Miracle-dz/UniFAR-FTUs huggingface dataset:
Treatment Outcomes of High-Risk Non-Muscle Invasive Bladder Cancer (HR-NMIBC) in Real-World Evidence (RWE) Studies: Systematic Literature Review (SLR)

Background To date, there has been limited synthesis of RWE studies in high-risk non-muscle invasive bladder cancer (HR-NMIBC). The objective of this research was to conduct a systematic review of published real-world evidence to better understand the real-world burden and treatment patterns in HR-NMIBC. Methods An SLR was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with the scope defined by the Population, Intervention Comparators, Outcomes, and Study design (PICOS) criteria. EMBASE, MEDLINE, and Cochrane databases (Jan 2015–Jul 2020) were searched, and relevant congress abstracts (Jan 2018–Jul 2020) identified. The final analysis only included studies that enrolled ≥100 patients with HR-NMIBC from the US, Europe, Canada, and Australia. Results The SLR identified 634 RWE publications in NMIBC, of which 160 studies reported data in HR-NMIBC. The average age of patients in the studies was 71 years, and 79% were males. The rates of BCG intravesical instillations ranged from 3% to 86% (29–95% for induction and 8–83% for maintenance treatment). Five-year outcomes were 17–89% recurrence-free survival (longest survival in patients completing BCG maintenance), 58–89% progression-free survival, 71–96% cancer-specific survival (lowest survival in BCG-unresponsive patients), and 28–90% overall survival (lowest survival in patients who did not receive BCG or instillation therapy). Conclusion BCG treatment rates and survival outcomes in patients with HR-NMIBC vary in the real world, with better survival seen in patients completing maintenance BCG, responding to treatment, and not progressing to muscle-invasive disease. There is a need to better understand the factors associated with BCG use and discontinuation and for an effective treatment that improves outcomes in HR-NMIBC. Generalization of these results is limited by variations in data collection, reporting, and methodologies used across RWE studies.
Living in the lab: an analysis of the work in eight living laboratories set up in care homes for technology innovation

Abstract Examinations of living labs are scarce even though the approach is increasingly used as an opportunity to bring design activities into real-life use context and consequently facilitate cooperations between designers and users in future technology innovation. Furthermore, examinations of living labs that focus on the people living in the labs do not exist. This paper presents a study of eight living labs set up in care homes for innovation in health technologies and focuses on the work carried out by care workers, service staff, residents and management, i.e. people living and working in the labs. The analysis reveals a comprehensive system of work carried out by people living and working in the labs and identifies that linking the lab-work-system to the ongoing existing work-system is a major challenge. The study demonstrates that the work of people living and working in the labs is critical to the success of living labs and recommends greater focus on work balance, user gains and collaborative innovation in living lab theories and methods.
ISO and DSO Coordination: A Parametric Programming Approach

In this paper, a framework is proposed to coordinate the operation of the independent system operator (ISO) and distribution system operator (DSO) to leverage the wholesale market participation of distributed energy resources (DERs) aggregators while ensuring secure operation of distribution grids. The proposed coordination framework is based on parametric programming. The DSO builds the bid-in cost function based on the distribution system market considering its market player constraints and distribution system physical constraints including the power balance equations and voltage limitation constraints. The DSO submits the resulting bid-in cost function to the wholesale market operated by the ISO. After the clearance of the wholesale market, the DSO determines the share of its retail market participants (i.e., DER aggregators). Case studies are performed to verify the effectiveness of the proposed method.
Correlation effects in high-order harmonic generation from finite systems

Using the Hubbard model we study how the process of high-order harmonic generation (HHG) is modified by beyond mean-field electron-electron correlation for both finite and bulk systems. A finite-size enhancement of the HHG signal is found and attributed to electrons backscattering off the lattice edges. Additionally, with increasing strength of the electron-electron correlation an enhancement of the high-frequency regime of the HHG spectrum is found. This is attributed to the on-site Coulomb repulsion between electrons giving rise to a localized quiver motion of the electrons. The finite-lattice enhancement dominates the HHG spectra from a few harmonic orders until a threshold from which the correlational enhancement dominates. This threshold is determined by the degree of correlation and decreases into the low-frequency regime for increasing electron-electron correlation. This infers that as the Mott insulator limit of high electron-electron correlation is approached, the finite-size effect on the electron dynamics becomes negligible.
Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Can world knowledge learned by large language models (LLMs) be used to act in interactive environments? In this paper, we investigate the possibility of grounding high-level tasks, expressed in natural language (e.g."make breakfast"), to a chosen set of actionable steps (e.g."open fridge"). While prior work focused on learning from explicit step-by-step examples of how to act, we surprisingly find that if pre-trained LMs are large enough and prompted appropriately, they can effectively decompose high-level tasks into mid-level plans without any further training. However, the plans produced naively by LLMs often cannot map precisely to admissible actions. We propose a procedure that conditions on existing demonstrations and semantically translates the plans to admissible actions. Our evaluation in the recent VirtualHome environment shows that the resulting method substantially improves executability over the LLM baseline. The conducted human evaluation reveals a trade-off between executability and correctness but shows a promising sign towards extracting actionable knowledge from language models. Website at https://huangwl18.github. io/language-planner
Code Sophistication: From Code Recommendation to Logic Recommendation

A typical approach to programming is to first code the main execution scenario, and then focus on filling out alternative behaviors and corner cases. But, almost always, there exist unusual conditions that trigger atypical behaviors, which are hard to predict in program specifications, and are thus often not coded. In this paper, we consider the problem of detecting and recommending such missing behaviors, a task that we call code sophistication. Previous research on coding assistants usually focuses on recommending code fragments based on specifications of the intended behavior. In contrast, code sophistication happens in the absence of a specification, aiming to help developers complete the logic of their programs with missing and unspecified behaviors. We outline the research challenges to this problem and present early results showing how program logic can be completed by leveraging code structure and information about the usage of input parameters.
Overview frequency principle/spectral bias in deep learning

Understanding deep learning is increasingly emergent as it penetrates more and more into industry and science. In recent years, a research line from Fourier analysis sheds lights into this magical"black box"by showing a Frequency Principle (F-Principle or spectral bias) of the training behavior of deep neural networks (DNNs) -- DNNs often fit functions from low to high frequency during the training. The F-Principle is first demonstrated by one-dimensional synthetic data followed by the verification in high-dimensional real datasets. A series of works subsequently enhance the validity of the F-Principle. This low-frequency implicit bias reveals the strength of neural network in learning low-frequency functions as well as its deficiency in learning high-frequency functions. Such understanding inspires the design of DNN-based algorithms in practical problems, explains experimental phenomena emerging in various scenarios, and further advances the study of deep learning from the frequency perspective. Although incomplete, we provide an overview of F-Principle and propose some open problems for future research.
Dynamics of an SIRWS model with waning of immunity and varying immune boosting period

SIRS models capture transmission dynamics of infectious diseases for which immunity is not lifelong. Extending these models by a W compartment for individuals with waning immunity, the boosting of the immune system upon repeated exposure may be incorporated. Previous analyses assumed identical waning rates from R to W and from W to S. This implicitly assumes equal length for the period of full immunity and of waned immunity. We relax this restriction, and allow an asymmetric partitioning of the total immune period. Stability switches of the endemic equilibrium are investigated with a combination of analytic and numerical tools. Then, continuation methods are applied to track bifurcations along the equilibrium branch. We find rich dynamics: Hopf bifurcations, endemic double bubbles, and regions of bistability. Our results highlight that the length of the period in which waning immunity can be boosted is a crucial parameter significantly influencing long term epidemiological dynamics.
Angular-momentum projection in coupled-cluster theory: structure of $^{34}$Mg

Single-reference coupled-cluster theory is an accurate and aﬀordable computational method for the nuclear many-body problem. For open-shell nuclei, the reference state typically breaks rotational invariance and angular momentum must be restored as a good quantum number. We perform angular-momentum projection after variation and employ the disentangled coupled-cluster formalism and a Hermitian approach. We compare our results with benchmarks for 8 Be and 20 Ne using a two-nucleon interaction from chiral eﬀective ﬁeld theory and for pf -shell nuclei within the traditional shell model. We compute the rotational band in the exotic nucleus 34 Mg and ﬁnd agreement with data.
The Analysis of the Operating Conditions of Farm Machinery Engines in Regard to Exhaust Emissions Legislation

Abstract. An analysis was conducted of actual operating conditions of farm machine engines with respect to currently applicable tests. The tests were performed on a farm tractor, farm harvester, and self-propelled forage harvester. Results of the investigations and analyses related to operating conditions of the said engines while operating in the field. An analysis was performed in which ranges of the most frequently used engine speeds and loads were determined. In addition, select results of the exhaust emissions measured under actual operating conditions of the farm machinery are presented. A PEMS (Portable Emissions Measurement System) equipment was used for the tests, and the emission of NO x (Nitric Oxides) and PM (Particulate Matter). NO x and PM are key pollutants in diesel engines as indicated by results. Conclusions following the comparison of the actual operating conditions with the NRSC (Non-Road Stationary Cycle) and NRTC (Non-Road Transient Cycle) research cycles indicate that the measurement range in the American NTE test seems too wide in the context of the obtained results. The obtained results force a discussion related to the necessity of modifying the applicable test procedures.
Structural changes across thermodynamic maxima in supercooled liquid tellurium: A water-like scenario

Significance Liquids are traditionally modeled as disordered systems with a given short-range order. This assumption has been challenged, however, by the observation of anomalous thermodynamic behavior in the most common liquid on earth—water—with accumulating evidence pointing to the possible existence of two liquid forms. Here, by measuring deeply supercooled liquid tellurium with X-ray scattering, we find anomalies that closely resemble those of water. A detailed look into the microscopic structure further reveals the mechanism behind and indicates the importance of bond-ordering for the properties of the liquid. Our results suggest that the physics behind water-like thermodynamic anomalies may be common to a family of liquids.
A Fast Radio Burst Progenitor Born in a Galaxy Merger

We report a Giant Metrewave Radio Telescope 21 cm mapping study of the neutral atomic hydrogen (Hi) in the host galaxy of the fast radio burst (FRB) FRB 20180916B at z ≈ 0.03399. We find that the FRB host has an Hi mass of M Hi = (2.74 ± 0.33) × 109 M ⊙ and a high Hi to stellar mass ratio, ≈1.3. The FRB host is thus a gas-rich but near-quiescent galaxy that is likely to have acquired a significant mass of Hi in the recent past. The Hi distribution is disturbed, with extended Hi 21 cm emission detected in a northeastern tail, a counter-tail toward the south, an Hi hole between the galaxy center and the FRB location, and a high Hi column density measured close to the FRB position. The FRB host is part of a group with four companions detected in their Hi 21 cm emission, the nearest of which is only 22 kpc from the FRB location. The gas richness and disturbed Hi distribution indicate that the FRB host has recently undergone a minor merger, which increased its Hi mass, disturbed the Hi in the galaxy disk, and compressed the Hi near the FRB location to increase its surface density. We propose that this merger caused the burst of star formation in the outskirts of the galaxy that gave rise to the FRB progenitor. The evidence for a minor merger is consistent with scenarios in which the FRB progenitor is a massive star, formed due to the merger event.
Universality of dilute solutions of ring polymers in the thermal crossover region between θ and athermal solvents

Due to their unique topology of having no chain ends, dilute solutions of ring polymers exhibit behavior distinct from their linear chain counterparts. The universality of their static and dynamic properties, as a function of solvent quality [Formula: see text] in the thermal crossover regime between [Formula: see text] and athermal solvents, is studied here using Brownian dynamics simulations. The universal ratio [Formula: see text] of the radius of gyration [Formula: see text] to the hydrodynamic radius [Formula: see text] is determined, and a comparative study of the swelling ratio [Formula: see text] of the radius of gyration, the swelling ratio [Formula: see text] of the hydrodynamic radius, and the swelling ratio [Formula: see text] of the mean polymer stretch [Formula: see text] along the [Formula: see text]-axis, for linear and ring polymers, is carried out. The ratio [Formula: see text] for dilute ring polymer solutions is found to converge asymptotically to a constant value as [Formula: see text], which is a major difference from the behavior of solutions of linear chains, where no such asymptotic limit exists. Additionally, the ratio of the mean stretch along the [Formula: see text]-axis to the hydrodynamic radius, [Formula: see text], is found to be independent of [Formula: see text] for polymeric rings, unlike in the case for linear polymers. These results indicate a fundamental difference in the scaling of static and dynamic properties of rings and linear chains in the thermal crossover regime.
Using Reinforcement Learning for Load Testing of Video Games

Different from what happens for most types of software systems, testing video games has largely remained a manual activity per-formed by human testers. This is mostly due to the continuous and intelligent user interaction video games require. Recently, rein-forcement learning (RL) has been exploited to partially automate functional testing. RL enables training smart agents that can even achieve super-human performance in playing games, thus being suitable to explore them looking for bugs. We investigate the pos-sibility of using RL for load testing video games. Indeed, the goal of game testing is not only to identify functional bugs, but also to examine the game's performance, such as its ability to avoid lags and keep a minimum number of frames per second (FPS) when high-demanding 3D scenes are shown on screen. We define a method-ology employing RL to train an agent able to play the game as a human while also trying to identify areas of the game resulting in a drop of FPS. We demonstrate the feasibility of our approach on three games. Two of them are used as proof-of-concept, by injecting artificial performance bugs. The third one is an open-source 3D game that we load test using the trained agent showing its potential to identify areas of the game resulting in lower FPS.
Geometric tilt-to-length coupling in precision interferometry: mechanisms and analytical descriptions

Tilt-to-length (TTL) coupling is a technical term for the cross-coupling of angular or lateral jitter into an interferometric phase signal. It is an important noise source in precision interferometers and originates either from changes in the optical path lengths or from wavefront and clipping effects. Within this paper, we focus on geometric TTL coupling and categorise it into a number of different mechanisms for which we give analytic expressions. We then show that this geometric description is not always sufficient to predict the TTL coupling noise within an interferometer. We, therefore, discuss how understanding the geometric effects allows TTL noise reduction already by smart design choices. Additionally, they can be used to counteract the total measured TTL noise in a system. The presented content applies to a large variety of precision interferometers, including space gravitational wave detectors like LISA.
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data

Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications. However, building a system that is able to quickly and accurately pinpoint anomalous observations is a challenging problem. This is due to the lack of anomaly labels, high data volatility and the demands of ultra-low inference times in modern applications. Despite the recent developments of deep learning approaches for anomaly detection, only a few of them can address all of these challenges. In this paper, we propose TranAD, a deep transformer network based anomaly detection and diagnosis model which uses attention-based sequence encoders to swiftly perform inference with the knowledge of the broader temporal trends in the data. TranAD uses focus score-based self-conditioning to enable robust multi-modal feature extraction and adversarial training to gain stability. Additionally, model-agnostic meta learning (MAML) allows us to train the model using limited data. Extensive empirical studies on six publicly available datasets demonstrate that TranAD can outperform state-of-the-art baseline methods in detection and diagnosis performance with data and time-efficient training. Specifically, TranAD increases F1 scores by up to 17%, reducing training times by up to 99% compared to the baselines.
FAT: An In-Memory Accelerator With Fast Addition for Ternary Weight Neural Networks

Convolutional neural networks (CNNs) demonstrate excellent performance in various applications but have high computational complexity. Quantization is applied to reduce the latency and storage cost of CNNs. Among the quantization methods, binary and ternary weight networks (BWNs and TWNs) have a unique advantage over 8 and 4-bit quantization. They replace the multiplication operations in CNNs with additions, which are favored on in-memory-computing (IMC) devices. IMC acceleration for BWNs has been widely studied. However, though TWNs have higher accuracy and better sparsity than BWNs, IMC acceleration for TWNs has limited research. TWNs on the existing IMC devices are inefficient because the sparsity is not well utilized, and the addition operation is not efficient. In this article, we propose FAT as a novel IMC accelerator for TWNs. First, we propose a sparse addition control unit, which utilizes the sparsity of TWNs to skip the null operations on zero weights. Second, we propose a fast addition scheme based on the memory sense amplifier (SA) to avoid the time overhead of both carry propagation and writing back the carry to memory cells. Third, we further propose a combined-stationary data mapping to reduce the data movement of activations and weights and increase the parallelism across memory columns. Simulation results show that for addition operations at the SA level, FAT achieves <inline-formula> <tex-math notation="LaTeX">$2.00\times $ </tex-math></inline-formula> speedup, <inline-formula> <tex-math notation="LaTeX">$1.22\times $ </tex-math></inline-formula> power efficiency, and <inline-formula> <tex-math notation="LaTeX">$1.22\times $ </tex-math></inline-formula> area efficiency compared with a state-of-the-art IMC accelerator ParaPIM. FAT achieves <inline-formula> <tex-math notation="LaTeX">$10.02\times $ </tex-math></inline-formula> speedup and <inline-formula> <tex-math notation="LaTeX">$12.19\times $ </tex-math></inline-formula> energy efficiency compared with ParaPIM on networks with 80% average sparsity.
TriCoLo: Trimodal Contrastive Loss for Text to Shape Retrieval

Text-to-shape retrieval is an increasingly relevant problem with the growth of 3D shape data. Recent work on contrastive losses for learning joint embeddings over multimodal data has been successful at tasks such as retrieval and classification. Thus far, work on joint representation learning for 3D shapes and text has focused on improving embeddings through modeling of complex attention between representations, or multi-task learning. We propose a trimodal learning scheme over text, multi-view images and 3D shape voxels, and show that with large batch contrastive learning we achieve good performance on text-to-shape retrieval without complex attention mechanisms or losses. Our experiments serve as a foundation for follow-up work on building trimodal embeddings for text-image-shape.
X-ray line diagnostics of ion temperature at cosmic ray accelerating collisionless shocks

A novel collisionless shock jump condition is suggested by modeling the entropy production at the shock transition region. We also calculate downstream developments of the atomic ionization balance and the ion temperature relaxation in supernova remnants (SNRs). The injection process and subsequent acceleration of cosmic rays (CRs) in the SNR shocks are closely related to the formation process of the collisionless shocks. The formation of the shock is caused by wave–particle interactions. Since the wave–particle interactions result in energy exchanges between electromagnetic fields and charged particles, the randomization of particles associated with the shock transition may occur at a rate given by the scalar product of the electric field and current. We find that order-of-magnitude estimates of the randomization with reasonable strength of the electromagnetic fields in the SNR constrain the amount of CR nuclei and the ion temperatures. The constrained amount of CR nuclei can be sufficient to explain the Galactic CRs. The ion temperature becomes significantly lower than that in the case without CRs. To distinguish the case without CRs, we perform synthetic observations of atomic line emissions from the downstream region of the SNR RCW 86. Future observations by XRISM and Athena can distinguish whether the SNR shock accelerates the CRs or not from the ion temperatures.