Negative / Null Result ReportOpen accessComputer Science
Yuval Kirstain, Patrick Lewis, Sebastian Riedel et al. · 2021 · arXiv
We investigate the dynamics of increasing the number of model parameters versus the number of labeled examples across a wide variety of tasks. Our exploration reveals that while scaling parameters consistently yields performance improvements, the contribution of additional examples highly depends on the task's format. Specifically, in open question answering tasks, enlarging the training set does not improve performance. In contrast, classification, extractive question answering, and multiple choice tasks benefit so much from additional examples that collecting a few hundred examples is often
Negative / Null Result ReportOpen accessMathematics
Nicolas Burq, Nicolas Camps, Mickaël Latocca et al. · 2024 · arXiv
We consider the Wick ordered cubic Schrödinger equation (NLS) posed on the two-dimensional sphere, with initial data distributed according to a Gaussian measure. We show that the second Picard iteration does not improve the regularity of the initial data in the scale of the classical Sobolev spaces. This is in sharp contrast with the Wick ordered NLS on the two-dimensional tori, a model for which we know from the work of Bourgain that the second Picard iteration gains one half derivative. Our proof relies on identifying a singular part of the nonlinearity. We show that this singular part is re
Negative / Null Result ReportOpen accessPhysics
Örs Legeza, Florian Gebhard, Jörg Rissler · 2005 · arXiv
For the one-dimensional Hubbard model subject to periodic boundary conditions we construct a unitary transformation between basis states so that open boundary conditions apply for the transformed Hamiltonian. Despite the fact that the one-particle and two-particle interaction matrices link nearest and next-nearest neighbors only, the performance of the density-matrix renormalization group method for the transformed Hamiltonian does not improve. Some of the new interactions act as independent quantum channels which generate the same level of entanglement as periodic boundary conditions in the o
Methods Dead-EndOpen accessComputer Science
Pierre Boullier · 1996 · arXiv
In this paper we present a new parsing algorithm for linear indexed grammars (LIGs) in the same spirit as the one described in (Vijay-Shanker and Weir, 1993) for tree adjoining grammars. For a LIG $L$ and an input string $x$ of length $n$, we build a non ambiguous context-free grammar whose sentences are all (and exclusively) valid derivation sequences in $L$ which lead to $x$. We show that this grammar can be built in ${\cal O}(n^6)$ time and that individual parses can be extracted in linear time with the size of the extracted parse tree. Though this ${\cal O}(n^6)$ upper bound does not impro
Negative / Null Result ReportOpen accessComputer Science
Ryoutaro Watanabe · 2017 · arXiv
We study possible new physics (NP) effects on $B_c \to J/ψτ\barν$, which has been recently measured at LHCb as the ratio of $R_{J/ψ} = \mathcal B(B_c \to J/ψτ\barν)/\mathcal B(B_c \to J/ψμ\barν)$. Combining it with the long-standing $R_{D^{(*)}}$ measurements, in which the discrepancy with the prediction of the standard model is present, we find possible solutions to the anomaly by several NP types. Then, we see that adding the $R_{J/ψ}$ measurement does not improve NP fit to data, but the NP scenarios still give better $χ^2$ than the SM. We also investigate indirect NP constraints from the li
Negative / Null Result ReportOpen accessComputer Science
A. Donini, E. Fernandez-Martinez, S. Rigolin · 2004 · arXiv
In this letter we present the study of the eightfold degeneracy in the $(θ_{13},δ)$ measurement including both appearance and disappearance channels. We analyse, for definiteness, the case of a standard low-$γ$ $β$-Beam and a 4 MWatt SPL Super-Beam facility, both aiming at a UNO-like Mton water Cerenkov detector located at the Frejus laboratory, $L = 130$ km. In the $β$-Beam case, the \nue disappearance channel does not improve the $(θ_{13},δ)$ measurement when a realistic (i.e. $\ge$ 2%) systematic error is included. In the Super-Beam case, the \numu disappearance channel could, instead, be q
Negative / Null Result ReportOpen accessComputer Science
Chen Cai, Yusu Wang · 2020 · arXiv
Graph Neural Networks (GNNs) have achieved a lot of success on graph-structured data. However, it is observed that the performance of graph neural networks does not improve as the number of layers increases. This effect, known as over-smoothing, has been analyzed mostly in linear cases. In this paper, we build upon previous results \cite{oono2019graph} to further analyze the over-smoothing effect in the general graph neural network architecture. We show when the weight matrix satisfies the conditions determined by the spectrum of augmented normalized Laplacian, the Dirichlet energy of embeddin
Negative / Null Result ReportOpen accessComputer Science
Ngoc Minh Lai · 2022 · arXiv
Processing complex and ambiguous named entities is a challenging research problem, but it has not received sufficient attention from the natural language processing community. In this short paper, we present our participation in the English track of SemEval-2022 Task 11: Multilingual Complex Named Entity Recognition. Inspired by the recent advances in pretrained Transformer language models, we propose a simple yet effective Transformer-based baseline for the task. Despite its simplicity, our proposed approach shows competitive results in the leaderboard as we ranked 12 over 30 teams. Our syste
Negative / Null Result ReportOpen accessComputer Science
Christian Mollière, Iker Cumplido, Marco Zeulner et al. · 2025 · arXiv
The rapid growth of data from satellite-based Earth observation (EO) systems poses significant challenges in data transmission and storage. We evaluate the potential of task-specific learned compression algorithms in this context to reduce data volumes while retaining crucial information. In detail, we compare traditional compression (JPEG 2000) versus a learned compression approach (Discretized Mixed Gaussian Likelihood) on three EO segmentation tasks: Fire, cloud, and building detection. Learned compression notably outperforms JPEG 2000 for large-scale, multi-channel optical imagery in both
Negative / Null Result ReportOpen accessMathematics
Gilles Chardon · 2014 · arXiv
The scattering of waves by obstacles in a 2D setting is considered, in particular the computation of the scattered field via the collocation or the least-squares methods. In the case of multiple scattering by smooth obstacles, we prove that the scattered field can be uniformly approximated by sums of multipoles. For a unique obstacle, the choice of the number of points and their positions for the estimation of the error on the border of the scatterer is studied, showing the benefit of using a non-uniform distribution of points dependent on the scatterer and the approximation scheme. In general
Negative / Null Result ReportMedicine
Schauer JM · 2023 · Multivariate behavioral research
A prominent approach to studying the replication crisis has been to conduct replications of several different scientific findings as part of the same research effort. The reported proportion of findings that these programs determined…
View details →DOI: 10.1080/00273171.2022.2066500 Negative / Null Result ReportOpen accessComputer Science
Charles Welch, Rada Mihalcea, Jonathan K. Kummerfeld · 2020 · arXiv
Many NLP applications, such as biomedical data and technical support, have 10-100 million tokens of in-domain data and limited computational resources for learning from it. How should we train a language model in this scenario? Most language modeling research considers either a small dataset with a closed vocabulary (like the standard 1 million token Penn Treebank), or the whole web with byte-pair encoding. We show that for our target setting in English, initialising and freezing input embeddings using in-domain data can improve language model performance by providing a useful representation o
Negative / Null Result ReportOpen accessComputer Science
Dhananjay Srivastava · 2023 · arXiv
Clinical conversation summarization has become an important application of Natural language Processing. In this work, we intend to analyze summarization model ensembling approaches, that can be utilized to improve the overall accuracy of the generated medical report called chart note. The work starts with a single summarization model creating the baseline. Then leads to an ensemble of summarization models trained on a separate section of the chart note. This leads to the final approach of passing the generated results to another summarization model in a multi-layer/stage fashion for better coh
Negative / Null Result ReportOpen accessComputer Science
Roy Abel, Idan Benami, Yoram Louzoun · 2019 · arXiv
In colored graphs, node classes are often associated with either their neighbors class or with information not incorporated in the graph associated with each node. We here propose that node classes are also associated with topological features of the nodes. We use this association to improve Graph machine learning in general and specifically, Graph Convolutional Networks (GCN). First, we show that even in the absence of any external information on nodes, a good accuracy can be obtained on the prediction of the node class using either topological features, or using the neighbors class as an inp
Negative / Null Result ReportOpen accessComputer Science
Guanxu Chen, Dongrui Liu, Jing Shao · 2026 · arXiv
Large Language Models (LLMs) often exhibit a gap between their internal knowledge and their explicit linguistic outputs. In this report, we empirically investigate whether Looped Transformers (LTs)--architectures that increase computational depth by iterating shared layers--can bridge this gap by utilizing their iterative nature as a form of introspection. Our experiments reveal that while increasing loop iterations narrows the gap, it is partly driven by a degradation of their internal knowledge carried by representations. Moreover, another empirical analysis suggests that current LTs' abilit
Negative / Null Result ReportOpen accessMathematics
Nicolas Dupin · 2019 · arXiv
The discrete unit commitment problem with min-stop ramping constraints optimizes the daily production of thermal power plants (coal, gas, fuel units). For this problem, compact Integer Linear Programming (ILP) formulations have been designed to solve exactly small instances and heuristically real-size instances. This paper investigates whether Dantzig-Wolfe reformulation allows to improve the previous exact method and matheuristics. The extended ILP formulation is presented with the column generation algorithm to solve its linear relaxation. The experimental results show that the Dantzig-Wolfe
Negative / Null Result ReportOpen accessComputer Science
Richard A. Formato · 2010 · arXiv
Central Force Optimization is a global search and optimization algorithm that searches a decision space by flying "probes" whose trajectories are deterministically computed using two equations of motion. Because it is possible for a probe to fly outside the domain of feasible solutions, a simple errant probe retrieval method has been used previously that does not include the directional information contained in a probe's acceleration vector. This note investigates the effect of adding directionality to the "repositioning factor" approach. As a general proposition, it appears that doing so does
Negative / Null Result ReportOpen accessComputer Science
Adrian Sauter, Willem Zuidema, Marianne de Heer Kloots · 2025 · arXiv
How does visual information included in training affect language processing in audio- and text-based deep learning models? We explore how such visual grounding affects model-internal representations of words, and find substantially different effects in speech- vs. text-based language encoders. Firstly, global representational comparisons reveal that visual grounding increases alignment between representations of spoken and written language, but this effect seems mainly driven by enhanced encoding of word identity rather than meaning. We then apply targeted clustering analyses to probe for phon
Negative / Null Result ReportOpen accessComputer Science
Ehsan Asadi Kangarshahi, Albert Guillén i Fàbregas · 2020 · arXiv
We derive a single-letter upper bound to the mismatched-decoding capacity for discrete memoryless channels. The bound is expressed as the mutual information of a transformation of the channel, such that a maximum-likelihood decoding error on the translated channel implies a mismatched-decoding error in the original channel. In particular, a strong converse is shown to hold for this upper-bound: if the rate exceeds the upper-bound, the probability of error tends to 1 exponentially when the block-length tends to infinity. We also show that the underlying optimization problem is a convex-concave
Negative / Null Result ReportOpen accessComputer Science
Yongsheng Lian · 2025 · arXiv
This study presents a systematic comparison of three Reinforcement Learning (RL) algorithms (PPO, GRPO, and DAPO) for improving complex reasoning in large language models (LLMs). Our main contribution is a controlled transfer-learning evaluation: models are first fine-tuned on the specialized Countdown Game and then assessed on a suite of general-purpose reasoning benchmarks. Across all tasks, RL-trained models outperform their corresponding base models, although the degree of improvement differs by benchmark. Our parametric analysis offers practical guidance for RL-based LLM training. Increas
Negative / Null Result ReportOpen accessComputer Science
Niladri S. Chatterji, Philip M. Long · 2022 · arXiv
We bound the excess risk of interpolating deep linear networks trained using gradient flow. In a setting previously used to establish risk bounds for the minimum $\ell_2$-norm interpolant, we show that randomly initialized deep linear networks can closely approximate or even match known bounds for the minimum $\ell_2$-norm interpolant. Our analysis also reveals that interpolating deep linear models have exactly the same conditional variance as the minimum $\ell_2$-norm solution. Since the noise affects the excess risk only through the conditional variance, this implies that depth does not impr
Negative / Null Result ReportOpen accessMathematics
Martina Zizza · 2024 · arXiv
In this paper we examine the discrete Shnirelman's inequality [Shnirelman A., 1985], which relates the $L^2$-distance of two discrete configurations of a fluid to the $L^1_tL^2_x$-norm of the vector field connecting them. Our proof is inspired by [Shnirelman A., 1985], where it was obtained $α=\frac{1}{64}$ in dimension $ν=2$, while here we get $α\geq\frac{2}{7}$. Moreover we prove that $α\geq\frac{1}{ν+1}$ for any dimension $ν\geq 3$. We point out that, even if this does not improve the bound in the continuous version, where it was proved that $α\geq\frac{2}{4+ν}$, with $ν\geq 3$, our bound i
Negative / Null Result ReportOpen accessComputer Science
Felix Krejca, Tobias Kietreiber, Alexander Buchelt et al. · 2025 · arXiv
The increasing volume of online discussions requires advanced automatic content moderation to maintain responsible discourse. While hate speech detection on social media is well-studied, research on German-language newspaper forums remains limited. Existing studies often neglect platform-specific context, such as user history and article themes. This paper addresses this gap by developing and evaluating binary classification models for automatic content moderation in German newspaper forums, incorporating contextual information. Using LSTM, CNN, and ChatGPT-3.5 Turbo, and leveraging the One Mi
Negative / Null Result ReportOpen accessComputer Science
Syed Assad, Mark Bradshaw, Ping Koy Lam · 2016 · arXiv
Amplification of quantum states is inevitably accompanied with the introduction of noise at the output. For protocols that are probabilistic with heralded success, noiseless linear amplification in theory may still possible. When the protocol is successful, it can lead to an output that is a noiselessly amplified copy of the input. When the protocol is unsuccessful, the output state is degraded and is usually discarded. Probabilistic protocols may improve the performance of some quantum information protocols, but not for metrology if the whole statistics is taken into consideration. We calcula
Failed Experiment ReportOpen accessComputer Science
Roman Aperdannier, Sigurd Schacht, Alexander Piazza · 2024 · arXiv
Speaker diarization answers the question "who spoke when" for an audio file. In some diarization scenarios, low latency is required for transcription. Speaker diarization with low latency is referred to as online speaker diarization. The DIART pipeline is an online speaker diarization system. It consists of a segmentation and an embedding model. The embedding model has the largest share of the overall latency. The aim of this paper is to optimize the inference latency of the DIART pipeline. Different inference optimization methods such as knowledge distilation, pruning, quantization and layer
Negative / Null Result ReportOpen accessMathematics
Aihua Xia, Fuxi Zhang · 2011 · arXiv
Random events in space and time often exhibit a locally dependent structure. When the events are very rare and dependent structure is not too complicated, various studies in the literature have shown that Poisson and compound Poisson processes can provide adequate approximations. However, the accuracy of approximations does not improve or may even deteriorate when the mean number of events increases. In this paper, we investigate an alternative family of approximating point processes and establish Stein's method for their approximations. We prove two theorems to accommodate respectively the po
Negative / Null Result ReportOpen accessComputer Science
Peratham Wiriyathammabhum · 2023 · arXiv
In this report, we describe our Transformers for euphemism detection baseline (TEDB) submissions to a shared task on euphemism detection 2022. We cast the task of predicting euphemism as text classification. We considered Transformer-based models which are the current state-of-the-art methods for text classification. We explored different training schemes, pretrained models, and model architectures. Our best result of 0.816 F1-score (0.818 precision and 0.814 recall) consists of a euphemism-detection-finetuned TweetEval/TimeLMs-pretrained RoBERTa model as a feature extractor frontend with a Ki
Negative / Null Result ReportOpen accessEngineering
Jingqi Li, Anand Siththaranjan, Somayeh Sojoudi et al. · 2024 · arXiv
Autonomous agents should coordinate effectively without prior knowledge of others' intents. While prior work has focused on intent inference, we address the inverse problem: how agents can strategically demonstrate their intents within general-sum dynamic games. We model this problem and propose an algorithm that balances intent demonstration with task performance. To handle nonlinear dynamic games with continuous state-action spaces, our method leverages iterative linear-quadratic game approximations and provides efficient intent-teaching guarantees: the uncertain agent's belief can be driven
Failed Experiment ReportOpen accessPhysics
Daixi Xia, Hassan Allami, Jacob J. Krich · 2025 · arXiv
We present a Poisson/drift-diffusion model that includes valley scattering effects for simulating valley photovoltaic devices. The valley photovoltaic concept is a novel implementation of a hot-carrier solar cell and leverages the valley scattering effect under large electric field to potentially achieve high voltage and high efficiency. Fabricated devices have shown S-shaped current-voltage curves, low fill factor, and thus low efficiency. We hence develop the first device model for valley photovoltaics. Our model includes electric-field-dependent valley scattering rates extracted from previo
Negative / Null Result ReportOpen accessEngineering
Karl Strecker, Sabit Ekin, John OHara · 2021 · arXiv
A theoretical framework and numerical simulations quantifying the impact of atmospheric group velocity dispersion on wireless terahertz communication link error rate were developed based upon experimental work. We present, for the first time, predictions of symbol error rate as a function of link distance, signal bandwidth, signal-to-noise ratio, and atmospheric conditions, revealing that long-distance, broadband terahertz communication systems may be limited by inter-symbol interference stemming from group velocity dispersion, rather than attenuation. In such dispersion limited links, increas
