Miracle-dz/UniFAR-FTUs huggingface dataset:
The Use Of Capital Budgeting Techniques In Businesses: A Perspective From The Western Cape

Capital budgeting is one of the most important areas of financial management. There are several techniques commonly used to evaluate capital budgeting projects namely the payback period, accounting rate of return, present value and internal rate of return and profitability index. Recent studies highlight that financial managers worldwide favor methods such as the internal rate of return (IRR) or non-discounted payback period (PP) models over the net present value (NPV), which is the model academics consider superior. In particular this research focused on small, medium and large businesses and investigated a number of variables and associations relating to capital budgeting practices in businesses in the Western Cape province of South Africa. The results revealed that payback period, followed by net present value, appears to be the most used method across the different sizes and sectors of business. It was also found that 64% of businesses surveyed used only one technique, while 32% of the respondents used between two to three different types of techniques to evaluate capital budgeting decisions. The findings show that the more complicated methods such as IRR and NPV are most favored by the large businesses as compared to the small businesses. The majority of the respondents believed that project definition was the most important stage in the capital budgeting process. Implementation stage appeared to be the most difficult stage for the manufacturing sector whereas Project definition, Analysis and selection and Implementation were generally rated as being the difficult stages by the retail sector. Project definition and Analysis and selection were found to be the most difficult stages by the service sector. Most businesses used the cost of bank loan as a basis in capital budgeting and more than two thirds of respondents used non-quantitative techniques to consider risk when making a decision on investing in fixed assets. This paper reports on the findings of a survey on managers of registered business businesses in the Western Cape Province of South Africa on how they undertook capital budgeting practices. The survey followed similar surveys that were conducted around the globe such as Sandahl & Sjogren, 2002; Kester & Chong, 2001; Kester, Chong, Echanis, Haikal, Isa, Skully, Tsui, & Wang, 1999. The survey is the first one of its kind in South Africa in general and in the Western Cape Province in particular.
EFFECT OF FOOD PROCESSING ON RESIDUES OF DINICONAZOLE, ETOXAZOLE AND IMIDACLOPRID IN STRAWBERRY FRUITS

: Strawberry fruits were sprayed with imidacloprid, diniconazole and etoxazole at the recommended rates of application. The effect of processing on removal of tested pesticide residues from strawberry fruits were studied. According to their half-life (t 1/2 ) values, strawberry fruits can be safely harvested for human consumption or for processing purposes 14 ,7 and 3 days after the spraying of imidacloprid, diniconazole and etoxazole , respectively . The removal ratio of imidacloprid, diniconazole and etoxazole residues in strawberries were in the range of 9.9–30.55%, 10.0–29.52% and 15.0 – 46.67%, respectively, by washing with tap water . The average amount of imidacloprid, diniconazole and etoxazole residues in strawberry fruits, juice , syrup under cold and hot break were greatly decreased compared with those in unwashed strawberry fruits. Moreover, the amount of residues of the three tested pesticides was more greatly decreased in strawberries syrup under hot break than cold break. Pesticide residues were concentrated into jam and increased to higher levels than strawberry juice. A change in physical and chemical properties of strawberry fruits and products was related mainly to the processing operations .
Large interfacial Rashba interaction and giant spin-orbit torques in atomically thin metallic heterostructures

The ability of spin-orbit interactions to convert charge current into spin current, most often in the bulk of heavy metal thin ﬁlms, has been the hallmark of spintronics in the last decade. In this study, we demonstrate how the insertion of light metal element interface profoundly aﬀects both the nature of spin-orbit torque and its eﬃciency in terms of damping-like ( H DL ) and ﬁeld-like ( H FL ) eﬀective ﬁelds in ultrathin Co ferromagnet. Indeed, we measure unexpectedly large H FL / H DL ratio ( ∼ 2.5) upon inserting a 1.4 nm thin Al layer in Pt|Co|Al|Pt as compared to a similar stacking including Cu instead of Al. From our modelling, these results strongly evidence the presence of large Rashba interaction at Co|Al interface producing a giant H FL , which was not expected from a metallic interface. The occurrence of such enhanced torques from an interfacial origin is further validated by demonstrating current-induced magnetization reversal showing a signiﬁcant decrease of the critical current for switching.
On the Limitations of Elo: Real-World Games, are Transitive, not Additive

Real-world competitive games, such as chess, go, or StarCraft II, rely on Elo models to measure the strength of their players. Since these games are not fully transitive, using Elo implicitly assumes they have a strong transitive component that can correctly be identified and extracted. In this study, we investigate the challenge of identifying the strength of the transitive component in games. First, we show that Elo models can fail to extract this transitive component, even in elementary transitive games. Then, based on this observation, we propose an extension of the Elo score: we end up with a disc ranking system that assigns each player two scores, which we refer to as skill and consistency. Finally, we propose an empirical validation on payoff matrices coming from real-world games played by bots and humans.
H i properties of satellite galaxies around local volume hosts

We present neutral atomic hydrogen (H i) observations using the Robert C. Byrd Green Bank Telescope (GBT) along the lines of sight to 49 confirmed or possible dwarf satellite galaxies around eight Local Volume systems (M104, M51, NGC 1023, NGC 1156, NGC 2903, NGC 4258, NGC 4565, NGC 4631). We detect the H i reservoirs of two candidates (dw0934+2204 and dw1238−1122) and find them to be background sources relative to their nearest foreground host systems. The remaining 47 satellite candidates are not detected in H i, and we place stringent 5σ upper limits on their H i mass. We note that some (15/47) of our non-detections stem from satellites being occluded by their putative host’s H i emission. In addition to these new observations, we compile literature estimates on the H i mass for an additional 17 satellites. We compare the H i properties of these satellites to those within the Local Group, finding broad agreement between them. Crucially, these observations probe a ‘transition’ region between −10 ≳ MV ≳ −14 where we see a mixture of gas-rich and gas-poor satellites. While there are many gas-poor satellites within this region, some are gas-rich and this suggests that the transition towards predominantly gas-rich satellites occurs at LV ∼ 107L⊙, in line with simulations. The observations presented here are a key step toward characterizing the properties of dwarf satellite galaxies around Local Volume systems and future wide-field radio surveys with higher angular resolution (e.g. WALLABY) will vastly improve upon the study of such systems.
Decoding Short LDPC Codes via BP-RNN Diversity and Reliability-Based Post-Processing

This paper investigates decoder diversity architectures for short low-density parity-check (LDPC) codes, based on recurrent neural network (RNN) models of the belief-propagation (BP) algorithm. We propose a new approach to achieve decoder diversity in the waterfall region, by specializing BP-RNN decoders to specific classes of errors, with absorbing set support. We further combine our approach with an ordered statistics decoding (OSD) post-processing step, which effectively leverages the bit-error rate optimization deriving from the use of the binary cross-entropy loss function. We show that a single specialized BP-RNN decoder combines better than BP with the OSD post-processing step. Moreover, combining OSD post-processing with the diversity brought by the use of multiple BP-RNN decoders, provides an efficient way to bridge the gap to maximum likelihood decoding.
Consensus for Hegselmann–Krause type models with time variable time delays

In this paper, we analyze a Hegselmann–Krause opinion formation model with time variable time delay and prove that if the influence function is always positive, then there is exponential convergence to consensus without requiring any smallness assumptions on the time delay function. The analysis is then extended to a model with distributed time delay.
THE STRONG AND SUPER TREE PROPERTIES AT SUCCESSORS OF SINGULAR CARDINALS

The strong tree property and ITP (also called the super tree property) are generalizations of the tree property that characterize strong compactness and supercompactness up to inaccessibility. That is, an inaccessible cardinal $\kappa$ is strongly compact if and only if the strong tree property holds at $\kappa$, and supercompact if and only if ITP holds at $\kappa$. We present several results motivated by the problem of obtaining the strong tree property and ITP at many successive cardinals simultaneously; these results focus on the successors of singular cardinals. We describe a general class of forcings that will obtain the strong tree property and ITP at the successor of a singular cardinal of any cofinality. Generalizing a result of Neeman about the tree property, we show that it is consistent for ITP to hold at $\aleph_n$ for all $2 \leq n<\omega$ simultaneously with the strong tree property at $\aleph_{\omega+1}$; we also show that it is consistent for ITP to hold at $\aleph_n$ for all $3<n<\omega$ and at $\aleph_{\omega+1}$ simultaneously. Finally, turning our attention to singular cardinals of uncountable cofinality, we show that it is consistent for the strong and super tree properties to hold at successors of singulars of multiple cofinalities simultaneously.
Is the Core-cusp Problem a Matter of Perspective? Jeans Anisotropic Modeling against Numerical Simulations

Mock member stars for 28 dwarf galaxies are constructed from the cosmological auriga simulation, which reflects the dynamical status of realistic stellar tracers. Axisymmetric Jeans Anisotropic Multi-Gaussian Expansion (jam) modeling is applied to 6000 star particles for each system to recover the underlying matter distribution. The stellar or dark matter component individually is poorly recovered, but the total profile is constrained more reasonably. The mass within the half-mass radius of tracers is recovered the tightest, and the mass between 200 and 300 pc, M(200–300 pc), is an unbiasedly constrained ensemble, with a scatter of 0.167 dex. If using 2000 particles and only line-of-sight velocities with typical errors, the scatter in M(200–300 pc) is increased by ∼50%. Quiescent Saggitarius dSph–like systems and star-forming systems with strong outflows show distinct features, with M(200–300 pc) mostly underestimated for the former, and likely overestimated for the latter. The biases correlate with the dynamical status, which is a result of contraction motions due to tidal effects in quiescent systems or galactic winds in star-forming systems, driving them out of equilibrium. After including Gaia DR3 proper motion errors, we find proper motions can be as useful as line-of-sight velocities for nearby systems at < ∼60 kpc. By extrapolating the actual density profiles and the dynamical constraints down to scales below the resolution, we find the mass within 150 pc can be an unbiasedly constrained ensemble, with a scatter of ∼0.255 dex. We show that the contraction of member stars in nearby systems is detectable based on Gaia DR3 proper motion errors.
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology

The prototypical approach to reinforcement learning involves training policies tailored to a particular agent from scratch for every new morphology. Recent work aims to eliminate the re-training of policies by investigating whether a morphology-agnostic policy, trained on a diverse set of agents with similar task objectives, can be transferred to new agents with unseen morphologies without re-training. This is a challenging problem that required previous approaches to use hand-designed descriptions of the new agent's morphology. Instead of hand-designing this description, we propose a data-driven method that learns a representation of morphology directly from the reinforcement learning objective. Ours is the first reinforcement learning algorithm that can train a policy to generalize to new agent morphologies without requiring a description of the agent's morphology in advance. We evaluate our approach on the standard benchmark for agent-agnostic control, and improve over the current state of the art in zero-shot generalization to new agents. Importantly, our method attains good performance without an explicit description of morphology.
Measuring Representational Robustness of Neural Networks Through Shared Invariances

A major challenge in studying robustness in deep learning is defining the set of ``meaningless'' perturbations to which a given Neural Network (NN) should be invariant. Most work on robustness implicitly uses a human as the reference model to define such perturbations. Our work offers a new view on robustness by using another reference NN to define the set of perturbations a given NN should be invariant to, thus generalizing the reliance on a reference ``human NN'' to any NN. This makes measuring robustness equivalent to measuring the extent to which two NNs share invariances, for which we propose a measure called STIR. STIR re-purposes existing representation similarity measures to make them suitable for measuring shared invariances. Using our measure, we are able to gain insights into how shared invariances vary with changes in weight initialization, architecture, loss functions, and training dataset. Our implementation is available at: \url{https://github.com/nvedant07/STIR}.
CoSP: Co-supervised pretraining of pocket and ligand

Can we inject the pocket-ligand interaction knowledge into the pre-trained model and jointly learn their chemical space? Pretraining molecules and proteins has attracted considerable attention in recent years, while most of these approaches focus on learning one of the chemical spaces and lack the injection of biological knowledge. We propose a co-supervised pretraining (CoSP) framework to simultaneously learn 3D pocket and ligand representations. We use a gated geometric message passing layer to model both 3D pockets and ligands, where each node's chemical features, geometric position and orientation are considered. To learn biological meaningful embeddings, we inject the pocket-ligand interaction knowledge into the pretraining model via contrastive loss. Considering the specificity of molecules, we further propose a chemical similarity-enhanced negative sampling strategy to improve the contrastive learning performance. Through extensive experiments, we conclude that CoSP can achieve competitive results in pocket matching, molecule property predictions, and virtual screening.
A Test for Evaluating Performance in Human-Computer Systems

The Turing test for comparing computer performance to that of humans is well known, but, surprisingly, there is no widely used test for comparing how much better human-computer systems perform relative to humans alone, computers alone, or other baselines. Here, we show how to perform such a test using the ratio of means as a measure of effect size. Then we demonstrate the use of this test in three ways. First, in an analysis of 79 recently published experimental results, we ﬁnd that, surprisingly, over half of the studies ﬁnd a decrease in performance, the mean and median ratios of performance improvement are both approximately 1 (corresponding to no improvement at all), and the maximum ratio is 1.36 (a 36% improvement). Second, we experimentally investigate whether a higher performance improvement ratio is obtained when 100 human programmers generate software using GPT-3, a massive, state-of-the-art AI system. In this case, we ﬁnd a speed improvement ratio of 1.27 (a 27% improvement). Finally, we ﬁnd that 50 human non- programmers using GPT-3 can perform the task about as well as–and less expensively than–the human programmers. In this case, neither the non-programmers nor the computer would have been able to perform the task alone, so this is an example of a very strong form of human-computer synergy. stimulate competition among teams of computer and social scientists developing human-computer systems that perform far better than either people or computers alone. We believe this could beneﬁt our economy and society by fostering the development of software to augment humans rather than just replace them. And in the long run, it might also lead to creating “superintelligent” human-computer systems.
Multi-Frequency Joint Community Detection and Phase Synchronization

This paper studies the joint community detection and phase synchronization problem on the stochastic block model with relative phase, where each node is associated with an unknown phase angle. This problem, with a variety of real-world applications, aims to recover the cluster structure and associated phase angles simultaneously. We show this problem exhibits a “multi-frequency” structure by closely examining its maximum likelihood estimation (MLE) formulation, whereas existing methods are not originated from this perspective. To this end, two simple yet efficient algorithms that leverage the MLE formulation and benefit from the information across multiple frequencies are proposed. The former is a spectral method based on the novel multi-frequency column-pivoted QR factorization. The factorization applied to the top eigenvectors of the observation matrix provides key information about the cluster structure and associated phase angles. The second approach is an iterative multi-frequency generalized power method, where each iteration updates the estimation in a matrix-multiplication-then-projection manner. Numerical experiments show that our proposed algorithms significantly improve the ability of exactly recovering the cluster structure and the accuracy of the estimated phase angles, compared to state-of-the-art algorithms.
Hierarchical Planning With Annotated Skeleton Guidance

We present a hierarchical skeleton-guided motion planning algorithm to guide mobile robots. A good skeleton maps the connectivity of the subspace of c-space containing significant degrees of freedom and is able to guide the planner to find the desired solutions fast. However, sometimes the skeleton does not closely represent the free c-space, which often misleads current skeleton-guided planners. The hierarchical skeleton-guided planning strategy gradually relaxes its reliance on the workspace skeleton as $\mathcal {C}_{space}$ is sampled, thereby incrementally returning a sub-optimal path, a feature that is not guaranteed in the standard skeleton-guided algorithm. Experimental comparisons to the standard skeleton guided planners and other lazy planning strategies show significant improvement in roadmap construction run time while maintaining path quality for multi-query problems in cluttered environments.
Learning sparse features can lead to overfitting in neural networks

It is widely believed that the success of deep networks lies in their ability to learn a meaningful representation of the features of the data. Yet, understanding when and how this feature learning improves performance remains a challenge. For example, it is beneficial for modern architectures to be trained to classify images, whereas it is detrimental for fully-connected networks to be trained on the same data. Here, we propose an explanation for this puzzle, by showing that feature learning can perform worse than lazy training (via the random feature kernel or the neural tangent kernel) as the former can lead to a sparser neural representation. Although sparsity is known to be essential for learning anisotropic data, it is detrimental when the target function is constant or smooth along certain directions of the input space. We illustrate this phenomenon in two settings: (i) regression of Gaussian random functions on the d-dimensional unit sphere and (ii) classification of benchmark data sets of images. For (i), we compute the scaling of the generalization error with the number of training points and show that methods that do not learn features generalize better, even when the dimension of the input space is large. For (ii), we show empirically that learning features can indeed lead to sparse and thereby less smooth representations of the image predictors. This fact is plausibly responsible for deteriorating the performance, which is known to be correlated with smoothness along diffeomorphisms.
On Certifying and Improving Generalization to Unseen Domains

Domain Generalization (DG) aims to learn models whose performance remains high on unseen domains encountered at test-time by using data from multiple related source domains. Many existing DG algorithms reduce the divergence between source distributions in a representation space to potentially align the unseen domain close to the sources. This is motivated by the analysis that explains generalization to unseen domains using distributional distance (such as the Wasserstein distance) to the sources. However, due to the openness of the DG objective, it is challenging to evaluate DG algorithms comprehensively using a few benchmark datasets. In particular, we demonstrate that the accuracy of the models trained with DG methods varies significantly across unseen domains, generated from popular benchmark datasets. This highlights that the performance of DG methods on a few benchmark datasets may not be representative of their performance on unseen domains in the wild. To overcome this roadblock, we propose a universal certification framework based on distributionally robust optimization (DRO) that can efficiently certify the worst-case performance of any DG method. This enables a data-independent evaluation of a DG method complementary to the empirical evaluations on benchmark datasets. Furthermore, we propose a training algorithm that can be used with any DG method to provably improve their certified performance. Our empirical evaluation demonstrates the effectiveness of our method at significantly improving the worst-case loss (i.e., reducing the risk of failure of these models in the wild) without incurring a significant performance drop on benchmark datasets.
A Fundamental Limit of Distributed Hypothesis Testing Under Memoryless Quantization

We consider a distributed binary hypothesis testing setup where multiple nodes send quantized information to a central processor, which is oblivious to the nodes’ statistics. We study the regime where the missed detection (type-II error) probability decays exponentially and the false alarm (type-I error) probability vanishes. For memoryless quantization, we characterize a tradeoff curve that yields a lower bound for the feasible region of type-II error exponents and the average number of bits sent under the null hypothesis. Moreover, we show that the tradeoff curve is approached at high rates with lattice quantization.