Negative / Null Result ReportOpen accessComputer Science
Isabel Segura-Bedmar · 2023 · arXiv
This paper describes our participation in SemEval-2023 Task 10, whose goal is the detection of sexism in social media. We explore some of the most popular transformer models such as BERT, DistilBERT, RoBERTa, and XLNet. We also study different data augmentation techniques to increase the training dataset. During the development phase, our best results were obtained by using RoBERTa and data augmentation for tasks B and C. However, the use of synthetic data does not improve the results for task C. We participated in the three subtasks. Our approach still has much room for improvement, especiall
Negative / Null Result ReportOpen accessComputer Science
Amir Homayounirad, Enrico Liscio, Tong Wang et al. · 2025 · arXiv
Aggregating multiple annotations into a single ground truth label may hide valuable insights into annotator disagreement, particularly in tasks where subjectivity plays a crucial role. In this work, we explore methods for identifying subjectivity in recognizing the human values that motivate arguments. We evaluate two main approaches: inferring subjectivity through value prediction vs. directly identifying subjectivity. Our experiments show that direct subjectivity identification significantly improves the model performance of flagging subjective arguments. Furthermore, combining contrastive l
Negative / Null Result ReportOpen accessComputer Science
Fahd Ahmed Khan, Kamel Tourki, Mohamed-Slim Alouini et al. · 2013 · arXiv
This paper studies the impact of using outdated channel state information for relay selection on the performance of a network where two sources communicate with each other via fixed-gain amplifyand- forward relays. For a Rayleigh faded channel, closed-form expressions for the outage probability, moment generating function and symbol error rate are derived. Simulations results are also presented to corroborate the derived analytical results. It is shown that adding relays does not improve the performance if the channel is substantially outdated. Furthermore, relay location is also taken into co
Negative / Null Result ReportOpen accessComputer Science
Matthew Aitchison · 2019 · arXiv
Although reinforcement learning has made great strides recently, a continuing limitation is that it requires an extremely high number of interactions with the environment. In this paper, we explore the effectiveness of reusing experience from the experience replay buffer in the Deep Q-Learning algorithm. We test the effectiveness of applying learning update steps multiple times per environmental step in the VizDoom environment and show first, this requires a change in the learning rate, and second that it does not improve the performance of the agent. Furthermore, we show that updating less fr
Negative / Null Result ReportOpen accessMathematics
Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin et al. · 2020 · arXiv
Many instances of algorithmic bias are caused by subpopulation shifts. For example, ML models often perform worse on demographic groups that are underrepresented in the training data. In this paper, we study whether enforcing algorithmic fairness during training improves the performance of the trained model in the \emph{target domain}. On one hand, we conceive scenarios in which enforcing fairness does not improve performance in the target domain. In fact, it may even harm performance. On the other hand, we derive necessary and sufficient conditions under which enforcing algorithmic fairness l
Negative / Null Result ReportOpen accessComputer Science
John S. Van Dyke, Zackary White, Gregory Quiroz · 2024 · arXiv
Zero-noise extrapolation (ZNE), a technique to estimate quantum circuit expectation values through noise scaling and extrapolation, is well-studied in the context of quantum computing. We examine the applicability of ZNE to the field of quantum sensing. Focusing on the problem of DC magnetometry using the Ramsey protocol, we show that the sensitivity (in the sense of the minimum detectable signal) does not improve upon using ZNE in the slope detection scheme. On the other hand, signals of sufficiently large magnitude can be estimated more accurately. Our results are robust across various noise
Negative / Null Result ReportOpen accessComputer Science
Baruch Lubinsky, Bekir Genc, Tshilidzi Marwala · 2008 · arXiv
Neural networks are powerful tools for classification and regression in static environments. This paper describes a technique for creating an ensemble of neural networks that adapts dynamically to changing conditions. The model separates the input space into four regions and each network is given a weight in each region based on its performance on samples from that region. The ensemble adapts dynamically by constantly adjusting these weights based on the current performance of the networks. The data set used is a collection of financial indicators with the goal of predicting the future platinu
Negative / Null Result ReportOpen accessComputer Science
Shouman Das, Syed A. Haque, Md. Iftekhar Tanveer · 2021 · arXiv
\emph{Topological data analysis} (TDA) has recently emerged as a new technique to extract meaningful discriminitve features from high dimensional data. In this paper, we investigate the possibility of applying TDA to improve the classification accuracy of public speaking rating. We calculated \emph{persistence image vectors} for the sentence embeddings of TEDtalk data and feed this vectors as additional inputs to our machine learning models. We have found a negative result that this topological information does not improve the model accuracy significantly. In some cases, it makes the accuracy
Negative / Null Result ReportOpen accessComputer Science
Hayato Arai, Yuuya Yoshida, Masahito Hayashi · 2019 · arXiv
We address perfect discrimination of two separable states. When available states are restricted to separable states, we can theoretically consider a larger class of measurements than the class of measurements allowed in quantum theory. The framework composed of the class of separable states and the above extended class of measurements is a typical example of general probabilistic theories. In this framework, we give a necessary and sufficient condition to discriminate two separable pure states perfectly. In particular, we derive measurements explicitly to discriminate two separable pure states
Negative / Null Result ReportOpen accessEconomics, Econometrics and Finance
Michael Pedersen · 2024 · arXiv
The present study applies observations of individual predictions of the first three releases of the US output growth rate to evaluate how the applied judgment affects prediction efficiency and accuracy as well as if judgment is persistent. While the first two issues have been assessed in other studies, there is little evidence on the formation of judgment in macroeconomic projections. Most of the forecasters produce unbiased predictions, but employing the median Bloomberg projection as baseline, it turns out that judgment generally does not improve accuracy. There seems to be persistence in th
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
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 accessMathematics
Peter Bürgisser · 2022 · arXiv
Consider a system $f_1(x)=0,\ldots,f_n(x)=0$ of $n$ random real polynomials in $n$ variables, where each $f_i$ has a prescribed set of exponent vectors described by a set $A_i \subseteq \mathbb{Z}^n$ of cardinality $t_i$, whose convex hull is denoted $P_i$. Assuming that the coefficients of the $f_i$ are independent standard Gaussian, we prove that the expected number of zeros of the random system in the positive orthant is at most $(2π)^{-\frac{n}{2}} V_0 (t_1-1)\ldots (t_n-1)$. Here $V_0$ denotes the number of vertices of the Minkowski sum $P_1+\ldots + P_n$. However, this bound does not imp
Negative / Null Result ReportOpen accessComputer Science
Chengbin Pang, Ruotong Yu, Dongpeng Xu et al. · 2021 · arXiv
Function entry detection is critical for security of binary code. Conventional methods heavily rely on patterns, inevitably missing true functions and introducing errors. Recently, call frames have been used in exception-handling for function start detection. However, existing methods have two problems. First, they combine call frames with heuristic-based approaches, which often brings error and uncertain benefits. Second, they trust the fidelity of call frames, without handling the errors that are introduced by call frames. In this paper, we first study the coverage and accuracy of existing a
Negative / Null Result ReportOpen accessComputer Science
Callum Sharrock, Lukas Petersson, Hanna Petersson et al. · 2025 · arXiv
We present Butter-Bench, a benchmark evaluating large language model (LLM) controlled robots for practical intelligence, defined as the ability to navigate the messiness of the physical world. Current state-of-the-art robotic systems use a hierarchical architecture with LLMs in charge of high-level reasoning, and a Vision Language Action (VLA) model for low-level control. Butter-Bench evaluates the LLM part in isolation from the VLA. Although LLMs have repeatedly surpassed humans in evaluations requiring analytical intelligence, we find humans still outperform LLMs on Butter-Bench. The best LL
