Hasil untuk "General works"

Qingqing Wu, Rui Zhang

Intelligent reflecting surface (IRS) is a cost-effective solution for achieving high spectrum and energy efficiency in future wireless networks by leveraging massive low-cost passive elements that are able to reflect the signals with adjustable phase shifts. Prior works on IRS mainly consider continuous phase shifts at reflecting elements, which are practically difficult to implement due to the hardware limitation. In contrast, we study in this paper an IRS-aided wireless network, where an IRS with only a finite number of phase shifts at each element is deployed to assist in the communication from a multi-antenna access point (AP) to multiple single-antenna users. We aim to minimize the transmit power at the AP by jointly optimizing the continuous transmit precoding at the AP and the discrete reflect phase shifts at the IRS, subject to a given set of minimum signal-to-interference-plus-noise ratio (SINR) constraints at the user receivers. The considered problem is shown to be a mixed-integer non-linear program (MINLP) and thus is difficult to solve in general. To tackle this problem, we first study the single-user case with one user assisted by the IRS and propose both optimal and suboptimal algorithms for solving it. Besides, we analytically show that as compared to the ideal case with continuous phase shifts, the IRS with discrete phase shifts achieves the same squared power gain in terms of asymptotically large number of reflecting elements, while a constant proportional power loss is incurred that depends only on the number of phase-shift levels. The proposed designs for the single-user case are also extended to the general setup with multiple users among which some are aided by the IRS. Simulation results verify our performance analysis as well as the effectiveness of our proposed designs as compared to various benchmark schemes.

A. Mostofi, J. Yates, G. Pizzi et al.

Ziwei Liu, Ping Luo, Xiaogang Wang et al.

Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.

D. Weiman

D. Goldberg, R. Gater, N. Sartorius et al.

A. Siepel, G. Bejerano, J. S. Pedersen et al.

We have conducted a comprehensive search for conserved elements in vertebrate genomes, using genome-wide multiple alignments of five vertebrate species (human, mouse, rat, chicken, and Fugu rubripes). Parallel searches have been performed with multiple alignments of four insect species (three species of Drosophila and Anopheles gambiae), two species of Caenorhabditis, and seven species of Saccharomyces. Conserved elements were identified with a computer program called phastCons, which is based on a two-state phylogenetic hidden Markov model (phylo-HMM). PhastCons works by fitting a phylo-HMM to the data by maximum likelihood, subject to constraints designed to calibrate the model across species groups, and then predicting conserved elements based on this model. The predicted elements cover roughly 3%-8% of the human genome (depending on the details of the calibration procedure) and substantially higher fractions of the more compact Drosophila melanogaster (37%-53%), Caenorhabditis elegans (18%-37%), and Saccharaomyces cerevisiae (47%-68%) genomes. From yeasts to vertebrates, in order of increasing genome size and general biological complexity, increasing fractions of conserved bases are found to lie outside of the exons of known protein-coding genes. In all groups, the most highly conserved elements (HCEs), by log-odds score, are hundreds or thousands of bases long. These elements share certain properties with ultraconserved elements, but they tend to be longer and less perfectly conserved, and they overlap genes of somewhat different functional categories. In vertebrates, HCEs are associated with the 3' UTRs of regulatory genes, stable gene deserts, and megabase-sized regions rich in moderately conserved noncoding sequences. Noncoding HCEs also show strong statistical evidence of an enrichment for RNA secondary structure.

Michael Warner

Clara B. Aranda-Jan, Neo Mohutsiwa-Dibe, S. Loukanova

BackgroundAccess to mobile phone technology has rapidly expanded in developing countries. In Africa, mHealth is a relatively new concept and questions arise regarding reliability of the technology used for health outcomes. This review documents strengths, weaknesses, opportunities, and threats (SWOT) of mHealth projects in Africa.MethodsA systematic review of peer-reviewed literature on mHealth projects in Africa, between 2003 and 2013, was carried out using PubMed and OvidSP. Data was synthesized using a SWOT analysis methodology. Results were grouped to assess specific aspects of project implementation in terms of sustainability and mid/long-term results, integration to the health system, management process, scale-up and replication, and legal issues, regulations and standards.ResultsForty-four studies on mHealth projects in Africa were included and classified as: “patient follow-up and medication adherence” (n = 19), “staff training, support and motivation” (n = 2), “staff evaluation, monitoring and guidelines compliance” (n = 4), “drug supply-chain and stock management” (n = 2), “patient education and awareness” (n = 1), “disease surveillance and intervention monitoring” (n = 4), “data collection/transfer and reporting” (n = 10) and “overview of mHealth projects” (n = 2). In general, mHealth projects demonstrate positive health-related outcomes and their success is based on the accessibility, acceptance and low-cost of the technology, effective adaptation to local contexts, strong stakeholder collaboration, and government involvement. Threats such as dependency on funding, unclear healthcare system responsibilities, unreliable infrastructure and lack of evidence on cost-effectiveness challenge their implementation. mHealth projects can potentially be scaled-up to help tackle problems faced by healthcare systems like poor management of drug stocks, weak surveillance and reporting systems or lack of resources.ConclusionsmHealth in Africa is an innovative approach to delivering health services. In this fast-growing technological field, research opportunities include assessing implications of scaling-up mHealth projects, evaluating cost-effectiveness and impacts on the overall health system.

Congcong Wen, Yuan Hu, Xiang Li et al.

The remarkable achievements of ChatGPT and Generative Pre-trained Transformer 4 (GPT-4) have sparked a wave of interest and research in the field of large language models (LLMs) for artificial general intelligence (AGI). These models provide intelligent solutions that are closer to human thinking, enabling us to use general artificial intelligence (AI) to solve problems in various applications. However, in the field of remote sensing (RS), the scientific literature on the implementation of AGI remains relatively scant. Existing AI-related research in RS focuses primarily on visual-understanding tasks while neglecting the semantic understanding of the objects and their relationships. This is where vision-LMs (VLMs) excel as they enable reasoning about images and their associated textual descriptions, allowing for a deeper understanding of the underlying semantics. VLMs can go beyond visual recognition of RS images and can model semantic relationships as well as generate natural language descriptions of the image. This makes them better suited for tasks that require both visual and textual understanding, such as image captioning and visual question answering (VQA). This article provides a comprehensive review of the research on VLMs in RS, summarizing the latest progress, highlighting current challenges, and identifying potential research opportunities. Specifically, we review the application of VLMs in mainstream RS tasks, including image captioning, text-based image generation, text-based image retrieval (TBIR), VQA, scene classification, semantic segmentation, and object detection. For each task, we analyze representative works and discuss research progress. Finally, we summarize the limitations of existing works and provide possible directions for future development. This review aims to provide a comprehensive overview of the current research progress of VLMs in RS (see Figure 1), and to inspire further research in this exciting and promising field.

W. R. Huang, Jonas Geiping, Liam H. Fowl et al.

Data poisoning--the process by which an attacker takes control of a model by making imperceptible changes to a subset of the training data--is an emerging threat in the context of neural networks. Existing attacks for data poisoning have relied on hand-crafted heuristics. Instead, we pose crafting poisons more generally as a bi-level optimization problem, where the inner level corresponds to training a network on a poisoned dataset and the outer level corresponds to updating those poisons to achieve a desired behavior on the trained model. We then propose MetaPoison, a first-order method to solve this optimization quickly. MetaPoison is effective: it outperforms previous clean-label poisoning methods by a large margin under the same setting. MetaPoison is robust: its poisons transfer to a variety of victims with unknown hyperparameters and architectures. MetaPoison is also general-purpose, working not only in fine-tuning scenarios, but also for end-to-end training from scratch with remarkable success, e.g. causing a target image to be misclassified 90% of the time via manipulating just 1% of the dataset. Additionally, MetaPoison can achieve arbitrary adversary goals not previously possible--like using poisons of one class to make a target image don the label of another arbitrarily chosen class. Finally, MetaPoison works in the real-world. We demonstrate successful data poisoning of models trained on Google Cloud AutoML Vision. Code and premade poisons are provided at this https URL

Dugang Liu, Pengxiang Cheng, Zhenhua Dong et al.

Recommender systems are feedback loop systems, which often face bias problems such as popularity bias, previous model bias and position bias. In this paper, we focus on solving the bias problems in a recommender system via a uniform data. Through empirical studies in online and offline settings, we observe that simple modeling with a uniform data can alleviate the bias problems and improve the performance. However, the uniform data is always few and expensive to collect in a real product. In order to use the valuable uniform data more effectively, we propose a general knowledge distillation framework for counterfactual recommendation that enables uniform data modeling through four approaches: (1) label-based distillation focuses on using the imputed labels as a carrier to provide useful de-biasing guidance; (2) feature-based distillation aims to filter out the representative causal and stable features; (3) sample-based distillation considers mutual learning and alignment of the information of the uniform and non-uniform data; and (4) model structure-based distillation constrains the training of the models from the perspective of embedded representation. We conduct extensive experiments on both public and product datasets, demonstrating that the proposed four methods achieve better performance over the baseline models in terms of AUC and NLL. Moreover, we discuss the relation between the proposed methods and the previous works. We emphasize that counterfactual modeling with uniform data is a rich research area, and list some interesting and promising research topics worthy of further exploration. Note that the source codes are available at \urlhttps://github.com/dgliu/SIGIR20_KDCRec.

Weihan Cao, Yifan Zhang, Jianfei Gao et al.

Knowledge distillation(KD) is a widely-used technique to train compact models in object detection. However, there is still a lack of study on how to distill between heterogeneous detectors. In this paper, we empirically find that better FPN features from a heterogeneous teacher detector can help the student although their detection heads and label assignments are different. However, directly aligning the feature maps to distill detectors suffers from two problems. First, the difference in feature magnitude between the teacher and the student could enforce overly strict constraints on the student. Second, the FPN stages and channels with large feature magnitude from the teacher model could dominate the gradient of distillation loss, which will overwhelm the effects of other features in KD and introduce much noise. To address the above issues, we propose to imitate features with Pearson Correlation Coefficient to focus on the relational information from the teacher and relax constraints on the magnitude of the features. Our method consistently outperforms the existing detection KD methods and works for both homogeneous and heterogeneous student-teacher pairs. Furthermore, it converges faster. With a powerful MaskRCNN-Swin detector as the teacher, ResNet-50 based RetinaNet and FCOS achieve 41.5% and 43.9% mAP on COCO2017, which are 4.1\% and 4.8\% higher than the baseline, respectively.

Enrique Flores

La vorágine es una gran obra disruptiva en el horizonte de la narrativa latinoamericana. Su radical desafío psíquico y su vínculo con lo fantasmal la vuelven única entre las novelas de la selva. Este ensayo profundiza su dimensión “espectral” asociada a la fotografía —aunque no únicamente a ella— a partir de otros estudios, del marco teórico expuesto en La cámara lúcida por Roland Barthes, del lugar de la fotografía en la novela, y de la figura de Eugène Robuchon, el fotógrafo francés cuya experiencia sirvió de modelo a José Eustasio Rivera.

Ibrahim Elassal, Andrea Chávez Triviño, Iván Darío Chávez Triviño

Reviving both the tangible and intangible aspects of lost heritage is essential to preserve awareness for future generations. The 9th-century city of Al-Qaṭāʾi in Egypt has been repeatedly destroyed during the conflicts between the Tulunids and the Abbasids. Virtual technologies and three-dimensional (3D) reconstruction serve to digitally raise awareness of the city and its historical significance, especially since it no longer exists. The challenge of digital revival in this project's framework is to imagine how life unfolded in the city during the 9th century. Our goal in this project is to leverage the advancements in 3D modelling technologies to reconstruct the lost historical Egyptian city. The methodology report begins with a description of the foundation for the 3D model reconstruction of the historically lost city, which serves as the main case study for this project. The methodology for this reconstruction is achieved through scope definition, wherein the main structures and complementary elements of the scene are identified. This is followed by the documentation phase, during which all available information on the scene is gathered through sketches, engravings, photographs, plans, and other sources. The next phase involves establishing hypotheses, where 2D and 3D sketches are produced to propose the general volumes of structures and elements. Finally, the creation of 3D models brings the structures and associated elements to life through the texturing process, as well as the inclusion of terrain, vegetation, fauna, characters, and other components, allowing the complete scene to emerge. The objective of the article is to revive the destroyed architecture of the non-existent city and communicate its value to the public through interactive virtual exploration. This study aims to demonstrate how the combination of digital methods for virtual reconstruction is valuable for both knowledge dissemination and research, through the case study of Al-Qaṭāʾi in Egypt.

Subhankar Patra, Bibhas Ranjan Majhi, Santabrata Das

We study the general relativistic transonic accretion flow around the primary black hole, which forms the circumprimary disc (CPD), within a binary black hole (BBH) system. The BBH spacetime is characterized by the mass ratio ($q$) and the separation distance ($z_2$) between the two black holes. We numerically solve the radial momentum and energy equations to obtain the accretion solutions. It is observed that the CPD can exhibit shock solutions, which exist for a wide range parameter space spanned by flow specific angular momentum ($λ$) and energy ($E$). We find that the shock parameter space is modified by $q$ and $z_2$. Investigations show that $q$ and $z_2$ also affect various shock properties, such as density compression and temperature compression across the shock fronts. Moreover, we calculate the spectral energy distributions (SEDs) of the CPD and examine how the SEDs are modified by $q$ and $z_2$ for both shock-free and shock-induced accretion solutions. SED is found to be nearly independent of the binary parameters. We essentially show that although $q$ and $z_2$ alter the effective horizon area of the primary black hole located at the center of the CPD, they have a minimal impact on the dynamical and spectral properties of the accretion flow around the primary black hole.

Kai Bao, Meihua Liu

This study compared the five-word lexical bundles (LBs) expressing gratitude in acknowledgments of dissertations written by Chinese and American PhD students of linguistics. Two corpora were built: (1) The Chinese University Dissertation Acknowledgments Collection (CUC) which contained 700 acknowledgments with a total of 300,686 tokens, and (2) the American University Dissertation Acknowledgments Collection (AUC) which contained 700 acknowledgments with a total of 493,045 tokens. We then retrieved five-word LBs, of which LBs expressing gratitude in CUC and AUC were identified, categorized, and compared with respect to frequency, forms and structures. Major findings were: (1) the Chinese students used a substantially greater number of gratitude LBs than the American students, (2) the two groups used considerably different gratitude LBs, and (3) the two groups mainly relied on verb phrase-based LBs to express gratitude, but the Chinese students used a larger proportion of noun phrase- yet a smaller proportion of verb phrase-based items than the American students, and (4) the two groups used dissimilar structures and words to construct gratitude LBs. These findings enrich our knowledge of linguistic patterns in dissertation acknowledgments as a unique genre of academic prose, and provide corpus-based learning materials for students tasked with properly expressing gratitude in their theses or dissertations.

Čorboloković Saša S., Gavranović Valentina M.

The paper investigates punctuation rules and their application in Serbian and English, focusing on the examples that comply with different normative solutions in the two languages. The main goal of the research is to compare and contrast the results obtained from a survey done by a group of seventh-grade primary school students. The paper aims to determine how well the respondents apply punctuation rules in Serbian and English, to examine whether there is interference in the application of rules, and to investigate to what extent the detected errors illustrate the tendency of spreading pseudo-norms that violate the orthography of both languages. The results show that the respondents use punctuation marks with more precision in Serbian than in English. The percentage of incorrect answers to each question and the types of errors indicate interference and the creation of hybrid forms that are incorrect in both languages, which represent the creation of pseudo-norms. Furthermore, the results show a greater influence of the application of the rules adopted in the Serbian language on the English language, which can be interpreted by the bigger number of Serbian classes and clearly stated topics within the syllabus of the Serbian language course.

Taner Arsan, Sehnaz Sismanoglu Simsek, Onder Pekcan

In this study, Nobel Laureate Orhan Pamuk's works are chosen as examples of Turkish literature. By counting the number of letters and words in his texts, we find it possible to study his works statistically. It has been known that there is a geometrical order in text structures. Here the method based on the basic assumption of fractal geometry is introduced for calculating the fractal dimensions of Pamuk's texts. The results are compared with the applications of Zipf's law, which is successfully applied for letters and words, where two concepts, namely Zipf's dimension and Zipf's order, are introduced. The Zipf dimension of the novel My Name is Red is found to be much different than his other novels. However, it is linguistically observed that there is no fundamental difference between his corpora. The results are interpreted in terms of fractal dimensions and the Turkish language.

Eugene Zhang

In this paper, we present a general formula for derived sets in general topology. Consequently, more results can be proved in general topology involving derived sets and isolated point sets. More specifically, we can prove that isolated point sets are nowhere dense in general topological space.

Maria-Jose Guzman

General relativity (GR) admits two alternative formulations with the same dynamics attributing the gravitational phenomena to torsion or nonmetricity of the manifold's connection. They lead, respectively, to the teleparallel equivalent of general relativity (TEGR) and the symmetric teleparallel equivalent of general relativity (STEGR). In this work, we focus on STEGR and present its differences with the conventional, curvature-based GR. We exhibit the 3+1 decomposition of the STEGR Lagrangian in the coincident gauge and present the Hamiltonian, the Hamiltonian and momenta constraints, and Hamilton's equations. For a particular case of spherical symmetry, we explicitly show the differences in the Hamiltonian and the Hamiltonian constraint between GR and STEGR. We finally discuss the implications that these differences, which represent genuine different features between the two formulations of gravity, might encompass to numerical relativity.