Search

With the global spread and increasing transmission rate of SARS-CoV-2, more and more laboratories and researchers are turning their attention to wastewater-based epidemiology (WBE), hoping it can become an effective tool for large-scale testing and provide more ac-curate predictions of the number of infected individuals. Based on the cases of sewage sampling and testing in some regions such as Hong Kong, Brazil, and the United States, the feasibility of detecting the novel coronavirus in sewage is extremely high. This study re-views domestic and international achievements in detecting SARS-CoV-2 through WBE and summarizes four aspects of COVID-19, including sampling methods, virus decay rate cal-culation, standardized population coverage of the watershed, algorithm prediction, and provides ideas for combining field modeling with epidemic prevention and control. Moreover, we highlighted some diagnostic techniques for detection of the virus from sew-age sample. Our review is a new approach in identification of the research gaps in waste water-based epidemiology and diagnosis and we also predict the future prospect of our analysis.

We introduce an inhomogeneous variant of Kaufman's measure, with applications to diophantine approximation. In particular, we make progress towards a problem related to Littlewood's conjecture.

This paper proposes one of the first clinical applications of multimodal large language models (LLMs) as an assistant for radiologists to check errors in their reports. We created an evaluation dataset from real-world radiology datasets (including X-rays and CT scans). A subset of original reports was modified to contain synthetic errors by introducing three types of mistakes: "insert", "remove", and "substitute". The evaluation contained two difficulty levels: SIMPLE for binary error-checking and COMPLEX for identifying error types. At the SIMPLE level, our fine-tuned model significantly enhanced performance by 47.4% and 25.4% on MIMIC-CXR and IU X-ray data, respectively. This performance boost is also observed in unseen modality, CT scans, as the model performed 19.46% better than the baseline model. The model also surpassed the domain expert's accuracy in the MIMIC-CXR dataset by 1.67%. Notably, among the subsets (N=21) of the test set where a clinician did not achieve the correct conclusion, the LLaVA ensemble mode correctly identified 71.4% of these cases. However, all models performed poorly in identifying mistake types, underscoring the difficulty of the COMPLEX level. This study marks a promising step toward utilizing multimodal LLMs to enhance diagnostic accuracy in radiology. The ensemble model demonstrated comparable performance to clinicians, even capturing errors overlooked by humans.

We characterized the dosimetric properties of a clinical linear accelerator configured to deliver ultra-high dose rate (UHDR) irradiation to mice and cell-culture FLASH radiobiology experiments. UHDR electron beams were controlled by a microcontroller and relay interfaced with the respiratory gating system. We produced beam collimators with indexed stereotactic mouse positioning devices to provide anatomically specific preclinical treatments. Treatment delivery was monitored directly with an ionization chamber, and charge measurements were correlated with radiochromic film at the entry surface of the mice. The setup for conventional (CONV) dose rate irradiation was similar but the source-to-surface distance was longer. Monte Carlo simulations and film dosimetry were used to characterize beam properties and dose distributions. The mean electron beam energies before the flattening filter were 18.8 MeV (UHDR) and 17.7 MeV (CONV), with corresponding values at the mouse surface of 17.2 MeV and 16.2 MeV. The charges measured with an external ion chamber were linearly correlated with the mouse entrance dose. Use of relay gating for pulse control initially led to a delivery failure rate of 20% ($+/-$ 1 pulse); adjustments to account for the linac latency improved this rate to <1/20. Beam field sizes for two anatomically specific mouse collimators (4x4 $cm^2$ for whole-abdomen and 1.5x1.5 $cm^2$ for unilateral lung irradiation) were accurate within <5% and had low radiation leakage (<4%). Normalizing the dose at the center of the mouse (~0.75 cm depth) produced UHDR and CONV doses to the irradiated volumes with >95% agreement. We successfully configured a clinical linear accelerator for increased output and developed a robust preclinical platform for anatomically specific irradiation, with highly accurate and precise temporal and spatial dose delivery, for both CONV and UHDR applications., Comment: Jinghui Wang and Stavros Melemenidis are co-first authors, and Emil Sch\"uler and Peter G. Maxim are co-senior/co-corresponding authors

It is well known that Random Serial Dictatorship is strategy-proof and leads to a Pareto-Efficient outcome. We show that this result breaks down when individuals are allowed to make transfers, and adapt Random Serial Dictatorship to encompass trades between individuals. Strategic analysis of play under the new mechanisms we define is given, accompanied by simulations to quantify the gains from trade.

Electromagnetically induced transparency (EIT) can be used to cool an atom in a harmonic potential close to the ground state by addressing several vibrational modes simultaneously. Previous experimental efforts focus on trapped ions and neutral atoms in a standing wave trap. In this work, we demonstrate EIT cooling of an optically trapped single neutral atom, where the trap frequencies are an order of magnitude smaller than in an ion trap and a standing wave trap. We resolve the Fano resonance feature in fluorescence excitation spectra and the corresponding cooling profile in temperature measurements. A final temperature of around 6 $\mu$K is achieved with EIT cooling, a factor of two lower than the previous value obtained using olarization gradient cooling., Comment: 7 pages, 6 figures

We give a description of the Picard group of a reductive group over a number field as an abelianized Galois cohomology group. It gives another approach of a result due to Labesse.

Over a global field (number field or function field of a curve over a finite field), theorems for the Galois cohomology of algebraic groups have long been known. For $F$ the function field of a curve over the formal series field $\mathbb{C}((t))$, under an additional assumption on the curve, we establish an explicit description of the localization map in Galois cohomology. This is achieved by extending the theory of $B(F,G)$ due to Kottwitz.

Polysemantic neurons -- neurons that activate for a set of unrelated features -- have been seen as a significant obstacle towards interpretability of task-optimized deep networks, with implications for AI safety. The classic origin story of polysemanticity is that the data contains more ``features" than neurons, such that learning to perform a task forces the network to co-allocate multiple unrelated features to the same neuron, endangering our ability to understand networks' internal processing. In this work, we present a second and non-mutually exclusive origin story of polysemanticity. We show that polysemanticity can arise incidentally, even when there are ample neurons to represent all features in the data, a phenomenon we term \textit{incidental polysemanticity}. Using a combination of theory and experiments, we show that incidental polysemanticity can arise due to multiple reasons including regularization and neural noise; this incidental polysemanticity occurs because random initialization can, by chance alone, initially assign multiple features to the same neuron, and the training dynamics then strengthen such overlap. Our paper concludes by calling for further research quantifying the performance-polysemanticity tradeoff in task-optimized deep neural networks to better understand to what extent polysemanticity is avoidable.

Multi-band light curves of two RR Lyrae variables in Segue II and Ursa Major II ultra-faint dwarf (UFD) galaxies were collected from near simultaneous observations using the Lulin One-meter Telescope in Vgri bands. Together with Gaia G-band light curves, we determined photometric metallicities using empirical relations involving pulsation period and Fourier parameter as dependent parameters. We demonstrated that the RR Lyrae photometric metallicity can be determined accurately when these empirical relations were employed at multiple wavelengths, which can potentially improve the distance determination based on RR Lyrae stars. The photometric metallicities based on our approach were found to be $-2.27\pm0.13$ dex and $-1.87\pm0.16$ dex for the RR Lyrae in Segue II and Ursa Major II UFD, respectively, with corresponding distance moduli of $17.69\pm0.15$ mag and $17.58\pm0.15$ mag, in agreement with previous literature determinations. This approach of photometric metallicity of RR Lyrae based on multi-band optical light curves will be particularly relevant for distance measurements in the era of the Vera C Rubin's Legacy Survey of Space and Time., Comment: 9 pages, 3 figures and 5 tables, AJ accepted

Rietsch constructed a candidate $T$-equivariant mirror LG model for any flag variety $G/P$. In this paper, we prove the following mirror symmetry prediction: the small $T\times\mathbb{G}_m$-equivariant quantum cohomology of $G/P$ equipped with quantum connection is isomorphic as $D$-modules to the Brieskorn lattice associated to the LG model equipped with Gauss-Manin connection., Comment: Reformulated main result; simplified exposition; added references

The increasing penetration of renewable energy resources and utilization of energy storage systems pose new challenges in maintaining power system's stability. Specifically, the cost function of regulation no longer remains smooth, which complicates the task of ensuring nominal frequency and power balance, particularly in a distributed manner. This paper proposes a distributed proximal primal-dual (DPPD) algorithm, based on a modified primal-dual dynamics equipped with operator splitting technique, to address this nonsmooth frequency regulation problem on load side. By Lyapunov stability and invariance theory, we prove that DPPD algorithm achieves global asymptotic convergence to the optimal solution that minimizes the nonsmooth regulation cost, while restoring the frequency under constraints such as capacity limits of load and power flow. Finally, we demonstrate the effectiveness and robustness of DPPD algorithm by simulations on the IEEE 39-bus system.

Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing popularity to study deep ensemble techniques in the deep learning community. Some mission-critical applications utilize a large number of deep neural networks to form deep ensembles to achieve desired accuracy and resilience, which introduces high time and space costs for ensemble execution. However, it still remains a critical challenge whether a small subset of the entire deep ensemble can achieve the same or better generalizability and how to effectively identify these small deep ensembles for improving the space and time efficiency of ensemble execution. This paper presents a novel deep ensemble pruning approach, which can efficiently identify smaller deep ensembles and provide higher ensemble accuracy than the entire deep ensemble of a large number of member networks. Our hierarchical ensemble pruning approach (HQ) leverages three novel ensemble pruning techniques. First, we show that the focal diversity metrics can accurately capture the complementary capacity of the member networks of an ensemble, which can guide ensemble pruning. Second, we design a focal diversity based hierarchical pruning approach, which will iteratively find high quality deep ensembles with low cost and high accuracy. Third, we develop a focal diversity consensus method to integrate multiple focal diversity metrics to refine ensemble pruning results, where smaller deep ensembles can be effectively identified to offer high accuracy, high robustness and high efficiency. Evaluated using popular benchmark datasets, we demonstrate that the proposed hierarchical ensemble pruning approach can effectively identify high quality deep ensembles with better generalizability while being more time and space efficient in ensemble decision making., Comment: To appear on ACM Transactions on Intelligent Systems and Technology

In recent years, certain luminous extragalactic optical transients have been observed to last only a few days. Their short observed duration implies a different powering mechanism from the most common luminous extragalactic transients (supernovae) whose timescale is weeks. Some short-duration transients, most notably AT2018cow, display blue optical colours and bright radio and X-ray emission. Several AT2018cow-like transients have shown hints of a long-lived embedded energy source, such as X-ray variability, prolonged ultraviolet emission, a tentative X-ray quasiperiodic oscillation, and large energies coupled to fast (but subrelativistic) radio-emitting ejecta. Here we report observations of minutes-duration optical flares in the aftermath of an AT2018cow-like transient, AT2022tsd (the "Tasmanian Devil"). The flares occur over a period of months, are highly energetic, and are likely nonthermal, implying that they arise from a near-relativistic outflow or jet. Our observations confirm that in some AT2018cow-like transients the embedded energy source is a compact object, either a magnetar or an accreting black hole., Comment: 79 pages, 3 figures (main text) + 7 figures (extended data) + 2 figures (supplementary information). Published online in Nature on 15 November 2023

RR Lyrae stars are pulsating stars, many of which also show a long-term variation called the Blazhko effect which is a modulation of amplitude and phase of the lightcurve. In this work, we searched for the incidence rate of the Blazhko effect in the first-overtone pulsating RR Lyrae (RRc) stars of the Galactic halo. The focus was on the Stripe 82 region in the Galactic halo which was studied by Sesar et al using the Sloan Digital Sky Survey (SDSS) data. In their work, 104 RR Lyrae stars were classified as RRc type. We combined their SDSS light curves with Zwicky Transient Facility (ZTF) data, and use them to document the Blazhko properties of these RRc stars. Our analysis showed that among the 104 RRc stars, 8 were rather RRd stars, and were excluded from the study. Out of remaining 96, 34 were Blazhko type, 62 were non-Blazhko type, giving the incidence rate of 35.42% for Blazhko RRc stars. The shortest Blazhko period found was 12.808 +/- 0.001 d for SDSS 747380, while the longest was 3100 +/- 126 d for SDSS 3585856. Combining SDSS and ZTF data sets increased the probability of detecting the small variations due to the Blazhko effect, and thus provided a unique opportunity to find longer Blazhko periods. We found that 85% of RRc stars had the Blazhko period longer than 200 d., Comment: 9 pages, 2 tables, 8 figures, AJ accepted

Hybrid cloud provides an attractive solution to microservices for better resource elasticity. A subset of application components can be offloaded from the on-premises cluster to the cloud, where they can readily access additional resources. However, the selection of this subset is challenging because of the large number of possible combinations. A poor choice degrades the application performance, disrupts the critical services, and increases the cost to the extent of making the use of hybrid cloud unviable. This paper presents Atlas, a hybrid cloud migration advisor. Atlas uses a data-driven approach to learn how each user-facing API utilizes different components and their network footprints to drive the migration decision. It learns to accelerate the discovery of high-quality migration plans from millions and offers recommendations with customizable trade-offs among three quality indicators: end-to-end latency of user-facing APIs representing application performance, service availability, and cloud hosting costs. Atlas continuously monitors the application even after the migration for proactive recommendations. Our evaluation shows that Atlas can achieve 21% better API performance (latency) and 11% cheaper cost with less service disruption than widely used solutions., Comment: To appear at EuroSys 2024

In today's data centers, the performance of interconnects plays a pivotal role. However, many of the underlying technologies for these interconnects have a history of several decades and existed long before data centers came into being.To better cater to the requirements of data center networks, particularly in the context of intra-rack communication, we have developed a new interconnect. This interconnect is based on a lossless link layer protocol, named RIFL. In this work, we designed and implemented RIFL Layer 2, a scalable network that supports up to multi-hundred Gbps communication. RIFL Layer 2 includes the RIFL switch and RIFL NIC. By utilizing a simple Batcher Banyan and iSLIP RIFL switch, we effectively keep the typical intra-rack latency under 400 nanoseconds. Moreover, for a 32-port 100Gbps network, under both Bernoulli arrival and bursty arrival traffic patterns, we ensure that the 99\% tail latency does not exceed 12microseconds.

With the rise in demand for local deliveries and e-commerce, robotic deliveries are being considered as efficient and sustainable solutions. However, the deployment of such systems can be highly complex due to numerous factors involving stochastic demand, stochastic charging and maintenance needs, complex routing, etc. We propose a model that uses continuous approximation methods for evaluating service trade-offs that consider the unique characteristics of large-scale sidewalk delivery robot systems used to serve online food deliveries. The model captures both the initial cost and the operation cost of the delivery system and evaluates the impact of constraints and operation strategies on the deployment. By minimizing the system cost, variables related to the system design can be determined. First, the minimization problem is formulated based on a homogeneous area, and the optimal system cost can be derived as a closed-form expression. By evaluating the expression, relationships between variables and the system cost can be directly obtained. We then apply the model in neighborhoods in New York City to evaluate the cost of deploying the sidewalk delivery robot system in a real-world scenario. The results shed light on the potential of deploying such a system in the future.

Preference elicitation plays a central role in interactive recommender systems. Most preference elicitation approaches use either item queries that ask users to select preferred items from a slate, or attribute queries that ask them to express their preferences for item characteristics. Unfortunately, users often wish to describe their preferences using soft attributes for which no ground-truth semantics is given. Leveraging concept activation vectors for soft attribute semantics, we develop novel preference elicitation methods that can accommodate soft attributes and bring together both item and attribute-based preference elicitation. Our techniques query users using both items and soft attributes to update the recommender system's belief about their preferences to improve recommendation quality. We demonstrate the effectiveness of our methods vis-a-vis competing approaches on both synthetic and real-world datasets.

The first science image released by the JWST reveals numerous galaxies in the distant background of the galaxy cluster SMACS J0723.3-7327. Some have claimed redshifts of up to $z \simeq 20$, challenging standard cosmological models for structure formation. Here, we present a lens model for SMACS J0723.3-7327 anchored on five spectroscopically-confirmed systems at $1.38 \leq z \leq 2.21$ that are multiply lensed, along with twelve other systems with proposed image counterparts sharing common colours, spectral energy distributions, and morphological features, but having unknown redshifts. Constrained only by their image positions and, where available, redshifts, our lens model correctly reproduces the positions and correctly predicts the morphologies and relative brightnesses of all these image counterparts, as well as providing geometrically-determined redshifts spanning $1.4 \lesssim z \lesssim 6.7$ for the twelve candidate multiply-lensed galaxies lacking spectroscopic measurements. From this lens model, we create a lens finder map that defines regions over which galaxies beyond a certain redshift are predicted to be multiply lensed. Applying this map to three galaxies claimed to be at $10 \lesssim z \lesssim 20$, we find no image counterparts at locations (with an uncertainty of $\sim$$0.^{\prime\prime}5$) where they ought to be sufficiently magnified to be detectable - suggesting instead that these galaxies lie at $z \lesssim 1.7-3.2$. In lieu of spectroscopy, the creation of reliable lens finder maps for cluster fields are urgently needed to test and constrain redshifts inferred from photometry for a rapidly increasing number of candidate high-$z$ galaxies found with the JWST.

Recommender systems (RSs) play a central role in connecting users to content, products, and services, matching candidate items to users based on their preferences. While traditional RSs rely on implicit user feedback signals, conversational RSs interact with users in natural language. In this work, we develop a comPelling, Precise, Personalized, Preference-relevant language model (P4LM) that recommends items to users while putting emphasis on explaining item characteristics and their relevance. P4LM uses the embedding space representation of a user's preferences to generate compelling responses that are factually-grounded and relevant w.r.t. the user's preferences. Moreover, we develop a joint reward function that measures precision, appeal, and personalization, which we use as AI-based feedback in a reinforcement learning-based language model framework. Using the MovieLens 25M dataset, we demonstrate that P4LM delivers compelling, personalized movie narratives to users.

Embeddings have become a pivotal means to represent complex, multi-faceted information about entities, concepts, and relationships in a condensed and useful format. Nevertheless, they often preclude direct interpretation. While downstream tasks make use of these compressed representations, meaningful interpretation usually requires visualization using dimensionality reduction or specialized machine learning interpretability methods. This paper addresses the challenge of making such embeddings more interpretable and broadly useful, by employing Large Language Models (LLMs) to directly interact with embeddings -- transforming abstract vectors into understandable narratives. By injecting embeddings into LLMs, we enable querying and exploration of complex embedding data. We demonstrate our approach on a variety of diverse tasks, including: enhancing concept activation vectors (CAVs), communicating novel embedded entities, and decoding user preferences in recommender systems. Our work couples the immense information potential of embeddings with the interpretative power of LLMs., Comment: Accepted to ICLR 2024

Solving a linear system $Ax=b$ is a fundamental scientific computing primitive for which numerous solvers and preconditioners have been developed. These come with parameters whose optimal values depend on the system being solved and are often impossible or too expensive to identify; thus in practice sub-optimal heuristics are used. We consider the common setting in which many related linear systems need to be solved, e.g. during a single numerical simulation. In this scenario, can we sequentially choose parameters that attain a near-optimal overall number of iterations, without extra matrix computations? We answer in the affirmative for Successive Over-Relaxation (SOR), a standard solver whose parameter $\omega$ has a strong impact on its runtime. For this method, we prove that a bandit online learning algorithm--using only the number of iterations as feedback--can select parameters for a sequence of instances such that the overall cost approaches that of the best fixed $\omega$ as the sequence length increases. Furthermore, when given additional structural information, we show that a contextual bandit method asymptotically achieves the performance of the instance-optimal policy, which selects the best $\omega$ for each instance. Our work provides the first learning-theoretic treatment of high-precision linear system solvers and the first end-to-end guarantees for data-driven scientific computing, demonstrating theoretically the potential to speed up numerical methods using well-understood learning algorithms., Comment: ICLR 2024 Spotlight

Deep neural network ensembles hold the potential of improving generalization performance for complex learning tasks. This paper presents formal analysis and empirical evaluation to show that heterogeneous deep ensembles with high ensemble diversity can effectively leverage model learning heterogeneity to boost ensemble robustness. We first show that heterogeneous DNN models trained for solving the same learning problem, e.g., object detection, can significantly strengthen the mean average precision (mAP) through our weighted bounding box ensemble consensus method. Second, we further compose ensembles of heterogeneous models for solving different learning problems, e.g., object detection and semantic segmentation, by introducing the connected component labeling (CCL) based alignment. We show that this two-tier heterogeneity driven ensemble construction method can compose an ensemble team that promotes high ensemble diversity and low negative correlation among member models of the ensemble, strengthening ensemble robustness against both negative examples and adversarial attacks. Third, we provide a formal analysis of the ensemble robustness in terms of negative correlation. Extensive experiments validate the enhanced robustness of heterogeneous ensembles in both benign and adversarial settings. The source codes are available on GitHub at https://github.com/git-disl/HeteRobust., Comment: Accepted by IEEE ICDM 2023