: Author re-identification privacy attacks in the era of pre-trained language models

The widespread use of pre-trained language models has revolutionised knowledge transfer in natural language processing tasks. However, there is a concern regarding potential breaches of user trust due to the risk of re-identification attacks, where malicious users could extract Personally Identifiable Information (PII) from other datasets. To assess the extent of extractable personal information on popular pre-trained models, we conduct the first wide coverage evaluation and comparison of state-of-the-art privacy-preserving algorithms on a large multi-lingual dataset for sentiment analysis annotated with demographic information (including location, age, and gender). Our results suggest a link between model complexity, pre-training data volume, and the efficacy of privacy-preserving embeddings. We found that privacy-preserving methods demonstrate greater effectiveness when applied to larger and more complex models, with improvements exceeding >20% over non-private baselines. Additionally, we observe that local differential privacy imposes serious performance penalties of ≈20% in our test setting, which can be mitigated using hybrid or metric-DP techniques.

Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images

Microscopy-based spatially resolved omic methods are transforming the life sciences. However, these methods rely on high numerical aperture objectives and cannot resolve crowded molecular targets, limiting the amount of extractable biological information. To overcome these limitations, here we develop Deconwolf, an open-source, user-friendly software for high-performance deconvolution of widefield fluorescence microscopy images, which efficiently runs on laptop computers. Deconwolf enables accurate quantification of crowded diffraction limited fluorescence dots in DNA and RNA fluorescence in situ hybridization images and allows robust detection of individual transcripts in tissue sections imaged with ×20 air objectives. Deconvolution of in situ spatial transcriptomics images with Deconwolf increased the number of transcripts identified more than threefold, while the application of Deconwolf to images obtained by fluorescence in situ sequencing of barcoded Oligopaint probes drastically improved chromosome tracing. Deconwolf greatly facilitates the use of deconvolution in many bioimaging applications.

Modulating the Microenvironment of Coumarin Dyes within Multihierarchical Supraparticles: En Route toward Versatile Luminescent Dual‐Threshold Temperature Indicators with Ratiometric Readout

AbstractTemperature indicators trace (un)desired thermal impacts across their thermal history upon readout at every point of interest via defined irreversible signal changes. Ratiometric luminescent temperature indicators are attractive due to their fast, sensitive, contactless, spatially‐resolved, and self‐referenced readout. However, they have a limited working range as commonly only one specific temperature‐induced physicochemical effect is exploited. Herein, dual‐threshold temperature indicator supraparticles (SPs) are introduced that expand the working range of individual luminescent probes and enhance the extractable information on the thermal history. These SPs derive their functionality from the synergistic interplay of three spectrally distinguishable luminescent species assembled in one multihierarchical, micrometer‐sized particle of hybrid organic‐inorganic nature. This engineered nanostructure enables the stepwise modulation of the physicochemical microenvironment of two incorporated coumarin species at programmable threshold temperatures, resulting in distinct changes in their emission. As these changes are based on dye‐specific mechanisms that are induced in consecutive temperature regimes, the SPs achieve a scarcely reported broad working range (60–200 °C) with temperature‐dependent response times of minutes to seconds. From a broader perspective, the conceptualized SPs pioneer in how multiple individual luminescent probes with different excitation‐emission properties and indicator mechanisms are synergistically united in one advanced indicator system by precise material engineering.

Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs.

Real-world data (RWD) in the medical field, such as electronic health records (EHRs) and medication orders, are receiving increasing attention from researchers and practitioners. While structured data have played a vital role thus far, unstructured data represented by text (e.g., discharge summaries) are not effectively utilized because of the difficulty in extracting medical information. We evaluated the information gained by supplementing structured data with clinical concepts extracted from unstructured text by leveraging natural language processing techniques. Using a machine learning-based pretrained named entity recognition tool, we extracted disease and medication names from real discharge summaries in a Japanese hospital and linked them to medical concepts using medical term dictionaries. By comparing the diseases and medications mentioned in the text with medical codes in tabular diagnosis records, we found that: (1) the text data contained richer information on patient symptoms than tabular diagnosis records, whereas the medication-order table stored more injection data than text. In addition, (2) extractable information regarding specific diseases showed surprisingly small intersections among text, diagnosis records, and medication orders. Text data can thus be a useful supplement for RWD mining, which is further demonstrated by (3) our practical application system for drug safety evaluation, which exhaustively visualizes suspicious adverse drug effects caused by the simultaneous use of anticancer drug pairs. We conclude that proper use of textual information extraction can lead to better outcomes in medical RWD mining.

Extractable information capacity in sequential measurements metrology

Extractable information capacity in sequential measurements metrology

Universal screen-shooting robust image watermarking with channel-attention in DCT domain

In this paper, we propose a universal screen-shooting robust image watermarking scheme, which can be used to embed extractable information into on-screen images for copyright protection or additional information acquisition. Specifically, to obtain higher robustness and watermarked image quality, we introduce the channel-attention mechanism into DCT domain and exploit the noise layer to guide the watermarking model, which integrates simulated distortions and screen-shooting distortions. After training, the universal watermark mask can be directly added on arbitrary cover image to produce a watermarked image that effectively resists multiple distortions. Experimental results show that the proposed scheme can achieve better performance than some state-of-the-art (SOTA) schemes in terms of the watermark embedding invisibility and the watermark extraction accuracy.

Systematic review of blue-light service collaboration for community health and well-being

Effective cross-service collaboration has been posed as a way of improving outcomes for people, enhancing community safety and well-being, reducing social and health inequalities, and improving service resource efficiencies. However, it was not known what evidence and frameworks existed for service leads to reform collaborative public service responses. This systematic review aimed to summarize evidence to understand best ways for police, fire, and ambulance services to collaborate to improve community safety and well-being. Standard methodology was used following PRISMA guidance. The search strategy optimized report retrieval from a broad range of academic databases, grey literature, and citation handsearching from January 2012 to March 2022. Endnote 8 supported data management. Eligible reports explored collaboration benefits between any two emergency services to improve any aspect of community safety or well-being and had to provide relevant extractable information. Critical appraisal and syntheses of findings were conducted. Studies could originate from any country. Records were screened and retrieved by one author and included reports independently double-screened. From the academic databases, 4,648 reports were identified and screened, of which 25 reports were retrieved and assessed for eligibility, but no relevant studies were retained following full text review. A further 27 records were identified from websites and citation searching, of which three were included following eligibility checks. The scant evidence uncovered in this review tentatively suggests service collaboration initiatives have potential for decreased resource use, increased public confidence, faster responses, increased survival rates, and reduced unnecessary emergency responses. Robust evidence is needed to influence policy and practice.

Expanding chiral metamaterials for retrieving fingerprints via vibrational circular dichroism

Circular dichroism (CD) spectroscopy has been widely demonstrated for detecting chiral molecules. However, the determination of chiral mixtures with various concentrations and enantiomeric ratios can be a challenging task. To solve this problem, we report an enhanced vibrational circular dichroism (VCD) sensing platform based on plasmonic chiral metamaterials, which presents a 6-magnitude signal enhancement with a selectivity of chiral molecules. Guided by coupled-mode theory, we leverage both in-plane and out-of-plane symmetry-breaking structures for chiral metamaterial design enabled by a two-step lithography process, which increases the near-field coupling strengths and varies the ratio between absorption and radiation loss, resulting in improved chiral light-matter interaction and enhanced molecular VCD signals. Besides, we demonstrate the thin-film sensing process of BSA and β-lactoglobulin proteins, which contain secondary structures α-helix and β-sheet and achieve a limit of detection down to zeptomole level. Furthermore, we also, for the first time, explore the potential of enhanced VCD spectroscopy by demonstrating a selective sensing process of chiral mixtures, where the mixing ratio can be successfully differentiated with our proposed chiral metamaterials. Our findings improve the sensing signal of molecules and expand the extractable information, paving the way toward label-free, compact, small-volume chiral molecule detection for stereochemical and clinical diagnosis applications.

Quantitative temperature and relative humidity changes recorded by the Lake Cuoqia in the southeastern Tibetan Plateau during the past 300 years

High-elevation lakes on the Tibetan Plateau have the advantage of sensitive response to climate changes. Multiple proxy records in lake sediments can provide a large amount of extractable information for paleoclimate reconstructions and assessing the position of recent global warming within the context of natural climate variability. In this study, we reconstruct the climatic and environmental changes over the past 300 years from a remote alpine lake (Lake Cuoqia) in the southeastern Tibetan Plateau using multiple proxies including branched glycerol dialkyl glycerol tetraethers (brGDGTs), n-alkanes, elements, fatty acids and their hydrogen isotopes. Due to ice-cover nature of lake surface during winter, brGDGTs mainly reflect the variation in warm-season temperature from March to October, supported by nearby instrumental data. Our reconstructed high-resolution temperature showed a continuous cooling trend between 1700 and 1950 AD, followed by a rapid warming afterward, in parallel with other proxies such as n-alkanes and fatty acids in the same core, which is also consistent with previously published regional temperature records. The hydrogen isotope (δD) of fatty acids, similar to regional tree-ring δ18O, can record the history of atmospheric precipitation isotope and further indicate the variations of regional relative humidity. Our record exhibited a long-term decrease since 1700 AD, in accord with the decreasing lake level inferred from the ratio of Fe/Mn. The combined pattern of reconstructed temperature and relative humidity showed consistent changes before 1950 AD toward to a gradually cold-dry trend, whereas started to decouple afterward. Before 1950 AD, the declined temperature and relative humidity are mainly driven by insolation and thermal contrast between the Indian-Pacific Ocean and south Asian continent. After 1950 AD, decoupling of temperature and relative humidity may be related to the increased regional evaporation and human-induced emission of greenhouse gases and aerosol.

Examination of vegetation indices based on multitemporal drone images

In Hungary, the use of drones (UAV) has been showing an increasing trend over the past few years. The available UAV’s and the camera systems placed a significant influence and, in some cases, can even limit, their effective and reliable - also appropriate from the data security point of view - application in the assessment of plant growing areas. In addition to the generation of the data, the extractable information content of the produced visual and non-visual data can also be greatly influenced by the processing and analysis methods. During our research, we took multitemporal aerial photographs of winter wheat at different times of the vegetation period. In our article, we will present our results for the NDVI, GNDVI, and VDVI vegetation indices, which include the processing of aerial photographs taken with two different UAVs, highlighting our analyses of the correlations between the discrete and nondiscrete bands of the aerial photographs.

Force Sensor Chip With High Lateral Resolution for Artificial Skin and Surgical Applications

The growing interest in artificial skin-based tactile systems requires sufficiently accurate, stretchable, low-power mechanical sensors. In this study, we present a novel capacitive device and an evaluation method capable of retrieving the force vector and determining the position of the loading point on an extended area without the application of a large matrix of individual sensors. With this tool, we can simultaneously increase the extractable information from an extended surface area and reduce the complexity, thereby the power consumption of the system. Thus, an alternative path is opened toward robotic tactile sensing by using a small array of sensors but covering a relatively large receptive field. Even a single sensor can meet the requirements of specific devices—for example, in catheters—to detect tissue hardness, bending, or narrowing of the arteries or veins. Apart from the medical and robotic applications, the flexibility in processing the Flex2Rigid platform enables the mimicry of human fingers or hands with integrated sensors and driving circuits.

Geometric Brownian information engine: Upper bound of the achievable work under feedback control.

We design a geometric Brownian information engine by considering overdamped Brownian particles inside a two-dimensional monolobal confinement with irregular width along the transport direction. Under such detention, particles experience an effective entropic potential which has a logarithmic form. We employ a feedback control protocol as an outcome of error-free position measurement. The protocol comprises three stages: measurement, feedback, and relaxation. We reposition the center of the confinement to the measurement distance (xp) instantaneously when the position of the trapped particle crosses xp for the first time. Then, the particle is allowed for thermal relaxation. We calculate the extractable work, total information, and unavailable information associated with the feedback control using this equilibrium probability distribution function. We find the exact analytical value of the upper bound of extractable work as (53-2ln2)kBT. We introduce a constant force G downward to the transverse coordinate (y). A change in G alters the effective potential of the system and tunes the relative dominance of entropic and energetic contributions in it. The upper bound of the achievable work shows a crossover from (53-2ln2)kBT to 12kBT when the system changes from an entropy-dominated regime to an energy-dominated one. Compared to an energetic analog, the loss of information during the relaxation process is higher in the entropy-dominated region, which accredits the less value in achievable work. Theoretical predictions are in good agreement with the Langevin dynamics simulation studies.

Suppressing Shot Noise Using Quadratic Variable Step-Size Quantization for the Initial Acquisition of Camera-Raw Image Data.

Digital images from today's premium digital cameras have a higher dynamic range than what humans can perceive. Maximumly keeping the image information captured by sensors while reducing the file size of the output raw image data is highly desirable. In this paper, we show that the extractable image information contained in high-quality HDR images taken by premium cameras can be preserved using a bit-depth of only 8 to 9 bits/pixel when the shot-noise-introduced extra entropy is suppressed. The quadratic variable step-size quantization is used to suppress the shot-noise-introduced entropy. The ratio of the quantization step-size of the quadratic quantizer over the shot noise amplitude is introduced as a parameter to measure the relative quantization noise level, and is used to design the quadratic quantizers that maximally suppress the shot noise. Finally, quadratically quantized real CFA camera-raw image data are losslessly compressed. Compared with the lossless compression on linearly quantized data, at least more than 50% bitrate savings are achieved. Such high bitrate savings without loss of extractable image information are very attractive. Thus, it is highly worthwhile to explore, in the future, hardware implementations of quadratic quantization in image sensors.

Lightweight Underwater Object Detection Based on YOLO v4 and Multi-Scale Attentional Feature Fusion

A challenging and attractive task in computer vision is underwater object detection. Although object detection techniques have achieved good performance in general datasets, problems of low visibility and color bias in the complex underwater environment have led to generally poor image quality; besides this, problems with small targets and target aggregation have led to less extractable information, which makes it difficult to achieve satisfactory results. In past research of underwater object detection based on deep learning, most studies have mainly focused on improving detection accuracy by using large networks; the problem of marine underwater lightweight object detection has rarely gotten attention, which has resulted in a large model size and slow detection speed; as such the application of object detection technologies under marine environments needs better real-time and lightweight performance. In view of this, a lightweight underwater object detection method based on the MobileNet v2, You Only Look Once (YOLO) v4 algorithm and attentional feature fusion has been proposed to address this problem, to produce a harmonious balance between accuracy and speediness for target detection in marine environments. In our work, a combination of MobileNet v2 and depth-wise separable convolution is proposed to reduce the number of model parameters and the size of the model. The Modified Attentional Feature Fusion (AFFM) module aims to better fuse semantic and scale-inconsistent features and to improve accuracy. Experiments indicate that the proposed method obtained a mean average precision (mAP) of 81.67% and 92.65% on the PASCAL VOC dataset and the brackish dataset, respectively, and reached a processing speed of 44.22 frame per second (FPS) on the brackish dataset. Moreover, the number of model parameters and the model size were compressed to 16.76% and 19.53% of YOLO v4, respectively, which achieved a good tradeoff between time and accuracy for underwater object detection.

Monte Carlo evaluation of out-of-field dose in 18 MV pelvic radiotherapy using a simplified female MIRD phantom

This study was devoted to determining the unwanted dose due to scattered photons to the out-of-field organs and subsequently estimate the risk of secondary cancers in the patients undergoing pelvic radiotherapy. A typical 18 MV Medical Linear Accelerator (Varian Clinac 2100 C/D) was modeled using MCNPX® code to simulate pelvic radiotherapy with four treatment fields: anterior-posterior, posterior-anterior, right lateral, left lateral. Dose evaluation was performed inside Medical Internal Radiation Dose (MIRD) revised female phantom. The average photon equivalent dose in out-of-field organs is 8.53 mSv Gy−1, ranging from 0.17 to 72.11 mSv Gy−1, respectively, for the organs far from the Planning Treatment Volume (Brain) and those close to the treatment field (Colon). Evidence showed that colon with 4.3049% and thyroid with 0.0020% have the highest and lowest risk of secondary cancer, respectively. Accordingly, this study introduced the colon as an organ with a high risk of secondary cancer which should be paid more attention in the follow-up of patients undergoing pelvic radiotherapy. The authors believe that this simple Monte Carlo (MC) model can be also used in other radiotherapy plans and mathematical phantoms with different ages (from childhood to adults) to estimate the out-of-field dose. The extractable information by this simple MC model can be also employed for providing libraries for user-friendly applications (e.g. ‘.apk’) which in turn increase the public knowledge about fatal cancer risk after radiotherapy and subsequently decrease the concerns in this regard among the public.

Readout of quantum information spreading using a disordered quantum walk

We design a quantum probing protocol using quantum walks to investigate the quantum information spreading pattern. We employ quantum Fisher information as a figure of merit to quantify extractable information about an unknown parameter encoded within the quantum walk evolution. Although the approach is universal, we focus on the coherent static and dynamic disorder to investigate anomalous and classical transport as well as Anderson localization. We provide a feasible experimental strategy to implement, in principle, the quantum probing protocol based on the quantum Fisher information using a Mach–Zehnder-like interferometric setup. Our results show that a quantum walk can be considered as a readout device of information about defects and perturbations occurring in complex networks, both classical and quantum.

Wave–particle duality relation with a quantum N-path beamsplitter* *Project supported by the National Natural Science Foundation of China (Grant No. 61632021).

The wave–particle duality relation derived by Englert sets an upper bound of the extractable information from wave and particle properties in a two-path interferometer. Surprisingly, previous studies demonstrated that the introduction of a quantum beamsplitter in the interferometer could break the limitation of this upper bound, due to interference between wave and particle states. Along the other line, a lot of efforts have been made to generalize this relation from the two-path setup to the N-path case. Thus, it is an interesting question that whether a quantum N-path beamsplitter can break the limitation as well. This paper systemically studies the model of a quantum N-path beamsplitter, and finds that the generalized wave–particle duality relation between interference visibility and path distinguishability is also broken in certain situations. We further study the maximal extractable information’s reliance on the interference between wave and particle properties, and derive a quantitative description. We then propose an experimental methodology to verify the break of the limitation. Our work reflects the effect of quantum superposition on wave–particle duality, and exhibits a new aspect of the relation between visibility and path distinguishability in N-path interference. Moreover, it implies the observer’s influence on wave–particle duality.

A Survey of Textual Emotion Recognition and Its Challenges

Textual language is the most natural carrier of human emotion. In natural language processing, textual emotion recognition (TER) has become an important topic due to its significant academic and commercial potential. With the advanced development of deep learning technologies, TER has attracted growing attention and has significantly been promoted in recent years. This article provides a systematic survey of the latest TER advances, focusing on approaches using deep neural networks. According to how deep learning works at each stage, TER approaches are reviewed on word embedding, architecture, and training levels, respectively. We discussed the remaining challenges and opportunities from four aspects: the shortage of large-scale and high-quality datasets, fuzzy emotional boundaries, incomplete extractable emotional information in texts, and TER in dialogue. This article creates a systematic and in-depth overview of deep TER technologies. It provides the necessary knowledge and new insights for relevant researchers to understand better the research state, remaining challenges, and future directions in this field.

Wave-Particle Duality Relation with a Quantum Which-Path Detector.

According to the relevant theories on duality relation, the summation of the extractable information of a quanton’s wave and particle properties, which are characterized by interference visibility V and path distinguishability D, respectively, is limited. However, this relation is violated upon quantum superposition between the wave-state and particle-state of the quanton, which is caused by the quantum beamsplitter (QBS). Along another line, recent studies have considered quantum coherence C in the -norm measure as a candidate for the wave property. In this study, we propose an interferometer with a quantum which-path detector (QWPD) and examine the generalized duality relation based on C. We find that this relationship still holds under such a circumstance, but the interference between these two properties causes the full-particle property to be observed when the QWPD system is partially present. Using a pair of polarization-entangled photons, we experimentally verify our analysis in the two-path case. This study extends the duality relation between coherence and path information to the quantum case and reveals the effect of quantum superposition on the duality relation.

Data Pseudonymization in a Range That Does Not Affect Data Quality: Correlation with the Degree of Participation of Clinicians.

Personal medical information is an essential resource for research; however, there are laws that regulate its use, and it typically has to be pseudonymized or anonymized. When data are anonymized, the quantity and quality of extractable information decrease significantly. From the perspective of a clinical researcher, a method of achieving pseudonymized data without degrading data quality while also preventing data loss is proposed herein. As the level of pseudonymization varies according to the research purpose, the pseudonymization method applied should be carefully chosen. Therefore, the active participation of clinicians is crucial to transform the data according to the research purpose. This can contribute to data security by simply transforming the data through secondary data processing. Case studies demonstrated that, compared with the initial baseline data, there was a clinically significant difference in the number of datapoints added with the participation of a clinician (from 267,979 to 280,127 points, P < 0.001). Thus, depending on the degree of clinician participation, data anonymization may not affect data quality and quantity, and proper data quality management along with data security are emphasized. Although the pseudonymization level and clinical use of data have a trade-off relationship, it is possible to create pseudonymized data while maintaining the data quality required for a given research purpose. Therefore, rather than relying solely on security guidelines, the active participation of clinicians is important.