Negative / Null Result ReportOpen accessComputer Science
O. Behrend, R. Harris, P. Warren · 1993 · arXiv
We present a numerical analysis of the validity of classical and generalized hydrodynamics for Lattice Boltzmann Equation (LBE) and Lattice BGK methods in two and three dimensions, as a function of the collision parameters of these models. Our analysis is based on the wave-number dependence of the evolution operator. Good ranges of validity are found for BGK models as long as the relaxation time is chosen smaller than or equal to unity. The additional freedom in the choice of collision parameters for LBE models does not seem to give significant improvement.
Negative / Null Result ReportOpen accessPhysics
Motonari Tonegawa, Tomonori Totani, Masayuki Akiyama et al. · 2013 · arXiv
The efficient selection of high-redshift emission galaxies is important for future large galaxy redshift surveys for cosmology. Here we describe the target selection methods for the FastSound project, a redshift survey for H alpha emitting galaxies at z=1.2-1.5 using Subaru/FMOS to measure the linear growth rate fσ8 via Redshift Space Distortion (RSD) and constrain the theory of gravity. To select ~400 target galaxies in the 0.2 deg^2 FMOS field-of-view from photometric data of CFHTLS-Wide (u*g'r'i'z'), we test several different methods based on color-color diagrams or photometric redshift est
Negative / Null Result ReportOpen accessComputer Science
V. S. Raghu Parupudi · 2025 · arXiv
Vector comparison in high dimensions is a fundamental task in NLP, yet it is dominated by two baselines: the raw dot product, which is unbounded and sensitive to vector norms, and the cosine similarity, which discards magnitude information entirely. This paper challenges both standards by proposing and rigorously evaluating a new class of parameter-free, magnitude-aware similarity metrics. I introduce two such functions, Overlap Similarity (OS) and Hyperbolic Tangent Similarity (HTS), designed to integrate vector magnitude and alignment in a more principled manner. To ensure that my findings a
Negative / Null Result ReportOpen accessPhysics
A. D. Kulkarni, R. M. Shannon, D. J. Reardon et al. · 2024 · arXiv
Pulse-to-pulse profile shape variations introduce correlations in pulsar times of arrival (TOAs) across radio frequency measured at the same observational epoch. This leads to a broadband noise in excess of radiometer noise, which is termed pulse jitter noise. The presence of jitter noise limits the achievable timing precision and decreases the sensitivity of pulsar-timing data sets to signals of interest such as nanohertz-frequency gravitational waves. Current white noise models used in pulsar timing analyses attempt to account for this, assuming complete correlation of uncertainties through
Failed Experiment ReportOpen accessComputer Science
D. Kiderlen, P. Danielewicz · 1996 · arXiv
Generalization of Gaussian trial wave functions in quantum molecular dynamics models is introduced, which allows for long-range correlations characteristic for composite nuclear fragments. We demonstrate a significant improvement in the description of light fragments with correlations. Utilizing either type of Gaussian wave functions, with or without correlations, however, we find that we cannot describe fragment formation in a dynamic situation. Composite fragments are only produced in simulations if they are present as clusters in the substructure of original nuclei. The difficulty is traced
Negative / Null Result ReportOpen accessComputer Science
Sungjin Park, Seungwoo Ryu, Edward Choi · 2022 · arXiv
Recent success of pre-trained language models (PLMs) has stimulated interest in their ability to understand and work with numbers. Yet, the numerical reasoning over measurements has not been formally studied despite their importance. In this study, we show that PLMs lack the capability required for reasoning over measurements. Furthermore, we find that a language model trained on a measurement-rich corpus shows better performance on understanding measurements. We propose a simple embedding strategy to better distinguish between numbers and units, which leads to a significant improvement in the
Negative / Null Result ReportOpen accessComputer Science
Ramnath Kumar, Tristan Deleu, Yoshua Bengio · 2022 · arXiv
Recent studies show that task distribution plays a vital role in the meta-learner's performance. Conventional wisdom is that task diversity should improve the performance of meta-learning. In this work, we find evidence to the contrary; (i) our experiments draw into question the efficacy of our learned models: similar manifolds can be learned with a subset of the data (lower task diversity). This finding questions the advantage of providing more data to the model, and (ii) adding diversity to the task distribution (higher task diversity) sometimes hinders the model and does not lead to a signi
Negative / Null Result ReportOpen accessPhysics
Dominic M. Bowman, Daniel L. Holdsworth · 2019 · arXiv
Context. Modern space telescopes are currently providing high-precision light curves for a large fraction of the sky, such that many new variable stars are being discovered. However, some stars have periodic variability with periods of order minutes and require high-cadence photometry to probe the physical mechanisms responsible. A cadence of less than a minute is often required to remove Nyquist ambiguities and confirm rapid variability which forces observers to obtain high-cadence ground-based photometry. Aims. We aim to provide a modern software package to reduce ground-based photometric ti
Negative / Null Result ReportOpen accessEconomics, Econometrics and Finance
Francisco Rodríguez · 2022 · arXiv
We revisit the results of a recent paper by Equipo Anova, who claim to find evidence of an improvement in Venezuelan imports of food and medicines associated with the adoption of U.S. financial sanctions towards Venezuela in 2017. We show that their results are consequence of data coding errors and questionable methodological choices, including the use an unreasonable functional form that implies a counterfactual of negative imports in the absence of sanctions, the omission of data accounting for four-fifths of the country's food imports at the time of sanctions and incorrect application of re
Failed Experiment ReportOpen accessComputer Science
P. Danielewicz, D. Kiderlen · 1997 · arXiv
Generalization of Gaussian trial wave functions in quantum molecular dynamics models is introduced, which allows for long-range correlations characteristic for composite nuclear fragments. We demonstrate a significant improvement in the description of light fragments with the correlations. Utilizing either type of Gaussian wave functions, with or without correlations, however, we find that we cannot describe fragment formation in a dynamic situation. Composite fragments are only produced in simulations if these fragments are present as clusters in the substructure of original nuclei. The diffi
Replication FailureOpen accessComputer Science
Supriyo Maji · 2017 · arXiv
Spin transfer torque oscillators (STOs) based on magnetic tunnel junction (MTJ) devices are emerging as a possible replacement for complementary metal-oxide semiconductors for radio-frequency (RF) signal generation. Advantages include low power consumption, small device area, and large frequency tunability. But such a single device cannot achieve the necessary noise performance for RF applications. It has been reported lately that a network of globally coupled STOs achieves significant improvement in phase noise. The study here is to propose use of such coupled STOs as self-oscillating RF mixe
Negative / Null Result ReportOpen accessPhysics
Sarbani Basu, H. M. Antia · 1999 · arXiv
Ring diagram analysis can be used to study large scale velocity fields in the outer part of the solar convection zone. All previous works assume that the peak profiles in the solar oscillation power spectrum are symmetric. However, it has now been demonstrated that the peaks are not symmetric. In this work we study how the explicit use of asymmetric peak profiles in ring-diagram analysis influences the estimated velocity fields. We find that the use of asymmetric profiles leads to significant improvement in the fits, but the estimated velocity fields are not substantially different from those
Negative / Null Result ReportOpen accessPhysics
Xian-Yu Wang, Yong-Hao Wang, Songhu Wang et al. · 2021 · arXiv
We present 127 new transit light curves for 39 hot Jupiter systems, obtained over the span of five years by two ground-based telescopes. A homogeneous analysis of these newly collected light curves together with archived spectroscopic, photometric, and Doppler velocimetric data using EXOFASTv2 leads to a significant improvement in the physical and orbital parameters of each system. All of our stellar radii are constrained to accuracies of better than 3\%. The planetary radii for 37 of our 39 targets are determined to accuracies of better than $5\%$. Compared to our results, the literature ecce
Negative / Null Result ReportOpen accessComputer Science
Andreas Hocker · 2001 · arXiv
The recent precise measurement of the muon magnetic anomaly (g-2)_{mu} at BNL opens a window into possible new physics, provided the contribution from hadronic vacuum polarization is well understood. This talk summarizes the development in the evaluation of the leading order hadronic contributions. Significant improvement has been achieved in a series of analyses which is presented historically in three steps: (1), use of tau spectral functions in addition to e+e- cross sections, (2), extended use of perturbative QCD and (3), application of QCD sum rule techniques. The uncertainties, in partic
Failed Experiment ReportOpen accessComputer Science
Adeel Mahmood, Aaron B. Wagner · 2024 · arXiv
Channel coding for discrete memoryless channels (DMCs) with mean and variance cost constraints has been recently introduced. We show that there is an improvement in coding performance due to cost variability, both with and without feedback. We demonstrate this improvement over the traditional almost-sure (per-codeword) cost constraint that prohibits any cost variation above a fixed threshold. Our result simultaneously shows that feedback does not improve the second-order coding rate of simple-dispersion DMCs under the almost-sure cost constraint. This finding parallels similar results for unco
Negative / Null Result ReportOpen accessComputer Science
Wojciech Zaremba, Evgenia Nitishinskaya, Boaz Barak et al. · 2025 · arXiv
We conduct experiments on the impact of increasing inference-time compute in reasoning models (specifically OpenAI o1-preview and o1-mini) on their robustness to adversarial attacks. We find that across a variety of attacks, increased inference-time compute leads to improved robustness. In many cases (with important exceptions), the fraction of model samples where the attack succeeds tends to zero as the amount of test-time compute grows. We perform no adversarial training for the tasks we study, and we increase inference-time compute by simply allowing the models to spend more compute on reas
Negative / Null Result ReportOpen accessMathematics
Guido De Philippis, Michael Goldman, Berardo Ruffini · 2023 · arXiv
We consider a branched transport type problem which describes the magnetic flux through type-I superconductors in a regime of very weak applied fields. At the boundary of the sample, deviation of the magnetization from being uniform is penalized through a negative Sobolev norm. It was conjectured by S. Conti, F. Otto and S. Serfaty that as a result, the trace of the magnetization on the boundary should be a measure of Hausdorff dimension $8/5$. We prove that this conjecture is equivalent to the proof of local energy bounds with an optimal exponent. We then obtain local bounds which are however
Negative / Null Result ReportOpen accessMathematics
Saahil Ognawala, Justin Bayer · 2014 · arXiv
Advancements in parallel processing have lead to a surge in multilayer perceptrons' (MLP) applications and deep learning in the past decades. Recurrent Neural Networks (RNNs) give additional representational power to feedforward MLPs by providing a way to treat sequential data. However, RNNs are hard to train using conventional error backpropagation methods because of the difficulty in relating inputs over many time-steps. Regularization approaches from MLP sphere, like dropout and noisy weight training, have been insufficiently applied and tested on simple RNNs. Moreover, solutions have been
Negative / Null Result ReportOpen accessEconomics, Econometrics and Finance
Yuda Bi, Vince D Calhoun · 2026 · arXiv
We address the attribution problem for apparent slow collective dynamics: is the observed persistence intrinsic, or inherited from a persistent driver? For the leading eigenvalue fraction $ψ_1=λ_{\max}/N$ of S\&P 500 60-day rolling correlation matrices ($237$ stocks, 2004--2023), a VIX-coupled Ornstein--Uhlenbeck model reduces the effective relaxation time from $298$ to $61$ trading days and improves the fit over bare mean reversion by $Δ$BIC$=109$. On the decomposition sample, an informational residual of $\log(\mathrm{VIX})$ alone retains most of that gain ($Δ$BIC$=78.6$), whereas a mechanic
Negative / Null Result ReportOpen accessComputer Science
Hongxu Zhou · 2026 · arXiv
Intrinsic self-correction in Large Language Models (LLMs) frequently fails in open-ended reasoning tasks due to ``hallucination snowballing,'' a phenomenon in which models recursively justify early errors during free-text reflection. While structured feedback can mitigate this issue, existing approaches often rely on externally trained critics or symbolic tools, reducing agent autonomy. This study investigates whether enforcing structured reflection purely through Outlines-based constrained decoding can disrupt error propagation without additional training. Evaluating an 8-billion-parameter mo
Negative / Null Result ReportOpen accessPhysics
Md Mamun Al-Rashid, Jayasimha Atulasimha, Supriyo Bandyopadhyay · 2014 · arXiv
Strain-clocked dipole-coupled nanomagnetic logic is an energy-efficient Boolean logic paradigm whose progress has been stymied by its propensity for high error rates. In an effort to mitigate this problem, we have studied the effect of nanomagnet geometry on error rates, focusing on elliptical and cylindrical geometries. We had previously reported that the out-of-plane excursion of the magnetization vector during switching creates a precessional torque that is responsible for high switching error probability in elliptical nanomagnet geometries. The absence of this torque in cylindrical magnets
Negative / Null Result ReportOpen accessComputer Science
Linas Vepstas · 2013 · arXiv
This report describes the suicidality prediction models created under the DARPA DCAPS program in association with the Durkheim Project [http://durkheimproject.org/]. The models were built primarily from unstructured text (free-format clinician notes) for several hundred patient records obtained from the Veterans Health Administration (VHA). The models were constructed using a genetic programming algorithm applied to bag-of-words and bag-of-phrases datasets. The influence of additional structured data was explored but was found to be minor. Given the small dataset size, classification between c
Negative / Null Result ReportOpen accessComputer Science
Shreya Chandrasekhar, Chieh-Yang Huang, Ting-Hao 'Kenneth' Huang · 2023 · arXiv
The rapid growth of scientific publications, particularly during the COVID-19 pandemic, emphasizes the need for tools to help researchers efficiently comprehend the latest advancements. One essential part of understanding scientific literature is research aspect classification, which categorizes sentences in abstracts to Background, Purpose, Method, and Finding. In this study, we investigate the impact of different datasets on model performance for the crowd-annotated CODA-19 research aspect classification task. Specifically, we explore the potential benefits of using the large, automatically
Negative / Null Result ReportOpen accessComputer Science
Egor Shevchenko, Elena Bruches · 2026 · arXiv
Data quality is a critical factor in the effectiveness of machine learning models. Label errors, present even in widely used benchmarks, introduce noise into training data and reduce model generalization. In this work, we conduct a comparative analysis of two automatic label error detection methods - Confident Learning and Dataset Cartography - on three Russian text classification corpora of varying size, number of classes, and domain: ru_emotion_e-culture (49,123 examples, emotion classification), RuCoLA (8,524 examples, linguistic acceptability), and TERRa (2,337 examples, textual entailment
Negative / Null Result ReportOpen accessPhysics
Mohamed M. Fadlallah, Ulrich Eckern · 2017 · arXiv
The structural, electronic, and optical properties of metal (Si, Ge, Sn, and Pb) mono- and co-doped anatase TiO$_{2}$ nanotubes are investigated, in order to elucidate their potential for photocatalytic applications. It is found that Si doped TiO$_{2}$ nanotubes are more stable than those doped with Ge, Sn, or Pb. All dopants lower the band gap, except the (Ge, Sn) co-doped structure, the decrease depending on the concentration and the type of dopant. Correspondingly, a redshift in the optical properties for all kinds of dopings is obtained. Even though a Pb mono- and co-doped TiO$_{2}$ nanotu
Negative / Null Result ReportOpen accessPhysics
Chris Pankow, Laura Sampson, Leah Perri et al. · 2016 · arXiv
The detection of electromagnetic counterparts to gravitational waves has great promise for the investigation of many scientific questions. It has long been hoped that in addition to providing extra, non-gravitational information about the sources of these signals, the detection of an electromagnetic signal in conjunction with a gravitational wave could aid in the analysis of the gravitational signal itself. That is, knowledge of the sky location, inclination, and redshift of a binary could break degeneracies between these extrinsic, coordinate-dependent parameters and the physical parameters,
Negative / Null Result ReportOpen accessPhysics
Sergei Manzhos, Pavlo Golub · 2020 · arXiv
We study the dependence of kinetic energy densities (KED) on density-dependent variables that have been suggested in previous works on kinetic energy functionals (KEF) for orbital-free DFT (OF-DFT). We focus on the role of data distribution and on data and regressor selection. We compare unweighted and weighted linear and Gaussian process regressions of KED for light metals and a semiconductor. We find that good quality linear regression resulting in good energy-volume dependence is possible over density-dependent variables suggested in previous literature. This is achieved with weighted fitti
Negative / Null Result ReportOpen accessComputer Science
Mario Sanz-Guerrero, Katharina von der Wense · 2025 · arXiv
In-context learning (ICL) has transformed the use of large language models (LLMs) for NLP tasks, enabling few-shot learning by conditioning on labeled examples without finetuning. Despite its effectiveness, ICL is prone to errors, especially for challenging examples. With the goal of improving the performance of ICL, we propose corrective in-context learning (CICL), an approach that incorporates a model's incorrect predictions alongside ground truth corrections into the prompt, aiming to enhance classification accuracy through self-correction. However, contrary to our hypothesis, extensive exp
Negative / Null Result ReportOpen accessComputer Science
Hui Zhang, Kiduk Yang, Elin Jacob · 2015 · arXiv
Despite limited success, information retrieval (IR) systems today are not intelligent or reliable. IR systems return poor search results when users formulate their information needs into incomplete or ambiguous queries (i.e., weak queries). Therefore, one of the main challenges in modern IR research is to provide consistent results across all queries by improving the performance on weak queries. However, existing IR approaches such as query expansion are not overly effective because they make little effort to analyze and exploit the meanings of the queries. Furthermore, word sense disambiguati
Negative / Null Result ReportOpen accessComputer Science
Philippe Laban, Tobias Schnabel, Jennifer Neville · 2026 · arXiv
Large Language Models (LLMs) are poised to disrupt knowledge work, with the emergence of delegated work as a new interaction paradigm (e.g., vibe coding). Delegation requires trust - the expectation that the LLM will faithfully execute the task without introducing errors into documents. We introduce DELEGATE-52 to study the readiness of AI systems in delegated workflows. DELEGATE-52 simulates long delegated workflows that require in-depth document editing across 52 professional domains, such as coding, crystallography, and music notation. Our large-scale experiment with 19 LLMs reveals that cu
